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"ECOLOGY AND FISHERIES OF SEAMOUNT ECOSYSTEMS by  TELMO Alexandre Fernandes MORATO Gomes M.Sc., Universidade de Coimbra, Portugal, 2001 B.Sc. (Hons), Universidade do Algarve, Portugal, 1995  A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR III hi DEGREE OF DOCTOR OF PHILOSOPH Y in THE FACULTY OF GRADUATE STUDIES (Resource Management and Environmental Studies)  THE UNIVERSITY OF BRITISH COLUMBIA February 2007 \u00A9 TELMO Alexandre Fernandes MORATO Gomes, 2006  \u000CABSTRACT This thesis explores some fundamental questions about seamount ecology and fisheries. Initially, I characterized the seamount distribution on the Azores Exclusive Economic Zone using two bathymetry datasets. The algorithm developed was able to map and describe 63 large and 332 small seamount-like features in the EEZ of the Azores. The distribution suggests that large proportion of seamounts occur in chains along the Mid Atlantic Ridge, however, few isolated seamounts are also present in the Azores. In clarifying how seamounts affect primary productivity, I show that primary production enhancement is not sufficient to support often-observed large aggregations of fish. My work supports an alternative hypothesis, that a horizontal flux of prey is the key factor in sustaining rich communities living on seamounts. Additionally, the importance of seamounts to some large pelagic fish, marine mammals and seabirds is also demonstrated. In the case of skipjack and bigeye tuna, common dolphin and Cory's shearwater, these species were significantly more abundant in the vicinity of some seamount summits. I found that seamounts may act as feeding stations for some of these visitors. The methodology developed, however, failed to demonstrate seamounts' association for bottlenose dolphins, spotted dolphin, sperm whale, terns, yellowlegged gull, and loggerhead sea turtles. Fisheries exploitation is a major threat to seamount ecosystems, but I am not presently able to quantify the amount of catches taken from seamounts around the world. Instead, 1 demonstrate that global landings of demersal marine fishes have shifted to deeper water species over the last 50 years, an indirect indication that seamounts have also increased in importance. Moreover, I show that 'deep-water', 'seamount' and 'seamount-aggregating' fish stocks may be at serious risk of depletion, since their life histories render them highly vulnerable to overfishing with little resilience to overexploitation. Finally, ecosystem modelling analyses indicate that sustainable seamount fisheries with tolerable ecosystem impacts can be found only by maximizing an 'ecological' objective function. This suggests that industrial fisheries are not viable on seamounts. However, regulated small-scale artisanal fishing fleets could be sustained by seamount ecosystems.  \u000CTABLE OF CONTENTS Abstract Table of Contents List of Tables List of Figures Acknowledgements Dedication Co-authorship statement  ii iii vi viii xii xiii xiv  CHAPTER 1 - Introduction: seamounts as hotspots of marine life 1 1.1 What are seamounts? 1 1.2 Currents and primary production around seamounts 3 1.3 Secondary production on seamounts: zooplankton communities 4 1.4 Seamount benthic communities 6 1.5 Seamount fishes 6 1.6 Seamount visitors: large pelagic fish, cephalopods, marine mammals, and seabirdsl2 1.7 Seamount fisheries and other threats 14 1.8 Thesis theme and objectives 17 1.9 References 20 CHAPTER 2 - Abundance and distribution of seamounts in the Azores 2.1 Introduction 2.2 Methods 2.3 Results 2.3.1 Peaks dataset 2.3.2 Small and large seamounts dataset 2.4 Discussion 2.5 References  33 33 34 38 38 39 45 47  CHAPTER 3 - Modelled effects of primary and secondary production enhancement by seamounts on local fish stocks 49 3.1 Introduction 49 3.2 Methods 53 3.2.1 Modelling approach 53 3.2.2 Generic model for seamounts in the North-Atlantic ..\u2022' 54 3.2.3 Impact of primary production enhancement 61 3.3.4 The \"advection model\" 61 3.3 Results . 63 3.3.1 The \"closed seamount model\". : 63 3.3.2 Impact of primary production enhancement 65 3.3.3 The \"advection model\" 65 3.4 Discussion 68 3.5 References 73  \u000CCHAPTER 4 - Testing a seamount effect on aggregating visitors 4.1 Introduction 4.2 Methods 4.2.1 Study area .-....:...: 4.2.2 Data collection: POPA Observer program 4.2.3 Species 4.2.4 Data analyses 4.3 Results \u2022..\". 4.3.1 Tuna ! 4.3.2 Other visitors 4.3.3 Seamounts  .'  80 80 81 81 83 83 85 86 86 89 93  4.4 Discussion  95  4.5 References  98  CH APTER 5 - Fishing down the deep 5.1 Introduction 5.2 Methods 5.3 Results 5.3.1 Global trends 5.3.2 Atlantic Ocean 5.3.3 Pacific Ocean 5.3.4 Indian Ocean 5.3.5 Antarctic 5.3.6 Mean longevity of the catch 5.4 Discussion 5.5 References  ;  104 104 105 109 109 Ill Ill 116 116 116 118 120  CHAPTER 6 - Vulnerability of seamount fish to fishing: fuzzy analysis of life history attributes 124 6.1 Introduction 124 6.2 Methods 125 6.2.1. Compilation of species list 125 6.2.2. Comparisons of biological characteristics and vulnerabilities 125 6.2.3. Responses to fishing 128 6.3 Results 128 6.4 Discussion 134 6.5 References 137 CHAPTER 7 - Ecosystem simulations of management strategies for data-limited seamount fisheries 141 7.1 Introduction 141 7.2 Methods 143 7.2.1. Trophic model of seamount ecosystems 143  \u000C7.2.2. Model analyses 7.3 Results 7.3.1 Optimal fishing scenarios 7.3.2 Trade-offs 7.4 Discussion 7.5 References CHAPTER 8 - Conclusions 8.1 Introduction 8.2 Main results 8.3 Conclusions 8.4 Future research APPENDICES 1 Compilation offish species recorded on seamounts 2 List of large and small seamounts identified in the Azores EEZ 3 Details of the generic seamount model  144 146 146 149 154 156 160 160 161 166 167  168 191 ....205  \u000CLISTS OF TABLES Table 1.1 - Examples of literature supporting or opposing the main hypotheses about seamount ecosystem functioning. Note this is not a complete list but some examples only 5 Table 1.2 - List of species considered as \"seamount-aggregating\" fishes  11  Table 3.1 - Input parameters and estimates (in parentheses) from the theoretical \"closed seamount\" (closed) and \"advection\" (adv.) models of a seamount. P/B is production to biomass ratio, Q/B is consumption to biomass ratio, EE is ecotrophic efficiency, and TL is trophic level of the groups. P/B, Q/B, TL and catch were the same in the immigration and no-migration models. Bold numbers show those groups with increased biomass as a result some type of seamount effect 57 Table 3.2 - Diet matrix in weight proportions for a generic seamount model in the Northeast Atlantic. Columns stand for predators while rows stand for prey. Numbers in columns headings represent the predator groups as defined in different rows of the first two columns 58 Table 3.3- Catch (t km\"2 year _i) and total catch (t-year 1) estimated for the different fisheries considered in the theoretical seamount. DL is demersal longline; DWL is deepwater longline; SP is small pelagics fishery; T is tuna pole-and-line fishery; SW isswordfish longlinefishery; and DWT is deep-water trawl 60 Table 3.4 - Effect of primary production (PP) enhancement on orange roughy biomass at a generic seamount in the Northeast Atlantic. Estimated primary productivity for the Northeast Atlantic (PP N EA) region was 2 0 3 0 t km\"2 year\"' (SeaWIFS data set) .....66 Table 3.5 - Total immigration rate required (t-km~2-year\"'), estimated as a function of potential biomass of predators as: / = a-Bj+b; where B, is the biomass of the predatory, a is the Q/B ratio of i and b is roughly the total consumption of i in the balanced \"closed seamount model\" 66 ; Table 4.1 - Species included in the present study, the number of observations by species and summary of the data on distance from observed data points to closest seamount (km).... 84 Table 5.1 - Summary of the likelihood ratio test (Hilborn and Mangel, 1997) used to compare the goodness-of-fit of different models where the more complex equation fits better than the simple equation. Where n is the number of parameters estimated in each model; d.f. the degrees of freedom; SS the sum-of-squares. L stands for Linear models, Q for quadratic, LL for the biphase linear-linear models, C for cubic models and F for fourth order models 108  \u000CTable 5.2 - Rate of increasing depth of fishing per decade before and after the breaking point (BP) estimated using a two phase model (linear-linear) as described in Hintze (1998). Coefficient of determination (r 2) for regressions also presented 111 Table 6.1 - Number of fish species considered as \"non-seamount fishes\", \"seamount fishes\" and \"seamount-aggregating\" fishes, and number of available estimates of life history attributes and intrinsic vulnerability. Sample sizes for fish species grouped by preferred habitat is also shown. NS are \"non-seamount\" fish species while S are fish occurring on seamounts 127 Table 6.2 - Intrinsic vulnerability weighted by the Log(catch) for \"seamount\" and \"seamount-aggregating\" species reported explicitly in FAO catches ...: 132  \u000CLISTS OF FIGURES Figure 1.1 - Number of seamounts large seamount (&gt; 1km height) estimated by various authors and methods, where a) is for the Pacific Ocean, b) for the Atlantic Ocean, c) for the Indian Ocean, and d) for world's oceans. Colours refer to the.methods used: yellow is contour maps; orange is wide-beam soundings; green is multi-beam soundings; and blue is satellite altimetry 2 Figure 2.1 - Height (h) frequency distribution of all identified peaks. Solid circles are actual counts while open circles are the cumulative counts. The grey circle data point was excluded from the exponential model fit. The relationship can be expressed as A^ = 4107.6-e\"2'897', with h in km; r2=0.99. If expressed by unit area (km -2), v(H)= 4.31 \u2022e\"2'89 /l  38  Figure 2.2 - Distribution of seamounts in the Azores Exclusive Economic Zone (black line). Green circles show large seamounts while black dots show small seamount-like features. Scale goes from dark blue (deep water; about 5000m) to dark red (shallow water) 39 Figure 2.3 - Depth of the summit frequency distribution of small (left) and large (right) seamounts-lilce features 40 Figure 2.4 - Distribution of large seamounts in the Azores EEZ by mean depth of summit: a) &lt;250m; b) 250-500; c) 501-1000; d) &gt;1000m. Black dots indicate seamounts of the re ferred category 41 Figure 2.5 - Distribution of small seamount-like features in the Azores EEZ by mean depth of summit: a) &lt;250m; b) 250-500; c) 501-1000; d) &gt;1000m. Black dots indicate seamounts of the referred category 42 Figure 2.6 - Histogram of the predicted height distribution of small (light grey bars) and large (black bars) seamounts-like features. Bin size is 100m 43 Figure 2.7 - Histogram of the seamounts height to radius ratio large (black bars) seamount-like features. Bin size is 0.010  for small (grey bars) and  44  Figure 2.8 - Slope angle (tf&gt;) and seamount height (h) linear relationship for small (open circles) and large (filled circles) seamount-like features. For all seamounts the relationship can be expressed as &lt;f&gt; = 0.003-h + 0.86; r 2 = 0.53 Figure 3.1 - Flow diagram of a seamount ecosystem. The size of the nodes is proportional to the biomass of each group 64 Figure 3.2 - Potential biomass of orange roughy (t-km\"2) that could be sustained by different rates of advected micronekton (t-km\"2-year\"1) and oceanic current velocities (cm s -1 ). Scale goes from white and light blue (low rates of advection) to dark red (high rates of advection) 68  44  \u000CFigure 4.1 - Map of the Azores archipelago and its seamounts  82  Figure 4.2 - Frequency distribution of the distances to seamount summit of the dataset of tuna fishing events and of a set of an equal number of randomly selected locations. Black bars indicate observed data while light grey bars indicate randomly selected locations 87 Figure 4.3 - Tuna catch per square kilometre per year (\u00B195%CL) in relation to the distance to the nearest seamount summit in the Azores, a) skipjack, b) bigeye tuna. Bin size is 10 km. Light grey circles are those significantly higher (Dunnett test) than the overall mean (light grey line) 88 Figure 4.4 - Marine mammals sightings per square kilometre per hour (\u00B195%CL) in relation to the distance to the nearest seamount summit in the Azores, a) common dolphin, b) bottlenose dolphins, c) spotted dolphin and d) sperm whale. Bin size is 10 km. Light grey circles are those significantly higher (Dunnett test) than the overall mean (light grey line) 90 Figure 4.5 - Loggerhead turtles sightings per square kilometre per hour (\u00B195%CL) in relation to the distance to the nearest seamount summit in the Azores. Bin size is 10 km 91 Figure 4.6 - Seabirds sightings per square kilometre per hour (\u00B195%CL) in relation to the distance to the nearest seamount summit in the Azores, a) Cory's shearwater, b) terns, c) yellow-legged gull. Bin size is 10 km. Light grey circles are those significantly higher (Dunnett test) than the overall mean (light grey line) 92 Figure 4.7 - Catches and sightings (\u00B195%CL) above seamounts of various depths for those species with a significant association with seamounts. a) skipjack, b) bigeye tuna, c) common dolphin, d) Cory's shearwater. Bin size is 10 km. Light grey circles are those significantly higher (Z test) than the overall mean (light grey line)'.;' 94 Figure 5.1 - (a) Global trend of mean depth of world marine fisheries catches from 1950 to 2001 for all marine fishes including pelagics (dark grey dots) and for bottom marine fishes only (light grey squares). Open symbols are estimates for high seas areas only (beyond countries EEZs). Trend lines are fitted using the piecewise-polynomial model linear-linear (Hintze, 1998) or simple linear regression, (b) Time series of world marine bottom fisheries catches by depth strata. Catch in tonnes are logio transformed 110 Figure 5.2 - Trend of mean depth of marine bottom fisheries catches for: (a) North Atlantic; (b) Central Atlantic; (c) South Atlantic; (d) North Pacific; (e) Central Pacific; (f) South Pacific; (g) the Indian Ocean; and (h) Antarctic. Trend lines are fitted using the piecewise-polynomial model linear-linear (Hintze, 1998) or simple linear regression 113 Figure 5.3 - Time series of marine bottom fisheries catches by depth strata for: (a) North Atlantic; (b) Central Atlantic; (c) South Atlantic; (d) North Pacific; (e) Central Pacific; (f) South Pacific; (g) the Indian Ocean; and (h) Antarctic. Catch in tonnes are loge transformed 114  \u000CFigure 5.4 - (a) Global trend of mean fish longevity of the catches for all marine fishes including pelagics (dark grey dots), and for bottom marine fishes only (light grey squares), (b) Global trend of mean longevity of the 2001 world bottom marine fisheries catch by depth. Line is the least squares fit through points by using the logarithmic equation (r 2 = 0.75). Mean age at maturity shows a similar pattern 117 Figure 6.1 - Comparison of some life-history characteristics of \"non-seamount\" fish species (NS), fish occurring on seamounts (S), and \"seamount-aggregating\" species (AGG). a) longevity (Jiviax); b) age at maturity (T m); c) natural mortality (M); d) VonBertalanffy growth parameter (K). In the graphs the middle point is the mean, the box is the Mean \u00B1 S.E., and the whisker is the Mean \u00B1 95% CL 129 Figure 6.2 - Intrinsic vulnerability (Vi) index for fish species no-occurring on seamounts (NS), occurring on seamounts (S), and \"seamount-aggregating\" species (AGG). a) including all fish species; b) for species reported in FAO official landing statistics. In the graphs the middle point is the median, the box the 25%-75% percentiles, and the whisker is the range 130 Figure 6.3 - Intrinsic vulnerability (Fi) index for fish species of different habitats nooccurring on seamounts (NS), occurring on seamounts (S). Vulnerability for \"seamountaggregating\" species (AGG) also presented. In the graphs the middle point is the Median, the box the 25%-75% percentiles, and the whisker is the range. ** indicates significant differences between medians (Mann-Whitney test; Pelagic: p= 0.471; Demersal: p= 0.003; Reef-Associated: p= 0.076; Benthopelagic: p= 0.001; Bathypelagic: p= 0.806; Bathydemersal: p= 0.833) 132 Figure 6.4 - Proportion of biomass change over time for fish groups with different intrinsic vulnerabilities (V\\). Biomass change was estimated by a generic seamount ecosystem model (Morato and Pitcher, 2002) and simulating the effect of a 0.3 fishing mortality rate for each group over a 20 years period 133 Figure 7.1 - Optimized fishing rates (F), expressed as proportions of the base model, obtained by maximizing 'economy' and 'ecology' objectives. Note differences in scale 146 Figure 7.2 - Changes in group biomasses (percent change of biomass from base model) under the three different fishing scenarios: no fishing, maximizing the 'ecology' objective, and maximizing the 'economy' objective 147 Figure 7.3 - Catches (t-km\"2 y\"') for the different fishing fleets under the base model and two fishing scenarios: maximizing the 'ecology' objective function, and maximizing the 'economy' objective function 148 Figure 7.4 - Value of the catches (relative value) for the different fishing fleets under the base model and two fishing scenarios: maximizing the 'ecology' objective, and maximizing the 'economy' objective 149  \u000CFigure 7.5 - Surface plots showing optimal scenario solutions for a range of weightings of ecological and economic objective functions: a) performance of 'economy' objective, maximizing net economic value; b) performance of'social' objective, maximizing number of jobs; c) performance of 'ecology' objective, maximizing ecological 'stability' of the ecosystem. Scale goes from light blue (low performance) to dark red (high performance). Smooth surface is interpolated 151 Figure 7.6 - Surface plots from EwE model showing the resulting fishing rates, as proportion of base model rates, of the optimal scenario solutions for a range of weightings of ecological and economic objective functions: a) deepwater longline; b) deepwater trawl; c) pelagic longline; d) small pelagic fishery; e) tuna fishery; f) demersal (bottom) longline. Scale goes from light blue (low proportion of base model rates) to dark red (high proportion of base model rates) 152 Figure 7.7 - Surface plots showing the resulting ecosystem indicators and total fisheries catch for the optimal scenario solutions for a range of weightings of ecological and economic objective functions: a) total catch; b) biodiversity index (Q75), please note that this figures is shown from a different viewpoint; c) mean trophic level of the catch. Scale goes from light blue (low) to dark red (high) 153  \u000CACKNOWLEDGEMENTS I acknowledge the financial support received from the \"Fundaipao para a Ciencia e Tecnologia\" (Portugal, BD/4773/2001) and European Social Fund -through the Third Framework Programme. I am very grateful to my supervisors Tony J. Pitcher and Ricardo S. Santos for providing extremely valuable guidance, advices, ideas and support. Also to my supervisory committee members Daniel Pauly, Amanda Vincent and Les Lavkulich who provided extremely helpful assistance, discussions and ideas. I also wish to acknowledge the contribution of other Fisheries Centre and RMES members for providing tremendous help, namely Reg Watson, Maria (Deng) Palomares, Hadi Dowlatabadi, Sylvie Guenette, Adrian Kitchingman and Sherman Lai. A special thank to several people from the Department of Oceanography and Fisheries of the University of the Azores, for help and support during my entire career. Many thanks to Janice Doyle, Ann Tautz, Gerard O'doherty and Rosalie Casison for making my life much easier by dealing with extensive paper work and by solving many computer problems. Finally, 1 have to thank several anonymous reviewers of the submitted papers for effective editorial work and many helpful comments I was very lucky to be part of a group of very enthusiastic fellow students and other FC members. I have to thank all of them for the support given during my stay in Vancouver and for the discussions we had during those times.  Xll  \u000CDEDICATION I'm delighted and excited having the opportunity to dedicate this work to my grandparents Joaquim and Margarida for everything what they mean to me and for what this moment represents for them. I also want to dedicate it to Patricia and little Matilde, with sincere apologies for the time I have been absent. And to my parents Manuel and Odete and brother Filipe, with no further words. Let me share with the non-Portuguese speaking readers a poem from Fernando Pessoa (1888-1935). \"E com emogcio que cledico este trabalho ao meu avo Joaquim e avo Margarida, por tudo o que eles significant para mini e pelo que este momento representa para eles. Uma dedicagdo muito especial a Patricia e a pequeninct Matilde, com um pedido sincero cle desculpa pelo tempo que estive ausente. E aos mens pais Manuel e Odete e ao irmao Filipe, sent mais palavras.\"  Fernando Pessoa Mar portugues  Fernando Pessoa Portuguese Sea  O mar salgado, quanta do teu sal Sao lagrimas de Portugal! Por te cruzarmos, quantas maes choraram, Quantos filhos em vao rezaram! Quantas noivas ficaram por casar Para que fosses nosso, o mar!  Salt-laden sea, how much of all your salt Is tears of Portugal! For us to cross you, how many sons have kept Vigil in vain, and.mothers wept! Lived as old maids how many brides-to-be Till death, that you might be ours, sea!  Valeu a pena? Tudo vale a pena Se a alma nao e pequena. Quern querer passar alem do Bojador Tern que passar alem da dor. Deus ao mar o perigo e o abismo deu, Mas nele e que espelhou o ceu.  Was it worth? It is worth while, all, If the soul is not small. Whoever means to sail beyond the Cape Must double sorrow - no escape. Peril and abyss has God to the sea given And yet made it the mirror of heaven Translated into English by J.Griffin  Xlll  \u000CCO-AUTHORSHIP STATEMENT  CHAPTER 2  Morato, T., M. Machete, A. Kitchingman, F. Tempera, S. Lai, G. Menezes, R.S. Santos and T.J. Pitcher (submitted) Abundance and distribution of seamounts in the Azores. Marine Ecology Progress Series. Co-authorship statement- my contribution to this paper was: 1) identification of research program, 2) performing the research, 3) data analyses, 4) manuscript preparation  CHAPTER 3  Morato, T., C. Bulman and T.J. Pitcher (submitted) Impact of primary production enhancement by seamounts on local fish stocks. Deep-Sea Research II. Co-authorship statement- my contribution to this paper was: 1) identification and design of research program, 2) performing the research, 3) data analyses, 4) manuscript preparation  CHAPTER 4  8- Morato, T., D.A. Varkey, C. Damaso, M. Machete, M. Santos, R. Prieto, R.S. Santos and T.J. Pitcher (submitted) Testing a seamount effect on aggregating visitors. Marine Ecology Progress Series. Co-authorship statement- my contribution to this paper was: 1) identification and design of research program, 2) performing the research, 3) data analyses, 4) manuscript preparation  \u000CCHAPTER 5  9- Morato, T., R. Watson, T.J. Pitcher and D. Pauly (2006) Fishing down the deep. Fish and Fisheries 7: 24-34. Co-authorship statement- my contribution to this paper was: 1) identification and design of research program, 2) performing the research, 3) data analyses, 4) manuscript preparation  CHAPTER 6  Morato, T., W.W.L. Cheung and T.J. Pitcher (2006) Vulnerability of seamount fish to fishing: fuzzy analysis of life history attributes. Journal of Fish Biology 68(1): 209-221. Co-authorship statement- my contribution to this paper was: 1) performing the research, 2) data analyses, 3) manuscript preparation  CHAPTER 7  Morato, T. and T.J. Pitcher (2005) Ecosystem simulations in support of management of data-limited seamount fisheries. In: G.H. Kruse, V.F. Gallucci, D.E. Hay, R.I. Perry, R.M. Peterman, T.C. Shirley, P.D. Spencer, B. Wilson and D. Woodby (eds.) Fisheries assessment and management, in data-limited situations. Alaska Sea Grant, University of  Alaska Fairbanks, Lowell Wakefield Fisheries Symposium Series 21, pp. 467-486. Co-authorship statement- my contribution to this paper was: 1) identification and design of research program, 2) performing the research, 3) data analyses, 4) manuscript preparation  \u000CCHAPTER 1 INTRODUCTION: SEAMOUNTS AS HOTSPOTS OF MARINE LIFE  1  1.1 WHAT ARE SEAMOUNTS?  Seamounts are undersea mountains (usually of volcanic origin) rising from the seafloor and peaking below sea level (Epp and Smoot, 1989). Underwater mountains of heights above 1000 m are considered to be seamounts, those between 500-1000 m as knolls, and those below 500 m as hills (Rogers, 1994). Typically, seamounts are formed by volcanic activity over hotspots in the earth's crust (Epp and Smoot, 1989). Spreading of the sea floor away from these hotspots via plate tectonic movements means that seamounts are often arranged in long chains or clusters that radiate out from such spreading zones (Menard and Dietz, 1951; Menard, 1964). Seamounts can have slopes of up to 60\u00B0, much greater than anywhere else in the deep sea, and are often much younger than the surrounding sea floor. A seamount tall enough to break the sea surface is called an oceanic island, e.g., the islands of Hawaii, the Azores and Bermuda were all underwater seamounts at some point in the past. :  y  \u2022  Though most people may be unaware of it, underwater seamounts are fairly common. Estimates vary largely (Figure 1.1), but studies suggest that there may be from 1.9 to 130 thousand large seamounts, those of heights over 1000 m, in the Pacific Ocean (Viau and Cailleux, 1971; Cailleux, 1975; Jordan et al., 1983; Smith and Jordan, 1988; Abers et ai, 1988; Wessel and Lyons, 1997), from 1 to 2.8 thousand the Atlantic Ocean (Viau and Cailleux, 1971; Litvin and Rudenko, 1972; Smith and Cann, 1990) and from 500 to 900 large seamounts in the Indian Ocean (Viau and Cailleux, 1971; Cailleux, 1975).  1  A version of this chapter has been published. Morato, T. 2003. Seamounts - hotspots of marine life. ICES Newsletter 40: 4-6. http://www.ices.dk/marineworld/seamounts.asp  \u000C1,900 (Viau and Caileux. 1971) 2,400 (Caileux, 1975)  \u202232;O0Oi (Smithand Jordan: 1988) 8,800:(Wessel lind Uyohs\u00BBl997) 9;200:(Kilchingman and Lai. 2004) 10.000 (Menard, 1959)  IK 1,000  10,000  33,000 (Jordanefa/S\"l983) 70;000: (WesseUnaiiyoBsSl 997) 130,000 (.J/it'i.? el al liM) y ; 100,000  1,000,000  1,000 (Viau and Cailleux. 1971) i  1,000 (Utviri aral:Riidenkfi;\u00BB1972)  2,800 (Smi(h and Cann&gt;!99o) | '3v300:(Rjtehingman and Lai: 2004)  ,000  10,000  100,000  1,000,000  10,000  100,000  ,000,000  i500:';(Cailleiix, 1975)  I  900 (Viau and Cailleux, 197l) :1 ;900;(Kilchingman and l,ai, 2004)  1,000  3,800 (Viau and Cailleux. 1971) 5,500s(Cailleiix, 1975) 8,600i(Gniig and Sandwell:4988)  k  andl.ai. 2004)  100,000 (Wcssel,200!)  15 ,000 (Wessel 2001)  1,000  10,000  100,000  1,000,000  Numbers of large seamount (&gt; 1km height) Figure 1.1 - Number of seamounts large seamount (&gt; 1km height) estimated by various authors and methods, where a) is for the Pacific Ocean, b) for the Atlantic Ocean, c) for the Indian Ocean, and d) for world's oceans. Colours refer to the methods used: yellow is contour maps; orange is wide-beam soundings; green is multi-beam soundings; and blue is satellite altimetry.  \u000CThe world's number of large seamounts is still unknown, but estimates vary from 3.8 to over 100 thousand (Viau and Cailleux, 1971 Cailleux, 1975; Craig and Sandwell, 1988; Marova, 2000; Wessel, 2001). Global seamount datasets containing information on world's seamount positions are rare, and most larger datasets often only contain data for single oceans (e.g., Fornari et al., 1987; Smith and Jordan, 1988; Epp and Smoot, 1989; Smith and Cann, 1990; Wessel and Lyons, 1997). Scientific knowledge on seamounts is still very poor and fundamental questions such as how many are out there are still hard to answer. In fact, only a small fraction of the world's seamounts have actually been mapped bathymetrically (Wessel and Lyons, 1997).  1.2 CURRENTS AND PRIMARY PRODUCTION AROUND SEAMOUNTS  Seamounts are said to be hotspots of marine life in the relatively empty open ocean (Rogers, 1994). They tend to enhance water currents (Genin et al., 1986; Boehlert, 1988) and can have their own localized tides, eddies and upwellings (Lueck and Mudge, 1997) where cold deepwater moves up from the deep along the steep sides of the seamount. These patterns may enhance primary production (PP) over and around seamounts due either to uplifting of isotherms into the euphotic zone and introducing biogenes into nutrient-poor water (Genin and Boehlert, 1985; Dower et al., 1992; Odate and Furuya, 1998; Mourino et al., 2001), or to stabilization of the water column above the seamount, maintaining phytoplankton cells in a suitable light regime, promoting the growth of diatoms, and increasing growth rates and PP (Comeau et al., 1995). For example, Mourino et al. (2001) showed that local increase in chlorophyll a, enhanced carbon incorporation rates and changes in phytoplankton species composition were associated with a seamount. Although some investigations have failed to demonstrate persistent high chlorophyll a patches over seamounts (Pelaez and McGowan, 1986), Mourino et al. (2001) demonstrated that production enhancement effects were subjected to a large degree of spatial and temporal variability both at seasonal and shorter time scales. The effects of seasonality on upwelling may be partially responsible for the large variation in the results and conclusions of seamount studies.  \u000CNevertheless, the hypothesis that seamounts enhance PP is not yet well tested (Table 1.1) and a general procedure to test this hypothesis is still lacking.  1.3 SECONDARY PRODUCTION ON SEAMOUNTS: ZOOPLANKTON COMMUNITIES  The biomass of zooplankton is often high over seamounts, but, as for PP, evidence concerning zooplankton features over seamounts is conflicting (Table 1.1). Huskin et al. (2001) concluded that mesoscale structures (such as seamounts) influence zooplankton distribution and abundance, although more detailed temporal and spatial studies were said to be required to determine the real influence of them. Fedosova (1974) reported increases in zooplankton abundances over seamounts of 2 to 8 fold, while Huskin et al. (2001) reported a 1.6 fold increase. On the other hand, an absence of zooplankton above seamounts due to grazing or other effects were detected in several studies (Genin et al., 1994; Haury et al., 2000), while other studies reported no differences in zooplankton biomass either on or off seamounts (Voronina and Timonin, 1986; Dower and Mackas, 1996). Sime-Ngando et al. (1992) reported an increase of ciliate biomass (micro-zooplankton) over seamounts, probably related to seamount-induced physical forcing, which likely generates microhabitats favourable to the growth of opportunistic or physiologically-adapted populations. Regarding pelagic crustaceans over and around seamounts, two main features have been observed (Vereshchaka, 1994): 1) the rise of lines of equal size, abundances and biomass of the pelagic animals, and 2) the decrease in abundance, biomass and sizes of pelagic animals near the bottom water layer. One of the possible important causes of the decrease in abundance and biomass of pelagic shrimps near the bottom is that they are consumed by benthic and benthopelagic predators. Vereshchaka (1996) concluded that the abundance of pelagic animals decreases while the concentration of benthopelagic predators increases near the seafloor and the role of the former in planktonic communities falls in the near-bottom layer.  \u000CTable 1.1 - Examples of literature supporting or opposing the main hypotheses about seamount ecosystem functioning. Note this is not a complete list but some examples only. Hypotheses Enclosed circulation cells around seamounts (Taylor columns) Increased phytoplankton biomass and primary productivity Increased zooplankton biomass (micro and meso) Increased fish larvae biomass Increased micronekton biomass Increased demersal and pelagic fish biomass Tuna aggregations Swordfish aggregations Increased occurrence of sharks Increased occurrence of cephalopods Increased occurrence of marine mammals Increased occurrence of seabirds Increased occurrence of sea-turtles Increased occurrence of corals and other epibenthic megafauna High endemism Increased demersal and pelagic fish biomass are supported by: bottom trapping of migrating zooplankton horizontal flux of non-migrating . zooplankton locally enhanced primary production  Supporting evidence  Opposing evidence  Judgment  17, 22, 32, 34, 52  35  7, 19,26, 40, 48  10, 54  Tested and supported by data Not fully tested  4, 5, 20, 35, 37, 47, 53,55,58 46 13, 31 6  11, 14, 24, 29, 42  Not fully tested  21, 56 25,28, 57  22 27, 30 60 9, 15  15  Not fully tested Not fully tested Tested and supported by data Not fully tested Not tested Not tested Not fully tested Not tested Not tested Not tested Not fully tested  12,36,39, 44,61  50,59  Not fully tested  18,41,43,45 49 16, 38  3,24,31,33,51 8,9,33 \u2022 1, 2, 6  52  . Not fully tested Not fully tested v.. Not fully tested  1) Uda and Ishino, 1958; 2) Hubbs, 1959; 3) Isaacs and Schwartzlose, 1965; 4) Simpson and Heydorn, 1965; 5) Fedosova, 1974; 6) Uchida and Tagami, 1984; 7) Genin and Boehlert, 1985; 8) Tseitlin, 1985; 9) Genin et al., 1986; 10) Pelaez and McGowan, 1986; 11) Voronina and Timonin, 1986; 12) Wilson and Kaufman, 1987; 13) Bohelert, 1988; 14) Genin et al., 1988; 15) Kaufman et al., 1989; 16) Klimley et al., 1988; 17) Brink, 1990; 18) Fonteneau, 1991; 19) Dower et al., 1992; 20) Sime-Ngando et al., 1992; 21) Boehlert and Mundy, 1993; 22) Reeves and Mitchell, 1993; 23) Freeland, 1994; 24) Genin et al., 1994; 25) Vereshchaka, 1994; 26) Comeau et al., 1995; 27) Haney et al., 1995; 28) Vereshchaka, 1995; 29) Dower and Mackas, 1996; 30) Monteiro e/ al., 1996; 31) Vereshchaka, 1996; 32) Goldner and Chapman, 1997; 33) Koslow, 1997; 34) Lueck and Mudge, 1997; 35) Mullimeaux and Mills, 1997; 36) Parin et al., 1997; 37) Saltzman and Wishner, 1997; 38) Hazin et al., 1998; 39) Koslow and Gowlett-Holmes, 1998; 40) Odate and Furuya, 1998; 41) Holland et al., 1999; 42) Haury et al., 2000; 43) Itano and Holland, 2000; 44) Richer de Forges et al., 2000; 45) Sibert et al., 2000; 46) Dower and Perry, 2001; 47) Huskin et al., 2001; 48) Mourino et al., 2001; 49) Sedberry and Loefer, 2001; 50) Fock et al., 2002a; 51) Fock et al., 2002b; 52) Diekmann and Piatkowski, 2004; 53) Fock et al., 2004; 54) Genin, 2004; 55) Martin and Nellen, 2004; 56) Nellen and Ruseler, 2004; 57) Pusch et al., 2004; 58) SchnackSchiel and Henmng, 2004; 59) Tracey et al., 2004; 60) Dellinger, 2005; 61) Vereshchaka, 2005.  \u000CAnother example of high zooplankton abundance over seamounts was reported by Dower and Perry (2001) who found a high abundance of larval rockfish over Cobb Seamount (SW of Vancouver Island, Canada). They suggested that a persistent clockwise eddy, consistent with a stratified Taylor cone, plays a critical role in retaining larval rockfish and may contribute to the process of self-recruitment.  1 . 4 SEAMOUNT BENTHIC COMMUNITIES  On the seamount floor there are often rich communities dominated by suspension feeders, e.g., gorgonians and other corals (Richer de Forges et al., 2000; Ohkushi and Natori, 2001; Koslow et al., 2001), that may be particularly susceptible and sensitive to disturbance by trawling (Probert et al., 1997; Koslow et al., 2001). Enhanced currents and steep slopes expose the volcanic rocks and favour the growth of suspension feeders in these benthic seamount communities (Genin et al., 1986; Grigg et al., 1987; Wilson and Kaufmann, 1987; Rogers, 1994), in contrast to the deposit feeders typical of most deep-sea benthos. The abundance and biomass of benthic organisms on some seamounts was, however, observed to be very low when compared to other hard bottom habitats at similar depths (Grigg et al., 1987; Gillet and Dauvin, 2000). Though the diversity and exceptionally localized distribution of species living in these communities are acknowledged (Richer de Forges et al., 2000), their biology and life history remain poorly studied, except for some indications that some of these species may be extremely long-lived, e.g., up to maximum ages of over 100 years (Grigg, 1993).  1 . 5 SEAMOUNT FISHES  Seamounts have received much attention mainly because of the presence of substantial aggregations of forage fishes in mid- and deep-water (Boehlert and Sasaki, 1988; Rogers, 1994; Koslow, 1996, 1997; Koslow et al., 2000), which became the prime target of a highly technological fishery.  \u000CWhat are \"seamount fishes\"? This is a simple question, yet the answer remains elusive. The designation of \"seamount fishes\" or seamount species has been widely employed (e.g. Koslow, 1996; Probert et al., 1997; Probert, 1999; Koslow et al., 2000; Fock et al., 2002a; Tracey et al., 2004), but the criteria used in identifying those taxa are rarely defined. Pioneering work on seamounts focused on the intriguing question: what species inhabit individual banks and seamounts? Since then, a large number of studies have described the fish fauna inhabiting these features. The results -of early studies have been summarised in a number of reviews (e.g., Wilson and Kaufman, 1987; Rogers, 1994; Froese and Sampang, 2004). The question is how can we appropriately classify those fishes that live in association with seamounts from those more typical of other habitats, or that span both. Most species appear to occupy a range of habitats. Many fish species occur on seamounts or congregate over their summits to feed due to enhanced levels of planktonic production, hydrographic retention mechanisms such as eddies, or being able to remain close to the bottom yet reach shallower depths (Tseytlin, 1985; Genin et al., 1988; Koslow, 1997). This may be the case for some commercially important species of deepwater fish, such as orange roughy, Hoplostethus atlanticus, pelagic armorhead, Pseudopentaceros wheeleri, oreosmatids, e.g. Allocyttus niger and Pseudocyttus maculatus, and alfonsinos, Beryx spp., as well as for some sharks (Klimley et al., 1988; Hazin et al., 1998), tunas (Holland et al., 1999; Itano and Holland, 2000; Sibert et al., 2000) and other large pelagic predators (Ward et al., 2000; Sedberry and Loefer, 2001).  A range of fish species sporadically aggregate around shallow seamounts mainly for spawning; for instance, reef-associated fish like serranids (Mycteroperca rosacea, Paranthias colonus) and jacks (Caranx sexfasciatus,  Seriola lalandi) (Sala et al., 2003). Recently,  Tsukamoto et al. (2003) found that the spawning site of the Japanese eel (Anguilla japonica) in the western North Pacific appears to be near three seamounts, 2000-3000 km away from their freshwater habitats. Further examples are the deep-bodied species of the orders Zeiformes  (mainly  the genera Antigonia,  Capros, Zenopsis  and  Cyttopsis) and  \u000CSyngnathiformes (in particular the genus, Macroramphosus), which are. the dominant fishes (&lt;500 m depth) of the Great Meteor seamount, a large, isolated, flat-topped feature in the central eastern Atlantic (Fock et al., 2002a). These fish are also the main prey of large demersal predators inhabiting the slopes of the Azores islands and seamounts (Morato et al., 1999, 2000, 2001, 2003). However, as well as occurring on seamount features, in some areas they are among the most abundant fishes from adjacent continental shelves. Coral reef scientists faced exactly the same problem when trying to provide a definition of \"reef fishes\" (see Choat and Bellwood, 1991; Bellwood, 1996; Bellwood, 1998; Robertson, 1998). They first tried to find potential taxonomic and ecological characteristics that could distinguish coral fish assemblages from other fish assemblages (Choat and Bellwood, 1991). They also proposed a consensus list of fish families that would better describe, not define, a coral reef assemblage (Bellwood, 1996). They concluded from this list that most reef fishes are characteristic of, but not restricted to, coral reefs (Bellwood, 1996). Coral reef scientists are still debating the meaning of reef fishes, and apparently they can only agree on the tautological definition of reef fishes: those that live on coral reefs. The definition of \"seamount fishes\" may be similar and involve the same redundancy with trying to define a functional type of label that applies only in part to the ecology of the species: seamount fishes are those individual fishes that live on seamounts. There is a group of fish species, however, living on (or visiting) seamounts that have raised much attention because of their high abundance and good flesh quality: they include orange roughy, pelagic armorhead and alfonsinos. These fish aggregate on top and around seamounts and have been object of intense exploitation since the late 1970's. The discovery of these commercially important aggregations of deepwater fish species on seamount features have changed the idea that significant commercial fisheries would never develop in the deep sea due to scarcity at those depths and poor palatability of fish flesh. Koslow (1996, 1997) explored the differences of fish species aggregating on seamounts from those generally considered typical of the deep-sea environment. By addressing this problem in an energetic  \u000Cperspective (see below), he concluded that fish species that aggregate around seamounts appeared to form a distinct guild. He found that these fishes differ markedly from other deepwater species in their relatively high levels of food consumption and energy expenditure, low growth and productivity and a robust body composition and body plan suited for strong swimming currents. Koslow called them \"seamount-associated fishes\" or \"seamountaggregating fishes\" (Koslow, 1996; Koslow et al., 2000). For the propose of this thesis we will consider: 1) \"seamount fishes\" as those individual fishes that live on seamounts; and 2) \"seamount-aggregating fishes\" as those species that form large aggregations around these features and that are the main target for fisheries that develop around seamounts. Numerous studies have described the species richness and diversity of fish fauna on seamounts. Wilson and Kaufman (1987) reviewed seamount biota worldwide and reported about 450 fishes collected from more than 60 seamounts. Rogers (1994) provided a list of 77 commercial species fished on seamounts. Since then, more detailed studies of certain seamounts and seamount chains provide more comprehensive species lists, especially with an increase in exploratory fishing in the last two decades (e.g., Parin et al., 1997; Koslow and Gowlett-Holmes, 1998; Grandperrin et al., 1999; Fock et al., 2002a; Moore et al., 2003; Clark and Roberts, 2003; Tracey et al., 2004). Froese and Sampang (2004) compiled a list of fish that have been reported on seamounts, and found 535 fish species recognized as seamount fishes. Based on the best available information, I collated species lists for fishes that occur or aggregate in and around seamounts. \"Seamount fishes\" are defined as fish that have been reported as occurring on seamounts, even if rare. A total of 798 species of marine fishes were classified as \"seamount fishes\" (see Appendix 1). I now have the most comprehensive checklist of seamount fishes, even if incomplete. The number of known seamount fishes represents about 2.8% of the total number of known fish species and belong to 165 families (32% of the 515 known). Although the number of known seamount fishes is comparatively small, because they encompass a third of fish families,  \u000Cabout half of the orders and many unique adaptations, they represent a relatively large and unique portion of fish biodiversity (Froese and Sampang, 2004). Currently recognized seamount fishes have different habitat preferences (associations). Forty-three species are pelagic, 94 are reef-associated, 118 demersal, 68 benthopelagic, 223 bathypelagic, and 252 bathypelagic. A large portion of the seamount fish community is composed by deep-sea fishes, but many shallow water species are also known to occur on these features. According to Froese and Sampamg (2004), only 6 seamount fishes are included in the 2000 IUCN Red List (Hilton-Taylor, 2000): Sebastes paucispinis  is listed as 'critically endangered',  Sphoeroides pachygaster and paucipinis are listed as 'vulnerable', and Squalus acanthias, Dalatias licha and Prionace glauca are listed as 'lower risk, near threatened.' Other seamount fishes have not been evaluated so far.  The second category of fish species living on seamounts considered for the purpose of this chapter is the \"seamount-aggregating fish\". A list of 23 fishes was compiled (Table 1.2). I acknowledge that this list is preliminary and its completeness and accuracy will improve as we gain more knowledge about the ecology of seamount and deepwater fish species. Some of the most well known representatives of this group include the deep-water fishes: orange roughy, alfosinos (Beryx splendens and B. decadactylus), Patagonian toothfish {Dissostichus eleginoides), oreos, pelagic armourhead, several species of rockfishes (Sebastes spp.) (Koslow, 1996; Koslow et al., 2000) and probably roundnose grenadier (Coryphaenoides rupestris) (Vinnichenko, 2002a). These species are the main target of the large-scale fisheries that occurs on top and around seamounts. These fish aggregations are supported in the otherwise food-poor deep sea by the enhanced flux of prey organisms past the seamounts and the interception and trapping of vertical migrators by the uplifted topography (Tseytlin, 1985; Genin et al., 1988; Koslow, 1997). It has long been held, however, that the high biomass of fish on seamounts result, at least in part, from locally enhanced primary production and the subsequent bottom-up transfer of this energy to higher trophic levels in seamount food chains (Uda and Ishino, 1958; Hubbs, 1959; Uchida and Tagami, 1984).  \u000CTable 1.2 - List of species considered as \"seamount-aggregating\" fishes. Species Alepocephalus bairdii Allocyttus niger  a a  Allocyttvs verrucosus Aphanopus carbo  b  Beryx decadactylus  .  .  ;  Aggregation  Reference  Maybe  6, 11  True  3,4  Maybe  12  True  10  True  4,9  Beryx splendens  True  Coryphaenoides rupestris  True  8, 5  Dissostichus eleginoides.  True  -\u2022-4  Epigonus telescopus *  True  10, 5  Hoplostethus atlanticus  True  3,4,8,5  Hoplostethus mediterraneus  Maybe  6  Lepidion eques *  Maybe  6  Maybe  6  Maybe  11  True  3,4  Pseudopentaceros richardsoni  True  9  Pseudopentaceros wheeleri *  True  2,3,4  Sebastes entomelas *' c  Maybe  1  Sebastes helvomaculatus *' c  Maybe  1  True  5  Mora moro Neocyttus rhomboidalis *' Pseudocyttus maculatus  Sebastes marinus Sebastes mentella  a  a  ' 3,4,7,9  True  8  Sebastes paucispinis  c  Maybe  1  Sebastes ruberrimus  c  Maybe  1  * intrinsic vulnerability index not estimated due to the lack of sufficient parameters, a) forming large shoals over rough ground near pinnacles and canyons; b) not a typical \"seamount-aggregating\" fish (sensu Koslow, 1996); c) juveniles form large schools. References: 1) Parker and Tunnicliffe, 1994; 2) Rogers, 1994; 3) Koslow, 1996; 4) Koslow et al., 2000; 5) Hareide and Games, 2001; 6) Pineiro et al., 2001; 7) Ramos et al., 2001; 8) Shibanov et al., 2002; 9) Vinnichenko, 2002a; 10) Vinnichenko, 2002b; 11) Allain et al., 2003; 12) Fishbase - Froese and Pauly, 2003.  \u000CStudies of fish composition on seamounts have often reported high levels of endemism, exceeding 40% in one case (e.g. Wilson and Kaufmann, 1987; Parin et al., 1997; Richer de Forges et al., 2000; Froese and Sampang, 2004). However, estimates can be variable, and some studies have found little evidences of endemic seamount fish species. For example, none of the fish species recorded by Tracey et al. (2004) on New Zealand seamounts were regarded as endemic to any seamount, seamount chain, or even the region. However, the data were from fish trawls designed to capture relatively large-sized fishes, and these fish will generally tend to have wider distributions. Small sampling gear used off Tasmania revealed previously unknown, and probably endemic, species of Paralaemonena (Family Moridae), and Cataetyx (Family Bythitidae) (Koslow and Gowlett-Holmes, 1998). The number of seamount endemic species or the number of fish that lives only on seamounts is still not known. Froese and Sampang (2004) speculated that of the 535 seamount fishes they identified, 62 species are reported from only one seamount, suggesting a high rate of endemism. Generally, species accounts from seamounts focus on just the samples collected from the seamount features, and studies have not considered how specific the species composition is to seamounts.  1 . 6 SEAMOUNT VISITORS: LARGE PELAGIC FISH, CEPHALOPODS, MARINE MAMMALS, AND SEABIRDS  While the importance of seamounts for bottom fishes is very well documented (see above), the importance for large pelagic or visiting organisms has been poorly tested (see Table 1.1). However, it has been hypothesised that there are higher abundances of some \"visiting\" animals, such as tuna, sharks, billfishes, marine mammals, sea-turtles and even seabirds, over seamounts but this has been based on sparse records, warranting further examination. Sharks appear to be attracted to seamounts as demonstrated by Klimley et al. (1988), who showed that hammerhead sharks remained grouped at a seamount in the Gulf of California (Mexico) during the day and moved separately into the surrounding pelagic environment at night. Hazin et al. (1998) showed that catches of gray sharks were significantly higher  \u000Caround seamounts, mainly in those with summits of about 300m and low-sloping depth profiles. The reasons for these aggregations are not clear, but Hazin et al. (1998) assumed that seamounts were used by some sharks as feeding stations. It is known by fishermen and researchers that large biomasses of tuna are sometimes concentrated on seamounts (Fonteneau, 1991; Holland et al., 1999; Itano and Holland, 2000; Sibert et al., 2000). Several thousand tons of tuna can be taken yearly on some remote seamounts, while other seamounts closer to land are apparently always poor in tuna, even when they are located in regular fishing areas (Fonteneau, 1991). It is possible that seamounts act both as feeding stations and as orientation points in the larger-scale movement patterns of these fish (Holland et al., 1999). The navigation role might explain why remote seamounts aggregate more tuna than seamounts located closer to land masses, as noticed by Fonteneau (1991). Swordfish and other billfishes appear also to be attracted to complex highrelief bottom structures. For example, swordfish that moved away from the Charleston Bump were frequently found associated with seamounts, submarine canyons, and with thermal fronts of the northern wall of the Gulf Stream (Sedberry and Loefer, 2001).  Cephalopods may drift passively over and around seamounts and be subject to predation (Nesis, 1986). The author suggested that this link might be one of the reasons for the high abundance of benthic and demersal fish species on certain seamounts. Cephalopod fauna of seamounts may consist of four main components (Nesis, 1986): 1) bottom and near-bottom species that reside there permanently (e.g., genera Froekenia, Danoctopus and Scaeurgus)\\ 2) pelagic species that descend to or near the bottom to spawn, and including either those that maintain themselves constantly in midwater above or near seamounts, or those that migrate actively to seamounts for sexual maturation (e.g., genera Ornithoteuthis, some Todarodes, Lycoteuthis); 3) mesopelagic species that may migrate vertically and descend to the bottom during the daytime (e.g., Enoploteuthidae and Octopoteuthidae); and 4) non-migrating pelagic  species  that  permanently  Vampyroteuthidae, Cranchiidae).  inhabit  the  water  column  (Mastigoteuthidae,  \u000CAlthough several works have correlated marine mammals occurrence with complex and steep topographies (e. g. Schoenherr, 1991; Balcomb, 1989; Canadas et al., 2002; Hooker et al., 2002; Hastie et al., 2004; Yen et al., 2004) the literature addressing their association with seamounts is scarce. Reeves and Mitchell (1993) noticed that when in pelagic areas Baird's beaked whales (Berardius bairdii) are observed close to submarine escarpments and seamounts. Seabird density and biomass has been reported to be higher around seamounts when compared to adjacent areas (Haney et al., 1995; Monteiro et al., 1996). Haney et al. (1995) showed that seabird biomass was eight times higher within a 30-km radius centred on a seamount summit. The authors attributed seabird aggregation observed at the seamount to be related to an increase of food availability.  1 . 7 SEAMOUNT FISHERIES AND OTHER THREATS  The steady and steep decline of global catches since the 1980s (Watson and Pauly, 2001) alludes to the fact that the world's fisheries resources are in serious danger of depletion (e.g. Pauly and Christensen, 1995; Pauly et al., 1998; Pitcher, 2001; Pauly et al., 2002), undoubtedly due to poor management practices and increased fishing pressure (Ludwig et al., 1993). Unsustainable fishing practices along with an excessive level of investment in fishing capacity have resulted in serious stock depletion on most continental shelves, thus creating new pressures on alternative fishing grounds (Pauly et al., 2002). Fisheries are evidently expanding offshore (e.g., Christensen et al., 2003; Myers and Worm, 2003; Pauly et al., 2003) and into deeper waters (Koslow et al., 2000; Garibaldi and Limongelli, 2003; FAO, 2004; Gianni, 2004). The expansion into offshore areas has been well documented, (for example, fisheries targeting oceanic tuna, billfishes and their relatives covered the world ocean by the early 1980s; Myers and Worm, 2003), but the extension into deeper waters is less well analysed, While many local examples of fisheries expansion into deeper waters have been reported (e.g., some European, Soviet, U.S.A., Canada, New  \u000CZealand and Australian fishing fleets: see references in Hopper, 1995; Moore, 1999; Koslow et al., 2000; Roberts, 2002), we still lack a global quantitative analysis. Seamounts are also among those \"newly\" targeted ecosystems that, since the second half of the 20th century, have been intensively fished (Rogers, 1994; Koslow et al., 2000). Deepwater fisheries in general and seamounts fisheries in particular usually exhibit a boom and bust sequence, crashing within about ten years of their initial development. This was the case of the orange roughy {Hoplostethus atlanticus) fisheries off New Zealand (Clark, 1995; Clark, 1999; Clark et al., 2000), Australia (Wayte and Bax, 2001; Lack et al., 2003), Namibia (Boyer et al, 2001; Branch,;2001) and even in the North Atlantic (Branch, 2001), the pelagic armourhead {Pseudopentaceros wheeleri) fisheries over seamounts in international waters off Hawaii (Boehlert and Genin, 1987), and the blue ling {Molva dipterygia) fisheries in the North Atlantic (Bergstad et al., 2003; Devine et al., 2006). As seamounts are rapidly depleted, the continued existence of a fishery depends upon the continuing discovery of unexploited seamounts with large fish aggregations. The species targeted by fisheries at seamounts have a very low overall abundance, but aggregate at seamounts as part of their life cycle strategy, e.g., for spawning (Clarke et al., 1996). They are often long-lived (some species to over 100 years), slow growing, late maturing (at about 30 years), and have low reproductive potential (Koslow, 1997). When they have been fished out it is estimated that it will take decades for these localised stocks to recover, as they are thought to have limited exchange with other seamounts (Koslow, 1997). This makes these fish communities very vulnerable to overfishing and the problem is even more pronounced in seamounts located in international waters where management strategies and agreements are absent. However, information on seamounts fisheries is very sparse, and it is difficult to make a distinction between deep-water fishing activities in general and those occurring on seamounts (Koslow et al., 2000). Moreover, fish species living on seamounts are also known to occur on other habitats, such as the continental slope, and landings statistics are not spatially allocated,  \u000Cmaking it difficult to make an estimate of the total fisheries occurring on seamounts worldwide. Depletion of fish stocks is not the only concern. Extensive trawling activities on seamounts are damaging benthic (bottom living) communities, particularly for dominant communities of corals and other suspension feeders (Koslow et al., 2001). The impact of trawling on complex seamount reefs appears to be dramatic, with the coral substrate and associated community largely removed from the most heavily fished seamounts (Koslow et al., 2001). Such massive removal of natural and structural components to the ecosystem has negative consequences on seamount biodiversity (Probert, 1999). There is a rising concern about the threats to seamount ecosystems in the Economic Exclusive Zones of coastal states and on the High Seas. Overfishing, even depletion, of the often slow-growing and late-reproducing fish populations, and the destructive impact of trawling activities on the benthic communities of seamounts, poses an immediate risk to these isolated ecosystems. Consequently, Canada, Australia, Portugal and New Zealand have begun to take the first steps towards protecting seamounts. In the Atlantic, no such protective measures have been established, but the Oslo and Paris Commission (OSPAR) is considering this issue at the moment within the framework of Annex V of the Convention on the Protection of the Marine Environment in the North-East Atlantic (OSPAR Convention). Seamounts are on an initial draft list of habitats and species that require conservation action, and the developing OSPAR Marine Protected Areas programme may provide one of the possible mechanisms. In addition, seamounts dominated by hard substrata in the waters of the European Community qualify for site protection under the European Habitats Directive (1992, Natura 2000 code 1170 \"reefs\" in the Interpretation Manual of European Union Habitats EUR 15/2).  \u000C1.8 THESIS THEME AND OBJECTIVES  The presence of numerous seamounts in the world's oceans has only become known to the scientific community during the last 50 years (Rogers, 1994). The potential importance of these steep-sided undersea mountains to biogeography and diversity was only recognized after Hubbs (1959) work, but this environment has remained very poorly investigated (Forges et al., 2000). Hence, the most fundamental questions remain incompletely answered (see Table 1.1). The general objective of this PhD was to explore some fundamental questions about seamount ecology and fisheries: 1) Estimate seamount numbers and locations around the Azores islands; The main goal of Chapter 2 is to infer potential seamount locations and thus to generate estimates of the actual number of seamounts in the Azores. I will also describe seamount population according to location, depth of the summit, height, basal area, height to radius ratio, the average slope, and distance to nearest seamount. The output of this chapter will be used later (Chapter 4) to test some hypothesis related to seamount ecosystem functioning. 2) Examine the impact of a potential increase of local primary production on higher trophic levels; In an effort to better understand seamount ecosystem functioning, Chapter 3 will address how complex seamount food web structures are sustained. A generic seamount ecosystem model from the Northeast Atlantic will be used to test the impact of a potential increase of local primary production on higher trophic levels. 3) Quantify the amount of advected prey necessary to sustain a \"typical\" seamount fish community and to explore if the necessary prey can be supported with food provided by local oceanographic conditions (also in Chapter 3);  \u000C4) Test if the reported high abundances of seamount \"visitors\" on top and around seamounts are true; Some previous studies have focused on analysing the auto-ecology of some organisms in relation to seamounts. In Chapter 4, I will use data from a fishery observer program to explicitly test if the abundances of tuna, marine mammals, sea turtles and seabirds observed at Azores seamounts are higher than expected by chance. This chapter will also use the seamount dataset produced in chapter 2. 5) Test if a reported historical expansion into deeper-waters can be detected in global landings datasets; Whereas previous studies on global trends of fisheries have focused on catch or biomass changes over time, in Chapter 5 I will analyse changes in the mean depth of fishing to test if the predicted expansion into deeper-waters can be detected in global landings datasets. I will also test for the predicted higher vulnerability of deep-water fisheries resources, using longevity as the main proxy for vulnerability. 6) Test the hypothesis that \"seamount fishes\" generally have a higher than average vulnerability to fishing exploitation; Previous studies have found that vulnerability of fishes to exploitation is correlated with their life history characteristics. However, no attempt has been made to review, summarize and compare the life-history of seamount species with species typical from other habitats. Therefore, Chapter 6 will test the generalization that \"seamount fishes\" possess specific life history characteristics that render them more vulnerable than other species. Vulnerability was estimated quantitatively by analysis of life-history characteristics using a fuzzy-logic algorithm. 7) investigate if whole-ecosystem simulations can help in understanding the impact of fishing on pristine seamounts and provide guidelines for sustainable fisheries. Using ecosystem modeling loosely structured on North Atlantic case studies, data gathered from elsewhere, and optimization niethods for policy search, in this Chapter I  \u000Cwill explore the types of fisheries that might be sustainable on seamount ecosystems. 1 will also investigate if ecosystem simulations can help in understanding the impact of fishing on pristine seamounts. Additionally, and in collaboration with the \"Sea Around Us Project\" I was involved in small ad-hoc projects that helped: a) estimating of the number of seamounts in the world's oceans by inferring potential seamount locations; b) detecting primary production enhancement by world's oceanic seamounts using remotely sensed data on primary production c) estimating potential yield from seamounts and determining how catches have changed in recent years.  \u000C1.9 REFERENCES  Abers, A.A., B. Parsons and J.K. Weissel (1988) Seamount abundances and distributions in the southeast Pacific. Earth and Planetary Science Letters 87: 137-151. Allain, V., A. Biseau and B. Kergoat (2003) Preliminary estimates of French deepwater fishery discards in the Northeast Atlantic Ocean. Fisheries Research 60: 185-192. Bakun, A. (1996) Patterns in the Oceans: ocean processes and marine population  dynamics.  California Sea Grant and Centro de Investigaciones Biologicas del Noroeste, La Jolla and La Paz. 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Suzuki (2003) Seamounts, new moon and eel spawning: the search for the spawning site of the Japanese eel. Environmental Biology of Fishes 66: 221-229. Uchida, R.N. and D.T. Tagami (1984) Groundfish fisheries and research in the vicinity of seamounts in the North Pacific Ocean. Marine Fisheries Reviews 46: 1-17. Uda, M. and M. Ishino (1958) Enrichment pattern resulting from eddy systems in relation to fishing grounds. Journal of the Tokyo University of Fisheries 1-2: 105-119. Vereshchaka, A. L. (1994) The distribution of pelagic macroplankton (mysids, euphausiids, and decapods) over the continental slope and seamounts of the western Indian Ocean. Oceanology 34(1): 81-86. Vereschchaka, A.L. (1995) Macroplankton in the near-bottom layer of continental slopes and seamounts. Deep-Sea Research I 42(9): 1639-1668 Vereshchaka, A.L. (1996) Distribution of benthopelagic shrimps over the continental slopes and seamounts of the Western Indian Ocean. Oceanology 35(4): 528-531. Vereshchaka, A.L. 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Hyrenbach (2004) Marine bird and cetacean associations with bathymetric habitats and shallow-water topographies: implications for trophic transfer and conservation. Journal of Marine Systems 50: 79-99.  \u000CCHAPTER 2 ABUNDANCE AND DISTRIBUTION OF SEAMOUNTS IN THE AZORES 1  2 . 1 INTRODUCTION  Seamounts have been recently recognized as highly important habitats for fisheries and biodiversity, and a target for conservation as they support often isolated but rich underwater ecosystems (Morato and Pauly, 2004). However, only a few seamount datasets containing information on positions are available (e.g., Fornari et al., 1987; Smith and Jordan, 1988; Epp and Smoot, 1989; Smith and Cann, 1990; Wessel and Lyons, 1997; Wessel, 2001). In fact only a small fraction of seamounts have actually been mapped (Wessel and Lyons, 1997). Recently, Kitchingman and Lai (2004) have conducted a global analysis with the goal of generating a spatial dataset of points across the world's oceans that indicate large peaked bathymetric anomalies with a high probability of being seamounts. In that study 14,287 potential large seamounts were identified in the world's oceans. Seamounts are thought to be common topographic features in the Azorean archipelago subarea of the Portuguese Economic Exclusive Zone (EEZ), hereafter named Azores EEZ. However, their numbers and locations are poorly known. The Azores archipelago is a group of nine volcanic islands and many small islets that are parts of the Mid-Atlantic Ridge in the Northeast Atlantic Ocean (an irregular area within 33.5-43\u00B0 N, 21-35.5\u00B0 W). Relatively shallow seabed less than 600 m deep cover less than 1 % of the 953,633 km 2 of the Azorean EEZ. This reflects the narrowness of the island shelves and means that most fishing grounds are scattered (Santos et al., 1995; Menezes, 2003). The only easily accessible seamount datasets that include the Azores are those of Wessel (2001) and Kitchingman and Lai (2004), with the later describing 58 seamounts in the Azores EEZ. Few other studies have been conducted in the Mid Atlantic Ridge, where estimates vary from 58 (Jaroslow et al., 2000) to 1  A version of this chapter has been submitted for publication. Morato, T.; M. Machete; A. Kitchingman; F. Tempera; S. Lai; G. Menezes; R.S. Santos; and T.J. Pitcher (submitted). Abundance and distribution of seamounts in the Azores. Marine Ecology Progress Series.  \u000C80 (Smith and Cann, 1990) seamounts per 1000 km 2. These estimates are about 1000 times higher than the numbers presented by Kitchingman and Lai (2004) because their study accounts only for those topographic features with a relief larger than 1000m. Seamounts are important areas for conservation and fisheries in the Azores (Santos et al., 1995) and the knowledge of their locations are highly important for choosing and implementing management measures. In this study, seamounts are defined as any topographically distinct seafloor feature that is at least 200 meters high but which does not break the sea surface. The 200 meters threshold was chosen for being the smallest peak size that fit the negative exponential model (see Results). I classify seamounts as being large or small, depending on whether the height exceeds 1000 meters (regardless of depth). For standard cone like seamounts height and width are highly correlated (Smith, 1988). Thus, this height separation is useful in isolating large seamounts, whose global distribution is well resolved by satellite altimetry, from small seamounts the distribution of which must be inferred from local acoustic mapping and therefore remain poorly sampled. The main goal of this paper is to infer potential seamount locations and thus to generate estimates of the actual number of seamounts in the Azores. I will also describe seamount population according to location, depth of the summit, height, basal area, height to radius ratio, the average slope, and distance to nearest seamount.  2 . 2 METHODS  I used an automated methodology adapted from Kitchingman and Lai (2004) to identified topographic structures with high probability of being seamounts. For this I used two bathymetric datasets with different resolutions. The MOMAR (Monitoring the Mid Atlantic Ridge) mid-resolution bathymetric map (Lourengo et al., 1998; http://www.motnar.org) was the finest available bathymetrical grid  \u000Cfor the Azores region at the date of the analysis (February 2006) and was used for the area comprised by the parallels 36\u00B0N and 41\u00B0N and the meridians 24\u00B0W and 32\u00B0W. The dataset is supplied at a 1 minute cell resolution (approx. 1.8 km in length), thus allowing a reasonable scale at which to perform an analysis for large seamount like features. The \"Global seafloor topography from satellite altimetry and ship depth soundings\" database (Smith and Sandwell, 1997; http://topex.ucsd.edu/sandwe-J 1/sandwel 1 .html; also called S&amp;S dataset) was used as the bathymetry dataset for the remaining area. This dataset is at a 2 minute cell resolution (approx. 3.7 km in length). This dataset contains some artefacts such as a spurious deep trough in the NE region of the study area. The methodology followed three succeeding steps: 1) identifying all detectable peaks in the bathymetry dataset; 2) isolating peaks with .heights greater than 200m and displaying an approximately circular or elliptical shape; and 3) isolating large seamount-like features. The datasets produced after step 1 will be called the peaks dataset, while the dataset produced after step 3 will be used as the Azores seamount dataset. The dataset produced after step 2 minus those produced after step 3 will be called the small seamounts dataset. The initial process of determining the locations of all detectable peaks (local maxima) in the elevation data was performed with the ESRI ArcGIS (ESRI, 1999-2004) software flow direction and sink algorithms. Both bathymetry datasets were used in an ESRI grid format for the cell-by-cell analysis. The ESRI flow direction algorithm was first used on the bathymetry data. This algorithm produces a grid in which each cell is allocated a flow direction value determined by the steepest descent from the immediate surrounding cells. There are eight valid flow direction values. Cells determined to have an undefined flow direction are given a value equal to the sum of the possible flow direction values. Undefined flow directions occur when all surrounding cells are higher than the focus cell or when two adjacent cells flow into each other. The ESRI sink algorithm is used on the resulting flow direction grid to identify all flow direction cells that have undefined flow directions. The resulting sink (seafloor peak) grid can then be overlaid with the depth grid to indicate all identifiable peaks on the seafloor.  \u000CThe bathymetry data was prepared by first eliminating all land cells (any elevation above 0) and then converting negative values (known as depths) to absolute numbers. This allowed the ESRI hydrology algorithms, designed to detect downhill flow direction and sinks, to identify the uphill flow directions and peaks. The next step of the analysis will isolate those the detected peaks that have a significant rise from the ocean floor and that have an approximate circular or elliptical base in an effort to eliminate small peaks found along the ridges or island slopes. The raw peak grid dataset was compared with the bathymetry data. An algorithm was developed that scanned depths around each peak, along 8 radii of 20 km each at 45\u00B0 intervals. The lowest and highest depths over the radii and the cells where those values were obtained were then recorded. Subsequently, I isolated a dataset of potential seamounts and then extracted the large seamounts. A peak was considered to be a potential seamount when the following conditions were met: 1 Each and all of the 8 radii included depths differing by at least 200 m. This helped eliminate all peaks of insignificant rises. 2 No more than one of the 8 radii has the highest depth shallower than the depth of the peak and if the distance between these two cells is greater than 10 km. This helped eliminate peaks that were part of a larger structure and peaks close to island slopes. 3 If 2 radii included depths between 200 and 1000 m with the shallowest point being closer to the peak than to the deepest point, and if the radii formed an angle of less than 135\u00B0. This condition was created to help separate ridges from seamounts. 4 At least 5 of the 8 radii around a peak included depths with a difference of at least 1000 m, with the shallowest point being closer to the peak than to the deepest point. 5 The average height of the peak is greater than 1000 m. Peaks that met all five conditions were considered large seamounts while those that met the first three conditions but failed to meet the fourth and fifth were considered small seamounts. For the detected small and large seamounts several characteristics were recorded: 1) location;  \u000C2) depth of the summit (m); 3) seamount height (h in m); 4) basal area (a/, in km ); 5) height to radius ratio (\u00A3,.); 6) the average slope (&lt;f&gt; in degrees); and 7) distance to nearest seamount (km). The location of the seamounts was recorded as the latitude and longitude of the centroid of the detected peak or seamount. The depth of the summit was recorded as the depth of the cell where the peak was located and must be interpreted as the average depth of the cell, not the absolute minimum depth of the seamount. The seamount height was estimated as the average height of the 8 radii of the seamount, where each radius height was estimated as the difference between the summit and the deepest record. The area of the base of the seamount was approximated by the area of the octagon formed by the location of the deepest cell in each radius. The slope of the seamount was estimated as the average steepness of the 8 radii of the seamount calculated by the slope algorithm of ArcGIS software. The ArcGIS 'Slope' function calculates the maximum rate of change between each cell and its neighbours using the average maximum technique. Finally, the distance to the nearest seamount was calculated by identifying the closest feature and then estimating its distance. Seamount size distribution is well characterized by a negative exponential model that considers the cumulative numbers of seamounts having heights greater than a certain value (Jordan et al., 1983; Smith and Jordan, 1988). This distribution is expressed as v(H)=v\u201E-exp(P'H), where v(H) is the number of peaks per unit area with height greater than H, v0 is the total number of peaks per unit area and P is the negative of the slope of a line fitting ln(v(//)) and H.  \u000C2 . 3 RESULTS  2.3.1 Peaks dataset A total of 3177 peaks were identified by the ArcGIS flow direction and sink algorithms, yielding an average density of 3.3 peaks per 1000 km . Of these peaks, 1104 were found with the MOMAR Azores dataset (24\u00B0W 36\u00B0N to 32\u00B0W 41\u00B0N), whereas 2073 were found with the S&amp;S dataset used to cover the rest of the EEZ. The peaks dataset adequately identified topographic structures with heights larger than 100m (Figure 2.1). The resolution of the bathymetry data seems to be inadequate for peaks smaller than 100m, leading to an underestimation of the counts. Thus, this data point was excluded from the fit. The exponential model adequately fits the Azores peaks counts with vG= 4.31 peaks per 1000 km 2 and /?= 2.89 km\"1, yielding a characteristic height (/?\"') of -350 m. According to this exponential model there are about 4100 potential peaks in the Azores where 1000 km contain an average of ~4 peaks of all sizes.  10000 3  0.1  1 0  500  1  .  1  ,  ,  ,  1000  1500  2000  2500  3000  3500  Predicted peak height (m)  Figure 2.1 - Height (h) frequency distribution of all identified peaks. Solid circles are actual counts while open circles are the cumulative counts. The grey circle data point was excluded from the exponential model fit. The relationship can be expressed as N = 4107.6-e\" 2 ' 89/i , with h in km; r 2=0.99. If expressed by unit area (km\"2), v(H)= 4.31 -e\"2 8 9 /'.  \u000C2.3.2 Small and large seamounts dataset Figure 2.2 shows the location of 461 potential small and large seamount-like features in the Azores EEZ. Detailed tables of results for each seamount with location, depth of the summit, base area and average slope are shown in Appendix 2 2 . Our methodology identified a total of 398 small features, which represents only 12% of the 3177 identified peaks. This discrepancy shows that our methodology successfully eliminated peaks of insignificant rises as well those that were part of larger structures or were in ridges or island slopes. Of the 398 potential small seamounts, 151 were identified around the islands with the Lourengo et al. (1999) bathymetrical dataset while 247 were identified offshore with the Smith and Sandwell (1997) dataset. I have also detected 63 large potential seamounts, which is only 2% of the identified peaks. Of these, 52 were identified with the Lourengo et al. (1999) dataset while 11 were identified with S&amp;S dataset. The mean abundance of small and large seamounts in the Azores EEZ is 0.42 and 0.07 per 1000 km 2, respectively.  Figure 2.2 - Distribution of seamounts in the Azores Exclusive Economic Zone (black line). Circles show large seamounts while black dots show small seamount-like features. Scale goes from dark grey (deep water; about 5000m) to light grey (shallow water).  2  A digital version of this table is provided in the webaddress http://www.horta.uac.pt/ppl/tmorato/pdf/Appendix2_Morato_PhD.pdf  \u000CMost of the seamounts in the Azores have deep summits (Figure 2.3) with a strong predominance of summit depths of 800-1500m.Only 4 large seamounts have a mean depth of the peak shallower than 250m while 14 lay in the depth range 250-500m. The other 45 large seamounts have their summits deeper than 500m. Small seamount-like features show a similar pattern with only 6 shallower than 500 m. Location of large and small seamounts by depth of the summit is presented in Figures 2.4 and 2.5.  Small seamounts numbers 0  10  20  30  Large seamounts numbers 40  0  1000 -  1000 -  2000 -  2000 \u2022  3000  3000  4000 -  4000 i  CL Q 5000 6000  5  10  15  5000  6000 \u2022  Figure 2.3 - Depth of the summit frequency distribution of small (left) and large (right) seamounts-like features.  \u000Cd.  i\"1^*^-  S^^VW ..\u2022i,!^-.  JI^^W  ^\"H j f\"\u00AE!* \u2022*\u2022\u201E.  ...i,..,.  A-  4  \u00AB\u00AB\u00AB  &gt;4  srwsr msfymt Figure 2.4 - Distribution of large seamounts in the Azores EEZ by mean depth of summit: a) &lt;250m; b) 250-500; c) 501-1000; d) &gt; 1000m. Black dots indicate seamounts of the referred category.  \u000C'.iKirv  I / 1 / \u2022.  il^flL-  1  *  -  ii^V  Figure 2.5 - Distribution of small seamount-like features in the Azores EEZ by mean depth of summit: a) &lt;250m; b) 250-500; c) 501-1000; d) &gt; 1000m. Black dots indicate seamounts of the referred category.  to  \u000CThe distribution of small and large seamounts heights (h) distribution is shown in Figure 2.6. Small seamounts had a mean height of 612 m (S.D. = 210) while the mean height for large seamounts was 1267 m (S.D. = 272). Seamounts with small heights are much more abundant than larger seamounts.  90 80  CO  70  u 60  -O E  % 50 I 40  E  8 30  V)  20  IJHTE  10  0 0  500  1000  1500  2000  Predicted seamount height (m)  Figure 2.6 - Histogram of the predicted height distribution of small (light grey bars) and large (black bars) seamounts-Iike features. Bin size is 100m.  Shapes of seamounts as characterized by basal radius (r h), by the height to radius ratio (\u00A3) and by the slope (&lt;/)) show marked differences between small and large seamount like features. Small seamount-like features showed a mean basal radius of rb= 9.4 km (SD=2.0) while large seamounts showed a mean basal radius of r/,= 11.2 km (SD= 1.8). Accordingly, basal area is smaller on small seamounts (db= 742 km2; SD= 213) than on large seamounts (a h = 961 km2; SD= 171). The mean height to radius ratio increase from \u00A3.= 0.07 (SD= 0.027) on small seamounts to  0.12 (SD= 0.030) on large seamounts (Figure 2.7). Average slope  angles ranged from &lt;jr= 0.73 to (jj= 9.27. The sample mean slope angle was &lt;ff= seamounts and  2.90 for small  5.23 for large seamounts. Slope angle and summit height relationship is  presented in Figure 2.8.  \u000C0.00  0.05  0.10  0.15  0.20  0.25  Height to radius ratio  Figure 2.7 - Histogram of the seamounts height to radius ratio  for small (grey bars) and  large (black bars) seamount-like features. Bin size is 0.010.  10  0 I 0  ' '  1 ' &lt;\u2014 500  -r- . \"I \u2022 . . \u00BB I r\u2014 1000  1500  2000  Height (m)  Figure 2.8 - Slope angle  and seamount height (h) linear relationship for small (open  circles) and large (filled circles) seamount-like features. For all seamounts the relationship can be expressed as &lt;f&gt;= 0.003 h + 0.86; r 1 = 0.53.  \u000C2 . 4 DISCUSSION  This work is the first attempt to identify the seamounts in the Azorean EEZ. It must be emphasized that there are some potential sources of uncertainty in this study. First, the bathymetry of the Azores EEZ is not perfectly known and has not been, to our knowledge, extensively surveyed. Both bathymetry datasets used (Smith and Sandwell, 1997; Lourenfo et al., 1999) may lack resolution and thus preclude the identification of some small seamount-like features. For this reason, our references to seamounts should be interpreted as potential seamounts. A better but very costly solution would be to perform extensive multibeam surveys that would provide not only excellent bathymetric data for mapping seamounts and estimating depths, areas and slopes but also backscatter data for mapping the nature of the seafloor. Our methodology of identification shows that peaks and seamounts are common features in the Azorean EEZ. The average density of 3.3 peaks of all sizes per 1000 km 2 is in the same order of magnitude of that obtained in some studies in the Mid Atlantic Ridge (Batiza et al., 1989) but is an order of magnitude lower than obtained by Smith and Cann (1990) and by Jaroslow et al. (2000). The observed discrepancies are due to the facts that the later studies focused only on the MAR region, an area with a higher abundance of topographic structures (Smith and Cann, 1990), and that the resolution of. our datasets. is inadequate to detected peaks smaller than 100m height. The difference of about 1000 peaks between the real counts and the exponential model estimates (n= -4100) are due to underestimation of the counts for peaks smaller than 100m, suggesting that our methodology successfully identified small and large seamount-like features (/7&gt;200m height). In this study, I was able to map and describe 63 large and 398 small seamount-like features in the whole EEZ of the Azores. The total area where seamounts are found is much larger than previous thought. However, most of the summits are in waters deeper than 1000m. Etnoyer (2005) presented some evidences that small features with deep peaks predicted by some bathymetry datasets can actually be large seamounts with shallower summits. Therefore, our  \u000Cestimates of seamount abundance may perhaps be biased by underestimating seamounts heights and overestimating depth of summits. For that reason, the large seamount abundance in the Azores may be even higher than presented here. Also, the depth of the summits may be shallower than I have estimated. The distribution suggests that a large proportion of the seamounts occur in chains along the Mid Atlantic Ridge. However, isolated seamounts are also present in the Azores. Seamounts showed a wide range of sizes (heights), depth of summits, slopes, and areas being difficult to make generalization about the seamounts in the Azores. Our data suggests that seamounts provide a large diversity of habitats that can be suitable for different type of faunal associations. Similar findings and suggestion were made by Rowden et al. (2004) for New Zealand seamounts. The fact that most of the seamounts have small heights and deep summits has strong implications for fisheries exploitation in the region where most of the bottom longlining occurs at depths of up to 600 m (Morato et al., 2001). Our data suggests that only 29 of the 461 seamounts (small and large) are available for this type of fisheries. Thus, our work supports the idea that fishing grounds for existing fisheries are limited in the Azores. It should be noticed that deep-water trawl operates at deeper waters and thus the potential fishing grounds for this fishery could be slightly larger. Recently, the EU regulation 1568/2005 (European Commission, 2005) banned deep water trawling in a large area of the Azorean EEZ. According to our distribution of seamounts, this regulation protects 58 large and 207 small seamounts. Thus, 57% of the potential Azores seamounts are protected against deep-water trawling.  \u000C2 . 5 REFERENCES  Batiza, R., P.J. Fox, P.R. Vogt, S.C. Cande, and N.R. Grindlay (1989) Abundant Pacific-type near-ridge seamounts in the vicinity of the Mid-Atlantic Ridge 26 deg S. Journal of Geology 97: 209-220. Environmental Systems Research Institute (ESRI) (1999-2004) ArcGIS: Release 9.0 [software]. Redlands, California, USA. Epp, D., and N.C. Smoot (1989) Distribution of seamounts in the North Atlantic. Nature 337: 254-257. Etnoyer, P. (2005) Seamount resolution in satellite-derived bathymetry. Geochemistry Geophysics Geosystems 6(3), 8pp. European Commission (2005) Council Regulation No. 1568/2005 of 20 September 2004. amending Regulation (EC) No 850/98 as regard the protection of deep-water coral reefs from the effect of fishing in certain areas in the Atlantic Ocean. OJ L252/2, 28.09.2005. Fornari, D.J., R. Batiza and M.A: Luckman (1987) Seamount abundances and distribution near the East Pacific Rise 0\u00B0-24\u00B0N based on seabeam data. In: B.H. Keating, P. Fryer, R. Batiza and G.W. Boehlert (eds.) Seamounts, Islands, and Atolls. American Geophysical Union, Washington, D.C., pp. 13-21. Jaroslow, G.E., D.K. Smith and B.E. Tucholke (2000) Record of seamount production and off-axis evolution in the western North Atlantic Ocean, 25\u00B025' - 27\u00B010'N. Journal of Geophysical Research 105: 2721-2736. Jordan, T.H., W. Menard and D.K. Smith (1983) Density and size distribution of seamounts in the Eastern Pacific inferred form wide-beam sounding data. Journal of Geophysical Research 88(B12): 10508-10518. Kitchingman, A. and S. Lai (2004) Inferences on potential seamount locations from midresolution bathymetric data. In: T. Morato and D. Pauly (eds.) Seamounts: biodiversity and fisheries. Fisheries Centre Research Report 12(5), pp. 7-12. Lourenfo, N., J.M. Miranda, J.F. Luis, A. Ribeiro, L.A. Mendes-Victor, J. Madeira and H.D. Needham (1998) Morpho-tectonic analysis of the Azores volcanic plateau from a new bathymetric compilation of the area. Marine Geophysical Researches 20: 141-156. Menezes, G.M.M. (2003) Demersal fish assemblages  in the Atlantic archipelagos  of the  \u000CAzores, Madeira,  and Cape Verde.  Ph.D. Thesis, Universidade dos Agores, Horta,  Portugal, 228pp. Morato, T. and D. Pauly (2004) Seamounts: biodiversity and fisheries. Fisheries Centre Research Report 12(5), 78 pp. Morato, T., S. Guenette and T. Pitcher (2001) Fisheries of the Azores, 1982-1999. In: D. Zeller, R. Watson, T. Pitcher and D. Pauly (eds.) Fisheries impacts on North Atlantic Ecosystems:- Catch, effort  and national/regional  data sets. Fisheries \"Centre Research  Reports, University of British Columbia, 9(3), pp. 214-220. Rowden, A.A., M.R. Clark and I.C. Wright (2005) Physical characterisation and a biologically focused classification of \"seamounts\" in the New Zealand region. New Zealand Journal of Marine and Freshwater Research 39: 1039-1059. Santos, R.S., S. Hawkins, L.R. Monteiro, M. Alves and E.J. Isidro (1995) Case studies and reviews. Marine research, resources and conservation in the Azores. Aquatic Conservation: Marine and Freshwater Ecosystems 5: 311-354. Smith, D.K. (1988) Shape analysis of Pacific seamounts. Earth and Planetary Science Letters 90: 457-466. Smith, D.K. and J.R. Cann (1990) Hundreds of small volcanoes on the median valley floor of the Mid-Atlantic ridge at 24-30\u00B0 N. Nature 348:152-155. Smith, D.K. and T.H. Jordan (1988) Seamount statistics in the Pacific Ocean. Journal of Geophysical Research 93(B4): 2899-2918. Smith, W.H.F. and D.T. Sandwell (1997) Global seafloor topography from satellite altimetry and ship depth soundings. Science 277: 1957-1962. Wessel, P. (2001) Global distribution of seamounts inferred from gridded Geosat/ERS-1 altimetry. Journal of Geophysical Research 106(B9): 19431-19441. Wessel, P. and S. Lyons (1997) Distribution of large Pacific seamounts from Geosat/ERS-1: Implications for the history of intraplate volcanism. Journal of Geophysical Research, 102(B10): 22459-22475.  \u000CCHAPTER 3 MODELLED EFFECTS OF PRIMARY AND SECONDARY PRODUCTION ENHANCEMENT BY SEAMOUNTS ON LOCAL FISH STOCKS  1  3 . 1 INTRODUCTION  It has long been recognized that many seamounts may harbour large aggregations of demersal or benthopelagic fish (Rogers, 1994; Boehlert and Sasaki, 1988; Koslow, 1996, 1997; Koslow et al., 2000) such as orange roughy, Hoplostethus atlanticus, pelagic armorhead, Pseudopentaceros wheeleri, and alfonsinos, Beryx spp. (Morato et al., 2006). However, the mechanisms under which these aggregations are sustained are still under debate. Three hypotheses have been presented to explain how large aggregations of fish found on seamounts are energetically supported (see Genin, 2004 for a review). The first hypothesis (7) proposes that the high biomass of fish results, at least in part, from locally enhanced primary production (PP) and the subsequent bottom-up transfer of this energy to higher trophic levels in seamount food chains (Uda and Ishino, 1958; Hubbs, 1959; Uchida and Tagami, 1984). It is unlikely, however, that water could be retained around a seamount for the several months needed for production to work its way through the food-web to the higher trophic level fish residing on the seamount itself. Thus, is not surprising that evidence for enhanced primary production leading to concentrations of fish over seamounts is sparse (Rogers, 1994). Moreover, evidence of increased primary production over conical seamounts located in the path of marine currents is still contradictory. While upwelling that drives patches of high primary production has been detected using field sampling over certain well-studied seamounts (Genin and Boehlert, 1985; Dower et al., 1992; Comeau et al., 1995; Odate and Furuya, 1998; Mourino et al., 2001, 2005), many studies have failed to demonstrate persistent high chlorophyll a patches over seamounts (e.g., Pelaez and 1  A version of this chapter has been submitted for publication. T. Morato, C. Bulman and T.J. Pitcher (submitted) Impact of primary production enhancement by seamounts on local fish stocks. Deep-Sea Research  \u000CMcGowan, 1986). In general, seamounts reaching close to the surface enhance local primary production, but its contribution to primary production enhancement is generally low (Mourino et al., 2005). The second hypothesis (2) proposes that fish aggregations are sustained by the enhanced horizontal flux of prey organisms past the seamount (Tseytlin, 1985; Dower and Mackas, 1996; Koslow, 1997), named as the \"feed-rest\" hypothesis by Genin (2004). Enhanced fluxes of prey in regions of amplified currents augment feeding by site-attached fish but at the same time may have extremely high energetic costs. The \"feed-rest\" hypothesis suggests that the fish rest motionless in quiescent shelters during non-feeding intervals and, when conditions are right, the fish emerge from shelter, feed quickly, and then retreat back to rest. Another possible mechanism (hypothesis 3) is the interception and trapping of vertical migrators - both descending and ascending (Isaacs and Schwartzlose, 1965; Genin et al., 1988, 1994; Williams and Koslow, 1997; Fock et al., 2002), named as the \"topographic blockage\" hypothesis by Genin (2004). Most of the studies supporting hypotheses 2 and 3 are based on observations of potential prey (micronekton) rather than on the predatory fish themselves. Only two studies have examined fish energetic requirements and food availability on seamounts (Tseytlin, 1985; Koslow, 1997). On seamounts off southern Tasmania, Australia, Koslow (1997) assumed an orange roughy biomass of 100 t-km\"2, with a consumption of 1% of its body weight, a trophic level of four, a trophic efficiency of 10%. Net primary production in the region was 200 gC-m\"2-year\"'; enough to sustain only about one-tenth of the estimated biomass, or about 11 t-km\"2 of orange roughy, assuming a conversion of carbon as 5% of wet weight. Using a different approach, Koslow (1997) took into account the annual contribution to the fourth trophic level of the particle flux from the surface (sinking of ungrazed phytoplankton) and vertical migrators. He estimated that local production available to the fourth trophic level was about 1.25 gC-m\" 2  1  2  \u2022year\" which I estimate could only sustain about 7 t-km\" of orange roughy. Moreover, after an extensive field sampling program off southern Tasmania, Williams and Koslow (1997)  \u000Cestimated a biomass of micronekton migrating to about 900m depth between 0.94 to 3.36 2 I gC-m\" -year\" . Using the conversion ratios presented by Koslow (1997) this micronekton could sustain only between 5 to 18 t-km\"2 of orange roughy. All of these values are much less than orange roughy biomass estimates for Tasmania seamounts (50-125 t-km\"2; Koslow, 1997). From these calculations Koslow (1997) concluded that local productivity (hypothesis 1) and vertical migrators (hypothesis 3) are not sufficient to sustain known aggregations of seamount fishes on seamounts. Alternatively, they could be supported by advected sources (hypothesis 2). On the other hand, Tseytlin (1985) modelled the biomass of predatory fish that could be sustained at a seamount by food sources explained in hypothesis 2 and 3. With several assumptions, including a summit depth of 500m, a known mesozooplankton biomass of 50 mg-m3, a mean current velocity of 0.1 m-s\"1, and an average fish wet weight of 200g, he concluded that a maximum of 40 t km\"2 of fish could be supported by the horizontal flow of prey, while an extra 75 t-km\"2 of fish could be supported by vertical migrators, leading to a total of about 115 t-km\"2, in the range of known orange roughy biomasses. All of these studies strengthen hypothesis that imported food supplies support large fish aggregations on seamounts. However, these estimates account only for a single species and do not take into account the multispecies complexity of seamount ecosystems in which there may be more predators as well as additional sources of carbon and nutrients entering the food web from mesopelagic fish, visiting fish, squid and crustaceans, and, in some cases, detritus (Pitcher and Bulman, in press). On the south-eastern Australian and New Zealand seamounts, for instance, several species of oreo dories (e.g., Pseudocyttus maculatus and Allocyttus niger) are also aggregating in high densities (Koslow, 1997; Bulman, 2002) and other members of these communities such as the squalids and macrourids are also abundant (Koslow et al., 1994; Bulman, 2002). Many of these species consume similar prey as orange roughy and are therefore adding to the predation pressure on these prey (Bulman et al., 2001; Bulman et al., 2002a).  \u000CEcosystem-based modelling approaches can help in understanding the complex nature of ecosystem function. In seamount ecosystems, in particular, the complexity of the ecosystem is heightened by the apparent trophic inequities. However, there have been few attempts to fully model seamount ecosystems. Trophic models of the Tasmanian seamounts and large aggregations of orange roughy and oreos living on them were constructed using Ecopath with Ecosim to explore the hypothesis of Koslow (1997) that these large fish aggregations were supported by advection of prey past the seamounts (Bulman, 2002; Bulman et al., 2002b). Among the many tools for modelling marine ecosystems, the Ecopath with Ecosim (EwE) modelling approach and software has proved to be one of the most successful. It has seen widespread use and has generated helpful insights (Whipple et al., 2000; Robinson and Frid, 2003). The development of Ecopath in the early 1980s (Polovina, 1984) and its evolution in the following years into a dynamic modelling tool (Walters et al., 1997, 1999, 2000; Christensen and Walters, 2004a, b), has allowed us to address ecological questions, evaluate ecosystem effects of fishing, explore management policy options, analyse impact and placement of marine protected areas, and model the effect of environmental changes (e.g., Christensen and Pauly, 1993; Jarre-Teichmann, 1998; Pitcher et al., 2000; Shannon et al., 2000;. Watson et al., 2000; Guenette et al., 2001; Watson and Pauly, 2001; Walter et al., 2002; Christensen and Maclean, 2004; Morato and Pitcher, 2005; Pitcher et al., 2005). In an effort to better understand seamount ecosystem functioning, this paper will address how complex seamount food web structures are sustained. I used a generic seamount ecosystem model from the Northeast Atlantic to examine 1) the impact of a potential increase of local primary production on higher trophic levels, 2) to quantify the immigration of micronekton that would be required to maintain a \"typical\" seamount community, and 3) to quantify if the necessary immigration ratios could be supported by local oceanographic conditions.  \u000C3 . 2 METHODS  3.2.1 Modelling approach The modelling approach used in this study was Ecopath with Ecosim (EwE), including Ecospace (Christensen and Walters, 2004a). The parameterisation of an Ecopath model is based on satisfying two 'master' equations. The first describes how production for each group can be divided up, and the second is based on the principle of conservation of matter. As a trophic mass-balanced model Ecopath assumes that, for each functional group i in an ecosystem, mass balance should occur over a given time period. In the first 'master' equation biomass production of a compartment (/&gt;,) is balanced by catches (y,-), predation mortality (Bj.M2j,  where B, is the biomass of the group and M2, is the total predation rate for the  group), biomass accumulation (BAj), net migration (\u00A3, = emigration-immigration) and other mortality (MO,), such that: (1)  Pi = 7. + B,. \u2022 Ml, + E, + BAj + MO,  Production is usually estimated from the production/biomass ratio (P/B) and the average annual biomass (B) and can be expressed as (P, = Bi -(P/B),).  Predation mortality can be  expressed as the sum of consumption by all predators (J) preying upon group (/'), i.e.: (2)  B,-M2,=f jBj-(Q/B) j-DC j,  where (Q/B)j is the consumption/biomass ratio of the predator (J) and DC/, is the fraction of the prey (i) in the'average diet of the predator (/'). The other mortality can also be expressed as: (3)  MO, = /&gt; \u2022 (1 - EE,)  where EE is the ecotrophic efficiency, or the proportion of the production that is utilized in the system. Substituting (2) and (3) into the equation (1) means it can be re-expressed as: .  (4)  B\u2022 (P/B),  \u2022 EE, = Y, + \u00A3Bj 7=1  \u2022 (Q/B) . \u2022 DC.. + E, + BA,  \u000CThe second 'master' equation is: (5)  Qi=Pi+Ri+U i  where Rj is respiration and U-, is unassimilated food. Ecosim uses the Ecopath model to estimate its initial parameters. It then uses a system of differential equations of the form given in (6) to calculate the biomass fluxes between pools through time:  (6)  JT&gt; = at  j  QP  I, - (M,  B,  j  where dB/dt represents the growth rate of / during the time interval dt, g, is the net growth efficiency, Mj the natural mortality rate, F, is fishing mortality rate, e, is emigration rate, /, is immigration rate. The Q terms refer to consumption by group i (Qj,) and predation on i (Q,j), and are calculated using the 'forage arena' concept. For further details regarding the equations and their solutions see Walters et al. (1997, 2000) and Christensen and Walters (2004a). Ecospace represents biomass dynamics (as in eq. 6) over a two-dimensional rectangular grid space (x, y) and time (t). Such representations involve very complex sets of partial differential equations. For each cell the immigration rate /, of eq. (6) is assumed to consist of up to four immigration flows from the neighbouring cells in the grid. Similarly, the emigration flows e, in eq. (6) are represented as instantaneous movement rates to nearby cells. For further details see Walters et al. (1999). 3.2.2 Generic model for seamounts in the North-Atlantic A model of a hypothetical isolated seamount in the North Atlantic was built. The depth of the summit below the surface was set to be at around 300 m and the base at around 2000m. The area of the model was assumed to be 30 km radius from the summit, in order to include the theoretical area of its influence (see Chapter 4). As a result the total area under consideration was equal to 2827 km .  \u000CA total of 37 functional groups were included in the seamount EwE model, stratified by depth of habitat (see Appendix 3 for details). The models included three marine mammal groups (toothed whales, baleen whales and dolphins), seabirds, turtles, seven invertebrate groups (benthic filter feeders such as corals or gorgonians, benthic scavengers, benthic crustaceans, pelagic crustaceans, seamount resident cephalopods, small and large drifting cephalopods), three zooplankton groups (gelatinous, shallow and deepwater zooplankton), primary producers (phytoplankton), detritus and twenty fish groups. Fish groups were divided based on environment use (depth and habitat - e.g., benthic, pelagic or benthopelagic), size, energetics (Childress et al., 1980; Koslow, 1996) and lifehistory. A general, stratification by depth was used: epipelagic, between of 0-200 m depth; mesopelagic, the region of the oceanic zone from 200 m to 1000 m; and bathypelagic, between 1000 m to 4000 m depth. Seamount associated fishes were divided into three different groups. Two groups contained species that are targeted by the north Atlantic fishery (Hoplostethus atlanticus and Beryx spp.), and a third group had the other seamount associated species. The deep scattering layer (DSL; a 50-200 m thick, sound-reflecting layer in ocean waters, consisting of a stratified, dense concentration of zooplankton and fish) was not considered as a single group, but as several groups that take part in its formation (i.e. shallow and gelatinous zooplankton, pelagic crustaceans, and small mesopelagic migrating fishes).  The model parameters (Table 3.1) production to biomass ratios (P/B), consumption to 1 2 biomass ratios (Q/B) were calculated on a yearly basis (year\" ). Biomass (t-km\" ) and catch 2 1 (t-km\" -year\" ) were expressed in wet weight. The Q/B ratios for fish groups were estimated using an empirical equation (Palomares and Pauly, 1998). Temperature values (Celsius degrees) were established as being 18\u00B0C for the epipelagic region (0-200m), 8\u00B0C for the mesopelagic region (200-1000m), and 6\u00B0C for the bathypelagic region (1000-4000m). For some groups Q/B values were taken from other models. For most groups P/B ratios Were extracted from previously constructed models or were estimated assuming production and consumption ratio equal to 0.3. The proportion of food consumed and not assimilated was  \u000Ctaken as 0.2. When no biomass estimate was available, this parameter was estimated by Ecopath using a value of 0.95 for Ecological Efficiency (EE). A preliminary diet matrix was assembled using published data, unpublished local information, and empirical knowledge (Table 3.2). Unidentified diet categories were excluded from the diet matrix and data were re-scaled to 100%. The theoretical seamount was assumed to have a low initial level of exploitation. Modelled seamount fisheries were loosely based on those operating at the Azores / North Atlantic ridge, and thus divided in 6 fleets (Morato et al., 2001): demersal longline (targeting shallow water demersal and benthic fish species; operating mainly up to 600m depth); deepwater longline (targeting bathypelagic and bathybenthic; operating mainly at 800-1200m); small pelagics fishery (for small pelagic fishes); tuna fishery; swordfish fishery; deepwater trawl (targeting seamount associated species, including orange roughy and alfonsinos; operating mainly at 800-1200). Landings (Table 3.3) were assumed to be small and varied from 218 t-year\"1 for shallow benthic fishes to 2.7 t-year\"1 for billfishes. Initially, I assumed the seamount to be a closed system with no advection of micronekton from outside the system or biomass accumulation. Thus, this model was named \"closed seamount model\". Another model was built which estimated rates of advection of micronekton into the system required to sustain various levels of fish biomass. This later model was named \"advection seamount model\" (described in section 3:2.4).  \u000CTable 3.1 - Input parameters and estimates (in parentheses) from the theoretical \"closed seamount\" (closed) and \"advection\" (adv.) models of a seamount. P/B is production to biomass ratio, Q/B is consumption to biomass ratio, EE is ecotrophic efficiency, and TL is trophic level of the groups. P/B, Q/B, TL and catch were the same in the immigration and nomigration models. Bold numbers show those groups with increased biomass as a result some type of seamount effect . Biomass Biomass Catch P/B Q/B EE EE 2 (t-km\"2) (t-km\"2) Group name (t-km\" TL (year\"1) (year\"1) closed adv. 1 closed adv. \u2022year\" ) Toothed whales Baleen whales Dolphins Sea turtles Seabirds Tunas Bill fishes Pelagic sharks Benthopelagic sharks Rays and skates Large oceanic planktivores Small epipelagic fish Medium epipelagic fish Large epipelagic fish Small mig. mesopelagic fish Large mig. mesopelagic fish Non-mig. mesopelagic fish Shallow benthic fish Shallow demersal fish Seamouts-associated fish Hoplostethus atlanticus Beryx spp. Bathypelagic fihes Bathybenthic fishes Bathydemersal Fishes Benthic invert,filter feeders Benthic invert, scavengers Benthic crustaceans Pelagic crustaceans Cephalopods resident Cephalopods drifting small Cephalopods drifting large Gelatinous zooplankton Shallow zooplankton Deep zooplankton Phytoplankton Detritus  0.0001 0.123 0.040 0.001 0.0001 0.032 0.020 0.011 0.030 0.020 (0.003) 0.859 0.113 0.014 2.000 (0.970) (3.974) (0.723) (0.215) (0.592) (0.780) (0.531) (0.796) (1.264) (1.009) (0.755) (2.869) (3.425) (6.094) (0.120) (0.349) (0.006) (9.428) 16.684 6.849 7.160 100.000  0.0001 0.020 0.060 0.123 0.070 0.100 0.001 0.150 0.00025 0.040 0.742 0.191 0.020 0.500 0.011 0.300 0.030 0.510 0.020 0.170 0.003 0.112 0.859 2.053 0.113 1.080 0.014 0.690 2.000 1.980 0.970 0.600 3.974 0.500 (0.723) 0.590 (0.215) 0.660 0:592 0.060 0.048 41.930 0.060 5.313 0.796 0.500 (1.265) 0.200 (1.010) 0.200 0.755 0.800 (2.870) 1.830 (3.426) 1.600 6.094 1.450 (0.119) 2.890 0.349 4.450 0.006 2.500 9.428 0.850 16.684 (11.214) 6.849 (8.700) 7.160 283.500 100.000 -  10.270 5.563 11.410 3.500 84.390 16.291 4.200 3.100 6.900 1.500 2.066 19.867 10.750 5.095 8.000 3.550 1.570 4.700 5.200 2.200 2.000 2.000 1 All 0.500 0.600 9.000 13.567 10.000 9.667 10.000 16.863 10.000 2.000 37.379 29.000 -  (0.514) (0.024) (0.049) (0.900) (0.257) (0.706) (0.101) (0.916) (0.154) (0.678) 0.100 (0.734) (0.982) (0.870) (0.974) 0.950 0.950 0.950 0.950 0.950. 0.850 0.950 0.950 0.950 0.950 0.950 0.950 0.950 0.950 0.950 0.950 0.950 0.800 (0.710) (0.718) (0.261) (0.160)  0.514 0.024 0.020 0.900 0.103 0.118 0.101 0.914 0.154 0.678 (0.100) 0.734 0.983 0.870 0.974 (0.950) (0.950) 0.950 0.950 (0.945) (0.040) (0.095) (0.950) (0.950 0,950 (0.950) 0.950 0.950 (0.950) 0.950 (0.950) (0.951) (0.800) 0.710 0.718 0.267 0.162  5.03 3.45 4.31 3.83 4.25 4.34 4.53 4.57 4.33 3.84 3.50 3.07 3.53 4.10 3.30 3.98 3.17 3.64 3.98 4.08 4.19 3.87 3.82 3.27 3.86 2.00 2.37 2.22 2.69 3.39 3.60 4.15 2.84 2.11 2.11 1.00 1.00  0.011 0.001 0.002 0.002 0.002 0.050 0.010  0.080 0.020 0.011 0.010 0.010 0.006 0.003 0.002  \u000CTable 3.2 - Diet matrix in weight proportions for a generic seamount model in the Northeast Atlantic. Columns stand for predators while rows stand for prey. Numbers in columns headings represent the predator groups as defined in different rows of thefirst two columns. Prey \\ Predator 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 1 Toothed whales 0.001 2 Baleen whales 0.001 0.005 0.10 3 Dolphins 0.001 4 Sea turtles 0.001 5 Seabirds 0.001 6 Tunas 0.10 0.140 0.01 7 Billfishes 0.007 8 Pelagic sharks 0.08 0.01 0.005 9 Benthopelagic sharks 0.01 10 Rays and skates 0.007 0.002 11 Large oceanic planktivores 0.001 12 Small epipelagic fish 0.01 0.100.30 0.25 0.42 0.30 0.20 0.08 0.10 0.20 0.40 0.03 0.05 13 Medium epipelagic fish 0.05 0.08 0.35 0.05 0.20 14 Large epipelagic fish 0.10 15 Small mig. mesopelagic fish 0.100.100.050.050.04 0.05 0.01 0.09 0.05 0.01 0.167 0.20 0.15 16 Large mig. mesopelagic fish 0.05 0.01 17 Non-mig. mesopelagic fish 0.06 0.05 0.05 0.10 0.03 0.02 0.1560.01 18 Shallow benthic fishes 0.10 0.25 0.05 0.05 0.10 0.07 0.15 19 Shallow demersal fishes 0.10 0.10 0.100.01 0.150.05 0.015 20 Seamouts-associated fishes 0.05 0.05 0.005 0.001 21 Hoplostethus atlanticus 0.10 0.05 22 Beryx spp. 0.05 0.05 0.001 23 Bathypelagic fihes 0.08 0.05 0.056 24 Bathybenthic fishes 0.10 0.03 25 Bathydemersal Fishes 0.100.05 0.08 0.009 26 Benthic invert, filter feeders 0.05 0.08 0.07 0.05 27 Benthic invert, scavengers 0.02 0.05 0.18 0.150.15 28 Benthic crustaceans 0.01 0.01 0.05 0.07 0.25 0.05 29 Pelagic crustaceans 0.06 0.05 0.01 0.10 0.180.09 0.100.050.200.111 0.090.100.15 30 Cephalopods resident 0.20 0.05 0.10 0.050.10 0.01 0.100.08 0.05 31 Cephalopods drifting small 0.050.100.01 0.150.150.10 0.01 0.09 0.05 0.10 0.167 0.10 32 Cephalopods drifting large 0.20 0.05 0.06 0.04 33 Gelatinous zooplankton 0.05 0.89 0.03 0.03 0.158 0.1000.050.100.100.111 0.200.10 34 Shallow zooplankton 0.25 0.05 0.07 0.350.8000.550.100.35 0.90 35 Deep zooplankton 0.35 0.35 0.222 0.28 36 Phytoplankton 0.05 0.100 37 Detritus 0.05 38 Import  \u000CTable 3.2 - cont. Prey \\ Predator 1 Toothed whales 2 Baleen whales 3 Dolphins 4 Sea turtles 5 Seabirds 6 Tunas 7 Billfishes 8 Pelagic sharks 9 Benthopelagic sharks 10 Rays and skates 11 Large oceanic planktivores 12 Small epipelagic 13 Medium epipelagic fish 14 Large epipelagic fish 15 Small mig. mesopelagic fish 16 Large mig. mesopelagic fish 17Non-mig. mesopelagic fish 18 Shallow benthic fishes 19 Shallow demersal fishes 20 Seamouts-associated fishes 21 Hoplostethus atlanticus 22 Beryx spp. 23 Bathypelagic fihes 24 Bathybenthic fishes 25 Bathydemersal Fishes 26 Benthic invert, filter feeders 27 Benthic invert, scavengers 28 Benthic crustaceans 29 Pelagic crustaceans 30 Cephalopods resident 31 Cephalopods drifting small 32 Cephalopods drifting large 33 Gelatinous zooplankton 34 Shallow zooplankton 35 Deep zooplankton 36 Phytoplankton 37 Detritus 38 Import  20  21  22  23  24  25  26 27 28  fish  0.05 0.05  0.35 0.35 0.100.10 0.10 0.05 0.10 0.10 0.15 0.05 0.10 0.10 0.05  0.005 0.01 0.01 0.10 0.005 0.05 0.045 0.05 0.15 0.05 0.05 0.01 0.20 0.05 0.10 0.10 0.20 0.05 0.05 0.25 0.10 0.05 0.25 0.500.10 0.10 0.05 0.05 0.05 0.05 0.050.15 0.04 0.15 0.01 0.05  29' 30 31 32 33 34 35  0.05 0.150.10 0.05 0.100.100.10 0.05  0.005 0.005  0.15 0.05 0.05 0.05 0.05 0.05  0.055  0.20 0.30 0.10 0.10  0.10 0.150.050.100.10  0.15 0.20 0.05 0.10 0.375 0.15 0.10 0.35 0.10 0.05 0.20 0.050.10 0.25 0.200.200.25 0.05 0.015 0.25 0.10-0.25 0.100.800.10 0.185 0.15 0.75 0.70 0.70 0.125 0.20 0.10 0.20 0.10 0.80  \u000CTable 3.3- Catch (t km^ year\"1) and total catch (t-year\"1) estimated for the different fisheries considered in the theoretical seamount. DL is demersal longline; DWL is deep-water longline; SP is small pelagics fishery; T is tuna pole-and-line fishery; SW is swordfish longline fishery; and DWT is deep-water trawl. _  1  p  Landings by fleet (t-km\" -year\" ) Group name  DL  DWL  SP  Tunas  T  SW  t-year\" DWT  0.011  Billfishes Sharks Pelagic  0.001  Sharks Benthopelagic  0.001  Rays and Skates  0.002  Total 30.0  0.001  2.7  0.001  5.5 0.001  5.5 5.5  Epipelagic S  0.050  136.4  Epipelagic M  0.010  27.3  Shallow Benthic Fishes  0.080  218.2  Shallow Demersal fishes  0.020  54.6  Seamouts-associated Fishes Hoplostethus  0.001  atlanticus  0.005  Beryx spp.  0.010  30.0  0.010  27.3  0.005  27.3  Bathypelagic  0.005  0.001  16.4  Bathybenthic fishes  0.002  0.001  8.2  0.001  5.5  Bathydemersal Fishes z  Total (t-km\" -year\"')  0.001 0.110  0.008 0.060  0.011  0.002  0.029  \u000C3.2.3 Impact of primary production enhancement I explored the potential impact of heightened levels of primary production on higher trophic levels, using the \"closed model\". The estimated primary productivity for the Northeast 2  j  Atlantic region was 2030 t-km\" -year (SeaWIFS data set). Pauly et al. (pers. comm.) found that seamounts whose peak reaches to within 100 m of the surface would generate an increase in local primary production of between 50 to 70% of the surrounding areas, while those with summits at 300m depth would generate an increase of only between 2 and 5%. I used these values to estimate the biomasses of orange roughy that could be sustained under these conditions. Also, I quantified the amount of primary production required (PPR) to sustain different levels of orange roughy biomasses, as reported for several geographical areas. The \"closed\" seamount model was balanced by providing the biomasses of ten top predators: toothed whales, baleen whales, dolphins, sea-turtles, seabirds, tunas, billfishes, pelagic sharks, benthopelagic sharks, rays and skates. The Ecopath model was then run with different biomasses of orange roughy and the primary production required to sustain the new system was calculated. The linear relationship between PPR and orange roughy biomass was then computed and the levels of orange roughy sustained by different levels of local PP estimated. 3.2.4 The \"advection model\" Using the EwE approach, advection of prey can be modelled in two ways. The first approach, which is used only when there-is a permanent addition of organisms from'other areas outside the system, assumes treatment of migratory flows as dispersal (immigration / emigration) rates across the system boundaries (Christensen et al., 2005). Dispersal rates allow quantification of the amount of imported resources needed to maintain the system and simulation of the impact of different levels of immigration, due to inter-annual changes in climate, primary production, and currents. The second approach, which I did not use, involves the assignment of a high diet proportion as \"import\" in the Ecopath diet composition matrix (Christensen et al., 2005).  \u000CI simulated and quantified the immigration of prey organisms that would be required to maintain a \"hypothetical\" seamount community. Total immigration rates required were estimated as a function of potential biomass of predators as: (7)  I \u2014 a-Bj+b  where the slope a is the Q/B ratio of predator j and the intercept b is roughly the consumption of the predator in the balanced \"closed seamount model\". To estimate the total immigration required to sustain a \"typical\" seamount community in the northeast Atlantic, I assumed that the standing biomasses estimated by the \"closed seamount model\" would roughly represent the biomasses of most of the groups, except for seamount aggregating fish, orange roughy, Beryx spp., and some pelagic fish groups that are attracted to seamounts (i.e., seabirds, tunas, billfishes, and pelagic sharks). P/B, Q/B, EE, and the estimated standing biomasses were re-entered in the new model and the following assumptions were made: 1) biomass estimates for orange roughy in the North Atlantic are not available and so southern hemisphere data from Bulman (2002) were used. Biomass was rescaled to the total area, assuming that part of it is open water where seamount associated 2 I species are unlikely to occur (41.9 t-km\" -year\" ); 2) biomasses alfonsinos were estimated to 2 1 * be 10 times higher than in the \"closed seamount model\" (5.3 t-km\" -year\" ); 3) biomass of groups attracted to seamounts was increased based on data from Chapter 4, and thus considered greater than in the open ocean, i.e. abundances of marine mammals, seabirds and tuna were assumed to be 2.5, 2.5 and 6 times higher than in the \"closed seamount model\". No changes were made to the benthic filter feeding group. In order to check if the necessary immigration ratios could be supported by local oceanographic conditions I estimated the available micronekton in t-km\"2-year\"' that could be advected to the system. Ocean current velocity (V c) was derived from local measurements at the Sedlo seamount North of the Azores island of Graciosa while the standing biomass (B) of migrating groups (j) were assumed to be those of the \"closed seamount model\". The immigration (7) of micronekton can thus be estimated as:  \u000C(8)  I rV c-BrW'  where W is the average width of the area covered by the model. Sedlo seamount (40\u00B0 20'N, 26\u00B0 54'W) current patterns have been described by White et al. (2006). Sedlo is an elongated, multi-peaked seamount located within the sub tropical north Atlantic gyre. Current residual flow at Sedlo seamount may average 5 cm-s\"1 in the upper 800m while in deeper waters (&gt;800m) may average 1 cm-s\"1. Tidal currents appeared to be amplified by about 2-4x the surrounding ocean values. Since most of the orange roughy aggregations are found at 800m or deeper, and in order to have conservative estimates of immigration rates, I assumed V c of 1 cm-s\"1.  3 . 3 RESULTS  3.3.1 The \"closed seamount model\" The initial model was built with no special features in the seamount ecosystem, in order to estimate baseline standing biomasses for the different groups. This \"closed model\" was balanced after small changes in the diet matrix and the resulting parameter set is given in Tables 3.1 and 3.2; trophic structure is shown in Figure 3.1. The total biomass of the modeled ecosystem, excluding detritus, was estimated as 68 t-km\" . Primary producers account for 10.5% of the total biomass of our \"closed model\", whereas the highest trophic levels (7X&gt;4.30), i.e., toothed whales, pelagic sharks, billfishes, tunas, benthopelagic sharks and dolphins, account for 0.14%) of the total biomass. Aggregating fishes account for only 2.8% of the total biomass of the system. The estimated biomasses were  0.78 t-km\"2 of orange roughy, 0.53 t-km\"2 of alfonsinos, and 0.59 t-km\"2 of other seamount-associated fish species.  \u000CSharks Pelagic  jdphins  ^Tephsrfopocis Dri ^attydemersal F  V\\  ^ftytoFtanktcn  ~ \"  -  Figure 3.1 - Flow diagram of a seamount ecosystem. The area of the nodes is proportional to the biomass of each group.  ON  \u000C3.3.2 Impact of primary production enhancement In our system the biomass of orange roughy (Borh) can be expressed as a function of the primary production 542.l BORH+l607.6).  (PP)  as: B o r h (t-km'2) =  0.0018-PPR-2.965  (or  PPR  (t-km\"2-year_1) =  The average primary productivity for the Northeast Atlantic  (PPNEA)  could sustain only about 0.78 t-km\" 2-year' of orange roughy for our generic seamount model. This analysis is based on the assumption of a linear relationship between PP and biomass of higher trophic levels, which may not always be the case, thus warranting further examination mainly through dynamic simulations modelling. The effect of primary production (PP) enhancement on orange roughy biomass at the \"closed seamount model\" in the Northeast Atlantic is presented in Table 3.4. A 50-70% increase in local PP, typical for seamounts reaching to about 100 m of the surface (Pauly et al., pers. comm.), could sustain about 3.0-7.3 t-km\" of orange roughy biomass, which represents a four to nine fold increase in biomass when compared to the non PP enhancement scenario. Our results suggested that seamounts with summits at about 300m depth (depth of the summit in the generic seamount model), and which generate an increase in PP between 2 and 5%, could 2  sustain only 0.8-1.0 t-km\"' of orange roughy. As previously suggested, the values of PP required to sustain the levels of orange roughy biomass reported from different seamounts around the world are much higher than the observed values. For example, to sustain 100 * \u2022 t-kiri 2 of orange roughy (similar to that observed off Tasmania; Koslow, 1997; Bulman, 2002) the PPR is about 55800 t-km\"2-year_1, 28 times higher than the observed values for the Northeast Atlantic. 3.3.3 The \"advection model\" Total immigration biomass was estimated as a function of the potential biomass of different predatory groups that are somehow attracted to seamounts (Table 3.5). Taking as an example 2 I the orange roughy, the total immigration rate required (t-km\" -year\" ) was estimated as: I = 2.00\\S\u201E,/,-l .560. Thus, a potential biomass of orange roughy of 100 t-km\" will require a total 2 I immigration rate of 198.4 t-km\"-year\" , which represents an import of 119%) the total  \u000Cstanding biomass of the system (excluding detritus). Prey groups with higher immigration 2 I rates were: small (vertical) migrating mesopelagic fish (69.5 t km\" -year\" ), pelagic crustaceans (49.6 t-km\"2 -year\" 1), mesopelagic non-migrating fish (29.8 t-km\"2 -year\"1), small drifting cephalopods (29.8 t-km\"2-year\"'), gelatinous zooplankton (9.9 t-km\"2-year\"') and large (vertical) migrating mesopelagic fish (9.9 t-km\"2-year\"1). Table 3.4 -Effect of primary production (PP) enhancement on orange roughy biomass at a generic seamount in the Northeast Atlantic. Estimated primary productivity for the Northeast Atlantic  (PPNEA)  region was 2030 t-km\"2-year\"' (SeaWIFS data set). PP Enhancement  PP  Biomass (orange roughy)  (t-km\"2-year\"1)  (t-km\"2)  1.00  2030  0.780  1.02  2071  0.855  1.05  2171  1.046  1.50  3261  3.052  1.70  5544  7.263  Table 3.5 - Total immigration rate required (t-km\"2-year\"'), estimated as a function of potential biomass of predators as: I = a-Bj+b\\ where Bj is the biomass of the predator j, the slope a is the Q/B ratio of i and the intercept b is roughly the consumption of i in the balanced \"closed seamount model\". Predator Group  a  b  Orange roughy  2.000  -1.560  Alfonsinos  2.000  -1.062  Dolphins  11.410  -0.456  Seabirds  84.390  -0.008  Tuna  16.291  -0.519  \u000CTo enable the model to estimate the total immigration required to sustain a \"typical\" seamount community in the northeast Atlantic, abundances of dolphins, seabirds and tuna were assumed to be 0.1, 0.00025 and 0.191 t-km\"2, respectively. Using these abundances to estimate the prey immigration rates required to sustain these predatory groups, I calculated that marine mammals (dolphins only) require 0.7 t-km\"2-year\"', seabirds 0.01 t-km\"2-year\"', 2 and tuna 2.6 t-km\"2 -year\" 1. Additionally, 1 assumed biomasses of orange roughy of 41.9 t-km\"  and of alfonsinos of 5.3 t-km\"2 requiring immigration rates of 82.3 t-km\"2-year\"' and 9.6 t-km\" 2 1 \u2022year\" , respectively. In this case, the total immigration rate required to sustain this \"typical\" 9  I  seamount community would be 95.2 t-km\" -year\" . If I assumed an average current velocity (V c) of 1 cm-s\"1 (or 315 km-year\"1), a model width (W) of 53 km and a standing biomass (B) of migrating groups of 23.787 t-km\"2 (i.e. small epipelagic fish, medium epipelagic fish, small migrating mesopelagic fish, large migrating mesopelagic fish, non-migrating mesopelagic fish, pelagic crustaceans, drifting small cephalopods, and gelatinous zooplankton), the total immigration rates available to the system ' than what may be needed by the \"typical\" was about 141.5 t-km\"2-year\" 1, about 50% more 2 1 seamount ecosystem (95.2 t-km\" -year\" ). Higher current velocities, typical from seamount summits, generated even higher biomasses of advected organisms. I also tested the extent to which current velocities and advected rates of organisms may influence the potential standing biomass of orange roughy. For most current velocities there was enough food to sustain high standing biomasses of orange roughy (Figure 3.2). Only when the biomass of advected micronekton and current velocity were both very low (below 2 I 10 t-km\" and 5 cm-s\" , respectively), may food be a limiting factor for orange roughy. Otherwise, it seems that food is not the limiting factor for orange roughy abundance.  \u000CZ axis; Potential biomass of orange roughy (t-km\"2)  0 0  1 1 1 j 1 1 1 1 1 1 r 1 1 1 1 1 T 1 1 1 \"1' v I \u2014 ' I I n I \u2014I I I | I I I t I I I I I | I I\u2014\" r r'' T rTTT 2  4  6  8  10  15  20  25  30  Advection rate of micronekton (t-km 2-year\"1)  35  40  45  50  2  Figure 3.2 - Potential biomass of orange roughy (t-km\" ) that could be sustained by different 2  1  rates of advected micronekton (t-km\" -year\" ) and oceanic current velocities (cm-s\" ). Scale goes from white and light blue (low rates of advection) to dark red (high rates of advection).  3 . 4 DISCUSSION  The \"closed seamount model\" was built to estimate baseline standing biomasses for the different groups. Compared to a general \"open ocean\" model built for the North Atlantic (Vasconcellos and Watson, 2004) the biomasses estimated for the pelagic groups were very similar. Such a result was expected because I did not take into account any ecological processes such as dense fish aggregations around seamounts, the hydrological trapping of small organisms or horizontal flux of micronekton. In contrast, estimated biomasses for benthic and demersal groups of the present study were higher than in the open ocean model. For example, Vasconcellos and Watson (2004) estimated biomass of small and large bathydemersal fish in the slope and abyss zones of the North Atlantic as being 0.45 t-km\" while in the seamount models the biomass of bathydemersal and bathybenthic fishes summed to almost five times as much, 2.06 t-km\"2. These differences are due to greater habitat  .1 0  \u000Cavailability at suitable depths for these groups on seamounts. On the other hand, our biomass estimates of benthic organisms were very similar to the estimates for a deepwater flat bottom model off Tasmania (Bulman, 2002). The estimated biomasses of seamount associated species (orange roughy, alfonsinos, and other seamount associated fishes) in the \"closed model\" were very low (&lt;2 t-km\" ) when compared to reported abundances found in the literature (Koslow, 1997; Bulman, 2002). For example, off Tasmania Bulman (2002) estimated biomasses of different seamount aggregating fish as 106.7 t-km\"2 of orange roughy, 4.11 t-km\"2 of oreos (Pseudocyttus maculatus, Neocyttus rhomboidalis  and Allocyttus niger) and 8.21  t-km\" of warty dory (Allocyttus verrucosus). These estimates were based on many assumptions and were later found to be conservative. Using commercial catch data from Tasmania seamounts Bulman et al. (2002b) found the biomass of oreos to be 300 t-km\" . Thus, these results suggest lack of resources in the system to support such amounts of seamount aggregating fish. In other words, local seamount production may be responsible for sustaining only a small amount of its total biomass. The question of whether increased primary production could explain the occurrence of large aggregations of seamount fishes was thus tested. The second part of this work was to obtain more accurate estimates of the primary production required (PPR) to sustain large aggregations of fish around seamount ecosystems. Our study supports the idea that local primary productivity enhancement cannot sustain large aggregations of seamount fishes. However, our results differ markedly from, those of the previous study based on a simpler approach. Koslow (1997), estimated that the PPR to 2 2 1 sustain 2 i 100 t-km\" of PPR to sustain the same biomass would be about about 56500 difference is duethe to orange roughy was 18000 t-km\" t-km\" -year\" -year\" ., The while in our study our use of an ecosystem approach instead of single species perspective, i.e., not all PP will be available to orange roughy as in Koslow (1997) assumptions. Instead, there will be many other predators feeding on same prey as roughy that will also require some PP. From these calculations, ! can conclude that aggregations of seamount fishes on seamounts cannot likely  \u000Cbe sustained by local biological productivity. This finding refutes the first hypothesis and supports Koslow (1997). Therefore, how are large aggregations of fish sustained around seamounts? Both local production and enhanced primary production (Uda and Ishino, 1958; Hubbs, 1959; Uchida and Tagami, 1984) are clearly not sufficient sources of energy to these aggregations. Our simulations using the \"advection\" model clearly show that these aggregations might be sustained by the horizontal flux of prey organisms that past seamounts, thus supporting the \"feed-rest\" hypothesis (term used by Genin, 2004) originally proposed by Tseytlin (1985). I suggest that the required flux of micronekton to an intermediate seamount can be measured as I = 2.00-i?,\u201E./j-l .560 (where / the immigration to the system and Borh is the biomass of orange roughy). This seamount model, which took into account high abundances of fish, marine mammals, seabirds and tuna, required a total immigration of micronekton of 95.2 t-km\"2-year\"', which is less than the potential available biomass after considering currents (141.5 t-km\"2-year\"'). Therefore, I suggest that the horizontal flux of prey may be sufficient to sustain the rich communities living on seamounts. Problems with these calculations may arise from overestimation of standing biomasses of micronekton (23.8 t-km\"2 in our example); or from overestimation of current velocities (0.1 cm s\"1 in our example). However, using acoustic and trawling methods, the standing biomasses of mesopelagic fishes and pelagic crustaceans off southern Tasmania were estimated to be about 100 t-km\"2 and 5 t-km\"2 respectively (Koslow et al., 1997; Williams and Koslow, 1997). These values are four times higher than the values I used in our model. Furthermore, the current velocity I used is a very conservative value. Current velocities measured in different intermediate or shallow seamounts in northeast Pacific Ocean range from about 12 cm-s\"1 at Cobb seamount (Freeland, 1994) to 20-40 cm-s\"1 at Fieberling seamount (Eriksen, 1991) or 15-35 cm-s\"' at Emperor Seamount (Roden, 1987). In the north Atlantic current velocities at Great Meteor Seamount measured 12-15 cm-s\"1 (Mohn and Beckmann, 2002) and at Gorringe seamount measured 15-20 cm-s\"' (Serra and Ambar, 2002).  \u000CThese results suggest that food may not be a limiting factor for orange roughy at some seamounts. Only when the biomass of advected micronekton and current velocity are both extremely low (below 10 t-km\"2 and 5 cm-s\"', respectively) will the flux of prey be insufficient to sustain large aggregations of orange roughy (Figure 3.2). The question now is what can influence seamount fish abundance and explain the high variability of abundances from one seamount to the other (e.g., Clark et al., 2001; McClatchie and Coombs, 2005; Rowden et al., 2005). There is a considerable literature dealing with the composition of fish assemblages and their association with various environmental factors. Aspects of bottom depth, latitude, longitude, sediment type, bottom temperature, and oceanographic water masses are frequently recorded as important in determining fish species composition and abundance (e.g., Haedrich and Merrett, 1990; Koslow, 1993; Koslow et al., 1994; Francis et al, 2002). Clark et al. (2001) examined relationships between physical variables of seamounts around New Zealand, and the estimated size of orange roughy populations from those seamounts. They used multiple regression procedures to model the effects of the physical variables. Seamount location, depth of the peak, slope of the seamount flanks, and geological association (continental or oceanic) were significant factors in determining stock size in various analyses. Since I have shown that food may not be a limiting factor, other factors may explain the high variability of abundances between seamounts (Melo and Menezes, 2002). For large aggregations of seamount fish the availability of sheltered or resting areas may be a determinant factor in explaining intra-seamount differences in abundance. This is probably the reason why Clark et al. (2001) found the slope of the seamount flanks a significant factor.. For example, orange roughy and some oreos have a relatively high metabolic rate compared to many other deep-sea fishes as they in order to maintain position in highly dynamic current regimes require strong locomotory performance (Koslow, 1996; Bulman, 2002). However, they will also need quiescent areas for resting between feeding events. Some observational studies support this hypothesis (Lorance et al., 2002), showing that perhaps the fish has  \u000Cevolved to efficiently utilize enhanced horizontal fluxes in strong currents and to effectively reduce their metabolic expenditure by resting motionless in topographic shelters (Genin, 2004). 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Marine Biology 130: 259-276.  \u000CCHAPTER 4 TESTING A SEAMOUNT EFFECT ON AGGREGATING VISITORS  1  4 . 1 INTRODUCTION  Seamounts have been recently recognized as highly important for fisheries, biodiversity and conservation as they often support isolated but rich underwater ecosystems (Morato and Pauly, 2004). One important characteristic of seamounts is that they tend to enhance water currents (Genin et al., .1986; Boehlert, 1988) thus enhancing the flux of prey organisms that pass the seamounts (Chapter 3; Tseitlin, 1985; Genin et al., 1986; Koslow, 1997). This characteristic has been used to explain the occurrence of large aggregations of bottom fishes around seamounts. While the importance of seamounts for bottom fishes is very well documented (Boehlert and Sasaki, 1988; Koslow, 1996; Koslow, 1997; Koslow et al., 2000), the importance for large pelagic or visiting organisms has been poorly examined. However, it has been hypothesised that there are higher abundances of some \"visiting\" animals, such as tuna, sharks, billfishes, marine mammals, sea-turtles and even seabirds, over seamounts but this has been based on sparse records, warranting further examination. It is known from tagging studies that some seamounts host transient populations of bigeye tuna Thunnus obesus and yellowfin tuna T. albacares (Holland et al., 1999; Itano and Holland, 2000; Sibert et al., 2000; Klimley et al., 2003; Musyl et al., 2003; Sibert et al., 2003), consequently some fisheries have taken advantage of these and other tuna aggregations (like albacore T. alalunga and skipjack Katsuwonus pelamis) to increase their yields (Yasui, 1986; Fonteneau, 1991; Adam et al., 2003). Other pelagic fishes such as billfishes (Ward et al., 2000; Sedberry and Loefer, 2001) and pelagic sharks (Hazin et al., 1  A version of this chapter has been submitted for publication. Morato, T.; M. Machete; A. Kitchingman; F. Tempera; S. Lai; G. Menezes; R.S. Santos; and T.J. Pitcher (submitted). Abundance and distribution of seamounts in the Azores. Marine Ecology Progress Series.  \u000C1998; Klimley et al., 1988) appear also to be attracted to complex high-relief bottom structures where they may be subject to local depletion. The reasons for these aggregations are still under debate but most seamounts act both as feeding stations and as orientation points in the larger-scale movement patterns of these visitors (e.g., Freon and Dagorn, 2000). Although several works have correlated cetacean occurrence with complex and steep topographies (e. g. Schoenherr, 1991; Balcomb, 1989; Canadas et al., 2002; Hooker et al., 2002; Hastie et al., 2004; Yen et al., 2004) the literature addressing their association with seamounts is scarce. Santos et al. (in press) concluded that seamounts are \"hotspots\" for seaturtles because they found that tracked loggerhead Caretta caretta move toward seamounts where they have increased residence time. As to seabirds, Cory's shearwater Calonectris diomedea,  yellow-legged  gull Larus  cachinnans  atlantis,  Madeiran  storm  petrel  Oceanodroma castro (Monteiro et al. 1996), Cassin's auklet Ptychoramphus aleuticus (Yen et al., 2004, 2005), and black-footed albatross Diomedea nigripes (Haney et al., 1995) have also been observed aggregating above seamounts summits where they feed on zooplankton, small fish and small cephalopods. Whereas previous studies have focused on analysing the auto-ecology of some organisms, in this paper, using data from a fishery observer program, I explicitly test if the abundances of tuna, marine mammals, sea turtles and seabirds observed at Azores seamounts are higher than expected by chance.  4 . 2 METHODS  4.2.1 Study area The Azores archipelago is a group of nine volcanic islands and many small islets that are parts of the Mid-Atlantic Ridge in the Northeast Atlantic Ocean (an irregular area within 33.5-43\u00B0 N, 21-35.5\u00B0 W). Shallow seabed less than 600 m deep cover less than 1% of the 953,633 km of the Azorean EEZ. This reflects the narrowness of the island shelves and  \u000Csupports scattered fishing grounds. This study was conducted in a small rectangular area of the Azores EEZ (36.20-40.20\u00B0 N, 24.20-32.00\u00B0 W) chosen because it includes most of the seamounts, has better bathymetry data, and because all the tuna fishing effort and thus the fishery observer program was conducted within this area. In this study, I used seamounts mapped (using an automated technique -Chapter 2) in the designated area of the Azores EEZ as described above which contains 39 large and 151 small seamount-like features (Figure 4.1) with different shapes, heights and depth of summits. The seamounts included in this study are those with numbers 1, 3-28, 30, 31, 33-39, and 50-52 in Appendix 2. Data from island shores (&lt;30 km) were excluded from the analyses because they may be biased by the Island Mass Effect and because I wanted to test the effect of offshore seamounts.  JW*  fww  3HWW  jbwv  5\u00ABreTf  nsyttm  i \u2022 -WW -  ~  \u2022\u2022  t m. &gt;1 }H 1i  WW  II  mmmm  5r:i rr'f.y Figure 4.1 - Map of the Azores archipelago and its seamounts.  rwt  \u000Cr  4.2.2 Data collection: POPA Observer program The  observer  program  for  fisheries  in  the  Azores  (POPA,  http://www.horta.uac.pt/projectos/popa) was launched in 1998 with the main goal of gathering data on the pole-and-line tuna fisheries in order to certify the fisheries as 'dolphin safe'. The program covers about 50% of the fleet of around 20 vessels and often covers over 50% of the tuna catches. The program operates with observers onboard tuna fishing vessels recording data on fishing activities and gathering other scientifically-relevant information. Geo-referenced data collected onboard includes: fishing effort, tuna catch, sighting effort for different species, and sighting of named species of marine mammals, sea turtles, and seabirds. Fishing effort is estimated as the amount of time fishermen spent looking for tuna schools. Sighting effort is defined as the time the observer spent looking at the sea searching for species other than tuna. Marine mammals, sea-turtles and seabirds were quantified by counting individuals (or estimation when in large groups) that were up to 300 meters from the vessel. The average speed of the boats when searching was 8 knots  15 km-h\"1).  4.2.3 Species The present study focused on different species of tuna, marine mammals, sea turtles and seabirds (Table 4.1). Tuna are present in the Azores EEZ only during summer months: our study focuses on adult bigeye and skipjack, the most common tuna species caught in the Azores. The first species is present during April to June, while the second is usually caught from June to October. Albacore, yellowfin and bluefin tuna (Thunnus thynnus) are also caught in the Azores in small quantities. Tunas are caught with pole-and-line, usually with water spray and live bait. Only about 20 medium-sized Azorean boats (28-32 m long) fish within the EEZ. In fact, tuna fishery catches are relatively small and can be around 5000 tons in a good year.  \u000CTable 4.1 - Species included in the present study, the number of observations by species and summary of the data on distance from observed data points to closest seamount (km). observations n  Species Tuna Skipjack, Katsuwonus  pelamis  Bigeye tuna, Thunnus obesus Marine mammals Common dolphin, Delphinus delphis Spotted dolphin, Stenella frontalis Bottlenose dolphins, Tursiops truncatus Sperm whale, Physeter macrocephalus Sea Turtles Loggerhead turtle, Caretta carettaSeabirds  Dist. to seamount (km) Min - max Mean \u00B1 SD  1675 1497  0.4-&gt;100 2.0-&gt;100  50.7\u00B149.2 28.4\u00B136.3  2008 528 303 233  0.2-&gt;100 0.7-&gt;100 0.3-&gt;100 1.6-&gt;100  30.U33.0 44.3\u00B153.9 29.7\u00B133.1 44.1\u00B135.8  566  0.6-&gt;100  52.5\u00B165.6  1681 329 134  0.4-&gt;100 1,7-&gt;l 00 3.2-&gt;100  37.0\u00B146.8 43.2\u00B152.5 54.2\u00B158.9  * '  Cory's shearwater, Calonectris diomedea borealis Yellow-legged gull, Larus cachinnans atlantis Terns, Sterna hirundo and S. dougalli  Over twenty-three species of cetaceans have been reported for the Azores (Gon9alves et al., 1992, 1996; Silva et al., 2003). From these only a part is recorded yearly and from those the most common species are the spotted dolphin Stenella frontalis, common dolphin Delphinus delphis,  bottlenose dolphin Tursiops  truncatus,  sperm whale Physeter  macrocephalus,  Risso's dolphin Grampus griseus, stripped dolphin Stenella coeruleoalba, short-finned pilot whale Globicephala macrorhynchus (Silva et al., 2003). Our study will focus on only the first four species because of the lack of data for the others. The sea-turtles occurring in the Azores are loggerhead, leatherback Dermochelys coriacea, and green turtles Chelonia mydas, the first being the most commonly sighted species. Juvenile loggerhead turtles are transported by the North Atlantic Gyre current and live a pelagic life for about 8 years in the Eastern Atlantic, including around the Azores (Bjorndal et al., 2000). This study will focus only on the loggerhead turtle.  \u000CSeveral seabird species are found in the Azores. However, the most commonly-sighted species are Cory's shearwater, yellow-legged gull, terns Sterna hirundo and S. dougalli, Madeiran storm petrel Oceanodroma castro, greater shearwater Puffmus shearwater Puffmus  gravis, and the little  assimilis. The petrels feed on vertical migrating fish while Cory's  shearwater feeds often in association with marine predators such as dolphins and tuna (Monteiro et al., 1996). This study will focus only on Cory's shearwater, the yellow-legged gull and the terns. 4.2.4 Data analyses I used spatial data from the POPA program to test if tuna catches and sightings of sea-turtles, marine mammals and seabirds were higher closer to seamount summits. Data was available for the period 1998 to 2004 for tuna and marine mammals and for the period 2000-2004 for seabirds and sea turtles. A preliminary analyses showed no inter annual differences in the tuna catch or sighting effort. Thus, data for all years was pooled. I built grids of tuna catch, sighting effort, and sightings of different species by allocating each data point to a 0.05 x 0.05 degree cell (~5.5 x 5.5 km). This procedure produced two grids of tuna catch per 30.9 km 2, one grid of sighting effort in hours per 30.9 km 2, and several grids of numbers of individuals per 30.9 km . The latter grids where then divided by the effort grid to produce grids of numbers of individuals per 30.9 km 2 per hour of search. These grids included cells with zero values, i.e., those cells with fishing or sighting effort but zero catch or observations. Null data cells were those with no fishing or sighting effort. Distances of observation cell to nearest seamount summit were separated into 10 classes of 10 km intervals from 0 to 100 km. Each cell with data was then allocated to one of the distance classes and the average catch and sightings were calculated for each class. The estimated averages were transformed in catches or sightings per km . I test if fishing locations were randomly selected by comparing the frequency distribution of the distances to seamount summit of the dataset of tuna fishing events and of a set of an equal  \u000Cnumber of randomly selected locations using the G statistic for the log-likelihood ratio goodness of fit. One-way analysis of variance (ANOVA) was used to test for significant differences between Log (x+1) transformed mean abundances (catch or sightings) at different distances from seamount summits. When significant F tests from the ANOVA were found, I looked for those means contributing to the effect using a post-hoc comparison. In this case, I used Dunnett's multiple comparison test to determine the significant differences between a control group mean and the treatment group means in the analysis of variance setting (Zar, 1999). The overall mean value was used as the control group mean, since I wanted to compare each distance to seamount to the overall mean (C.W. Dunnett, pers. comm.). Additionally, I have tested for seamounts that produced a significant effect on species association. Firstly, for those species that showed significant associations with seamounts I have estimated its abundance in the first 10 km for each seamount and compared these values with the overall mean for the 10 km bin. These analyses were preformed with a common Z test on log (x+1) transformed data. Finally, I have estimated species abundance in relation to seamount summits depth. Mean values of abundance for each seamount depth interval were then compared with the overall mean abundance to test if seamounts depth could be an important factor on explaining species abundances. These analyses were also preformed with a common Z test (Zar, 1999).  4 . 3 RESULTS  4.3.1 Tuna Observers recorded over four thousand tuna fishing events during the period from 1998 to 2004. The most common species caught with observers onboard during this period were skipjack and bigeye tuna with about 1500 and 962 tons, respectively. The frequency distribution of the distances to seamount summit of the dataset of tuna fishing events and of a  \u000Cset of an equal number of randomly selected locations is shown in Figure 4.2. Calculation of the G statistic for the log-likelihood ratio goodness of fit test show significant differences (G = 2344, df = 25, /?\u00AB0.001) between both distributions. This test allows us to assume that fishing locations were not randomly selected and thus to explore the influence of seamounts on tuna catches.  600  500 400 &gt;&gt; oC 0) 2- 300 C T 0) S-i 200  100 0  1  in i o  in O (N  III in m\u2022 o  in o  m mi o in  &gt;n l o  in r-i o  I...  &gt; ooni o 00  in oON  m Distance to seamount summit (km)  in o O, o  in  \u2014\u2022  Ill m  &lt;N O  Figure 4.2 - Frequency distribution of the distances to seamount summit of the dataset of tuna fishing events and of a set of an equal number of randomly selected locations. Black bars indicate observed data while light grey bars indicate randomly selected locations.  \u000CTuna catches per square kilometre per year were significantly different (ANOVA p\u00AB  0.001  for skipjack and bigeye tuna) at different distances from seamount summits (Figure 4.3). For skipjack (Figure 4.3a), catches occurring within 30 km from seamount summits were significant higher than the overall mean (Dunnett tests for 10km, 20km and 30km /?&lt;0.01) with all other catches being smaller. Bigeye tuna catches per square kilometre per year (Figure 4.3b) were significant higher within 20 km from the summits (Dunnett tests for 10km and 20km /?&lt;0.01; Dunnett tests for 30km p&gt;0.05).  b) 0.25  a) 0.20  0.20  0.15  0.15 0.10  0  9  0.10  0.05  0.05 -  0.00 0  0.00 10 20 30 40 50 60 70 80 90 100  Distance to seamount summit (Km)  0  II  10 20 30 40 50 60 70 80 90 100  Distance to seamount summit (Km)  Figure 4.3 - Tuna catch per square kilometre per year (\u00B195%CL) in relation to the distance to the nearest seamount summit in the Azores, a) skipjack, b) bigeye tuna. Bin size is 10 km. Light grey circles are those significantly higher (Dunnett test) than the overall mean (light grey line).  \u000C4.3.2 Oth er visitors A total of 3910 sightings of marine mammals were recorded from 1998 to 2004. The most commonly sighted species were common dolphin, spotted dolphin, bottlenose dolphin and sperm whale. Common dolphins showed significant different sightings at different distances from seamounts (ANOVA p\u00AB  0.001) whereas all other species showed no significant  differences (ANOVAs bottlenose dolphin, p= 0.278; spotted dolphin, p= 0.392; sperm whale, p= 0.233). The common dolphin (Figure 4.4a) showed some association with seamounts where the highest observations per square kilometre per hour being recorded close to their summits (Dunnett (10km) p&lt;0.02; Dunnett (20km) /?&lt;0.01; Dunnett (30km) /?&gt;0.05). On the other hand, bottlenose dolphins (Figure 4.4b), spotted dolphins (Figure 4.4c), and sperm whales (Figure 4.4d) showed no association with seamount and were not more abundant in the vicinity of the features.  From 2000 to 2004 there were 566 valid observer sighting records of loggerhead turtle. The analyses of the number of loggerheads per square kilometre per hour (Figure 4.5) show no differences with distance to seamount summits (ANOVA sea turtles, p= 0.403). On the other hand, the distance to seamount summit influenced the abundance of Cory's shearwater (ANOVA p\u00AB  0.001). The abundance of Cory's shearwater (Figure 4.6a) was higher in the  first 20 km from the seamount summit. However, only the second distance bin (10-20km) was significant higher than the overall mean (Dunnett 10km /?&gt;0.05; Dunnett (20km)/K0.01; Dunnett (30km) /?&gt;0.05): Terns (Figure 4.6b) and yellow-legged gull (Figure 4.6c) abundances where not different at different distances from seamounts (ANOVA yellowlegged gull,/?=0.364; terns,p= 0.998).  .  .  .  \u000Ca\") 0.05  b) 0.012 0.010  0.04 O  JG  0.008 -  _o  0.03  e  0.006  Jul ^  c0  0.02 -  0.004  03  1 0.01  0.002  x&gt; O  0.00  1 0  0.000  10 20 30 40 50 60 70 80 90 100  Distance to seamount summit (Km)  c) 0.040  -i0  10 20 30 40 50 60 70 80 90 100  Distance to seamount summit (Km)  d) 0.004  0.035 - P 0.030  0.003  M 0.025 ^  0.020  0.002  c cn 0.015 C O 0.010 '-s &gt;  0.001  S-i  &lt;u 0.005 -  X)  o 0.000  0.000 0  10 20 30 40 50 60 70 80 90 100  Distance to seamount summit (Km)  0  10 20 30 40 50 60 70 80 90 100  Distance to seamount summit (Km)  Figure 4.4 - Marine mammals sightings per square kilometre per hour (\u00B195%CL) in relation to the distance to the nearest seamount summit in the Azores, a) common dolphin, b) bottlenose dolphins, c) spotted dolphin and d) sperm whale. Bin size is 10 km. Light grey circles are those significantly higher (Dunnett test) than the overall mean (light grey line).  \u000C0.000 0  10 20 30 40 50 60 70 80 90 100  Distance to seamount summit (Km)  Figure 4.5 - Loggerhead turtles sightings per square kilometre per hour (\u00B195%CL) relation to the distance to the nearest seamount summit in the Azores. Bin size is 10 km.  \u000Ca) 0.07 0.06 -  0.004 0.05  3 o 0.04 \"6  0.003  )  M  C 0.03 e e&gt;S 0.02  () \u2022  0.002  )  f 1  (-1  &lt;D M JO 0.01  0.001  O  0.00 -  0.000 -I-  0  10 20 30 40 50 60 70 80 90 100  Distance to seamount summit (Km)  0  10 20 30 40 50 60 70 80 90 100  Distance to seamount summit (Km)  c) 0.012 0.010 o  0.008  (N  'g. 0.006  m  0 0  .2\u00BB 0.004  t  O.  0.002  0.000 0  10 20 30 40 50 60 70 80 90 100  Distance to seamount summit (Km)  Figure 4.6 - Seabirds sightings per square kilometre per hour (\u00B195%CL) in relation to the distance to the nearest seamount summit in the Azores, a) Cory's shearwater, b) terns, c) yellow-legged gull. Bin size is 10 km. Light grey circles are those significantly higher (Dunnett test) than the overall mean (light grey line).  \u000C4.3.3 Seamounts Catches and sightings above seamounts of various depths are showed in Figure 4.7, for those species with a significant association with seamounts. Skipjack and bigeye tuna showed significantly higher catches on seamounts shallower than 400 m depth (Figure 4.7a,b), whereas common dolphin (Figure 4.7c) and Cory's shearwater (Figure 4.7d) showed significantly higher sightings on seamounts with depths between 200 and 400 meters. In all cases, seamounts with deep summits had relatively low catches and sightings. I have also tested which seamounts produced a significant effect on species association. For skipjack, \"Formigas and Dollabarat\" (seamount # 13; P=0.012), \"Princesa Alice''' (# 50, P=0.014), \"D. Joao de Castro\" (# 25, P=0.026), \"Agores\" (#51, P=0.030) and \"Pico Leste\" of the Princesa Alice (# 23, P=0.031), all showed significant higher catches than the overall mean. For bigeye, only \"Agores\" (#51, P=0.004), \"Princesa Alice\" (# 50, P=0.009), and \"\u00A3&gt;. Joao de Castro\" (# 25, Z^O.044), showed significant higher catches than the overall mean. Common dolphin showed higher abundances on top of \"Agores\" (#51, / &gt;=0.002), \"Princesa Alice\" (# 50, P=0.014), \"Agulhas do Sul do Gigante\" (# 36, P=0.044), whereas Cory's shearwater showed higher sightings on top of \"Princesa Alice\" (# 50, .P=0.003) and \"Agores\" (#51, /M3.006).  \u000CCatch (kg-km\"2-year\"') a)  0.0  0.2  0.4  0.6  Catch (kg-km\"2-year\"') l.o '  O.J  b)  1  0  500  cP o 603 &lt; CU5/ tu J3  1500 o  o  o  o  -L  2  1  0.06  0.08  2000  Observations (n-km\" -hour\" ) 0.00  0  0.02  ,\u20141\u2014_ - KM  0.04  1 ,  1 ,  !  0.10  cT&gt;  -L  2  I 0.00Observations 0.02 0.04(nkm\" 0.06 hour\" 0.08)  0.10  , 1  500  xn &lt;a  0.8  1500 o  Oh  c)  0.6  o F o  o  2000  0.4  1000  o  &lt;D Q  0.2  500  1000  O .G  G P o C (D3  o.o 0  500  - HCH HCH 1000 -o  1000 -  1500 o?  1500 o  -G  o si  &lt;u 0 2000 J_ Figure 4.7 - Catches and sightings (\u00B195%CL) above seamounts of various depths for those 2000  species with a significant association with seamounts. a) skipjack, b) bigeye tuna, c) common dolphin, d) Cory's shearwater. Bin size is 10 km. Light grey circles are those significantly higher (Z test) than the overall mean (light grey line).  \u000C4 . 4 DISCUSSION  This study has demonstrated that some marine predators are associated with seamounts with shallow summits. This was the case of tuna species skipjack and bigeye, common dolphin and Cory's shearwater. These species were significantly more abundant in the vicinity of some seamount summits than far from these features. Our methodology, however, failed to demonstrate seamounts' association for bottlenose dolphins, spotted dolphin, sperm whale, terns, yellow-legged gull, and loggerhead sea turtles. Seamounts play a major role in localizing pelagic prey and thus attracting some pelagic fish, seabirds and marine mammals. Therefore, some seamounts in the Azores may act as feeding stations for some of these visitors. Tuna species such as skipjack and bigeye have previously been acknowledged to occur on seamounts. However, this study was the first to quantitatively demonstrate these associations. Common dolphins are know to feed mostly on small pelagic fish and squids (Silva, 1999) and may, therefore, take advantage of the localized abundance of prey on seamounts. These species are also known to be comparatively more characteristic of offshore habitats than the bottlenose or spotted dolphins. Thus, the common dolphins may use seamounts as important feeding areas (Lopez et al., 2004). Similarly, Monteiro et al. .(1996) inferred from stomach contents data that Cory's shearwaters feed often in association with seamounts, therefore, supporting our results. Not all seamounts, however, seemed to be equally important for these associations. For all species only seamounts shallower than 400m depth showed significant aggregation effects. The important seamounts in the Azores for these visitors are \"Princesa Alice\" and \"Agores\" for all four species, also \"D. Joao de Castro\" for both tuna species, \"Formigas and Dollabarat\" and \"Pico Leste\" of the Princesa Alice for skipjack, and \"Agulhas do Sul do Gigante\" for common dolphin. These seamounts should be considered hotspots of marine life in the Azores and a special effort should be made in order to ensure a sustainable management of these habitats. These seamounts are also known to be heavily exploited by local fishermen, with the exception of \"Formigas and Dollabarat\" which are a marine reserve. Reconciling fisheries with conservation on these seamounts should be a priority for the local  \u000Cmanagement authorities. On the other hand, the area of influence of the seamounts seemed to be about 20 to 30 km, since only the observations at these distances were significant higher than the average. The lack of association of sperm whales with seamounts summit is not surprisingly since these animals prey mostly in deep waters on large squid (Clarke et al., 1993) by echolocating deep prey patches in the mesopelagic environment (Watwood et al., 2006). However, the lack of association with seamounts for bottlenose and spotted dolphins is more difficult to explain. Stomach contents data for these two species in the Azores are not available. Bottlenose dolphins have a high degree of foraging plasticity (Spitz et al., 2006) but in coastal waters seem to prefer benthic species. For instance, around Scotland (UK) this species feeds mostly on benthic fish such as cod (Gadus morhua), saithe (Pollachius virens) and whiting (Merlangius merlangus), and also cephalopods (Santos et al., 2001). Similar patterns were observed for bottlenose dolphins from the U.S. mid-Atlantic coast (Gannon and Waples, 2004). On the other hand, bottlenose dolphins off Galicia (Lopez et al., 2004) and off Gulf of Mexico (Jefferson and Schiro, 1997) have been considered to be more common in coastal areas when compared to common dolphin. This pattern may also be true in the Azores since depth was found to be a significant but negative factor explaining bottlenose dolphins' distribution (Seabra et al., 2005), and therefore this species has been considered to be more associated with coastal habitats than common dolphin. Pelagic and mesopelagic prey may thus have less importance in bottlenose dolphins' diet as compared to common dolphin, meaning that the seamount effect on enhancing secondary and tertiary production may not be important for bottlenose dolphins. Spotted dolphins are known to associate with relatively low seafloor relief gradients (Davis et al., 1998; Mignucci-Giannoni, 1998; Perrin, 2002) and thus may display a stronger association with coastal habitats than with seamounts.  Yellow-legged gulls feed opportunistically on several prey such as fish, barnacles and rats (Monteiro et al., 1996), therefore there is no reason to expect an association of this species with seamounts. However, the lack of association with seamounts of terns was somehow unexpected because in the Azores these species feed mainly on small pelagic and  \u000Cmesopelagic fish (Monteiro et al., 1996), which are expected to have higher abundances close to seamounts. Probably, the number of observation available for these species was not sufficient to detect such associations. The presence of large concentrations of forage fish during the summer in the Azores originates mixed-species feeding aggregations (Clua and Grosvalet, 2001). Tuna fish may help prey rising to the surface before being concentrated by common dolphins, resulting in the formation of a compact 'ball' of forage fish close to the. surface. Tuna, common dolphins and Cory's shearwater collectively feed on this concentration of prey. Therefore, an alternative explanation for the association with seamounts of commpn dolphins and Cory's shearwater and the lack of association for bottlenose and spotted dolphin is the interaction with tuna: I cannot exclude the hypothesis that the seamount association of these species is an indirect effect of the seamount. The rational for this would be seamounts attracting tuna species and, in turn, tuna species attracting common dolphins and Cory's shearwater but not bottlenose and spotted dolphins. Silva et al. (2002) have demonstrated that common dolphin is significantly associated with tuna in the Azores and that bottlenose and spotted dolphins show lower degree of interaction. Loggerhead sea turtle is widely distributed in the North Atlantic where it feeds mainly on jellyfish. The lack of association of loggerhead with seamounts-may be simply explained by the lack of reasons to believe that jellyfish abundance should be higher on top of seamounts then elsewhere. This species may be significantly associated with large oceanographic anomalies, such as fronts and currents (Polovina et al., 2004, 2006), where food for loggerheads may be more abundant. Etnoyer et al. (2006) also found topographic features like the shelf break and seamounts not important factors in explaining the distribution of these turtles. Loggerheads, however, may use seamounts for large-scale navigation proposes (Lohmann and Lohmann, 2003).  \u000C4 . 5 REFERENCES  Adam, M.S., J. Sibert, D. Itano and K. Holland (2003) Dynamics of bigeye (Thunnus obesus) and yellowfin (T. albacares) tuna in Hawaii's pelagic fisheries: analysis of tagging data with a bulk transfer model incorporating size-specific attrition. Fishery Bulletin US 101:215-228. Balcomb, K.C. 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Dutton (2006) The Kuroshio Extension Bifurcation Region: A pelagic hotspot for juvenile loggerhead sea turtles. Deep-sea Research II 53(3-4): 326-339. Santos, M.A., A.B. Bolten, H.R. Martins, B. Riewald and K.A. Bjorndal (in press) Airbreathing visitors to seamounts: sea-turtles. Chapter 12a. In: T.J. Pitcher, T. Morato, P.J.B Hart, M. Clark, N. Haggan and R.S. Santos (eds). Seamounts: Ecology, Conservation and Management. Fish and Aquatic Resources Series, Blackwell, Oxford, UK. Santos, M.B., G.J. Pierce, R.J. Reid, I.A.P. Patterson, H.M. Ross and E. Mente (2001) Stomach contents of bottlenose dolphins (Tursiops truncatus) in Scottish waters. Journal of the Marine Biological Association of the UK 81: 873-878. Schoenherr, J.L. (1991) Blue whales feeding on high concentrations of euphausiids around Monterey Submarine Canyon. Canadian Journal of Zoology 69:583-594. Seabra, M.I.,.M.A. Silva, S. Magalhaes, R. Prieto, P. August, K. Vigness-Raposa, V. Lafon, and R.S. Santos (2005) Distribution and habitat preferences of bottlenose dolphins (Tursiops truncatus) and sperm whales (Physeter macrocephalus) with respect to physiographic and oceanographic factors in the waters around the Azores. European Research on Cetaceans. Proceedings of the 19th Annual Conference of the European. http:.^vAw Jioila.nac.pt/proiectos/cefamarh/Artigos/pub projeclo/scabraeta Iecs05.pdf  \u000CSedberry, G.R. and J.K. Loefer (2001) Satellite telemetry tracking of swordfish, Xiphias gladhts, off the eastern United States. Marine Biology 139: 355-360. Sibert, J., K. Holland and D. Itano (2000) Exchange rates of yellowfin and bigeye tunas and fishery interaction between Cross seamount and near-shore fads in Hawaii. Aquatic Living Resources 13(4): 225-232. Sibert, J.R., M.K. Musyl and R.W. Brill (2003) Horizontal movements of bigeye tuna (Thunnus obesus) near Hawaii determined by Kalman filter analysis of archival tagging data. Fisheries Oceanography 12(3): 141-151. Silva, M.A. (1999) Diet of common dolphins, Delphinus delphis, off the Portuguese continental coast. Journal of the Marine Biological Association of the UK 79: 531-540. Silva, M.A., R. Feio, R. Prieto, J.M. Gongalves, R.S. Santos (2002) Interactions between cetaceans and the tuna fishery on the Azores. Marine Mammal Science 18(4): 893-901. Silva, M.A., R. Prieto, S. Magalhaes, R. Cabecinhas, A. Cruz, J.M. Gongalves and R.S. Santos (2003) Occurrence and distribution of cetaceans in the waters around the Azores (Portugal), Summer and Autumn 1999-2000. Aquatic Mammals 29(1): 77-83. Spitz, J., E. Richard, L. Meynier, C. Pusineri and V. Ridoux (2006) Dietary plasticity of the oceanic striped dolphin, Stenella coeruleoalba, in the neritic waters of the Bay of Biscay. Journal of Sea Research 55: 309-320. Tseytlin, V.B. (1985) Energetics of fish populations inhabiting seamounts. Oceanology 25: 237-239. Ward, P., J.M. Porter and S. 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Fourth edition, Prentice Hall, New Jersey, USA.  \u000CCHAPTER 5 FISHING DOWN THE DEEP  1  5.1 INTRODUCTION  A global crisis in marine fisheries was regarded with scepticism by many fisheries scientists as recently as ten years ago. Today, however, few dispute worrying trends (Pitcher and Pauly, 1998; Pitcher, 2001; Pauly et al., 2002; Christensen et al., 2003; Hilborn et al., 2003; Pauly and Maclean, 2003; FAO, 2004). Historical data from marine ecosystems clearly suggest that overfishing has had, for thousands of years, a major impact on target species and have fundamentally altered marine ecosystems (Jackson et al., 2001; Pitcher, 2001), including coral reefs (Pandolfi et al., 2003). A dramatic depletion of large predators (Baum et al., 2003; Christensen et al., 2003; Myers and Worm, 2003) has triggered fisheries to target species of lower trophic levels in a process called \"fishing down marine food webs\" (Pauly et al., 1998a). More recently, fisheries exploitation has spread from coastal areas to the open ocean and a general decline in fish biomass has been reported (Baum et al., 2003; Christensen et al., 2003; Myers and Worm, 2003): as a consequence, many marine species are of serious conservation concern (Casey and Myers, 1998; Spotila et al., 2000; Baum et al., 2003; Sadovy and Cheung, 2003). Not surprisingly, there has been a decline in global fisheries catches since the late 1980s (Watson and Pauly, 2001; Zeller and Pauly, 2005) at an approximate rate of 0.36 million tonnes per year. Nevertheless, a global increase of fishing effort and catching power has continued (Greboval, 2003).  With the decline of shallow coastal waters resources, increasing demand, and new technology, fisheries are evidently expanding offshore (e.g., Christensen et al., 2003; Myers and Worm, 2003; Pauly et al., 2003) and into deeper waters (Koslow et al., 2000; Garibaldi and Limongelli, 2003; FAO, 2004; Gianni, 2004). The expansion into offshore areas has been well documented (for example, fisheries targeting oceanic tuna, billfishes and their relatives covered the world ocean by the early 1980s; Myers and Worm, 2003), but the extension into 1 A version of this chapter has been published. Morato, T., R. Watson, T.J. Pitcher and D. Pauly. 2006. Fishing down the deep. Fish and Fisheries 7:24-34.  \u000Cdeeper waters is less well analysed. While many local examples of fisheries expansion into deeper waters have been reported (e.g., some European, Soviet, U.S.A., Canada, New Zealand and Australian fishing fleets: see references in Hopper, 1995; Moore, 1999; Koslow et al., 2000; Roberts, 2002), we lack a global quantitative analysis. Deep-water fish resources are generally considered to have high longevity, slow growth, late maturity, and low fecundity. Thus, they have been considered more vulnerable to fishing than most species exploited on the continental shelf, upper continental slope or in open ocean pelagic ecosystems (Merrett and Haedrich, 1997; Koslow et al., 2000). Deep-water stocks can be rapidly depleted and recovery can be very slow, although this will not apply to a few deep-water species with life history traits comparable to shallow water species (Large et al., 2003). Whereas previous studies on global trends of fisheries have focused on catch or biomass changes over time (e.g., Christensen et al., 2003; Myers and Worm, 2003), in this paper I have analysed changes in the mean depth of fishing to test if the predicted expansion into deeper-waters can be detected in global landings datasets. I also tested for the predicted higher vulnerability of deep-water fisheries resources, using longevity as the main proxy for vulnerability.  5 . 2 METHODS  I used three publicly-available databases; official landings statistics from the Food and Agriculture Organization (FAO) from 1950 to 2001, which are based on reports submitted annually by FAO member states; FishBase (htip://\\vww.fishbase.or g), an information system with key data on the biology of fishes (Froese and Pauly, 2003); and the Sea Around Us Project database (SAUP: http://www.seaaroundtis.org), which contains estimated maps of global fisheries catches from 1950 to the present. The SAUP database includes data from the Food and Agriculture Organization (FAO), International Council for the Exploration of the Seas (ICES), Northwest Atlantic Fisheries Organization (NAFO), and other sources (Watson et al., 2004) and was used to compile catch data for high seas areas.  \u000CIn this study, depth range is defined as the extremes of the depths reported for juveniles and adults (but not larvae), while common depth is the range where adults are most often found, and is more precisely defined as the range within which approximately 95% of the biomass of a species occurs (Froese and Pauly, 2003). For those taxa not reported to species level, the average for the genus or family was calculated using the species most likely to be present at that locality. FishBase was used to estimate the average depth of occurrence for most of the 775 different species or groups of marine fishes included in the FAO landings statistics, and to gather data on their longevity. Average depth of occurrence for taxa identified at species level in the landings statistics was estimated as the mean of the common depth range or as 1/3 of the total depth range. In the latter case, I assume fish to have a lognormal distribution with depth, whose peak in abundance is at 1/3 of their range (Alverson et al., 1964; Pauly and Chua, 1988). I have tested this assumption using FishBase data on full depth ranges and common depth ranges for 136 fish species; the only species with both ranges in the database. The average peak abundance was 0.302 of the full depth range (95% confidence interval; 0.280.33): this value is not significantly different from a 1/3 assumption (t-test: P&gt;0.01). By combining mean depths and catch series, time series of the mean depth of the catch of marine bottom fishes (excluding pelagic) and for all marine fishes were estimated for the world and for different groupings of FAO statistical areas (ocean basis). The mean depth of the fisheries catch by year and ocean basis was estimated as the average depth of occurrence of the species (or group caught) weighted by the logarithm of their catch. Visual inspection of different datasets suggested an inflection point such that a single regression line would not suffice. I therefore fitted simple linear biphase regression models, using the algorithm described in Hintze (1998). I then compared biphase regression models to other simpler and more complex models. For this, I have fitted simple linear regression models as well as quadratic, cubic and fourth order models to the data. If the simpler model fits better (has a smaller sum-of-squares) than the more complex model (more parameters),  \u000Cthen no statistical analysis was preformed and the simpler model was accepted. Since this rarely occurred, I used the likelihood ratio test (Hilborn and Mangel, 1997) to compare the goodness-of-fit of two models, where the more complex equation fits better than the simple equation. For most of the cases (8 out of 10) biphase regression models fitted the data significantly better than any other tested model (Table 5.1). Thus, biphase regression models were preferred. The only cases where biphase regression models were not preferred were for the time series data of mean depth of the fisheries catch for the whole world, where quadratic models fitted the data significantly better. Additionally, I estimated a time series of the mean longevity of fish in the world catch by combining data on fish longevity from FishBase with fish landings from FAO. The mean longevity of landings for each year and FAO area were estimated as the mean of the longevity of each species or group, weighted by the logarithm of their catch. The mean fish longevity of the catch was also estimated as a function of depth of fish occurrence. Since this has to be done in a yearly basis, I used year 2001 in FAO dataset.  \u000CTable 5.1 - Summary of the likelihood ratio test (Hilborn and Mangel, 1997) used to compare the goodness-of-fit of different models where the more complex equation fits better than the simple equation. Where n is the number of parameters estimated in each model; d.f. the degrees of freedom; SS the sum-of-squares. L stands for Linear models, Q for quadratic, LL for the biphase linear-linear models, C for cubic models and F for fourth order models. Ocean basis Model  n d.f. SS  N. Atlantic  Linear Quadratic Linear-Linear Cubic Fourth  2 j 4 4 5  50 49 48 48 47  2378.2 777.6 351.7 429.5 334.1  L/Q Q/LL LL/C LL/F  100.86 58.12  &gt;0.001 Quadratic &gt;0.001 Linear-Linear  2.48  0.122 Linear-Linear  Linear Quadratic Linear-Linear Cubic Fourth  2 j 4 4 5  50 49 48 48 47  184.5 148.8 129.0 130.1 126.1  L/Q Q/LL LL/C LL/F  11.76 7.35  0.001 Quadratic 0.009 Linear-Linear  1.10  0.300 Linear-Linear  Linear Quadratic Linear-Linear Cubic Fourth  2 3 4 4 5  50 49 48 48 47  2928.9 2002.3 1270.6 1125.9 1105.1  L/Q Q/LL LL/C LL/F  22.67 27.64  &gt;0.001 Quadratic &gt;0.001 Linear-Linear  7.04  0.011 Linear-Linear  Linear Quadratic Linear-Linear Cubic Fourth  2 j 4 4 5  50 49 48 48 47  1501.5 1258.0 876.9 800.1 791.6  L/Q Q/LL LL/C LL/F  9.48 20.86 5.07  0.003 Quadratic &gt;0.001 Linear-Linear Cubic 0.029 Linear-Linear  Linear Quadratic Linear-Linear Cubic Fourth  2 3 4 4 5  50 49 48 48 47  1287.7 672.6 426.7 476.7 381.3  L/Q Q/LL LL/C LL/F  44.81 27.67  &gt;0.001 Quadratic &gt;0.001 Linear-Linear  5.59  0.022 Linear-Linear  Linear Quadratic Linear-Linear Cubic Fourth  2 3 4 4 5  50 \u2022'49; 48 48 47  13764.4 6441.9 3645.6 4576.5 4568.7  ' 37.51 24.54  &gt;0.001 Quadratic &gt;0.001 Linear-Linear  Linear Quadratic Linear-Linear Cubic Fourth  2 3 4 4 5  50 49 48 48 47  3135.4 1124.9 946.8 921.2 823.0  L/Q Q/LL LL/C C/F  87.57 9.03  &gt;0.001 Quadratic 0.006 Linear-Linear  7.07  0.011 Linear-Linear  Linear Quadratic Linear-Linear Cubic Fourth  2 j 4 4 5  50 49 48 48 47  48560.6 37051.8 32769.0 36762.0 36415.9  L/Q Q/LL LL/C LL/F  15.22 6.27  &gt;0.001 Quadratic 0.016 Linear-Linear  C. Atlantic  S. Atlantic  N. Pacific  C. Pacific  S. Pacific  Indian Ocean  Antarctic  Comparisons Ratio (F)  L/Q '. Q/LL LL/C LL/F  '  P Best Fit  \u000C5 . 3 RESULTS  5.3.1 Global trends Our results (Figure 5.1a) show that, for bottom marine fishes, the overall trend over the past 50 years has been a 42 m increase in the mean depth of the catch, from around 103 m in the early 1950's to 145 m in 2001. The biphase linear regression fitting the data (overall r 2= 0.94) suggests two periods with different trends: a period of slow increase in the mean depth of fishing from 1950 to 1978 with a slope of about 2 mdecade\"1, followed by a period of marked increase in the mean depth of fishing at a rate of about 13 m- decade\"1 (Table 5.2). If I include pelagic fishes in the analysis (Figure 5.1a), the increase-in mean depth of the catch is lower but still considerable, with two distinguishable periods (overall r 2= 0.93). In both cases, the early plateau and the estimated break point can be attributed to either a real increase in the fishing deeper trend, or to a lack of taxonomic resolution in the FAO landing statistics before the 1970's. Application of our method to catches from high seas areas only (i.e., beyond countries' EEZ) showed a more dramatic decline in the mean depth of fishing, at a rate of 22 m decade\"1 for bottom fishes only and 9.0 m decade\"1 when considering pelagic fishes. In general, fishing began to operate deeper from the late 1960's. Since the taxonomic resolution in the FAO landing statistics improved after the 1970's, this increase in depth could be caused by, 1) a proportional decrease in the catches of shallow water species (resulting from collapse of coastal resources); 2) a proportional increase in the landings of deep-water species (from the expansion of fisheries into deep water); or 3) both. Figure 5.1b helps elucidate this by showing that, at a global level, the increase in the mean depth of fishing has been caused by an increase in landings of deep-water species such as the orange roughy (Hoplostethus atlanticus, Trachichthyidae), grenadiers (Macrouridae), alfonsinos (Beryx spp., Berycidae) and several deepwater sharks. The steepest rates of depth increase match the development of most of the deepwater fisheries around the world (Hopper, 1995; Merrett and Haedrich, 1997; Moore, 1999; Koslow et al., 2000; Roberts, 2002; Garibaldi and Limongelli, 2003).  \u000CSimilar trends of increased mean depth of fishing were observed for all oceans, with rates ranging from 1 m decade\"1 deeper for the North Pacific to 180 m-decade\"1 for Antarctic fisheries (Table 5.1).  Figure 5.1 - (a) Global trend of mean depth of world marine fisheries catches from 1950 to 2001 for all marine fishes including pelagics (dark grey dots) and for bottom marine fishes only (light grey squares). Open symbols are estimates for high seas areas only (beyond countries EEZs). Trend lines are fitted using the piecewise-polynomial model linear-linear (Hintze, 1998) or simple linear regression, (b) Time series of world marine bottom fisheries catches by depth strata. Catch in tonnes are logio transformed.  \u000CTable 5.2 - Rate of increasing depth of fishing per decade before and after the breaking point (BP) estimated using a two phase model (linear-linear) as described in Hintze (1998). Coefficient of determination (r ) for regressions also presented. Linear-linear two model Slope (mdecade\"1) 1950-B.P  Slope  BP Year  (mdecade\"1) SE  B.P.-2001  r  2  All fish  1.06  1978  2.0  8.80  0.93  Demersal fish  2.13  1978  2.0  13.17  0.94  Atlantic, North  5.49  1989  1.8  32.05  0.97  Central  8.36  1985  5.6  5.05  0.98  South  -3.05  1966  1.9  15.17  0.92  20.03  1959  1.4  0.58  0.59  Central  -1.80  1992  6.0  21.76  0.67  South  -7.64  1968  1.3  35.55  0.95  Indian Ocean  -0.09  1986  2.4  22.54  0.83  Antarctica  99.84  1985  4.6 180.13  0.96  Pacific, North  5.3.2 Atlantic Ocean In the Atlantic Ocean, the mean depth of the catch has increased steadily over the last decades at a rate of 32, 5 and 15 metres per decade for the North, Central and South Atlantic, respectively. In the North Atlantic (Figure 5.2a), the simplest biphase linear regression that fit the data suggests two periods with different trends: a period with a small rate of fishing deeper from 1950 to 1989 (\u00B11.8 S.E.), with a slope of 5.5 mdecade 1 , followed by a period of marked increase in fishing deeper at a rate of 32.1 m decade\"1.  \u000CThe first period corresponds to the relative increase in the reported landings of some deepwater species (Figure 5.3a) such as the roundnose grenadier (Coryphaenoides rupestris, Macrouridae), alfonsinos, ling (Molva molva, Lotidae), blue ling (M. dypterygia, Lotidae), and tusk (Brosme brosme, Lotidae). The steepest increase observed for the second period, after 1989, matches the development of most of the deepwater fisheries in the North Atlantic (Hopper, 1995; Merrett and Haedrich, 1997; Moore, 1999; Koslow et al., 2000; Gianni, 2004). In Figure 5.3a one can clearly see some new deepwater species, with an average depth of about 1000m, being reported for the first time after 1989. These were orange roughy, bulls-eye (Epigonus telescopus,  Epigonidae), and deepwater sharks (Centroscymnus  coelolepis, Dalatiidae; Dalatias licha, Dalatiidae; Centrophorus squamosus, Centrophoridae; Deania calcea, Centrophoridae). Similar trends are apparent throughout the time series for the Central (Figure 5.2b) and South Atlantic (Figure 5.2c), although in the Central Atlantic fishing operates in more shallow waters (Figure 5.3b). In the South Atlantic (Figure 5.2c), the mean depth of fishing time series suggests two periods with different depth of fishing trends: a period of fluctuating mean depth of fishing from 1950 to 1966 (\u00B11.9 S.E), with a slope of -3.0 m-decade\"', followed by a period of marked increase in the mean depth of fishing at a rate of 15.2 m decade\"1. In the South Atlantic basis, some deepwater fisheries have developed since the 1970's on the Patagonian shelf, Western South Atlantic (Catarci, 2004), and on the deeper continental shelf and slope of the Eastern South'Atlantic (Boyer et al., 2001). The highest rate of fishing deeper corresponds with increased landings of species with average depths at about four, seven and eleven hundred meters (Figure 5.3c).  \u000Ca)  North Atlantic  b)  Central Atlantic  Year 1950 120  tj  1975  2000  1950 40 \u2022  I \u2022 I \u2022 I \u2022 I \u2022I  1975  South Atlantic Year  2000  1950 80  I \u2022 I \u2022 I \u2022 I \u2022I  60  140 - -  ro o  c)  Year  100 \u2022 \u2022  1975  I \u2022 I \u2022 I \u2022 I \u2022I \\  # 160 \u2022 \u2022  Q.  80  \\ \u00AB \u00AB \u00AB\\  120 - \u2022  \u2022\u2022  S&gt;  O) \u2022O  m\\  S  180+  100  \u2022\u2022  140 \u2022\u2022  200 -L  120 -L  160 J-  d)  North Pacific  e)  Central Pacific  Year 1950 100  tJro u CL tu \"O  1975  f)  South Pacific  Year 1950 40  2000  I \u2022 I \u2022 I \u2022I  1975  Year 2000  I.I.I  1950 100  120 \u2022\u2022 V  60  \u2022\u2022  150 \u2022 \u2022  140 \u2022\u2022  80 - -  200 - \u2022  1975  I . I . I .  2000  i\u2014  V 160  S  +  180 J-  g)  Indian Ocean 1950  1975  2000  60 | i I \u2022 I \u2022 I \u2022 I \u2022 I  ro u C L (U  80  100  250 -\u2022  120 J-  300 J-  Antarctic  h)  Year  Year 1950  1975  2000  0| \u2022 I \u2022 I \u2022 I . I \u2022I  --  200 --  \\  Aw  100 - \u2022  \"O  cro cu  2000  400 -120 - -  140 J-  600 -L  Figure 5.2 - Trend of mean depth of marine bottom fisheries catches for: (a) North Atlantic; (b) Central Atlantic; (c) South Atlantic; (d) North Pacific; (e) Central Pacific; (f) South Pacific; (g) the Indian Ocean; and (h) Antarctic. Trend lines are fitted using the piecewisepolynomial model linear-linear (Hintze, 1998) or simple linear regression.  \u000Ca)  North Atlantic  b)  Central Atlantic  Year 1950  c)  South Atlantic  Year  Year  1960 1970 1980 1990 2000 1950 1960 1970 1980 1990 2000  1950 1960 1970 1980 1990 2000  0  u^smm  e)  North Pacific  Central Pacific Year  Year  1950 1960 1970 1980 1990 2000 1950 1960 1970 1980 1990 2000  South Pacific  f)  Year  1950 1960 1970 1980 1990 2000  Or  m i \u2022WHS Q- 1000  o0)  1500  Indian Ocean  Antarctic  h)  Year  Year  1950 1960 1970 1980 1990 2000 1950 1960 1970 1980 1990 2000  or  *  sssBm\"\" WSSSm  -T- i  14 12  500  wm^w^m IMflpi*\u00AE  CL  OJ Q  1000  10 8 6  4  1500  MfMSm  2  2000 0 Figure 5.3 - Time series of marine bottom fisheries catches by depth strata for: (a) North Atlantic; (b) Central Atlantic; (c) South Atlantic; (d) North Pacific; (e) Central Pacific; (f) South Pacific; (g) the Indian Ocean; and (h) Antarctic. Catch in tonnes are loge transformed.  \u000C5.3.3 Pacific Ocean The Pacific Ocean shows somewhat contrasting trends. The North Pacific (Figure 5.2d) has a steep increase in the mean depth of fishing (20 m-decade\"') for the period from 1950 to 1959 (\u00B11.4 S.E), but no significant change after that. Large-scale deepwater fisheries have a long history in the North Pacific Ocean with some of the targeted species been fished since the early 1900s (Moore, 1999). There are also established deepwater fisheries for dover sole (Microstomus pacificus, Pleuronectidae), thornyheads (Sebastolobus spp., Sebastidae), other rockfishes (Seabastes spp., Sebastidae), pelagic armorhead (Pseudopentaceros wheeleri, Pentacerotidae) and alfonsinos. The lack of a clear trend in the mean depth of fishing in the North Pacific may be attributed to the time scale used in the analysis, because most of the deepwater fisheries started before 1950s or early 1960s (Moore, 1999; Koslow et al., 2000). Nevertheless, Figure 5.3d clearly shows the start of fisheries of some deep-water species, observed in the early 1960s and the late 1970s.  The overall trend for Central Pacific Ocean (Figure 5.2e) is not clear. Until early 1990's the mean depth of the catch got shallower. This trend may be explained by three alternative hypotheses: 1) a problem with the official landings statistics mainly.by assigning catches to broad categories; 2) a proportional increase in shallow water fish landings greater than for deeper water species; and 3) a real lack of fisheries expansion into deeper waters. The last is not likely to be true, because it is clear from Figure 5.3b that in early 1970's some deeper water fish species like sablefish were being reported in the official landing statistics. After 1992 (\u00B16.0 S.E.) the mean depth of fishing in the central Pacific increased at a rate of 21.8 m-decade\"1, with the increase in importance of some deeper water fish species (Figure 5.3e), such as the dover sole. In the South Pacific (Figure 5.2f) the mean depth of fishing has increased rapidly since 1968, at a rate of 36 m-decade\"1, coinciding with the start of orange roughy and other deepwater fisheries around New Zealand and Australia (Koslow et al., 2000). Figure 5.3f clearly shows that some deepwater species with average depths of fishing at about four, seven and nine hundred meters start being reported during the 1970s.  \u000C5.3.4 Indian Ocean The Indian Ocean (Figure 5.2g) shows no clear trend until 1986, but a steep increase in depth afterwards, at a rate of 22m per decade. The second period matches the appearance of deepwater species, such as the silver gemfish, Rexea solandri, orange roughy and Patagonian toothfish in landing statistics (Figure 5.3g). 5.3.5 Antarctic Finfish fisheries in Antarctica began only during the mid 1960s (Koch, 1992). This region (Figure 5.2h) exhibits the most dramatic increase in mean depth of the catch, from about 100m in the mid 1960's to 600m in 2001, a rate of more than 100 metres per decade. The observed trend in Antarctica clearly reflects the collapse and the implementation of fisheries restrictions for some shallower water fishes (Figure 5.3h) such as marble rockcod (Notothenia rossii, Nototheniidae) and other Nototheniidae species in the late 1980s (CCAMLR, 2004). It also reflects the increase landings of the deepwater Patagonian toothfish (Dissostichus eleginoides, Nototheniidae) during late 1980s (Koch, 1992; Constable etal., 2000).  5.3.6 Mean longevity of the catch The mean longevity of the catch (Figure 5.4a) has increased during the past 50 years, but most dramatically since the early 1.990's. Mean longevity of the catch by depth (Figure 5.4b) in landings from shallow waters has a lower mean longevity (about 15 years) when compared to intermediate (about 40 years) or deeper waters (over 100 years). Hence, fishing deeper means fishing for increasingly longer-lived and thus more vulnerable species.  \u000Cb) 0  17 1950  1975 Year  2000  Mean longevity (years) 50 100  150  #  1500  Figure 5.4 - (a) Global trend of mean fish longevity of the catches for all marine fishes including pelagics (dark grey dots), and for bottom marine fishes only (light grey squares), (b) Global trend of mean longevity of the 2001 world bottom marine fisheries catch by depth. Line is the least squares tit through points by using the logarithmic equation (r 2 = 0.75). Mean age at maturity shows a similar pattern.  \u000C5 . 4 DISCUSSION  I have shown that global landings of fishes have shifted in the last 50 years from shallow to deeper water species, and also that, as a likely consequence, the mean longevity of the fish species caught has increased dramatically. This trend is a serious concern because species with larger body size, longer life span, later sexual maturity, and slow growth are more vulnerable to overfishing and pseudo-extinction (Jennings et al., 1998; Dulvy et al., 2003; Dulvy et al., 2004; Chueng et al., 2005). Deep-water fishes are thus highly vulnerable to overfishing and potentially have little resilience to overexploitation (Koslow et al., 2000; Clark, 2001; Morato et al., 2006). Moreover, deep waters act as the last refuge for some coastal stocks with an extensive vertical distribution where no fishing was occurring some decades ago (Caddy, 1993). With a fisheries expansion to deeper waters those refuges will no longer operate. There is a recent tendency in fisheries development to argue for a diversification of target fish species, mainly through the exploitation of 'under-utilised' deepwater species (see Moore, 1999). In fact we are already seeing the well-documented declines observed for shallow water fish stocks repeated in deepwater stocks (see Roberts, 2002 for some examples). Because of their life-history characteristics (Merrett and Haedrich, 1997; Morato et al., 2006) this phenomenon will be much faster with a smaller likelihood of recovery after collapse. Hence, deep-sea fisheries cannot be seen as a replacement for declining shallowwater resources; instead, deep-water habitats should be considered as the new candidates for conservation.  This work is based on the FAO catch statistics and on the reported average depth range of fish from FishBase. The reliability the FAO catch statistics is of some concern (for more details see FAO, 2002; Pauly et al., 2002; Watson et al., 2004) and the lack of taxonomic resolution can be a problem when drawing general conclusions at a global scale (Watson et al., 2004). However, I have demonstrated global and regional trends towards fishing deeper in the oceans in spite of a large portion of the world's landings being assigned to broad categories. This is especially true for newly-developed or undocumented fisheries as is the case of many deep-water demersal fisheries. As an example, the dropline fishery around the  \u000CMadeira Islands for the deepwater black scabbardfish (Aphanopus carbo, Trichiuridae) is known to have operated since the early 19th century (Martins and Ferreira, 1995), but the first official record of landings of this species is in 1986. As in this case, landing statistics may include a great proportion of deepwater species in broader categories, and because many deep-sea fishes are not very well known, the likelihood of having them aggregated in broader categories is higher when compared to well-known shallow coastal bottom fishes. Assigning catches to broad categories is often the case in tropical developing countries with strongly multispecies fisheries (Pauly et al., 1998b) and I did, in fact, find trends in the tropics less evident. In both cases I believe that, if better taxonomic resolution were to be available, the effect would be stronger because more deepwater fish species would be taken into account. I used the average depth range of fish distribution as an indicator of fishing depths because fisheries will mainly operate at depths where higher abundances of target species occur. Although this is probably not true for non-target species and by-catch, I do not think it unduly affected the analysis because a), the proportion of non-target landings is smaller and thus will not have a significant effect on the general trends; or, b), by-catch species are not generally reported in FAO statistics.  \u000C5 . 5 REFERENCES  Alverson, D.L., A.T. Pruter and L.L. 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Pitcher (2006) Vulnerability of seamount fish to fishing: fuzzy analysis of life history attributes. Journal of Fish Biology 68(1): 209-221. Myers, R.A. and B. Worm (2003) Rapid worldwide depletion of predatory fish communities. Nature 423: 280-283. Pandolfi, J.M., R.H. Bradbury, E. Sala, T.P. Hughes, K.A. Bjorndal, R.G. Cooke, D. McArdle, L. McClenachan, M.J.H. Newman, G. Paredes, R.R. Warner and J.B.C. Jackson (2003) Global trajectories of the long-term decline of coral reef ecosystems. Science 301: 955-958. Pauly, D. and J. Maclean (2003) In a Perfect Ocean: fisheries and ecosystems in the North  Atlantic. Island Press, Washington, D.C.  \u000CPauly, D. and T.E. Chua (1988) The overfishing of marine resources: socioeconomic background in Southeast Asia. AMBIO: a Journal of the Human Environment 17: 200206. Pauly, D., V. Christensen, J. Dalsgaard, R. Froese and F.Jr. Torres (1998a) Fishing down marine food webs. Science 279: 860-863. Pauly, D., R. Froese and V. Christensen (1998b) Response to Caddy et al. How pervasive is \"fishing down marine food webs?\" Science 282: 1384-1386. Pauly, D., V. Christensen, S. Guenette, T.J.P. Pitcher, U.R. Sumaila, C.J. Walters, R. Watson and D. Zeller (2002) Towards sustainability in world fisheries. Nature 418: 689-695. Pauly, D., J. Alder, E. Bennett, V. Christensen, P. Tyedmers and R. Watson (2003) World fisheries: the next 50 years. Science 302(5649): 1359-1361. Pitcher T.J. (2001) Fisheries managed to rebuild ecosystems: reconstructing the past to salvage the future. Ecological Applications 11: 601-617. Pitcher, T.J. and D. Pauly (1998) Rebuilding ecosystems, not sustainability, as the proper goal of fishery management. In: T.J. Pitcher, P.J.B. Hart and D. Pauly (eds.) Reinventing fisheries management. Fish and Fisheries Series 23, Kluwer Academic Press, Netherlands, pp. 311-329. Roberts, C.M. (2002) Deep impact: the rising toll of fishing in the deep sea. Trends in Ecology and Evolution 17: 242-245. Sadovy, Y. and W.W.L. Cheung (2003) Near extinction of a high fecund fish: the one that nearly got away. Fish and Fisheries 4: 86-99. Spotila, J.R., R.D. Reina, A.C. Steyermark, P.T. Plotkin and F.V. Paladino (2000) Pacific leatherback turtles face extinction. Nature 405: 529-530. Watson, R. and D. Pauly (2001) Systematic distortion in world fisheries catch trends. Nature 414:534-536. Watson, R., A. Kitchingman, A. Gelchu and D. Pauly (2004) Mapping global fisheries: sharpening our focus. Fish and Fisheries 5: 168-177. Zeller, D. and D. Pauly (2005) Good news, bad news: global fisheries discards are declining, but so are total catches. Fish and Fisheries 6(2): 156.  \u000CCHAPTER 6 VULNERABILITY OF SEAMOUNT FISH TO FISHING: FUZZY ANALYSIS OF LIFE HISTORY ATTRIBUTES  1  6.1 INTRODUCTION  Seamounts are biologically distinctive habitats of the open ocean exhibiting a number of unique features (see Rogers, 1994). Seamounts have received much attention mainly because of the presence of substantial aggregations of forage fishes in mid- and deep-water (Boehlert and Sasaki, 1988; Rogers, 1994; Koslow, 1996, 1997; Koslow et al., 2000), which became the prime target of a highly technological fishery. Based on life history and ecological characteristics, several authors have placed \"seamount fishes\" at the extreme end of the vulnerability spectrum (Koslow, 1997; Branch, 2001; Boyer et al., 2001; Clark, 2001). However, with the exception of works by Koslow (1996) and Froese and Sampang (2004), few attempts have been made to review, summarize and compare the life-history of seamount species. Therefore, this paper tests the generalization that \"seamount fishes\" possess specific life history characteristics that render them more vulnerable than other species. A detailed description of what are seamount fishes is given in Chapter 1.5. Responses of a fish species to exploitation may be partly determined by life history and ecological characteristics (Adams, 1980; Roff, 1984;-'Stokes et al., 1993; Kirkwood et al., 1994). Fish that are large in size, mature late, have slow growth and low mortality rates, are likely to have higher vulnerability to fishing (Jennings et al., 1998, 1999; Russ and Alcala, 1998; Musick, 1999; Denney et al., 2002). In addition, species that display social aggregation behaviours such as shoaling, schooling (Pitcher and Parrish, 1993) or shoal spawning may have higher vulnerability because of increased catchability when their abundance declines, often referred to as hyperstability of catch rates (Hilborn and Walters, 1992; Pitcher, 1995, 1  A version of this chapter has been published. Morato, T., W.W.L. Cheung and T.J. Pitcher. (2006) Vulnerability of seamount fish to fishing: fuzzy analysis of life history attributes. Journal of Fish Biology 68(1): 209-221.  \u000C1997; Walters, 2003), and their spawning behaviour may also be disrupted by fishing (Johannes, 1998; Sala et al., 2001; Sadovy and Domeier, 2005). This paper attempts to test the hypothesis that \"seamount fishes\" generally have a high vulnerability to fishing exploitation. Previous studies have found that vulnerability of fishes to exploitation is correlated with their life history characteristics. Vulnerability was estimated quantitatively by analysis of life-history characteristics using a fuzzy-logic algorithm (Cheung et al., 2005).  6 . 2 METHODS  6.2.1 Compilation of species list Based on the best available information, I collated species lists for fishes that occur or aggregate in and around seamounts. \"Seamount fishes\" are defined as fish that have been reported as occurring on seamounts, even if rare. A total of 798 species of marine fishes were classified as \"seamount fishes\" and 23 as \"seamount-aggregating\" fishes (Appendix 1). 6.2.2 Comparisons of biological characteristics and vulnerabilities I compiled estimates of life history and ecological attributes for over 14,000 marine fish species based on information available on Fishbase (Froese and Pauly, 2004). These attributes include longevity (T  Ma x),  age at maturity (T m ), asymptotic length (L m), total  fecundity (FT), von-Bertalanffy growth parameter (AT), natural mortality rate (M), together with information on spatial behaviour and preferred habitat (pelagic, demersal, reefassociated, bethopelagic, bathypelagic and bathydemersal). Only those parameters directly estimated from empirical studies were used, while those that were calculated from empirical relationships between life history parameters were excluded. If more than one estimate was available for a particular life history parameter of a particular species, the arithmetic mean was used.  \u000CI used a fuzzy expert system developed by Cheung et al. (2005) to predict 'intrinsic vulnerability to fishing. This index was demonstrated to provide vulnerability estimates that correlate better with observed data than existing alternatives (Musick, 1999; Hilton-Taylor 2000; ICES, 2001; Froese and Pauly, 2004). Cheung et al. (2005) defined intrinsic vulnerability as inherent capacity to respond to fishing that relates to the fish's maximum rate of population growth and strength of density dependence. The fuzzy expert system classifies fishes into different levels of vulnerability based on basic life history and ecological characteristics. The input variables include maximum length, age at first maturity, longevity, von Bertalanffy growth parameter' K, natural mortality rate, fecundity, strength of spatial behaviour, and geographic range. Heuristic rules were incorporated to describe the relationships between these biological traits and fish's intrinsic vulnerability, through which the latter can be predicted. Intrinsic vulnerability was expressed on an arbitrary scale from 1 to 100, with 100 being the most vulnerable. Comparisons with empirical data showed that the fuzzy expert system successfully predicted the intrinsic vulnerability of fishes to fishing (Cheung et al., 2005).  Here, I have predicted the intrinsic vulnerability for 1,600 species of fish for comparison between seamount and non-seamount associated fishes (Table 6.1). To increase robustness, I excluded species for which total length was the only available life-history parameter. Biological characteristics and the estimated fuzzy intrinsic vulnerabilities were compared between \"non-seamount fishes\", \"seamount fishes\" and \"seamount-aggregating\" fishes. To test if commercially-targeted seamount fish species were also more vulnerable than other target fish species, intrinsic vulnerability was also estimated for those species reported in the Food and Agriculture Organization (FAO) official landing statistics. Additionally, to correct for the confounding factor that deep-sea and bathydemersal fish may have higher vulnerability in general, multiple regression analysis was used to explore the relationship between depth (log transformed median of depth range), habitats (seamount fishes, seamountaggregating), and environmental preferences (pelagic, demersal or bathypelagic) with vulnerability. Differences between the biological characteristics estimates were tested by one-  \u000Cway ANOVA, while multiple comparisons were preformed by Tukey test with unequal sample sizes. Differences between intrinsic vulnerability estimates were tested with MannWhitney (U) non-parametric statistics (see Zar, 1999).  Table 6.1 - Number of fish species considered as \"non-seamount fishes\", \"seamount fishes\" and \"seamount-aggregating\" fishes, and number of available estimates of life history attributes and intrinsic vulnerability. Sample sizes for fish species grouped by preferred habitat is also shown. NS are \"non-seamount\" fish species while S are fish occurring on seamounts.  \"Non-Seamount\" \"Seamount\" \"Seamount-aggregating\" Pelagic NS Pelagic S Demersal NS Demersal S  Number of T^vlax T M M Species 14924 430 460 176  K  LOO  FT  VI*  1087  11900  482  1407  798  92  85  38  150  726  77  193  23  19  16  10  18  22  11  18  1013  76  84  45  165  871  74  193  40  11  13  7  18  36  9  18  200 201  66  417  4538  220  596  6047 118  23  23  9  31  96  21  39  4159  99  109  45  365  3828  111  421  Reef-associated S  94  11  13  8  29  92  10  35  Benthopelagic NS  831  39  54  18  92  646  39  114  Benthopelagic S  67  15  12  6  20  60  7  23  Bathypelagic NS  1051  7  4  1  26  736  5  31  223  11  9  3  25  214  9  33  1827  11  10  2  24  1284  34  54  252  19  13  4  25  225  20  43  Reef-associated NS  Bathypelagic S Bathydemersal NS Bathydemersal S  Where: r M a x is the longevity, T m the age at maturity, Mis the natural mortality, K is the Von-Bertalanffy Growth function K, Lx is the Total Length infinity, Fj is total fecundity, and V\\ is the intrinsic vulnerability. * Even though vulnerability was estimated for almost all fish species (n= 14148), this table only shows the number of species included in further analysis, i.e. excluding those species from which total length was the only available life-history parameter.  / r  -  127  \u000C6.2.3 Responses to fishing The relationship between vulnerability estimates and biomass change over time caused by fishing was evaluated using a simulation model. A mass-balanced ecosystem model (Ecopath with Ecosim, see Christensen and Walters, 2004 for details) developed for a theoretical isolated North Atlantic seamount (Morato and Pitcher, 2002) was used. This model included 37 functional  groups, of which twenty were fish groups assembled according to  environmental preference (i.e., depth and habitat: e.g., benthic, pelagic or benthopelagic), body size, energetics and life-history characteristics (see Morato and Pitcher, 2002 for a complete description of the model). The seamount fisheries were loosely based on those operating at the Azores / Mid Atlantic Ridge, and thus divided into six fleets (see Morato and Pitcher, 2002). Biomass changes over 20 years were simulated by assuming a fishing mortality rate of 0.3 for one fish group at a time.  6 . 3 RESULTS  Significant differences  in longevity (ANOVA, d.f.= 2; /?&lt;0.001) and age at maturity  (ANOVA, d.f.= 2; /?&lt;0.001) were found among \"seamount\", \"non-seamount\" and \"seamountaggregating\" fishes (Figure 6.1). The longevity (Figure 6.1a) of seamount fishes was significantly higher than \"non-seamount\" fishes (mean T Ms x-  41.6 years and 16.9 years  respectively; Tukey test; d.f.= 519; p&lt; 0.001). \"Seamount-aggregating\" fishes have the highest longevity among the three categories (mean 7iviax= 62.7 years), although the difference is significant only when comparing with \"non-seamount\" fishes (Tukey test; d.f.= 519;p&lt; 0.001). Accordingly, age at maturity (Figure 6.1b) of both seamount and \"seamountaggregation\" fishes were significantly higher (mean T m= 6.5 years and 15.6 years respectively) than the \"non-seamount fishes\" (mean T m= 3.9 y) (all Tukey test; d.f.= 542; p&lt; 0.001). \"Seamount-aggregating\" fishes also have a significantly higher age at maturity than \"seamount fishes\" (Tukey test; p&lt;  0.001).  \u000Cb)  24  _ 20  11 C3  pS 16 3 12  cd  2a 8 v &lt;bO NS  AGG  NS  d)  AGG  1.0  U 0.8 (U  0 1 S a.  0.6  |  0.4  o a ^ 0.2 NS  S  AGG  0.0  NS  S  AGG  Figure 6.1 - Comparison of some life-history characteristics of \"non-seamount\" fish species (NS), fish occurring on seamounts (S), and \"seamount-aggregating\" species (AGG). a) longevity (r Ma x); b) age at maturity (I'm); c) natural mortality (A/); d) Von-Bertalanffy growth parameter (AT). In the graphs the middle point is the mean, the box is the Mean \u00B1 S.E., and the whisker is the Mean \u00B1 95% CL.  \u000CComparisons of natural mortality rate (Figure 6. lc) and the von Bertalanffy growth parameter K (Figure 6.Id) among the three categories of fishes show similar, but reciprocal, trends as longevity and age at maturity. \"Seamount-aggregating\" fishes have the lowest natural mortality and von Bertalanffy growth parameter (mean M= 0.16 and mean K= 0.10) while \"non-seamount fishes\" have highest among the three fish categories (mean M= 1.05 and mean K= 0.57). Although, paired comparisons (Tukey test) were only significantly different at the 5% confidence level for \"seamount fishes\" and \"non-seamount fishes\". Significant differences in the estimated intrinsic vulnerabilities were observed between \"seamount\",  \"non-seamount\"  and  \"seamount-aggregating\"  fishes.  Median  intrinsic  vulnerabilities (Figure 6.2a) for \"non-seamount fishes\", \"seamount\" and \"seamountaggregating\" fishes were estimated to be 45.0, 51.8 and 68.2 respectively. The differences in intrinsic vulnerabilities are significant when comparing both \"non-seamount\" and \"seamounts fishes\" (\u00A37; p&lt; 0.001), and comparing \"seamount fishes\" and \"seamount-aggregating\" fishes (U,P&lt; 0.007).  a) 100  b) 100  90  j?  90  80  80  j5  70 60  3  50  &gt;  40  70 60  3  &gt;  \u2022ill  50 40  30  30  20  20  10  10  0  NS  AGG  0  NS  AGG  Figure 6.2 - Intrinsic vulnerability (V\\) index for fish species no-occurring on seamounts (NS), occurring on seamounts (S), and \"seamount-aggregating\" species (AGG). a) including all fish species; b) for species reported in FAO official landing statistics. In the graphs the middle point is the median, the box the 25%-75% percentiles, and the whisker is the range.  \u000CVulnerabilities offish reported as catches in the FAO landings statistics were also higher for \"seamount fishes\", and significantly different from the median for \"non-seamount fishes\" (U; p&lt; 0.001). However, there were no significant differences between the vulnerabilities of \"seamount fishes\" and \"seamount-aggregating\" species (U; p&lt; 0.111), even though the median was higher for the later. Additionally, the mean of vulnerability weighted by volume of catch (log-transformed) (Table 6.2) was estimated. Similarly, vulnerability was higher for \"seamount-aggregation\" species and lower for \"non-seamount fishes\". The differences in vulnerability between \"seamount\" and \"non-seamount fishes\" were mainly due to benthopelagic and demersal fishes (Figure 6.3), which were found to have significantly  different  medians of intrinsic vulnerability (U;  Demersal p= 0.003;  Benthopelagic p= 0.001). For all other fish groups paired comparisons of medians were not significantly different. Bathydemersal fishes, benthopelagic and demersal fishes were among the most vulnerable fish groups, with \"seamount-aggregating\" fishes having the highest intrinsic vulnerability. Vulnerabilities of the \"deep-sea fish\" group (bathydemersal fishes not occurring on seamounts) (median Vf= 64.0) were not significantly different from seamount demersal fishes (median Vf=  61.0; U, p= 0.194), seamount benthopelagic fishes (median V\\= 64.0; U, p=  0.819) and seamount bathydemersal fishes (median V\\= 63.5; U, p= 0.833). \"Seamountaggregating\" fishes (median V\\= 68.2) were the only group having higher vulnerability estimates than the \"deep-sea fish\" group, but this difference was not statistically significant {U,p= 0.335). Multiple regression analysis showed that \"seamount-aggregating\" was a significant factor (/?=0.011) in explaining the variance in vulnerability between species, when differences in depth range (log-transformed) were accounted for in the regression model. However, the \"seamount-aggregating\" factor was significant only when the lower vulnerability of bathypelagic fishes was accounted for in the regression model.  \u000CTable 6.2 - Intrinsic vulnerability weighted by the Log(catch) for \"seamount\" and \"seamount-aggregating\" species reported explicitly in FAO catches. \"Non-seamount\"  \"Seamounts\"  \"Seamount-aggregating\"  Number of Species  508  102  13  Vulnerability  39.9  47.9  64.5  100 90  U  80 70  XI c3  u e \"3  &gt;  1  60  50 40  \u2022  T  \u2022  30 20  I  10  0  NS S Pelagic  NS S NS S NS S NS S Demersal** Reef-Ass. Benthopel.**Bathypel.  Figure 6.3 - Intrinsic vulnerability (VI)  NS S AGG Bathydem.  index for fish species of different habitats no-  occurring on seamounts (NS), occurring on seamounts (S). Vulnerability for \"seamountaggregating\" species (AGG) also presented. In the graphs the middle point is the Median, the box the 25%-75% percentiles, and the whisker is the range. ** indicates significant differences between medians (Mann-Whitney test; Pelagic: p= 0.471; Demersal: p= 0.003; Reef-Associated:  p=  0.076;  Bathydemersal: p= 0.833).  Benthopelagic:  p=  0.001;  Bathypelagic:  p=  0.806;  \u000CThe intrinsic vulnerabilities estimated from the fuzzy system were significantly related (R 2= 0.738, p= 0.007) to the simulated population declines of marine fish groups (Figure 6.4) caused by fishing. Groups of species with higher vulnerabilities had larger biomass declines than species with lower vulnerabilities. Simulations showed that even at modest levels of fishing, seamount species were depleted, not sustained.  l.o 0.9  I 0.! 0.7 C3  0.6  O 0.5  &lt;u 0.4 &lt;C 0.3 0.2 H  0.1 \u00AE  \u2022  0.0  10  20  30  40  50  60  70  80  90  Intrinsic Vulnerability  Figure 6.4 - Proportion of biomass change over time for fish groups with different intrinsic vulnerabilities (Fj). Biomass change was estimated by a generic seamount ecosystem model (Morato and Pitcher, 2002) and simulating the effect of a 0.3 fishing mortality rate for each group over a 20 years period.  \u000C6 . 4 DISCUSSION  Despite rather broad definitions, this global analysis of over 1,600 species shows that \"seamount fishes\", particularly \"seamount-aggregating\" fishes, have higher intrinsic vulnerability than other groups of fishes. Similar patterns were found when considering commercially exploited  species only. Biological  characteristics  leading to greater  vulnerability include a longer lifespan, later sexual maturation, slower growth and lower natural mortality. However, as fish vulnerability was strongly related to depth range, seamount-association (as in \"seamount fishes\" group) may not be the proximal factor; higher vulnerability of fish found at seamounts may be confounded because they include more deepwater species. These findings are in agreement with life-history qualities for \"seamount fishes\" proposed by Koslow (1996, 1997), although very few complete case studies are available. In any case, seamounts provide physical and biological conditions for aggregation of vulnerable deep-water fishes and thus unsustainable exploitation in such habitats should be a concern. Deep-water species were considered sensitive to exploitation because of their vulnerable biological characteristics (Merrett and Haedrich, 1997; Roberts, 2002). The analysis presented here supports this theory. Particularly, bathydemersal fishes were more vulnerable than any other \"non-seamount\" group of fish; only \"seamount aggregating\" fish had higher vulnerabilities. As these species are often targeted by deep-sea fishing (trawling in particular), proper monitoring and management of their exploitation should be a priority. Conservation concerns about the exploitation of \"seamount fishes\" were supported by results from our simulation modelling. The simulation model confirmed that the biomass of fish with higher vulnerabilities declined more rapidly under exploitation. Although data limitations prevent prevents us from validating the modelling results using empirical data, evidence from other species assemblages suggests a significant positive correlation between vulnerability and population decline (Cheung et al., 2005). Considering that seamount fishes are increasingly being targeted by fishing (Watson and Morato, 2004), populations of highly  \u000Cvulnerable seamount species such as orange roughy, alfonsinos and other \"seamountaggregating\" fishes may be under considerable risk of local extinction (extirpation) under only moderate fishing intensity (F=0.3 in simulation model). In the light of this analysis, one may ask whether seamount fisheries may be sustainable in the long term (Clark, 2001). Examples from all over the world have showed the \"boom and bust\" characteristic of seamount trawling fisheries, with rapid stock reduction and serial depletion of successively-exploited new seamounts. The case of the orange roughy, a \"seamount-aggregating\" fish, is well known. In Namibian waters, orange roughy has been fished down to 10% of its virgin biomass in six years (Branch, 2001), while in Australia biomass levels dropped to 7-13%) in about 15 years (Lack, 2003). The orange roughy stock in New Zealand was fished down to 15-20%) of virgin biomass in less than 15 years (Clark, 2001) while annual sustainable levels of fishing have been estimated to be less than 2% of virgin biomass (Francis et al., 1995), which may not be economically viable. Another example is Russian fishing on seamounts at the Mid Atlantic Ridge. Vinnichenko (2002) showed that the total catch (mainly of alfonsino and scabbardfish Lepidopus caudatus Euphrasen 1788) at 9 seamounts in the South Azores area and in three seamounts at the Corner Rising area declined, in each area, from 12000t to below 2000t in just two years. In a larger area of the ridge that included 34 seamounts, catches declined from 30000t to below 2000t in about 15 years (mainly roundnose grenadier and orange roughy).  The high vulnerability of \"seamount fishes\" should encourage more precaution in managing seamount resources. Collapse of seamount fisheries have been partially attributed to the lack of management. However, even in places where detailed research programmes where in place when exploitation by trawl fisheries commenced, and where scientific recommendations for management were followed and the fisheries controlled, catches declined unexpectedly fast and stocks have been depressed well below Bmsv  (Boyer et al., 2001). Our research supports  the conclusion that fishing on seamounts is not sustainable, at current levels and with current methods. A number of seamount populations have already been depleted. More will be  \u000Cdepleted and some will be extirpated if fishing on seamounts continues at current or even more moderate levels. The fuzzy-logic life-history attributes method of estimating intrinsic vulnerability to depletion by fishing, followed by evaluation of sensitivity and local extinction risk using simulation, is a relatively new technique, but it has proved more robust than previous methods (Cheung et al., 2005). It provides a quantitative basis for more precautionary management of fisheries for \"seamount\" and \"seamount-aggregating\" fish in the future.  \u000C6 . 5 REFERENCES  Adams, P.B. (1980) Life history patterns in marine fishes and their consequences for fisheries management. Fishery Bulletin 78: 1-12. Boehlert,  G.W. and T.  Sasaki  (1988) Pelagic biogeography of the  armorhead,  Pseudopentaceros wheeleri, and recruitment to isolated seamounts in the North Pacific Ocean. Fishery Bulletin 86: 453-466. Boyer, D.C., C.H. Kirchner, M.K. McAllister, A. Staby and B.I. Staalesen (2001) The orange roughy fishery of Namibia: Lessons to be learned about managing a developing fishery. In: A.I.L. Paine, S.C. Pillar and R.J.M. Crawford (eds.) A decade of Namibian fisheries science. South African Journal of Marine Science 23, pp. 205-211. Branch, T.A. (2001) A review of orange roughy Hoplostethus atlanticus fisheries, estimation methods, biology and stock structure. In: A.I.L. Paine, S.C. Pillar and R.J.M. Crawford (eds.) A decade of Namibian fisheries science. South African Journal of Marine Science 23, pp. 181-203. Cheung, W.W.L., T. J. Pitcher and D. Pauly (2005) A fuzzy logic expert system to estimate intrinsic extinction vulnerabilities of marine fishes to fishing. Biological Conservation 124: 97-111. Christensen, V. and C. Walters (2004) Ecopath with Ecosim: methods, capabilities and limitations. Ecological Modelling 172: 109-139. Clark, M. (2001) Are deepwater fisheries sustainable? \u2014 the example of orange roughy (Hoplostethus atlanticus) in New Zealand. Fisheries Research 51: 123-135. Denney, N.H., S. Jennings and J.D. Reynolds (2002) Life-history correlates of maximum population growth rates in marine fishes. Proceedings of the Royal Society of London: Biological Science 269: 2229-2237. Francis, R.I.C.C., M.R. Clark, R.P. Coburn, K.D. Field and P.J. Grimes\"(1995) Assessment of the ORH 3B orange roughy fishery for the 1994-1995 fishing  year. New Zealand  Fisheries Assessment Research Documents 95/4. NIWA. Froese, R. and D. Pauly (eds.) (2004) FishBase.  www.fishbase.org , version 16 February 2004.  World Wide Web electronic  publication.  \u000CFroese, R. and A. Sampang (2004) Taxonomy and biology of seamount fishes. In: T. Morato and D. Pauly (eds.) Seamounts: Biodiversity and Fisheries. 'University of British Columbia, Vancouver, Fisheries Centre Research Report 12(5), pp. 25-31. Hilborn, R. and C.J. Walters (1992) Quantitative fisheries  stock assessment:  choice,  dynamics and uncertainty. New York: Chapman and Hall. Hilton-Taylor, C. (2000) (compiler) 2000 IUCN Red List of Threatened Species. IUCN,  Gland, Switzerland and Cambridge, UK. ICES (International Council for the Exploration of the Sea) (2004) Report of the ICES advisory committee on fishery management, 2001. ICES Cooperative Research Report 246, 895 pp. Jennings, S., J.D. Reynolds and S.C. Mills (1998) Life history correlates of responses to fisheries exploitation. Proceedings of the Royal Society of London: Biological Science 265: 333-339. Jennings, S., S.P.R. Greenstreet and J.D. Reynold (1999) Structural change in an exploited fish community: a consequence of differential  fishing effects  on species with  contrasting life histories. Journal of Animal Ecology 68: 617-627. Johannes, R.E. (1998) The case for data-less marine resource management: examples from tropical nearshore fin fisheries. Trends in Ecology and Evolution 13: 243-246. Kirkwood, G.P., J.R. Beddington and J.A. Rossouw (1994) Harvesting species of different lifespans. In: P.J. Edwards, R.M. May and N.R. Webb (eds.) Large-Scale Ecology and Conservation Biology. Blackwell Science, Oxford, pp. 199-227. Koslow, J.A. (1996) Energetic and life-history patterns of deep-sea benthic, benthopelagic and seamount-associated fish. Journal of Fish Biology 49(Supplement A): 54-74. Koslow, J.A. (1997) Seamounts and the ecology of deep-sea fisheries. American Scientist 85: 168-176. Koslow, J.A., G.W. Boehlert, J.D.M. Gordon, R.L. Haedrich, P. Lorance and N. Parin (2000) Continental slope and deep-sea fisheries: implications for a fragile ecosystem. ICES Journal of Marine Science 57: 548-557.  \u000CLack, M., K. Short and A. Willock (2003) Managing risk and uncertainty in deep-sea fisheries: lessons from orange roughy. Traffic Oceania and WWF Endangered Seas Programme, Australia. Merrett, N.R. and R.L. Haedrich (1997) Deep-sea demersal fish and fisheries. Chapman and Hall, London. Morato, T. and T. Pitcher (2002) Challenges and problems in modelling seamount ecosystems and their fisheries. ICES (International Council for the Exploitation of the Sea) 2002/M:8. Musick, J.A. (1999) Criteria to define extinction risk in marine fishes. Fisheries 24: 6-14. Pitcher, T.J. and J. Parrish (1993) The functions of shoaling behaviour. In: T.J. Pitcher (ed.) The behaviour of teleost fishes. Chapman and Hall, London, pp. 363-439. Pitcher, T.J. (1995) The impact of pelagic fish behaviour on fisheries. Scientia Marina 59: 295-306. Pitcher, T.J. (1997) Fish shoaling behaviour as a key factor in the resilience of fisheries: shoaling behaviour alone can generate range collapse in fisheries. In: D.A Hancock, D.C. Smith, A. Grant and J.P. Beumer (eds.) Developing and Sustaining World Fisheries Resources:  the State of Science and Management.  CSIRO, Collingwood,  Australia, pp. 143-148. Roberts, C.M. (2002) Deep impact: the rising toll of fishing in the deep sea. Trends in Ecology and Evolution 17: 242-245. Roff, D.A. (1984) The evolution of life history parameters in teleosts. Canadian Journal of Fisheries and Aquatic Sciences 41: 898-1000. Rogers, A.D. (1994) The biology of seamounts. Advances in Marine Biology 30: 305-350. Russ, G.R. and A.C. Alcala (1998) Natural fishing experiments in marine reserves 19831993: roles of life history and fishing intensity in family responses. Coral Reefs 17: 399-416. Sadovy, Y. and M. Domeier (2005) Are aggregation-fisheries sustainable? Reef fish fisheries as a case study. Coral Reefs 24: 254-262  \u000CSala, E., E. Ballesteros and R.M. Starr (2001) Rapid decline of Nassau grouper spawning aggregations in Belize: fishery management and conservation needs. Fisheries 26: 2330. Stokes, T.K., J.M. McGlade and R. Law (eds.) (1993) The Exploitation of Evolving Resources. Springer-Verlag, Berlin. Vinnichenko, V.I. (2002) Prospects of fisheries on seamounts. ICES CM2002/M32. Poster. Walters, C. (2003) Folly and fantasy in the analysis of spatial catch rate data. Canadian Journal of Fisheries and Aquatic Science 60: 1433-1436. Watson, R. and T. Morato (2004) Exploitation patterns in seamount fisheries: a preliminary analysis. In: T. Morato and D. Pauly (eds.) Seamounts: Biodiversity and Fisheries. Fisheries Centre Research Report 12(5), pp 61-66. Zar, J.H. (1999) Biostatistical Analysis, 4th edition. Prentice Hall, New Jersey.  \u000CCHAPTER 7 ECOSYSTEM SIMULATIONS OF MANAGEMENT STRATEGIES FOR DATA-LIMITED SEAMOUNT FISHERIES  1  7 . 1 INTRODUCTION  With global catches declining since the late 1980's (Watson and Pauly, 2001), the world's fisheries resources have been characterized as seriously depleted or in danger of depletion (e.g. Jackson et al., 2001; Pauly et al., 2002; Baum et al., 2003; Christensen et al., 2003; Myers and Worm, 2003), with very little evidence for any recovery (Hutchings, 2000). What has caused this phenomenon has been the subject of serious debate (Pitcher, 2001). Indeed, poor management practices and increased fishing pressure (Ludwig et al., 1993), along with an excessive level of investment in fishing capacity, have resulted in serious stock depletion on continental shelves and have created serial depletion from new pressures on alternative fishing grounds (Pauly et al., 2002). Seamounts are among those \"newly\" targeted ecosystems that have been intensively fished since the second half of the 20 th century (Rogers, 1994; Koslow et al., 2000). Deep-water fisheries in general, and seamounts fisheries in particular, are usually characterized by a boom and bust sequence (Koslow et al., 2000; Watson and Morato, 2004), with the targeted fish stocks showing signs of overexploitation within a short period after the beginning of the fishery. For example, this has been the case with the orange roughy (Hoplostethus atlanticus) fishery off the waters of New Zealand (Clark, 1999; Clark et al., 2000), Australia (Koslow, 1997), Namibia (Boyer et al., 2001), and the North Atlantic (Branch, 2001); the pelagic armourhead (Pseudopentaceros wheeleri) fishery over seamounts  1  A version of this chapter has been published. Morato, T. and T.J. Pitcher (2005). Ecosystem simulations in support of management of data-limited seamount fisheries. Pp: 467-486 in G.H. Kruse, V.F. Gallucci, D.E. Hay, R.I. Perry, R.M. Peterman, T.C. Shirley, P.D. Spencer, B. Wilson and D. Woodby (eds.) Fisheries assessment and management in data-limited situations. Alaska Sea Grant, University of Alaska Fairbanks. Lowell Wakefield Fisheries Symposium Series 21.  \u000Cin international waters off Hawaii (Sasaki, 1986); and the blue ling (Molva dipterygia) fishery in the North Atlantic (Bergstad et al., 2003). Seamount aggregating fish stocks are long-lived, slow growing species, with late maturity and low recruitment rates (Koslow, 1997; Rico et al., 2001; Morato et al., 2004), often forming highly localized aggregations (Clark, 1996). Thus, seamount fish stocks are rapidly depleted and maintenance of seamount fisheries depends on the discovery of unexploited seamounts. Moreover, many seamounts are located in international waters where no management is applied., Once depleted, seamount populations likely require decades to recover (Koslow, 1997). Side effects caused by overfishing or extensive trawling on seamounts raise serious concerns: for example, damage to benthic communities dominated by corals and other fragile suspension feeders is common (Richer de Forges, 2000; Koslow et al., 2001) and impacts on transient migratory species whose life histories rely on seamount food webs (Haney, 1995; Holland et al., 1999; Weimerskirch et al., 2002). The prevention of further negative impacts on these sensitive ecosystems is now an important policy objective (Probert, 1999; Roberts, 2002).  There is rising concern about threats to seamount ecosystems in the Exclusive Economic Zones of coastal states and the high seas; several countries, such as Canada, Australia, New Zealand and Portugal have begun to take action for the protection of such 'fragile' ecosystems. However, seamount ecosystems remain one of the worst cases of data-limited situations, comprising a true challenge for fishery scientists and managers who are urged to develop new fisheries under the precautionary approach of the Code of Conduct for Responsible Fisheries (FAO, 1995). Little is known about seamount ecosystems in the NE Atlantic and elsewhere, or the impact of human activities upon them. A recent attempt to tackle this global lack of information has been made by the European Commission, which has funded the first European Seamount Project to integrate physical, biogeochemical and biological research - the 'OASIS project' (OceAnic Seamounts: an Integrated Study).  In this paper I investigate if ecosystem simulations can help in understanding the impact of fishing on pristine seamounts. By using ecosystem modeling loosely structured on North  \u000CAtlantic case studies, data gathered from elsewhere, and optimization methods for policy search, 1 explore the types of fisheries that might be sustainable on seamount ecosystems.  7 . 2 METHODS  7.2.7 Trophic model of seamount ecosystems In this study, I used a general ecosystem model developed for North Atlantic seamounts (Morato and Pitcher, 2002) based on the 'Ecopath with Ecosim' approach (EwE; http://www.ecopath.org), a software for ecosystem trophic mass-balance analysis (Ecopath), with a dynamic modeling capability (Ecosim) (for details see Christensen and Walters, 2004). This model was developed for a theoretical isolated seamount. Habitat covered by the model was defined by the summit, set at around 300 m, down to the base of the seamount at around 2000 m. The model covered a small area of about 3000 km 2 and included 37 functional groups: three marine mammal groups (i.e., toothed whales, baleen whales and dolphins), seabirds, turtles, seven invertebrate groups (i.e., benthic filter feeders, such as corals or gorgonians, benthic scavengers, benthic crustaceans, pelagic crustaceans, seamount resident cephalopods, small and large drifting cephalopods), three zooplankton groups (i.e., gelatinous, shallow and deepwater zooplankton), primary producers (i.e., phytoplankton), detritus and twentyfish groups assembled according to their environmental preferences (i.e., depth and habitat: e.g. benthic, pelagic or benthopelagic), body size, energetics and lifehistory characteristics (see Appendix 3 and Morato and Pitcher, 2002 for a complete description of the model).  The theoretical seamount was assumed to have a low initial level of exploitation and its fisheries were loosely based on those operating at the Azores / Mid Atlantic Ridge comprising 6 fleets (see Appendix 3 and Morato et al, 2001): (a) demersal longline (targeting shallow water demersal and benthic fishes); (b) deepwater longline (targeting bathypelagic and bathybenthic fishes); (c) small pelagics fishery (for small pelagic fishes); (d) tuna fishery; (e) swordfish fishery; and (f) deepwater trawl (targeting seamount associated  \u000Cfishes, including orange roughy and alfonsinos, Beryx splendens and B. decadactylus). Landings, prices, and job estimates were loosely based on the Azores case study (Morato et al, 2001; Morato and Pitcher, 2002). Ecopath outputs are known to be very sensitive to the vulnerability parameters (see Walters et al, 1997; Pitcher and Cochrane, 2002). In this study a standard value of 0.3 representing mixed predator/prey control was used. A brief sensitivity analysis of the policy simulations to different vulnerability settings was conducted by repeating simulations with vulnerabilities of 0.2, 0.3 and 0.5. Results from simulations were generally consistent between different vulnerabilities. 7.2.2 Model analyses The impacts of alternative time patterns of fishing mortalities were explored using an optimization method (Walters et al, 2002; Christensen and Walters, 2004) to search for patterns of relative fishing effort by fishing fleets which would maximize one or more of the considered objectives: 1) 'Economy', or net present economic value (i.e., total present value of the catch); 2) 'Jobs', or employment (i.e., a social indicator, assumed proportional to gross landed value of catch for each fleet with a different jobs/landed value ratio for each fleet); 3) 'Ecology', or ecological 'stability' (i.e., measured by assigning a weighting factor to each group based on their longevity, and optimizing for the weighted sum). Net present economic value of landed catches was calculated as the discounted sum over all fleets and times of catches multiplied by the prices of landed fish species. A discount rate of 0.04 was used. The ecological criterion component is based on Odum's (1971) definition of 'maturity', with mature ecosystems being dominated by large, long-lived organisms. Thus, it is intended to identify the fleet structure that maximizes biomass of long-lived organisms, defined by the inverse of their production/ biomass ratios (P/B).  \u000CThe objective function can be thought of as a 'multi-criterion objective function', represented as a weighted sum for the three above-mentioned criteria indicators: OBJ = W y -X-W,  + W, \u2022\u00A3./,,  +WE \u2022  +*  where W= weighting factors; V= value; J= jobs; E= ecology; z'= fleets; y\u2014species caught; t= time in years; NPV= net present value; and B/P= biomass production ratio, assumed to be proportional to species longevity and thus ecological stability, with s a normally distributed error term. The Davidon-Fletcher-Powell (DFP) non-linear optimization procedure was used to iteratively optimize the three above-mentioned objectives by changing relative fishing rates (F) (Walters et al., 2002). This search procedure results in what control systems analysts call an 'open loop policy'; a recommendation for what to do at different future times without reference to what the system actually ends up doing along the way (Christensen and Walters, 2004). The resulting 'optimum' fishing rates by year/fleet served as input for the dynamic simulation, 'Ecosim', where they replaced the baseline relative efforts by fleet/gear type. Ecosim, was then run for a 50-year-period to simulate the effect of the optimized fishing rates and to estimate the biomass, catch and value variation. These two scenarios were compared with a 'no fishing' scenario where all the fishing rates were set to 0. Non-linear optimization methods, such as DFP, can be difficult to use and can be misleading. In particular, the method can 'hang up' on a local maximum and can give extreme answers due to an inappropriate objective function (Walters et al., 2002). To check for false convergence to local maxima, random starting F's were used. To search for trade-offs among objective functions, optimal scenario solutions for a range of weightings of ecological and economic objectives were accessed. Additionally, at the end of each run, ecosystem indicators such as the mean trophic level of the catch (see equation in Pauly et al., 1998) and biodiversity index (modified from Kempton and Taylor, 1976, Q75; Ainsworth and Pitcher, 2006) which resulted from the suggested fishing effort in each range of weighting, were estimated.  \u000C7 . 3 RESULTS  7.3.1 Optimal fishing scenarios The optimized fishing rates (F) for the 'economy' and 'ecology' objectives, expressed as proportions of the base model fishing rates are summarized in Figure 7.1. Maximizing net economic value led to an increase in fishing rate in all fisheries (deepwater trawl, Ffj na|/FbaSe= 97.6; swordfish fisheries (Fnnai/Fbase= 18.1); small pelagic fisheries, Ffi na|/Fbase= 16.2), except for the deepwater longline, where the fishing rate was reduced to 0.86 of the base model value. In contrast, maximizing 'ecosystem' stability led to a large decrease in all fishing rates (swordfish fisheries. (Ffi nai/Fbase= 0.01; demersal longline, Ffi nai/FbaSe= 0.16; deepwater trawl, Ffina|/Fbase  =  0.32).  Economy  0.0  '  Ecology  Figure 7.1 - Optimized fishing rates (F), expressed as proportions of the base model, obtained by maximizing 'economy' and 'ecology' objectives. Note differences in scale.  \u000CThe effects of the optimized fishing rates on biomass (i.e., percentage of biomass change from base model) after a 50-year simulation are presented, together with a 'no fishing' scenario, in Figure 7.2. Not surprisingly, the 'no fishing' scenario produced a general increase in biomass for most of the groups, but particularly for sea turtles, rays and skates, and pelagic sharks. However, this was not the case for the most important prey groups of the system: mesopelagic fish and benthic invertebrates. The optimized fishing rates for the 'ecology' objective function produced very similar results when compared with the 'no fishing' scenario, producing a large increase in groups that have slow turnover and higher trophic levels. When economic value was maximized, a general decrease in biomass was observed associated with collapse of ten functional  groups (pelagic sharks, tunas,  benthopelagic sharks, seamount-associated fishes, bathypelagic fishes, sea-turtles, rays and skates, alfonsinos, medium-sized epipelagic fish, and benthic invertebrate filter feeders, e.g. deep-sea corals).  150 w &lt; to  100  \u2022 NO FISHING \u2022 ECOLOGY ECONOMY  E  .\u00A3  50  -50 -100  Figure 7.2 - Changes in group biomasses (percent change of biomass from base model) under the three different fishing scenarios: no fishing, maximizing the 'ecology' objective, and maximizing the 'economy' objective.  \u000CThe effects of the different fishing policies on the total landed catches are shown in Figure 7.3. Maximizing the 'economy' objective led to an increase in landings when compared to the base model. In this scenario, the deepwater trawl fishery was favoured and had the highest contribution to the total catch. In contrast, maximizing the 'ecology' objective required an overall decrease in catches and fishery operations conducted mostly by small pelagic and bottom longline fishing fleets. Comparing the total value of the catches for the three scenarios (i.e., base model, maximizing 'economy', and maximizing 'ecology'; Figure 7.4), maximizing 'economy' generated more money than the base model and the 'ecology' scenario. In all cases, deepwater trawl and bottom longline fishing fleets contributed the most to the total value.  \u2022  1.6  ECOLOGY  \u2022 BASE B ECONOMY  U 0.4 0.2  |  Demersal Longline  DeepWater  Small Pelagics  Tuna  Swordfish  Deep Water trawl  Total  Longline  Fisheries  Figure 7.3 - Catches (t-km\"2-y\"') for the different fishing fleets under the base model and two fishing scenarios: maximizing the 'ecology' objective function, and maximizing the 'economy' objective function.  \u000C3.5 \u2022 ECOLOGY  3.0  2.5  2.0  u &gt; 1.5  1.0 0.5  0.0  Demersal Longliie  DeepWater  Small Pelagics  Tuna  Swordfish  DeepWater trawl  Total  Longline  Fisheries Figure 7.4 - Value of the catches (relative value) for the different fishing fleets under the base model and two fishing scenarios: maximizing the 'ecology' objective, and maximizing the 'economy' objective.  7.3.2 Trade-offs Surface plots of optimal scenario solutions for a range of weightings of ecological and economic objective functions are shown in Figures 7.5 to 7.7. They show that it was not possible to maximize the performance of all three objectives (i.e., net economic value, number of jobs, and ecological 'stability') simultaneously. This is true because net economic value (Figure 7.5a) and number of jobs (Figure 7.5b) reach a maximum with a high weighting factor on the economy objective and a small weight on ecology. This results in a decrease in the stability of the system. On the contrary, to maximize 'ecosystem stability' a high weighting was assigned to the 'ecology\" objective (Figure 7.5c). Assigning a low weighting factor to 'economy' and a high weighting factor to 'ecology' resulted in a decrease of net economic value and number of jobs, with a corresponding increase in the system's stability. Intermediate weightings produced, in general, intermediate performances for the three objective functions.  \u000CThe fishing rates required to achieve different performances of the objective functions (i.e., fishing policies) are presented in Figure 7.6. In order to maximize the net economic value of the system all fisheries, except deepwater longline (Figure 7.6a), required an increase in their fishing rates. These increases were of approximately: 50 times the base model rate for the deepwater trawl fishery (Figure 7.6b); 16 times for the swordfish fishery (Figure 7.6c); 15 times for the small pelagic fish fishery (Figure 7.6d); 3.5 times for the tuna fishery (Figure 7.6e); and 2.5 times for the bottom longline fishery (Figure 7.6f). To achieve ecological stability in the system, however, a decrease in the fishing rates of most of the fisheries was required with the exception of the tuna, swordfish and, to a lesser extent, the bottom longline fisheries. The latter, along with the small pelagic fisheries, reached their highest fishing rates when a high weighting factor was assigned to the 'ecology' objective and an intermediate weight to 'economy'. Ecosystem indicators (i.e., mean trophic level of the catch and biodiversity) and total catches derived from the optimal fishing strategies for the overall range of weighting factors for 'ecology' and 'economy' are presented in Figure 7.7. Total catches were maximized when weighting was high for 'economy' and low for 'ecology' objective functions (Figure 7.7a). In contrast, the biodiversity index (Figure 7.7b) was high only when a very small weight was assigned to 'economy'. The mean trophic level of the catch (Figure 7.7c), in general, decreased with a corresponding increase in the weighting of 'economy' and a decrease in the weighting of 'ecology' objective functions. However, maximum trophic level was achieved with a high weighting of 'ecology' and an intermediate weighting of 'economy'.  \u000COJ  1ET O do _o ooJ U c o \u20224\u2014\u00BB  bp '5 \u00A3  C 0.00  1.0 0.9  0.01 0.02 0.03 0.04 0.05 0 06 0.07 0.08 0.09 0.10  0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10  c  08  0.7 0.6  0.5 0.4 0.3 0.2 0.1 0.0  0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10  Weight on Economy Objective Function Figure 7.5 - Surface plots showing optimal scenario solutions for a range of weightings of ecological and economic objective functions: a) performance of 'economy' objective, maximizing net economic value; b) performance of 'social' objective, maximizing number of jobs; c) performance of 'ecology' objective, maximizing ecological 'stability' of the ecosystem. Scale goes from light blue (low performance) to dark red (high performance). Smooth surface is interpolated.  \u000C1.0  0.9  co \u2014 -t &gt; o cp F-L,  u&gt; o&lt;u x? O &gt;&gt; bO _o o o W e o  \u00A3  W) 'u \u00A3  a  1.0  0.9  0.8  0.8  0.7  0.7  0.6  0.6  0.5  0.5  0.4  0.4  0.3  0.3  0.2  0.2  0.1  0.1  0.0  0.0  i0.00 0.01 0 02 0.03 0.04 0.05 0.06 0.07 0.08 0 09 0.10  0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 1.0  1.0  0.9  0.8  0.7  0.7  0.6  0.6  0.5  0.5  0.4  0.4  0.3  0.3  0.2  0.2  01  0.1  0.0  0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 0.9  d)  09  0.8  1.0  L. )  \u00AEmm e)  00 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 1.0  0.9  0.8  0.8  0.7  0.7  0.6  f  0.6  0.5  0.5  0.4  0.4  0.3  0.3  0.2  0.2  0.1  0.1  0.0  0.0  0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0 09 0.10  0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10  Weight on Economy Objective Function Figure 7.6 - Surface plots from EwE model showing the resulting fishing rates, as proportion of base model rates, of the optimal scenario solutions for a range of weightings of ecological and economic objective functions: a) deepwater longline; b) deepwater trawl; c) pelagic longline; d) small pelagic fishery; e) tuna fishery; f) demersal (bottom) longline. Scale goes from light blue (low proportion of base model rates) to dark red (high proportion of base model rates).  \u000CO DO O  000 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10 1.0  0.9 o o OJ 08 e 0.7 o -C \u2022bO &gt;\u2014&lt;n* V  \u00A3  0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10  c)  0.5  0.4 0.3 02 0.1 0.0 0.00 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.10  Weight on Economy Objective Function Figure 7.7 - Surface plots showing the resulting ecosystem indicators and total fisheries catch for the optimal scenario solutions for a range of weightings of ecological and economic objective functions: a) total catch; b) biodiversity index (Q75), please note that this figures is shown from a different viewpoint; c) mean trophic level of the catch. Scale goes from light blue (low) to dark red (high).  \u000C7 . 4 DISCUSSION  The analyses in this paper are not meant to describe actual fisheries for seamounts, but rather as an exercise to explore the overall responses of seamount ecosystems to various multi-species management strategies. The use of 'open loop policy' search procedures can be unrealistic because it can entrust a fishery to fishing rates calculated at some time in the past and from data available from that time (Walters et al., 2002; Christensen and Walters, 2004). Fisheries management needs to be implemented using 'feedback policies' in which harvest .goals are adjusted over time as new information becomes available and in response to unpredicted ecological changes (Christensen and Walters, 2004). However, 'open loop policy' calculations can give insights and directional guidance to where the system might be heading. In this study, this method appeared to be appropriate due to both the exploratory characteristic of the study and the data-limited situation of the N. Atlantic seamounts. Different extreme policy objectives for seamount fisheries may require different fleet configurations. Simulations that maximize economic performance favour deepwater trawling and require an increase in the fishing rates of all other fishing fleets, the only exception being the deepwater longline. On the other hand, maximization of ecological performance is achieved by favouring the operation of small pelagic and bottom longline fisheries. At the same time, a decrease in the fishing rates of all other fishing fleets is necessary. Different fishing rates and fleet configurations produced different impacts on catches and consequently in the whole ecosystem. Optimizing for economics yielded six times the amount of landed catch and money than the base model scenario and, 17 and 23 times the amount of landed catches and money yielded by the scenario where ecology was maximized. This would, however, have implications to the whole ecosystem. While maximizing ecology produces an overall increase in biomass of most functional groups in the model, maximizing economics leads to a decrease and further collapse of some groups such as tuna, seamount associated fishes, alfonsinos, as well as some charismatic  \u000Cspecies such as sea turtles and sharks. This point was well illustrated some time ago by Clark (1973) who pointed out that for populations that are economically valuable but possess low reproductive capacities (such as seamount associated fishes, alfonsinos and sharks), common property competitive exploitation and private property maximization of profits may lead to overexploitation and even to extinction of the population. It is interesting to note that major collapses in deepwater fisheries, for example off New Zealand (Clark, 1999; Clark et al., 2000), Tasmania (Koslow, 1997) and Namibia (Boyer et al., 2001), and habitat degradation (Probert et al., 1997; Koslow et al., 2001) were attributed to extensive deepwater trawling. In the Azores where no deepwater trawling is known to occur, seamount fisheries are mainly longline, handline and pole-and-line, and are believed to be more sustainable. However, signs of stock decline are becoming apparent even in these systems (Santos et al., 1995; Menezes, 2003). Thus, the question of whether deepwater (mainly trawl) fisheries are sustainable in the long term remains open (Clark, 2001). Some authors (e.g. Probert, 1999; Roberts, 2002), agencies (WWF; IUCN), and governments strongly advocate an urgent need for fishing regulations and/or the establishment of marine reserves in such areas. It is apparent that major conflicts among stakeholders might emerge when different optimization scenarios result in completely different fishing policies (Figure 7.5, 7.6). In addition, our. results illustrate that maximizing 'economy' affects biodiversity in the ecosystem and probably the trophic level of the catch, while maximizing the total landed catches (see Figure 7.7). The opposite is true when 'ecology' is favoured; the total catch and the number of jobs are decreased in order to achieve high biomass of long-lived species and increased biodiversity in the ecosystem. In conclusion, sustainable seamount fisheries with minimal ecosystem effects appear to be achieved when the 'ecology' objective is maximized. However, more information for these fragile ecosystems and the long-term impacts of fishing and other human activities needs to be acquired. 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Science 295: 1259.  \u000CCHAPTER 8 CONCLUSIONS: ECOLOGY AND FISHERIES OF SEAMOUNT ECOSYSTEMS  8.1 INTRODUCTION  The general objectives of this PhD were to explore some fundamental questions about seamount ecology and fisheries, namely to: a) examine the impact of a potential increase of local primary production on higher trophic levels; b) quantify the amount of advected prey necessary to sustain a \"typical\" seamount fish community and to explore if the necessary prey can be supported with food provided by local oceanographic conditions; c) test if the reported high abundances of seamount \"visitors\" such as tuna, marine mammals, sea turtles and seabirds on top and around seamounts are true; d) analyse changes in the mean depth of fishing to test if reported historical expansion into deeper-waters can be detected in global landings datasets. Also to test the hypothesis that deep-water fisheries resources are vulnerable; e) test the hypothesis that \"seamount fishes\" generally have a higher than average vulnerability to fishing exploitation; f) investigate if whole-ecosystem simulations can help in understanding the impact of fishing on pristine seamounts and provide guidelines for sustainable fisheries. Additionally, and in collaboration with the \"Sea Around Us Project\" I participated in small ad-hoc projects that helped: a) estimating of the number of seamounts in the world's oceans by inferring potential seamount locations; b) estimating seamount numbers and locations around the Azores islands;  \u000Cc) detecting primary production enhancement by world's oceanic seamounts using remotely sensed data on primary production d) estimating potential yield from seamounts and determining how catches have changed in recent years.  8 . 2 MAIN RESULTS  # 1 Introduction: Seamounts as hotspots of marine life Seamounts are underwater volcanoes occurring throughout the world's oceans. In Chapter # 1 I introduced the topic highlighting the main characteristics of seamount ecosystems. Additionally, I have tried to compile a list of fish species living on seamounts. This is because the designation of \"seamount\" species has been widely employed, but rigorous criteria used in identifying these taxa have not been defined. The definition of \"seamount fishes\" may involve a redundancy as we try to define a functional type of label that applies only in part to the ecology of the species: seamount fishes are those individual fishes that live on seamounts. Species that aggregate in association with seamounts and other topographic features have been called \"seamount-aggregating\" fishes. I compiled a total of 798 species of marine fishes that fall under the classification of seamount fishes and a total of 23 potential seamount-aggregating species. This list is preliminary and its accuracy will improve as we gain more knowledge about the ecology of seamount and deepwater fish species.  #2 Abundances and distribution of seamounts in the Azores Seamounts are important areas for conservation and fisheries in the Azores and the knowledge of its location is highly important for implementing management options and for testing hypothesis about ecosystem functioning. In Chapter #2, I characterized the seamount distribution on the Azores Economic Exclusive Zone using two bathymetry datasets. I identified seamount locations from local maxima in the bathymetry grid. The algorithm developed showed that peaks and seamounts are common features in the Azores EEZ. The real count average density of 3.3 peaks of all sizes per 1000 km 2 is in the same order of magnitude than obtained in some studies in the Mid Atlantic Ridge. Nevertheless, I was able  \u000Cto map and describe 63 large and 332 small seamount-like features in the EEZ of the Azores. The distribution suggests that large proportions of seamounts occur in chains along the Mid Atlantic Ridge, however, few isolated seamounts are also present in the Azores. Our distribution of seamounts predicts that about 63% of the potential Azores seamounts are protected against deep water trawling by the European Commission Council Regulation No. 1568/2005. #3 Modelled effects  of primary and secondary production enhancement by seamounts on  local fish stocks Chapter #3 addresses how large aggregations of fish found on many seamounts are sustained. I used a generic seamount ecosystem model from the Northeast Atlantic to examine the impact of a potential increase of local primary production on higher trophic levels, to quantify the immigration of micronekton that would be required to maintain a \"typical\" seamount community, and to quantify if the necessary immigration ratios could be supported by local oceanographic conditions. The results indicate lack of resources in the system to support large amounts of seamount aggregating fish. In other words, local seamount production may be responsible for sustaining only a small amount of its total biomass. Additionally, our study supports the idea that enhancement of primary productivity can also not sustain large aggregations of seamount fishes. The seamount model, which took into account high abundances of fish, marine mammals, seabirds and tuna, required a total immigration of micronekton of 95.2 t-km\"2-year\"' less than the potential available biomass after considering currents of 141.5 t-km\"2-year\"'. Therefore, I suggest that the horizontal flux of prey is sufficient to sustain the rich communities living on seamounts. Additionally, the simulation results suggest that food may not be a limiting factor for fish aggregations at intermediate seamounts. I discussed what could influence seamount fish abundance and explain the high variability of abundances from one seamount to the other.. # 4 Testing a seamount effect  on aggregating visitors  The importance of seamounts for large pelagic or visiting organisms has been poorly tested. However, it has been hypothesised higher abundances of some \"visiting\" animals, such as  \u000Ctuna, sharks, billfishes, marine mammals, sea-turtles and even seabirds, over seamounts. Surprisingly, this has been based on some sparse records, warranting further examination. In Chapter #4 I tested-if the predicted high abundances of tuna, marine .mammals, sea turtles and seabirds on top and around the Azores seamounts are true. This study has demonstrated that some marine predators are associated with shallow waters seamounts. This was the case of tuna species skipjack and bigeye, common dolphin and Cory's shearwater. These species were significantly more abundant in the vicinity of some seamount summits than far from these features. The methodology developed, however, failed to demonstrate seamounts' association for bottlenose dolphins, spotted dolphin, sperm whale, terns, yellow-legged gull, and loggerhead sea turtles. Seamounts play a major role in localizing pelagic prey and thus attracting some pelagic fish, seabirds and marine mammals. Therefore, some seamounts in the Azores may act as feeding stations for some of these visitors. Not all seamounts, however, seemed to be equally important for these associations. Only seamounts shallower than 400m depth showed significant aggregation effects. The important seamounts in the Azores for these visitors are \"Princesa Alice\" and \"Acres'' for all four species, also \"D. Joao de Castro\" for both tuna species, \"Formigas and Dollabarat\" and \"Pico Leste\" of the Princesa Alice for skipjack, and \"Agulhas do Sul do Gigante\" for common dolphin. These seamounts should be considered hotspots of marine life in the Azores and a special effort should be made in order to ensure a sustainable management of these habitats.  # 5 Fishing down the deep With the decline of shallow coastal waters resources, increasing demand, and new technology, fisheries are evidently expanding offshore and into deeper waters. In particular, seamounts are among those \"newly\" targeted ecosystems. However, information on seamounts fisheries is very sparse, and it is difficult to make a distinction between deepwater fishing activities in general and those occurring on seamounts. Moreover, fish species living on seamounts are also known to occur on other habitats, such as continental slope, and landings statistics are not spatially allocated, making it difficult to make an estimate of the total fisheries occurring on seamounts worldwide. The expansion into offshore areas has been well documented, but the extension into deeper waters is less well analysed. Whereas  \u000Cprevious studies on global trends of fisheries have focused on catch or biomass changes over time, in Chapter #5 I have analysed changes in the mean depth of fishing to test if the predicted expansion into deeper-waters can be detected in global landings datasets. I also tested for the predicted higher vulnerability of deep-water fisheries resources, using longevity as the main proxy for vulnerability. Global landings of demersal marine fishes are demonstrated to have shifted to deeper water species over the last 50 years. Our analysis suggests deep-water fish stocks may be at serious risk of depletion, since their life histories render them highly vulnerable to overfishing with little resilience to overexploitation. Deepsea fisheries are exploiting the last refuges for commercial fish species and should not be seen as a replacement for declining resources in shallower waters. Instead, deep-water habitats are new candidates for conservation. # 6 Vulnerability of seamount fish to fishing: fuzzy analysis of life history attributes Chapter #6 attempts to test the hypothesis that \"seamount fishes\" generally have a high vulnerability to exploitation and that this is correlated with their life history characteristics. Despite rather broad definitions, global analysis shows that \"seamount fishes\", particularly \"seamount-aggregating\" fishes, have higher intrinsic vulnerability than other groups of fishes. The pattern is similar when considering only commercially exploited species. Biological characteristics leading to greater vulnerability include a longer lifespan, later sexual maturation, slower growth and lower natural mortality. This research supports the contention that \"seamount fishes\", especially those that aggregate on seamounts, are highly vulnerable to exploitation and that fishing on seamounts may not be sustainable at current levels and with current methods. A number of seamount populations have already been depleted; more depletion, extirpations, and even species extinctions may follow if fishing on seamounts is not reduced. # 7 Ecosystem simulations of management strategies for data-limited seamount fisheries Traditional fisheries stock assessment requires large amounts of information, mainly from long-term data series, a requirement that is hard to apply to new or poorly-documented fishing grounds. In Chapter #7 I investigate if ecosystem simulations can help to understand  \u000Cthe impact of fishing on pristine seamounts. Using ecosystem modelling tools, data gathered from elsewhere, and methods that search for optimal fishing policies, I explored what types of fisheries might be sustainable on seamounts. Although the analyses in this paper are not meant to describe actual fisheries for seamounts, some generalizations can be made. Simulations with policy objectives that maximize economic performance favour fleet configurations based on deepwater trawling, but entail a cost to biodiversity. Maximizing ecological performance favours fleets on based on small pelagic and bottom longline fisheries, and maximizes biomass of long-lived species and biodiversity, but sacrifices total catches and jobs. The overall study suggested that sustainable seamount fisheries with tolerable ecosystem impacts appear to be closer to those found by maximizing an 'ecological' objective function.  8 . 3 CONCLUSIONS  In conclusion, this PhD have shown that the primary production enhancement by seamounts is not sufficient to support the well-documented large aggregations of fish; my work supports the alternative hypothesis that the horizontal flux of prey is the key factor sustaining the rich communities living on seamounts. This PhD has also demonstrated the importance of seamounts to some large pelagic fish, marine mammals and seabirds. Fisheries exploitation is a major threat to seamount ecosystems, however I was not able to quantify the amount of catches taken from the seamounts around the world. Instead, I was able to demonstrate that global landings of demersal marine fishes have shifted to deeper water species over the last 50 years. This result is an indirect indication that seamounts have also increased in commercial importance over the last years. I have also demonstrated that \"deep-water\", \"seamount\" and \"seamount-aggregating\" fish stocks may be at serious risk of depletion, since their life histories render them highly vulnerable to overfishing, and provide with little resilience to overexploitation. Finally, ecosystem modelling analyses showed that sustainable seamount fisheries with tolerable ecosystem impacts appear to be closer to those found by maximizing an 'ecological' objective function. This suggested that industrial-scale fishery  \u000Ccannot be supported by seamount. However, some regulated small-scale artisanal like fleet could be supported by seamounts ecosystems.  8 . 4 FUTURE RESEARCH  Despite considerable recent research effort, including that presented in this thesis, our knowledge on seamounts ecosystems is still limited when compared to coastal / shallow ecosystems. A landmark on seamounts research was the launch in 2005 of a Global Census of Marine Life on Seamounts (CenSeam). This program was build towards \"a global understanding of seamount ecosystems, and the roles they have in biogeography, biodiversity, productivity, and evolution of marine organisms.\" The CenSeam project along with SeamountsOnline (an online information system for seamount biology) will hopefully help in the development of new understandings on seamount ecosystems. In my view, three goals should guide the future work on seamounts: 1 - Assess and monitor biodiversity both at global and local scales A detailed catalogue of species occurring on seamounts should be the top priority when assessing these habitats. This should 1) produce exhaustive lists of species living seamounts; 2) help findings new species to science; 3) identify the fraction of endemic species living on seamounts. This task could involve 1) the synthesis of seamount studies that have been conducted and 2) promote field efforts to improve our knowledge on the species inhabiting worldwide seamounts. 2- Increase understanding of seamount ecosystem functioning in terms of productivity and the underlying biological characteristics Extensive experimental fieldwork should be carried in different seamounts around the world to examine particular aspects of seamount ecosystem functioning that are not yet well understood. Topics to be addressed should include 1) examine zooplankton biomass over and around seamounts, with emphasis to fish larvae biomass; 2) using tagging techniques to better examine tuna, swordfish, sharks, aggregations on seamounts; 3)  \u000Cunderstand how cephalopods interact with seamounts; 4) better examine the importance of seamounts for marine mammals, seabirds and sea-turtles; 5) better understanding the importance of corals and other epibenthic megafauna to the seamount biota; 6) to validate the assumption that seamount fish biomass are supported by bottom trapping of migrating zooplankton and by the horizontal flux of non-migrating zooplankton. 2 - Assess the human impacts on seamount communities both at global and local scales Describing the catch of fishes from worldwide seamounts presents a major challenge, because information on seamounts fisheries is very sparse, and it is difficult to make a distinction between deep-water fishing activities in general and those occurring on seamounts. We should be able 1) to quantify total catches from world's seamounts and 2) to quantify catches from selected individual seamounts. The first goal could be achieved by automated mapping techniques as the ones developed by the Sea Around Us Project at the University of British Columbia. This algorithm could then be run to analyses the development of fishing catches from seamounts. The second goal could only be achieved with information from fishing vessels, i.e., where individual fishing vessels were operating. This could be accomplished by analyzing satellite tracking of fishing vessels along with data from landings. This could produce the first dataset with real information of catches from individual seamounts and would help identifying those seamounts that are under most threats.  3 - Long term monitoring of seamount communities There is also an urgent need for the establishment of long-term programmes to assess population trends of selected species in seamounts as a whole or in reference sites subject to different ecological and exploitation regimes. For the later, focus could be on seamounts where exploitation is more intense. Selected species should be those more vulnerable and those that are likely to be heavily damaged by fishing activities. The methodologies involved in assessing the status of the communities and species should be standardized and involve in situ observations using advanced technology such as ROV's and submersibles.  \u000CAPPENDIX 1 COMPILATION OF FISH SPECIES RECORDED ON SEAMOUNTS  Table 1 - List of species considered as S \"seamount fishes\" and AGG \"seamountaggregating\" fishes. Intrinsic Vulnerability (Vf)  index and * V-, excluding species from only  Total length infinity is available, are also presented. Species  S vs AGG  Habitat  Alepocephalus  agassizii  Alepocephalus  australis  Alepocephalus  bairdii  Alepocephalus  productus  S .S s s s s s s s s s s s s AGG s  Alepocephalus  rostratus  s  Bathydemersal  Alepocephalus  tenebrosus  s AGG AGG S s s s  Bathydemersal  Acanthocybium  solandri  Acantholabrus  palloni  Acanthurus  olivaceus  Acromycter  perturbator  Adelosebastes  I a tens  Ahliesaurus  berryi  Albatrossia  pectoralis  Aldrovandia affin is Aldrovandia  oleosa  Aldrovandia  phalacra  Aldrovandia  rostrata  Aiepisaurus  brevirostris  Allocyttus  niger  Allocyttus  verrucosus  Amphichaetodon Anarhichas  howensis  denticulatus  Anarrhichthys  ocellatus  Anatolanthias  apiomycter  Anoplogaster  cornuta  Pelagic Reef-associated Reef-associated Bathydemersal Bathydemersal Bathypelagic Bathydemersal Bathydemersal Bathypelagic Bathydemersal Bathypelagic Pelagic Bathydemersal Bathydemersal Bathydemersal Bathydemersal  Bathypelagic Bathypelagic Reef-associated Benthopelagic Demersal  50.22  40  90  76.97 10 49.55 62.35 65.75  10 82.43 82.43 90 90 42  Bathydemersal  62.24  62.24  s  Reef-associated  18.35  s  Bathydemersal  Anoplopoma fimbria  s  Anthias  aurorosea  50.22 16.29 32.23 76.17 21.45 18.25 90 40 40 40 64.7 60 52.62 44.89 71.14 32.77 10 49.55 40  42  Bathypelagic  Antigonia  Vi*  Pelagic  s  anthias  Vi  \u000CSpecies Antigonia  S vs AGG capros  Antigonia eos Antigonia  malayana  Antigonia  rubescens  Antimora  microlepis  Antimora rostrata  s s s s s s s  Habitat  v,  Vj*  Demersal  21.97  Bathydemersal Demersal Bathydemersal  10  Bathypelagic  51  Bathypelagic  49.89  Bathypelagic  52.13  Aphanopus  capricornis  Aphanopus  carbo  AGG  Benthopelagic  74.48  Aphanopus  intermedins  s s s s, s s 's s s s s s s s s s s s s s s s s s s s s s  Bathypelagic  73.91  Reef-associated  29.48  Reef-associated  40.73  Demersal  48.15  Bathydemersal  48.15  Bathydemersal  55.06  Aphareus furca Aprion  virescens  Apristurus  brwmeiis  Apristurus  laurussonii  Apristurus  manis  Apristurus profundorum Aptocyclus  ventricosus  Arctozenus risso Argentina  sphyraena  Argyripnus  atlanticus  Argyripnus  electronus  Argyripnus  iridescens  Argyropelecus  aculeatus  Argyropelecus afjinis Argyropelecus  gigas  Argyropelecus  hemigymnus  Argyropelecus  lychnus  Ariomma bondi Ariomma lurida Ariomma  melanum  Ariosoma  balearicum  Ariosoma  marginatum  Aristostomias  tittmanni  Arnoglossus  imperialis  Arnoglossus  multirastris  Arnoglossus  rueppelii  A rnoglossus sept em ventral is  49.89 54.09  40.73  Bathydemersal Benthopelagic  32.26  Bathypelagic  34.41  Bathydemersal  37.09  Benthopelagic  50  37.09  Bathydemersal Bathypelagic  10  Bathypelagic  10  Bathypelagic  17.57  Bathypelagic  10  Bathypelagic  10  Bathypelagic  10  Demersal Bathypelagic  55.73 26.6  Bathydemersal  21.45  Reef-associated  26.6  Demersal Pelagic  29.69 10  Demersal  16.29  Demersal  76.67  Demersal  10  Bathydemersal  80.42  17.57 10  \u000CSpecies  S vs AGG  Habitat  Vi  Arothron firmamentum  s  Demersal  26.6  Aspitrigla  s  Demersal  32.41  Assurger anzac  s  Benthopelagic  90  Astronesthes gemmifer  s  Bathypelagic  10  Astronesthes  ijimai  s  Bathypelagic  10  Astronesthes  macropogon  s  Bathypelagic  10  s s s s s s s s  Demersal  54.65  Demersal  40  Demersal  90  Banjos banjos  s  Demersal  10  Barbantus curvifrons  Bathypelagic  10  Bathophilus flemingi  s s s  Bathophiius  longipinnis  Bathophilus  pawneei  cucuius  Atheresthes  stomias  Aulopus filamentosus Aulopus  japonicus  Aulostomus Avocettina  chinensis bowersii  Avocettina infans Bajacalifomia Batistes  megalops  capriscus  Bassogigas gillii  Bathygadus favosus Bathygadus  melanobranchus  Bathylagus  euryops  Bathylagus  pacijicus  Bathymicrops  regis  Bathypterois  atricolor  Bathypterois  dubius  Bathypterois  longipes  Bathypterois  phenax  Bathypterois quadrifilis Bathyraja  shuntovi  Bathysaurus ferox Bathytroctes  oligoiepis  Bathytyphlops  marionae  Beilottia apoda Bembradium furici  Reef-associated  V* 32.41  54.65  53.03  Bathypelagic Bathypelagic  50.79  Bathypelagic  31.74  Reef-associated  27.73  Bathydemersal  55.06  Bathypelagic  29.17  s  Bathypelagic  10  s s s s s s s s s s  Bathypelagic  10  Bathydemersal  40  Bathydemersal  40  Bathypelagic  40  Bathypelagic  43.12  Bathydemersal  10  Bathydemersal  10  Bathydemersal  32.77  Bathydemersal  16.18  Bathydemersal  10  s  Bathydemersal  10  \" s  Bathydemersal  81.35  s s s s s  Bathydemersal  46.52  Bathypelagic  ' 23.82  Bathydemersal  29.69  Bathydemersal  34.18  Bathydemersal  27.73  29.17  43.12  32.77  \u000CBembradium  S-. s s  roseum  Bembrops fdifera Benthalbella  dentata  Benthodesmus  elongatus  Benthodesmus  tenuis  Benthosema  Bathydemersal  10  Bathydemersal  10  Bathypelagic  10  Bathydemersal -  s s  glaciate  Bathydemersal Pelagic  64.72  64.72  90 28.72  28.72  Beryx decadactylus  AGG  Bathydemersal  60  74.07  Beryx splendens  AGG  Bathydemersal  59.53  66.84  S s s s s s s s s s s s s s s s s s s s s s s s s s s s  Reef-associated  40  Reef-associated  10  Bathypelagic  10  Bathypelagic  10  Bodianus  bilumilatus  Bodianus cy/indriatus Bolinichthys Bonapartia  photothorax pedaliota  Borostomias Bothrocara Bothrocara  antarcticus brunneum molle  Brama brama Brosme brosme Brotulotaenia  brevicauda  Brotulotaenia  crassa  Caelorinchns  bollonsi  Caelorinchus caelorhincus Caelorinchus  celaenostomus  Caelorinchus fasciatus Caelorinchus  immaculatus  Caelorinchus  innotabilis  Caelorinchus  kaiyomaru  Caelorinchus  labiatus  Caelorinchus  matamua  Caelorinchus  multifasciatus  Caelorinchus  nazcaensis  Caelorinchus  spilonotus  Caelorinchus  trachycarus  Callanthias  parini  Callanthias ruber Callionymus  maculatus  Cantliigaster  callisterna  caelorhincus  Bathydemersal  21.45 '  Bathydemersal  47.33  Bathydemersal  40  Bathypelagic  53.16  53.16  Demersal  54.22  54.22  Bathypelagic  52.74  Bathypelagic  55.46  Benthopelagic  60  Benthopelagic  50.49  Bathydemersal  51.76  Bathydemersal  40  Bathydemersal  50.09  Bathydemersal  23.51  Bathydemersal  40  Bathydemersal  40  Bathydemersal  46.92  Bathydemersal  10  Bathypelagic  31.74  Bathydemersal  10  Bathydemersal  40  Reef-associated  43.33  Demersal  44.89  Demersal  10  Demersal  10  50.49  \u000CCanthigaster  coronata  s  Reef-associated  10  Canthigaster  epilampra  s  Reel-associated  10  Canthigaster  rivulata  s  Reef-associated  10 40  Caprodon  longimanus  s  Reef-associated  Caprodon  schlegelii  s  Benthopelagic  s  Demersal  s  Reef-associated  30.5  Caranx ignobilis  s  Reef-associated  61.4  61.4  Caranx lugubris  s  Reef-associated  53.38  53.38  Caranx  s  Reef-associated  45.28  45.28  s  Bathydemersal  29.26  29.26  Caristius maderensis  s  Pelagic  56.76  Cataetyx laticeps  s  Bathydemersal  Centracanthus  s  Demersal  25.57  Centroberyx affinis  s  Benthopelagic  61.77  Centrodraco  acanthopoma  s  Bathydemersal  10  Centrodraco  nakaboi  s  Bathydemersal  Centrodraco  otohime  s  Bathydemersal  10  Centrodraco  striatus  s  Bathydemersal  43.33  Centrolophus  niger  s  Bathypelagic  50  Centrophorus  granulosus  s  Bathydemersal  90  Centrophorus  squamosus  s  Benthopelagic  90  90  Centroscyllium fabricii  s  Bathydemersal  79.32  79.32  Centroscyllium  s  Demersal  Capros aper Carangoides  orthogrammus  melampygus  Careproctus  melanurus  cirrus  ritteri  26.6 55.73  26.6 61.77  .49.37  Centroscymnus  coelolepis  s  Bathydemersal  80.4  80.4  Centroscymnus  crepidater  s  Bathydemersal  83.94  83.94  Centroscymnus  cryptacanthus  s  Bathydemersal  71.92  Centroscymnus  owstoni  s  Bathydemersal  74.44  Centroscymnus  plunketi  s  Bathydemersal  78.83  Ceratoscopelus  maderensis  s  Bathypelagic  50  Ceratoscopelus  warmingii  s  Bathypelagic  10  Cetonurus  crassiceps  s  Bathydemersal  40  Cetorhinus  maximus  78.83  s  Pelagic  60.53  60.53  Cetostoma regani  s  Bathypelagic  18.02  18.02  Chaetodon fremblii  s  Reef-associated  10  Chaetodon kleinii  s  Reef-associated  10  \u000CSpecies  S vs AGG  Chaetodon miliaris Chascanopsetta  megagnatha  Chascanopsetta  prorigera  Chauliodus  macouni  Chauliodus  sloani  Chaunax fimbriatus Chaunax  latipunctatus  Chaunax pictus Chaunax  umhrinus  Chelidonichthys Chelidoperca  gurnardus lecromi  Chiasmodon niger Chimaera lignaria Chimaera  monstrosa  Chimaera owstoni Chirostomias  .  pliopterus  Chlamydoselachus  anguineus  CMopsis bicolor Chlorophthalmus  agassizi  Chlorophthalmus  albatrossis  Chlorophthalmus  ichthyandri  Chlorophthalmus  zvezdae  Chrionema pallidum Chromis verater Coccorella atlantica Conger conger Conger oligoporus Conocara  macropterum  Cookeolus  japonicus  Coris ballieui Coryphaena  hippurus  Coryphaenoides  acrolepis  Coryphaenoides  alateralis  Coryphaenoides  armatus  Coryphaenoides  carapinus  Coryphaenoides  cinereus  s s s s s s s s s s s s s s s s  s s s s s s s s s s s s s s s s s s s s  Habitat  Vi  V,*  Reef-associated  51.89  51.89  Bathydemersal  10  Bathydemersal  10  Bathypelagic  31.05  Bathypelagic  26.6  Bathydemersal  31.05  10  Bathydemersal Bathydemersal  31.74  Demersal Demersal  37.07  Demersal  10  Bathypelagic  \u2022  16.29  Bathydemersal  90  Bathydemersal  50  Bathydemersal  60  Bathypelagic  10  Bathydemersal  90  Demersal Bathydemersal  60.87 10  Demersal  76.67  Demersal  76.67  Bathydemersal  10  Reef-associated  32.22  Demersal  90  33.8  Demersal  Bathypelagic  37.07  10 81.18  81.18  Reef-associated Bathypelagic  25.57  Reef-associated  48.15  Reef-associated  21.45  Pelagic \u2022  48.15  50  50  Bathydemersal  79.66  79.66  Bathydemersal  17.84  Bathydemersal  60  Bathydemersal  40  Bathydemersal  40  \u000CSpecies  S vs AGG  Habitat  S S s s AGG S s s s s s s s s s s  Bathydemersal  Dalatias licha Dasyatis  Coryphaenoides  guentheri  Coryphaenoides longifilis Coryphaenoides  murrayi  Coryphaenoides  rudis  Coryphaenoides  rupestris  Coiyphaenoides  serruiatus  Coryphaenoides  subserrulatus  Cottunculus  thomsonii  Cryptopsaras Cubiceps  couesii  pauciradiatus  Cyciothone  braueri  Cyciothone  microdon  Cyciothone pallida Cyttomimus stelgis Cyttopsis rosea Dactylopsaron  dimorphicum  Bathypelagic Bathydemersal  76.67  Bathypelagic  56.24  Bathydemersal  43.33  s  Bathydemersal  72.94  72.94  Demersal  67.52  67.52  Bathydemersal  78.24  78.24  Bathydemersal  71.31  71.31  Pelagic  43.85 43.85 29.34 29.34 65.51 14.75 14.75 10 31.74 10 10  Bathydemersal Bathydemersal Bathypelagic Bathypelagic Bathydemersal Bathydemersal Bathydemersal Bathypelagic Bathypelagic Bathypelagic Bathypelagic  Decapterus  macarellus  Decapterus  maruadsi  Decapterus  muroadsi  s  Pelagic  Decapterus  russelli  s s s  Reef-associated  s  Bathypelagic Bathypelagic  Diaphus lucidus  s s s 's  Diaphus parini  s  Benthopelagic  Diaphus  s  Bathypelagic  Diaphus rafmesquii  s  Bathypelagic  Diaphus splendidus  s s  Bathypelagic  Deania calcea Deania profundorum  Dendrochirus Derichthys Diaphus Diaphus  barberi serpentinus  adenomus brachycephalus  Diaphus confusus Diaphus dumerilii  perspicillatus  Diaphus theta  V*  36.18 36.18 48.96 28.66 68.38 75.4 76.25 40 28.66 26.6 35 35 10 10 10 10 10 27.5 27.5  s s s s s  pastinaca  V;  Reef-associated  Reef-associated Bathypelagic  Bathydemersal Pelagic Bathypelagic  Bathypelagic  10 10 10 10 10 10  10  \u000CSpecies  S vs AGG  Diastobranchus Dibranchus  capensis  tremendus  Dicrolene  \u2022 \u2022  \u2022  introniger  Dicrolene nigra Diodon  .  holocanthus'  Diplospinus  multistriatus  Dipturus batis Dipturus  oxyrinchus  Diretmichthys Diretmus  parini  argenteus  Dissostichus  eleginoides  Dolicholagus  longirostris  Dolichopteryx  longipes  Echinorhinus  cookei  Echiodon dentatus Echiostoma  barbatum  Ectreposebastes Einara  imus  macrolepis  Emmelichthys  elongatus  Emmelichthys  nitidus  Emmelichthys  nitidus nitidus  cyanescens  Emmelichthys  struhsakeri  Engyprosopon  regani  Enigmapercis  acutirostris  Eopsetta jordani Epigonus  atherinoides  Epigonus  denticulatus  Epigonus  elegans  Epigonus  notacanthus  Epigonus  robustus  Epigonus  telescopus  '  Habitat  Vi  Vi*  s  Bathydemersal  76.86  s \u2022  Bathydemersal  10  s s  Bathydemersal  26.6  Bathydemersal  45.22  s.  Reef-associated  24.69  s s s s s  Bathypelagic  35.98  35.98  Demersal  79.58  79.58  AGG  s s s s s s s s s s s s s s s s s s s AGG  Bathydemersal  90  Bathypelagic  31.74  Bathypelagic  17.58  Pelagic  78.84  Bathypelagic  10  Bathypelagic  25  Demersal  90  Demersal  10  Bathypelagic  28.45  Bathypelagic  10  Bathypelagic  34.74  68.94 25  Demersal Bathydemersal  51.22  Bathydemersal  65.51  Demersal  21.45  Demersal  76.67  Bathydemersal  76.67  Demersal  64.92  Bathydemersal  10  Bathydemersal  10  64.92  Bathydemersal Bathydemersal  10  Bathydemersal  10  Bathydemersal  51  51  Epinephelus  quernus  S  Demersal  Epinephelus  septemfasciatus  S  Reef-associated  90  Erilepis zonifer  S  Bathydemersal  90  Etelis  carbunculus  S  Reef-associated  70.9  70.9  Etelis  coruscans  S  Reef-associated  63.22  63.22  75.05  \u000CEtmopterus  baxteri  S  Bathydemersal  51  Etmopterus  gracilispinis  S  Benthopelagic  26.6  Etmopterus  litvinovi  S  Bathydemersal  Etmopterus lucifer  S  Bathydemersal  65.46  Etmopterus  princeps  S  Bathydemersal  51  Etmopterus  pusillus  S  Bathydemersal  40  Etmopterus  pycnolepis  S  Bathydemersal  Etmopterus  spinax  S  Bathydemersal  66.56  Eurypharynx  pelecanoides  S  Bathypelagic  60  Eurypleuron  owasianum  S  Bathydemersal  10  Eustomias  obscurus  S  Bathypelagic  10  Eustomias  schmidti  S  Bathypelagic  10  Euthynnus afjinis  S  Reef-associated  59.78  Evistias  acutirostris  S  Reef-associated  33.04  Facciolelta  castlei  S  Demersal  76.67  Fistularia  commersonii  S  Reef-associated  90  Fistularia  petimba  S  Reef-associated  90  Flagellostomias  boureei  66.56  59.78  S  Bathypelagic  Foetorepus  kanmuensis  S  Bathydemersal  10  Foetorepus  kinmeiensis  S  Bathydemersal  10  Gadella maraldi  S  Benthopelagic  21.45  Gadella norops  S  Benthopelagic  18.15  Gadella  obscurus  S  Benthopelagic  10  Gadiculus argenteus thori  S  Pelagic  Gadomus  aoteanus  S  Benthopelagic  Gadomus  arcuatus  S  Bathypelagic  44.07  Gadomus dispar  S  Bathydemersal  77.28  Gadomus  S  Bathydemersal  S  Demersal  S  Bathydemersal  26.6  S  Benthopelagic  31.74  S  Demersal  27.63  S  Demersal  16.29  S  Demersal  23.75  23.75  S  Demersal  S  Benthopelagic  72.2  72.2  Gadus  melanopterus macrocephalus  Gaidropsarus  argentatus  Gaidropsarus  ensis  Gaidropsarus  granti  Gaidropsarus  macrophthalmus  Gaidropsarus  mediterraneus  Gaidropsarus  parini  Galeorhinus  galeus  23.72  16.38  16.38  60  54.58  54.58  \u000CGaleus  melastomus  Galeus murinus Gephyroberyx  darwinii  Gephyroberyx  japonicus  Gigantura indica Glossanodon  danieli  Glossanodon  leioglossus  Glossanodon  nazca  Glyptocephalus  cynoglossus  Gnathophis  andriashevi  Gnathophis  cinctus  Gnathophis  codoniphorus  Gnathophis  mystax  Gnathophis  parini  Gnathophis  smithi  Gonichthys  cocco  Goniistius vittatus Gonostoma  bathyphilum  S S S S s s s s s s s s s . s s . s \u2022\u2022 s s  Bathydemersal  51  Bathydemersal  68.05  Benthopelagic  44.89  Benthopelagic  10  Bathypelagic  15.1  Bathydemersal  10  Bathydemersal  10  Bathydemersal Demersal  60.18  Bathydemersal  76.67  Demersal  33.8  Demersal Demersal \u2022  44.89  Bathydemersal  43.33  Demersal  54.77  Bathypelagic Reef-associated  10 32.77  Bathypelagic  10  Gonostoma  denudatum  s  Bathypelagic  10  Gonostoma  elongatum  s  Bathypelagic  18.87  s  Demersal  75.18  dentatus  s  Bathypelagic  50  brachiusculus  Bathypelagic  46.52  Grammatonotus  laysanus  Grammatostomias  Gymnothorax  flavimarginatus  Gymnothorax  hepaticus  s s s  Gymnothorax  maderensis  s  Demersal .  Gymnothorax  punctatofasciatus  s  Reef-associated  40  Gymnothorax  steindachneri  s s s  Reef-associated  60  Bathypelagic  40  Bathydemersal  60  s  Bathypelagic  40  raleighana  s  Bathydemersal  avius  s  Bathydemersal  s  Bathydemersal  59.44  s s  Bathydemersal  18.35  Bathydemersal  34.58  Grammicolepis  Halargyreus  johnsonii  Halosauropsis  macrochir  Haplomacrourus Harriotta Helicolenus  nudirostris  Helicolenus dactylopterus Helicolenus fedorovi Helicolenus  lengerichi  dactylopterus  Reef-associated  90  Reef-associated  60 \u2022  60  73.84  \u000CHem ilep ido tus hem Hep idotus  S  Demersal  40  Hemilepidotus  S  Demersal  20.42  s  Reef-associated  51.48 90  Heniochus  spinosus  diphreutes  Heptranchias  perlo  s  Bathydemersal  Heterophotus  ophistoma  s  Bathypelagic  27.22  s  Reef-associated  24.58  s  Reef-associated  77.65  s  Bathypelagic  50.04  s  Demersal  65.86  65.86  Heteropriacanthus Hexanchus  cruentatus  griseus  Himantolophus  albinares  Hippoglossoides  platessoides  90  77.65  Hippoglossus  hippoglossus  s  Demersal  76.46  76.46  Hippoglossus  stenolepis  s  Demersal  79.18  79.18  Hollardia goslinei  s  Demersal  Holtbyrnia  anomala  s  Bathypelagic  Holtbyrnia  macrops  s  Benthopelagic  10  Hoplichthys  citrinus  s  Demersal  60  Hoplichthys gilberti  s  Demersal  10  ,  52.87  AGG  Bathypelagic  63.79  S  Bathypelagic  34.97  AGG  Benthopelagic  45.11  66.22  Howella brodiei  S  Bathypelagic  10  10  Hozukius  S  Bathydemersal  40  Hydrolagus afjinis  S  Bathydemersal  79.89  Hydrolagus  bemisi  s  Bathydemersal  67.09  Hydrolagus  mirabilis  s  Bathydemersal  29.69  Hydrolagus pallidus  s  Bathydemersal  83.27  Hydrolagus trolli  s  Bathydemersal  68.69  Hygophum  hygomii  s  Bathypelagic  10  Hygophum  taaningi  s  Bathypelagic  10  Hoplostethus  atlanticus  Hoplostethus  crassispinus  Hoplostethus mediterraneus  mediterraneus  guyotensis  Hymenocephalus  aterrimus  s  Bathypelagic  10  Hymenocephalus  gracilis  s  Bathypelagic  10  Hymenocephalus  italicus  s  Benthopelagic  36.96  Hymenocephalus  longibarbis  s  Benthopelagic  60  Hymenocephalus  longiceps  s  Benthopelagic  Hymenocephalus  neglectissimus  s  Bathydemersal  10  Hymenocephalus  semipellucidus  s  Bathydemersal  10  Hymenocephalus  striatulus  s  Bathypelagic  10  68.65  36.96  \u000CHyperoglyphe  japonica  s  Benthopelagic  Hyperoglyphe perciformis  s  Pelagic  36.11  Icichthys australis  s  Pelagic  53.43  Icosteus  s  Bathypelagic  79.44  Idiacanthus fasciola  s  Bathypelagic  40  ldiolychnus  s  Pelagic  s  Bathypelagic  44.07  s  Bathypelagic  17.47  s  Pelagic  48.31  Kentrocapros flavofasciatus  s  . Demersal  Kuronezumia pallida  s  Bathydemersal  40  Lactoria fornasini  s  Reef-associated  10  Laemonema  longipes  s  Bathydemersal  50.79  Laemonema  rhodochir  s  Benthopelagic  10  Laemonema  yarrellii  s  Bathydemersal  50  Laemonema  yuvto  s  Bathydemersal  Lampadena  luminosa  s  Bathypelagic  10  Lampadena  speculigera  s  Bathypelagic  10  Lampadena urophaos atlantica  s  Bathypelagic  10  Lampadena urophaos  s  Pelagic  10  s  Bathypelagic  10  s  Reef-associated  50  Ilyophis  aenigmaticus  urolampus  brunneus  Kali indica Katsuwonus  pelamis  Lampanyctus  urophaos  photonotus  Lappanella fasciata Lepidion eques  60  79.44  19.2 .  48.31  10  AGG  Benthopelagic  40  Lepidion  guentheri  S  Benthopelagic  55.87  Lepidion  inosimae  S  Bathydemersal  45.09  Lepidion  microcephalus  s  Bathydemersal  40  s  Bathydemersal  55.06  s  Bathypelagic  90  s  Pelagic  10  10  s  Demersal  66.75  66.75  s  Bathydemersal  52.62  s  Bathydemersal  78.61  78.61  s  Demersal  41.98  41.98  s  Bathydemersal  58.43  58.43  s  Bathypelagic  s  Pelagic  Lepidion schmidti Lepidocybium  flavobrunneum  Lepidophanes  guentheri  Lepidopsetta  bilineata  Lepidopus calcar Lepidopus caudatus Lepidorhombus  boscii  Lepidorhombus  whifjiagonis  Lepidorhynchus  denticulatus  Leptostomias  haplocaulus  40 36.18  \u000CLeptostomias  longibarba  s  Bathypelagic  45.64  Lestrolepis  intermedia  s  Bathypelagic  16.29  Leucoraja  circuiaris  s  Demersal  73.84  Leucoraja fullonica  s  Bathydemersal  73.84  Leuroglossus  s  Bathypelagic  10  schmidti  Lobianchia  dojleini  s  Bathypelagic  10  Lobianchia  gemellarii  s  Bathypelagic  10  Lophiodes  miacanthus  s  Bathydemersal  31.09  s  Bathydemersal  68.48  s  Bathydemersal  10  Lutjanus kasmira  s  Reef-associated  43.94  Lycodes esmarkii  s  Bathydemersal  51  Lycodes  terraenovae  s  Bathydemersal  40  Lyconus  brachycolus  s  Bathydemersal  36.49  s  Bathypelagic  10 42  42 27.97  Lophius  piscatorius  Lucigadus  microlepis  Macropinna  microstoma  10  68.48 43.94  Macroramphosus  gracilis  s  Pelagic  Macroramphosus  scolopax  s  Demersal  27.97  s  Demersal  10  Macroaroides inflaticeps  s  Bathypelagic  53.06  Macrourus  berglax  s  Benthopelagic  78.08  78.08  Macrourus  carinatus  s  Bathydemersal  60  60  Macrorhamphosodes  uradoi  Macruronus  magelianicus  s  Benthopelagic  71.19  71.19  Macruronus  novaezelandiae  s  Benthopelagic  69.18  69.18  s  Pelagic  s  Bathydemersal  Magnisudis  atlantica  Malacocephalus Malacosteus  laevis  niger  40 44.89  s  Bathypelagic  10  Malthopsis annulifera  s  Bathydemersal  10  Malthopsis lutea  s  Bathydemersal  10  Malthopsis tiarella  s  Demersal  10  Manducus  s  Bathypelagic  19.28  s  Bathydemersal  31.55  s  Bathydemersal  10  s  Bathypelagic  10  s  Bathypelagic  33.08  s  Bathypelagic  10  s  Bathypelagic  44.55  maderensis  Marukawichthys Marukawichthys  ambulator pacificus  Mataeocephalus  acipenserinus  Maulisia argipalla Maulisia mauli Maulisia  microlepis  \u000Cs  Bathypelagic  10  Maurolicits rudjakovi  s  Bathypelagic  10  Maurolicus  s  Bathypelagic  10  s  Bathypelagic  10  s \u2022  Bathypelagic  10  Maurolicus  muelleri  weitzmani  Melamphaes lugubris \u2022 K  10  Melamphaes  microps  Melamphaes  suborbitalis  s  Bathypelagic  10  Melanocetus  murrayi  s  Bathypelagic  10  s  Demersal  s  Bathypelagic  10  s  Bathypelagic  19.39  s  Bathypelagic  10  s  Bathypelagic  17.53  australis  s  Benthopelagic  72.28  antipodum  s  Bathypelagic  47.33  multiradiatus  s  Benthopelagic  19.39  strigatus  s  Reef-associated  32.22  s  Pelagic  s  Bathydemersal  Microsiomus kitt  s  Demersal  40.41  40.41  Microstomus pacificus  s  Demersal  64.75  64.75  Microstomus  shuntovi  s  Bathydemersal  Mirognathus  normani  s  Bathypelagic  Melanogrammus Melanolagus  aeglefmus  bericoides  Melanonus  zugmayeri  Melanostigma  atlanticum  Melanostomias Merluccius Mesobius Metavelifer  bartonbeani  Microcanthus Micromesistius Microstomus  Mitsukurina  poutassou bathybius  owstoni  43.31  34.1  43.31  72.28  34.1  40  31.13  s  Bathydemersal  Mola mola  s  Pelagic  71.33  71.33  Molva dypterygia  s  Demersal  78.39  64.99  Molva  macrophthalma  90  s  Demersal  66.56  Molva molva  s  Demersal  75.34  75.34  Monocentris reedi  s  Demersal  49.47  49.47  Bathypelagic  53.03  47.36  Mora moro  AGG  Muraena helena Myctophum affine Myctophum  selenops  Myripristis murdjan Nannobrachium  atrum  Nannobrachium  cuprarium  Nannobrachium  lineatum  s  Reef-associated  90  s  Bathypelagic  10  s  Bathypelagic  10  s  Reef-associated  s  Bathypelagic  10  s  Bathypelagic  50.82  s  Bathypelagic  10  19.86  19.86  \u000CNannobrachium  regale  s  Bathypelagic  10  Nannobrachium  ritteri  s  Bathypelagic  10  Benthopelagic  10  Nansenia Candida  s s  Bathypelagic  10  Narcetes  stomias  s  Bathypelagic  40  Naso brevirostris  s s s s s  Reef-associated  44.89  Reef-associated  51  Reef-associated  44.89  Reef-associated  41.21  Bathypelagic  16.29  s  Demersal  76.67  s s  Bathypelagic Bathydemersal  50.13  s  Bathypelagic  55.98  AGG  Bathypelagic  31.74  Nansenia  Naso  ardesiaca  hexacanthus  Naso maculatus Naso unicornis Nealotus tripes Nemadactylus  gayi  Nemichthys  scolopaceus  Neobythites  zonatus  Neocyttus  helgae  Neocyttus  rhomboidalis  56  Neomerinthe  procurva\u2022  s  Demersal  Neoscopelus  macrolepidotus  s  Bathypelagic  16.29  Neoscopelus  microchir  s s s' s s s s s s s s s s s s s s s s  Bathypelagic  21.97  Bathydemersal  79.89  Nesiarchus nasutus Nessorhamphus  ingolfianus  Nettastoma falcinaris Nettastoma  parviceps  Nezumia  aequalis  Nezumia  convergens  Nezumia  longebarbata  Nezumia  propinqua  Nezumia  sclerorhynchus  Normichthys  operosus  Notacanthus  bonaparte  Notacanthus  chemnitzii  Notacanthus  sexspinis  Notopogon  fernandezianus  Notopogon lilliei Notopogon  xenosoma  Notoscopelus  caudispinosus  Notoscopelus  resplendens  Bathypelagic  44.8  Bathydemersal  76.67  Bathydemersal  53.84  Benthopelagic  27.63  Bathydemersal  21.45  Bathypelagic  32.26  Bathydemersal  18.35  Bathypelagic  44.02  Bathypelagic  10  Bathypelagic  60  Benthopelagic  73.84  Bathydemersal  44.89  Bathydemersal  10  Demersal  18.35  Bathydemersal  10  Bathypelagic  10  Bathypelagic  10  \u000Cs  Bathydemersal  s  Bathypelagic  46.52  Omosudis lowii  s  Bathypelagic  10  Oneirodes  macrosteus  s  Bathypelagic  83.27  Oneirodes  thompsoni  10  Odontaspis ferox Odontomacrurus  murrayi  90  s  Bathypelagic  Osopsaron karlik  s  Bathydemersal  43.33  Ostichthys  kaiamts  s  Bathydemersal  27.63  Ostracion  cubicus  s  Reef-associated  40  Oxycheilinus  unifasciatus  s  Reef-associated  40  Oxynotus  bruniensis  s  Bathydemersal  67.44  67.44  Pagellus  bogaraveo  s  Benthopelagic  54.73  54.73  Pagrus pagrus  s  Reef-associated  38.85  38.85  Parabothus  amaokai  s  Demersal  76.67  Parabothus  coarctatus  s  Bathydemersal  10  s  Bathypelagic  10  macrops  s  Demersal  40  coregonoides  s  Pelagic  40  Parabrotula  plagiophlhalmus  Paraconger Paralepis Parapercis  dockinsi  s  Demersal  43.33  Parapercis  roseoviridis  s  Demersal  31.09  Parapristipomoides  squamimaxillaris  s  Reef-associated  39.4  Paraulopus Jilamentosus  s  Bathydemersal  31.09  Parupeneus  chrysonemus  s  Demersal  Parupeneus  multifasciatus  s  Reef-associated  37.06  Parupeneus  pleurostigma  s  Reef-associated  24.54  Parupeneus  porphyreus  s  Reef-associated  47.85  microphthalmus  s  Bathydemersal  26.6  Pentaceros  decacanthus  s  Bathydemersal  10  Pentaceros  japonicus  s  Benthopelagic  16.29  Pentaceros  quinquespinis  s  Pelagic  76.67  s  Demersal  s  Pelagic  s  Bathypelagic  10  s  Bathypelagic  10  s  Benthopelagic  48.86  48.86  s  Benthopelagic  52.3  52.3  s  Benthopelagic  21.45  Penopus  Phenacoscorpius Photonectes  dinema  Photostomias Photostylus  eschmeyeri  guernei pycnopterus  Phycis blennoides  '  Phycis phycis Physiculus dahvigki  10 16.29  \u000CPhysiculus  hexacytus  s  Bathydemersal  43.33  Physiculus  japonicus  s  Bathydemersal  40.69  Physiculus  longicavis  s  Benthopelagic  10  Physiculus  luminosus  s  Bathydemersal  21.45  Physiculus  parini  s  Bathypelagic  43.33  Physiculus  sazonovi  s  Bathypelagic  43.33  Physiculus  therosideros  s  Bathypelagic  10  Plagiogeneion  geminatum  s  Demersal  Plagiogeneion  unispina  s  Bathydemersal  10  s  Demersal  10  opalescens  s  Pelagic  69.99  Plectranthias  exsul  s  Demersal  43.33  Plectranthias  kelloggi  s  Demersal  10  Plectranthias  parini  s  Bathypelagic  Plagiopsetta  glossa  Platyberyx  Plectrogenium  barsukovi  s  Bathydemersal  Plectrogenium  nanum  s  Bathydemersal  s  Demersal  s  Bathypelagic  Pollachius  virens  Polyacanthonotus  challengeri  19.39  76.67  10 63.3 44.89  Polyipnus  clarus  s  Bathydemersal  Polyipnus  inermis  s  Bathypelagic  Polyipnus  kiwiensis  s  Benthopelagic  10  Polyipnus  matsubarai  s  Benthopelagic  31.09  10 43.33  Polymetme  andriashevi  s  Bathydemersal  10  Polymetme  corythaeola  s  Benthopelagic  . 17.32  Polymixia berndti  s  Reef-associated  Polymixia  s  Bathypelagic  21.45  s  Bathydemersal  .  s  Bathydemersal  44.76  s  Bathydemersal  10  s  Bathydemersal  34.81  s  Bathydemersal  90  s  Bathydemersal  40  s  Demersal  28.15  Demersal  60  japonica  Polymixia lowei Polymixia Polymixia  nobilis salagomeziensis  Polymixia yuri Polyprion  americanus  Pontinus kuhlii Pontinus  macrocephalus  Pontinus  tentacularis  Porogadus  miles  Poromitra  capito  s  .  40  10  s  Bathydemersal  21.45  s  Bathypelagic  22.97  \u000CPoromitra  crassiceps  s  Bathypelagic  10  Poromitra  megalops  s '  Bathypelagic-  10  s  Reef-associated  26.71  s  Reef-associated  24.54  s '  Pelagic  75.01  Priacanthus  macracanthus  Priacanthus  meeki  Prionace glauca  26.71 75.01  Pristipomoides a rgyro gram mic lis  ' s  Reef-associated  31.74  Pristipomoides auriciUa  . s  Reef-associated  31.21  31.21  Pristipomoides  filamentosus  s  Reef-associated  45.39  45.39  Pristipomoides  multidens  s  Demersal  66.85  66.85  Pristipomoides  sieboldii  s  Reef-associated  45.69  45.69  Pristipomoides  zonatus  s  Reef-associated  27.39  27.39  s  Benthopelagic  55.06  55.06  s  Bathydemersal  10  s  Bathypelagic  10  s  Bathypelagic  10  Psenes maculatus  s  Pelagic  19.75  Psenopsis  s  Benthopelagic  37.71  s  Reef-associated  32.47  s  Bathypelagic  s  Reef-associated  47.2  s  Bathydemersal  30.72  AGG  Bathydemersal  57.31  s  Pelagic  44.91  Promethichthys  prometheus  Protogrammus  sousai  Protomyctophum Psenes  thompsoni  cyanophrys  anomala  Pseudanthias  thompsoni  Pseudobathylagus Pseudocaranx  milleri  dentex  Pseudocetomtrus septifer Pseudocyttus  maculatus  Pseudopentaceros  pectoral is  10 47.2 72.9  Pseudopentaceros  richardsoni  AGG  Pelagic  40  Pseudopentaceros  wheeleri  AGG  Benthopelagic  60  S  Bathydemersal  79.18  79.18  S  Reef-associated  78.77  78.77  Pseudotriakis  microdon  Pteroplatytrygon  violacea  Pterycombus  brama  S  Pelagic  Pterygotrigla  picta  S  Bathydemersal  s  Benthopelagic  Pycnocraspedum  armatum  78.25  40 25.99  Pyramodon  parini  s  Benthopelagic  21.97  Pyramodon  ventralis  10  s  Benthopelagic  Raja brachyura  s  Demersal  70.35  70.35  Raja clavata  s  Demersal  75.51  75.51  Raja maderensis  s  Bathydemersal  70.94  Raja rhina  s  Bathydemersal  60  60  \u000CSpecies  S vs AGG  Habitat  Vi  Rajella bigelowi  S  Bathydemersal  40  Regalecus  glesne  s  Pelagic  90  Rexea antefurcata  s  Benthopelagic  49.13  Rexea  s  Benthopelagic  60.07  s  Bathypelagic  32.77  brevilineata  Rhadinesthes  decimus  V*  60.07  Rhinochimaera  atlantica  s  Bathydemersal  47.09  Rhinochimaera  pacijica  s  Bathydemersal  79.89  s  Bathypelagic  28.33  28.33  Rostroraja alba  s  Demersal  90  90  Rouleina attrita  s  Bathypelagic  40  Rouleina  maderensis  s  Bathypelagic  23.51  Ruvettus  pretiosus  s  Benthopelagic  s  Bathypelagic  77.15  s  Bathypelagic  18.57  s  Bathypelagic  18.35  s  Pelagic  48.46  Demersal  38.91 50.21  Rondeletia  loricata  Saccopharynx  ampullaceus  Sagamichthys  abei  Sagamichthys  schnakenbecki  Sarda sarda Satyrichthys  engyceros  S  Satyrichthys  quadratorostratus  s  Bathydemersal  Schedophilus  medusophagus  s  Pelagic  40  s  Benthopelagic  60  s  Reef-associated.  10  s  Pelagic  46.46  s  Benthopelagic  21.45  Schedophilus ovalis Schindleria Scomber  ,\u2022  praematura japonicus  Scombrolabrax  heterolepis  \u2022  90  Scopelarchus  guentheri  s  Bathypelagic  10  Scopeloberyx  opisthopterus  s  Bathypelagic  10  Scopelogadus  beanii  s  Bathypelagic  Scopelosaurus  harryi  s  Bathypelagic  17.9  Scopelosaurus  mauli  s  Bathypelagic  10  Scorpaena  azorica  s  Demersal  10  Scorpaena  loppei  s  Demersal  29.61  Scorpaena  neglecta  s  Demersal  29.17  Scorpaena  notata  s  Demersal  10  Scorpaena scrofa  s  Demersal  60.76  Scorpaena  s  Demersal  76.67 27.63  uncinata  Scorpaenopsis  oxycephala  s  Reef-associated  Scymnodalatias  garricki  s  Bathypelagic  18.57  48.46  46.46  17.9  60.76  \u000CScynmodon  obscurus  Scynmodon  ringens  Searsia koefoedi Sebastes  aleutianus  Sebastes alutus Sebastes aurora Sebastes  babcocki  Sebastes borealis Sebastes  brevispinis  Sebastes crameri Sebastes diploproa Sebastes elongatus Sebastes  entomelas  Sebastes flammeus Sebastes flavidus Sebastes  hehomaculatus  s s s s s s s s s s s s AGG  s s AGG  Benthopelagic  48.96  Bathypelagic  67.78  Bathypelagic  10  Bathydemersal  60  Bathydemersal  70.24  Bathydemersal  32.77  Demersal  46.52  46.52  Bathydemersal  66.56  66.56  Demersal  61.17  61.17  Demersal  61.99  61.99  Bathydemersal  50.36  50.36  Demersal  30.72  30.72  Pelagic  63.15  63.15  Bathydemersal  48.64  Reef-associated  67.15  67.15  Demersal  32.77  32.77  70.24  Sebastes iracundus  S  Bathydemersal  50.79  Sebastes maliger  S  Demersal  45.29  45.29  Sebastes  marinus  AGG  Pelagic  68.01  68.01  Sebastes mentella  AGG  Bathypelagic  67.81  67.74  Sebastes  miniatus  S  Reef-associated  60  60  Sebastes  nebulosus  Reef-associated  40  40  Sebastes  nigrocinctus  s s  Reef-associated  45.29  45.29  Sebastes  paucispinis  AGG  Reef-associated  55.24  60.87  Sebastes  pinniger  \u2022S  Reef-associated  46.3  46.3  Sebastes proriger  s s  Bathydemersal  42.54  42.54  Bathydemersal  56.28  56.28  Reef-associated  70.9  65.31  Sebastes reedi Sebastes  ruberrimus  AGG  Sebastes  variegatus  S  Demersal  29.69  29.69  s s s s s s s  Bathydemersal  53.03  53.03  Bathydemersal  30.72  30.72  Reef-associated  54.2  54.2  Reef-associated  43.62  43.62  Demersal  58.57  Demersal  33.64  Bathypelagic  52.22  Sebastolobus  alascanus  Sebastolobus  altivelis  Seriola dumerili Seriola lalandi Serranus atricauda Serranus cabrilla Serrivomer  beanii  33.64  \u000CSetarches  guentheri  s  Bathydemersal  16.29  Sladenia remiger  s  Bathydemersal  31.74  Sladenia shaefersi  s  Bathydemersal  Somniosus  microcephalus  s  Benthopelagic  90  90  Sonmiosus pacificus  s  Benthopelagic  90  90  Somniosus  s  Bathydemersal  90  s  Bathydemersal  78.07  rostratus  Spectrunculus  grandis  Sphagemacrurus  grenadae  s  Bathypelagic  16.29  Sphagemacrurus  hirundo  s  Bathydemersal  53.92  Sphoeroides  pachygaster  s  Demersal  32.26  Sphyraenops  bairdianus  s  Pelagic  Spiniphryne gladisfenae  s  Bathypelagic  Squaliolus  s  Bathypelagic  10  Squalogadus modificatus  s  Bathypelagic  26.6  Squalus  acanthias  s  Benthopelagic  79.42  79.42  Squalus blainville  s  Demersal  77.21  77.21  Stenobrachius  s  Bathypelagic  36.51  36.51  laticaudus  leucopsarus  60  Sternoptyx  diaphana  s  Bathypelagic  10  Sternoptyx  pseudobscura  s  Bathypelagic  10  s  Bathypelagic  76.67  Stomias afjinis  s  Bathypelagic  10  Stomias boa ferox  s  Bathypelagic  21.45  Styiephorus  s  Bathypelagic  19.39  s  Bathypelagic  10  s  Bathypelagic  60  s  Reef-associated  s  Bathypelagic  10  s  Demersal  10  s  Bathydemersal  26.6  s  Bathydemersal  10  s  Bathydemersal  90'  s  Bathydemersal  68.38  s  Bathydemersal  60  \u2022S. \u2022  Demersal  10  s  Reef-associated  19.39  s  Reel-associated  24.54  Stethopristes  eos  chordatus  Sudis atrox Sudis hyalina Sujjlamen  jraenatum  Symbolophorus Symphysanodon  veranyi maunaloae  Synagrops  japonicus  Synagrops  philippinensis  Synaphobranchus  ajjinis  Synaphobranchus  brevidorsalis  Synaphobranchus  kaupii  Synchiropus  phaeton  Synodus doaki Synodus synodus  29.69  \u000CSpecies  S vs AGG  Habitat  Vi  S  Bathypelagic  10  macropus  s  Bathypelagic  38.52  Talismania antiUarum  s  Bathypelagic  10  Talismania longifilis  s  Bathypelagic  40  s.  Pelagic  60  s  Bathypelagic  Taaningichthys Tactostoma  batliyphilus  Taractichthys  '  longipinriis  Thalassobathia  pelagica  Vi* 38.52  52.31  Thamnaconus  analis  s  Reef-associated  Thamnaconus  tessellatus  s  Bathydemersal  10  chalcogratnma  s  Benthopelagic  42.36  42.36  s  Reef-associated  51.79  51.79  Torpedo fairchildi  s  Bathydemersal  60  Torpedo  microdiscus  s  Demersal  76.67  Torpedo  nobiliana  s  Reef-associated  76.67  Torpedo  semipelagica  s  Demersal  76.67  Theragra Thunnus  albacares  Trachipterus  trachypterus  s  Bathypelagic  90  Trachonurus  sulcatus  s  Bathypelagic  40  Trachonurus  villosus  s  Bathypelagic  44.89  Trachurus  picturatus  s  Benthopelagic  67.44  Trachurus  symmetricus  Pelagic  60.41  Bathydemersal  44.89 \u2022  Trachyrincus  longirostris  s s  Trachyrincus  murrayi  s  Benthopelagic  90  Trachyrincus  scabrus  s  Bathydemersal  48.04  s s s  Bathydemersal  26.6  Bathydemersal  40  Reef-associated  40  s  Bathypelagic  10  Benthopelagic  27.89  Bathypelagic  47.74  Bathypelagic  10  Trachyscorpia  capensis  Trachyscorpia cristulata Triodon  echinata  macropterus  Tripterophycis  svetovidovi  Ventrifossa  macropogon  s s s s s s s  Ventrifossa  obtusirostris  s  Bathydemersal  21.45  Ventrifossa  teres  s  Bathydemersal  10  s  Bathypelagic  10  Trisopterus  minutus  Tubbia tasmanica Valenciennellus  tripunctulatus  Venefica procera Ventrifossa  johnboborum  Ventrifossa macrodon  Vinciguerria  nimbaria  Bathydemersal  67.05  Bathydemersal  57.76  Bathydemersal  31.74  Bathydemersal  40  76.67  60.41  48.04  27.89  \u000CSpecies  S us AGG  Habitat  V;  V*  Yarrella blackfordi  s s s s s  Bathypelagic  Zanclus cornutus  s  Reef-associated  10  Zaprora siiemts  s  Demersal  60  Zenion hololepis  s  Bathydemersal  10  Zenion leptolepis  s  Bathypelagic  10  Zenopsis conchifera  Benthopelagic  50.44  Bathydemersal  49.79  49.79  Zeus faber  s s s s  Benthopelagic  46.85  46.85  Zu cristatus  s  Bathypelagic  72.63  Xanthichthys  mento  Xenodermichthys  copei  Xenolepidichthys  dalgleishi  Xiphias gladius  Zenopsis  nebulosa  Zenopsis  oblongus  Reef-associated  55.21  Bathypelagic  10  Benthopelagic  10  Pelagic  73.5  73.5  35.98  Demersal  \u000CAPPENDIX 2 LIST OF LARGE AND SMALL SEAMOUNTS IDENTIFIED IN THE AZORES ECONOMIC EXCLUSIVE ZONE Table 1 - Seamounts characteristics: number; category (large and small); location; depth of the summit (m); seamount height (h in m); basal area (at, in km ); height to radius ratio ($.); the average slope ((/&gt; in degrees); distance to nearest large seamount (dL in km, for large seamounts only); and distance to nearest seamount (d in km). N  Category  Location  Depth  h  Ub  Longitude Latitude  (m)  (m)  Km2  \u00A3  &lt;t&gt; degrees  dL km  N  d  N  km  1  Large  -24.4850  37.3610  821 1252 1291 0.10  3.41  21.4 15 21.4  2  Large  -25.8794  37.6032  265 1050  974 0.09  4.71  48.4 19 18.2 100  3  Large  -29.5534  37.9873  364 1161  895 0.11  6.57  33.3 28 27.9 111  4  Large  -26.1300  38.5718  1154 1038  964 0.08  3.43  31.6 33 31.6  5  Large  -29.8791  39.6824  886 1020  796 0.10  3.23  60.9 39 19.7 142  6  Large  -26.9316  39.7074  756 1125 1159 0.09  3.84  69.4 40 29.5 146  7  Large  -25.7292  36.6179  1957 1144 1180 0.11  3.09  62.7 11 13.9  84  8  Large  -26.1049  37.1022  1048 1721 1004 0.15  8.00  50.8 11 12.6  93  9  Large  -28.1840  37.1690  1646 1141  654 0.10  3.80  18.0 12 18.0  12  10  Large  -31.4322  37.1774  413 1305 1295 0.09  2.10  71.1 57 25.0 174  11  Large  -25.6540  37.1773  270 2040  916 0.16  9.27  50.8  8 21.7  95  12  Large  -28.0504  37.2609  1478 1148 1114 0.08  4.09  18.0  9 18.0  9  13  Large  -24.7605  37.2609  65 1754 1054 0.12  8.32  32.6  1 32.6  1  14  Large  -25.0945  37.3945  195 1835 1022 0.13  7.99  40.0 13 40.0  13  15  Large  -24.3764  37.5197  1063 1086 1114 0.09  3.68  21.4  1 21.4  1  16  Large  -26.5725  37.5281  1265 1079  947 0.10  5.47  40.3 19 19.1  97  17  Large  -28.9689  37.7368  309  956 1062 0.08  2.74  35.7 23 35.7  23  18  Large  -30.6723  37.7535  735 1088  5.07  18.8 20 18.8  20  19  Large  -26.2886  37.7535  816 1448 1264 0.13 .  7.05  40.3 16 12.2 107  20  Large  -30.8394  37.7786  679 1096  6.20  18.8 18 18.8  596 0.11 568 0.10  15  33  18  \u000C21  Large  -31.4990  37.8287  824 1129 1009 0.11  3.88  31.8 22 13.5 181  22  Large  -31.2151  37.8621  707 1454  903 0.14  7.55  31.8 21 18.6 181  23  Large  -28.6851  37.8871  408 1297  557 0.12  7.24  35.7 17 23.4 182  24  Large  -30.3384  38.2044  592 1354 1150 0.11  3.86  27.0 27  25  Large  -26.6143  38.2211  390 1760  964 0.14  7.81  42.8 26 30.6 110  26  Large  -26.2301  38.2462  650 1747 1156 0.14  4.97  37.9  27  Large  -30.1046  38.2712  730 1166 1013 0.11  4.78  27.0 24 17.1 188  28  Large  -29.4366  38.2629  549 1134 1144 0.10  3.91  33.3  29  Large  -27.7832  38.2629  473 1103 1075 0.10  5.81 108.6 23 16.4 190  30  Large  -30.6389  38.2963  485 1414 1009 0.15  5.14  34.9 24 26.2 118  31  Large  -31.0231  38.3213  712 1197 1106 0.09  3.91  42.8 30 25.2 185  32  Large  -29.0357  38.5468  261 1269  718 0.11  7.35  39.2 51 23.3 189  33  Large  -25.8460  38.5885  1264 1260 1015 0.09  5.47  31.6  4 31.6  4  34  Large  -26.6143  38.6887  468  4.53  52.0 25 52.0  25  35  Large  -30.2298  38.6971  671 1055  798 0.11  4.24  27.2 36 15.0 191  36  Large  -29.9876  38.7305  380 1251  945 0.11  5.48  27.2 35 13.3 191  37  Large  -29.8875  38.9893  438 1069 1079 0.10  4.55  30.8 36 14.7 192  38  Large  -31.8831  39.3818  556 1223  6.21 151.7 31 43.6 128  39  Large  -29.3363  39.6072  603 1128 1144 0.09  5.24  60.9  40  Large  -26.6894  40.2835  705 1716 1095 0.12  6.11  29.1 43 29.1  41  Large  -30.1714  40.3587  661 1183  799 0.17  4.41  51.0 42 20.8 209  42  Large  -29.7121  40.3671  958 1447  839 0.13  5.93  38,6 44 34.0 209  43  Large  -26.9315  40.3838  804 1297  994 0.09  5.37  18.0 45 18.0  44  Large  -29.3698  40.4255  570 1884  790 0.23  5.61  36.6 47 35.3 207  45  Large  -27.0234  40.5174..  985 1227  991 0.11  5.89  18.0 43 18.0  46  Large  -28.8938  40.5424  896 1247  891 0.13  7.61  54.5 44 12.6 211  47  Large  -29.5034  40.7261  888 1511  921 0.17  5.57  22.3 48 22.3  48  48  Large  -29.7038 . 40.7428  75 f 1562  990 0.15 '  5.54  22.3 47 22.3  47  49  Large  -28.4346  40/8764  1230 1250  766 0.09  5.46  63.1 46 29.5 163  968 1065 0.09  906 0.09  9.3 118  4 10.4 119 3 32.4 111  5 25.8 144 43  45 43  \u000CN  Category  Location  Depth  h  ah  Longitude Latitude  (m)  (m)  Km 2  \u00A3  *  degrees  dL  N  km  d  N  km  50  Large  -29.2361  37.9873  206  625  796 0.06  2.86  35.3  51  Large  -28.9940  38.1961  204  876  961 0.07  4.06  35.5 50 17.8 116  52  Large  -26.7562  36.5260 . 2226 1344  940 0.10  7.64  96.6  53  Large  -27.5112  .34.8413  1241 1858 1048 0.17 ' . 5.75 107.8 54 37.1 239  54  Large  -27.0457  35.6925  2668 1220  832 0.14  8.13  .55  Large  -24.7980  35.9452  3176 1130  515 0.15  5.28 127.6  56  Large  -33.0307  36.7831  1146 1247  883 0.17  3.91  66.5 62 51.6 282  57  Large  -32.0598  37.3018  1057 1088 1040 0.12  3.67  51.9 58 37.4 176  58  Large  -32.0997  37.7673  3.46  51.9 57 29.6 105  59  Large  -34.5469 . 38.3259  2402 1062 1059 0.10  3.86 169.3 62 22.4 311  60  Large  -30.9426  41.5312  1080 1181 1045 0.11  5.14 155.9 41 29.7 365  61  Large  -29.1870  42.4090  1306 1103  4.37 189.7 49 29.1 390  62  Large  -33.4829  37.2353  606 1017 1083 0.10  4.39  51.4 63 51.4  63  Large  -33.0440  37.3816  377 1114  959 0.11  5.64  51.4 62 50.2 299  64  Small  -31.7161  36.1085  2377  466  606 0.04  1.99  20.0  68  65  Small  -28.5515  36.1503  2927  446  672 0.04  2.09  44.9  71  66  Small  -29.9877  36.2505  2798  414  535 0.04  1.47  39.1  76  67  Small  -24.3681  36.2505  3135  751  1004 0.06  2.25  38.6 269  68  Small  -31.7578  36.2839  2459  443  258 0.06  1.90  20.0  64  69  Small  -29.1193  36.4008  2949  578  487 0.06  2.46  34.9  72  70  Small  -29.6036  36.4175  3023  330  633 0.04  ' 1.57  22.1  72  71  Small  -28.3010 -36.4676  2777  506  917 0.04  1.76  22.4  78  72  Small  -29.4198  36.4927  3019  383  518 0.07  1.82  22.1  70  73  Small  -26.3137  36.551 1  2249 1214 1001 0.09  5.57  36.3 169  74  Small  -30.8393  36.5678  2337  495 1264 0.04  2.15  39.4  89  75  Small  -26.8647  36.5595  2530 1012 1054 0.09  5.08  12.6  52  76  Small  -29.9543  36.6012  2666  540 1096 0.04  2.24  30.1 172  77  Small  -27.0151  36.6012  2621  967  567 0.09  5.75  17.3  75  78  Small  -28.3845  36.6513  2828  476  689 0.05  2.33  22.4  71  989 1202  921 0.14  758 0.13  3  7.7 111  8 12.6  75  98.0 52 24.5 266 7 30.1 267  63  \u000CN  Category  Location  Depth  h  ab  Longitude Latitude  (m)  (m)  Km2  \u00A3  dL degrees  km  N  d  N  km  79  Small  -27.1987  36.6513  3080  544  455 0.06  2.51  21.2  77  80  Small  -27.4826  36.6597  2918  659  576 0.06  3.19  31.6  79  81  Small  -30.3133  36.6763  2683  320  820 0.03  1.76  17.0 172  82  Small  -27.8167  36.6847  2749  655  912 0.06  3.25  32.2  83  Small  -28.1005  36.7432  2595  648  602 0.06  4.07  15.9 173  84  Small  -25.7208  36.7432  1991  918 1174 0.09  4.44  7.1  86  85  Small  -29.1026  36.7682  3155  332  345 0.04  1.62  32.9  88  86  Small  -25.6958  36.8016  1881  982  839 0.12  4.17  7.1  84  87  Small  -30.3551  36.8350  2574  452  500 0.04  2.62  16.2  91  88  Small  -28.8187  36.8517  2659  547  676 0.07  2.51  32.9  85  89  Small  -30.6807  36.8851  2364  414  382 0.05  2.49  21.4  91  90  Small  -25.7292  36.8851  2088  866  472 0.11  4.89  10.0  86  91  Small  -30,4887  36.8935  2492  373  611 0.04  2.57  16.2  87  92  Small  -31.7579  37.0271  915  902  747 0.08  '6.14  93  Small  -26.1968  37.1690  1209 1406 1019 0.13  6.27  12.6  94  Small  -30.0545  37.2191  1900  771 0.06  2.48  50.5 102  95  Small  -25.7208  37.3610  393 1203 1114 0.11  6.03  16.0 100  96  Small  -31.2235  37.3861  678 1142  883 0.09  6.42  10.0  99  97  Small  -26.4890  37.3778  842 1000 0.07  4.62  19.1  16  98  Small  -30.9730  37.4028  816 1047  995 0.09  4.52  18.6  99  99  Small  -31.1400  37.4195  710 1132  797 0.11  6.46  10.0  96  100 Small  -25.8377  37.4445  393 1288 1175 0.12  5.02  16.0  95  101 Small  -26.7979  37.5364  1577  711 0.08  4.02  25.1  16  102 Small  -30.3217  37.5865  994  790 1298 0.07  2.20  26.4 106  103 Small  -28.1507  37.6032  1986  463  504 0.08  2.64  18.2 177  104 Small  -27.0986  37.6199  1729  707  600 0.08  3.15  34.7 101  105 Small  -31.8414  37.7034  1205  915  621 0.10  5.56  13.9 179  106 Small  -30.1380  37.7368  881  730 1020 0.09  3.80  26.4 102  107 Small  -26.1800  37.7368  919 1377  4.63  12.2  1618  688  857  993 0.15  83  15.0 174 8  19  \u000CN  Category  Location  Depth  h  ah  Longitude Latitude  (m)  (m)  Km2  108 Small  -29.7539  37.7535  804  109 Small  -31.8079  37.8537  110 Small  -26.6226  111 Small  &lt;t&gt; degrees  dL km  N  d  N  km  640 0.09  2.60  34.2  1014  959 1067 0.09  4.33  17.1 105  37.9456  1303  818 1230 0.06  3.20  30.6  -29.3030  38.0040  131  756  837 0.08  3.86  7.7. 50  112 Small  -27.4576  37.9957  1047  767 1174 0.06  2.60  29.6 117  113 Small  -28.4262  38.0124  1203  751  993 0.07  2.75  19.9 184  114 Small  -27.1653  38.0708  1068  812  905 0.07  4.29  13.7 186  115 Small  -31.6326  38.1126  1182  843  449 0.10  4.24  34.7 109  116 Small  -29.1360  38.1209  234  727  639 0.07  3.76  17.8  51  117 Small  -27.6580  38.1710  644  902 1012 0.09  5.15  17.3  29  118 Small  -30.4219  38.2044  866 1208 1060 0.10  4.36  9.3  24  119 Small  -26.1466  38.2044  960 1374 1082 0.13  6.12  10.4  26  120 Small  -28.8520  38.2796  502  613  344 0.09  4.37  13.9 189  121 Small  -31.5992  38.4382  962  635 1084 0.05  1.99  36.4 115  122 Small  -27.3741  38.4633  607  850  631 0.07  5.08  12.6 123  123 Small  -27.4659  38.5301  674  820  646 0.09  4.01  12.6 122  124 Small  -24.0759  38.5885  3302  389  521 0.04  2.21  15.8 316  125 Small  -29.3531  38.6971  1427  485  764 0.04  2.45  38.6 129  126 Small  -31.1483  38.8307  1149  740 1129 0.05  2.94  58.3  127 Small  -30.4803  38.8474  1140  735  738 0.11  3.50  19.7 193  128 Small  -31.7245  39.0227  965  502 1234 0.04  2.13  43.6  129 Small  -29.4950  39.0144  1514  489  510 0.06  2.40  24.7 196  130 Small  -27.5161  39.0561  753  831  688 0.08  4.70  26.5 131  131 Small  -27.3240  39.1981  882  527 1166 0.04  1.56  26.5 130  132 Small  -29.2862  39.2231  1437  568  544 0.06  2.37  14.9 196  133 Small  -28.5013  39.2231  1064  966  837 0.07  5.39  34.4 137  134 Small  -29.6119  39.2816  1067  909  684 0.12  5.10  13.2 199  135 Small  -29.7538  39.3066  967  881  690 0.14  4.29  10.5 199  \u2022 2:46  13.3 196  136 Small  -29.3698  39.3316  1552  639  \u00A3  468  660 0.07  ;  3 25  31 38  \u000C137 Small  . . -28.6433  39.4987  1217  777  706 0.06  3.67  34.4 133  138 Small  -30.5471  39.5320  1403  526  911 0.04  2.38  42.8 202  139 Small  -29.0023  39.6323  1226  730  611 0.08  3.19  37.2  140 Small  -29.6203  39.6657  1200  784  779 0.08  4.47  12.1 203  141 Small  -24.3180  39.6657  3195  537 1204 0.04  2.16  46.9 149  142 Small  -30.0545  39.7074  985  734  643 0.09  3.94  15.4 202  143 Small  -30.2131  39.7659  1210  636  720 0.07  2.63  13.5 202  144 Small  -29.4533  39.8076  1177  718  551 0.12  2.82  13.4 145  145 Small  -29.3865  39.9078  1242  615  253 0.09  3.45  13.4 144  146 Small  -27.0401  39.9496  1122  825  974 0.06  4.03  18.8 206  147 Small  -29.2612  40.0414  1245  503  632 0.06  3.17  15.8 207  148 Small  -25.9630  40.0581  2809  293  560 0.03  1.31  60.0 154  149 Small  -24.1844  40.0665  3260  420  985 0.03  1.58  46.9 141  150 Small  -30.0043  40.1082  1257  472  529 0.07  2.20  18.6 209  151 Small  -25.2115  40.1082  2893  391 1013 0.03  1.52  44.8 154  152 Small  -24.6270  40.1917  3249  370 1015 0.03  1.59  36.5 157  153 Small  -28.0504  40.3086  1574  579  755 0.06  2.64  16.4 208  154 Small  -25.5204  40.3671  2730  385 1100 0.04  1.29  44.8 151  155 Small  -28.6600  40.4840  1554  793  488 0.08  4.70  21.4 156  156 Small  -28.4679  40.4923  1943  505  531 0.05  2.72  21.4 155  157 Small  -24.5852  40.5174  3217  387 1055 0.03  1.65  36.5 152  158 Small  -30.1046  40.5424  1497  641  739 0.07  2.93  21.7  41  159 Small  -29.0524  40.5424  1062 1153  512 0.10  7.60  17.6  46  160 Small  -30.3884  40.5758  1455  784  880 0.07  ' 2.48  20.0 212  161 Small  -30.6724  40.5925  1792  590  517 0.06  2.43  12.4 162  162 Small  -30.6974  40.7010  1918  561  415 0.06 . ; 2.66  12.4 161  163 Small  -28.1757  40.8180  1926  798  921 0.06  4.45  29.5  164 Small  -30.3217  40.9432  1607 , 891  387 0.11  4.92  38.6 359  165 Small  -25.4870  36.1586  3286  1.75 ,  49.0 268  574 1073 0.04 '  39  49  \u000C166 Small  . -24.7522  36.2422  3063  681 1264 0.05  1.64  33.4  55  167 Small  -27.3324  36.4008  2960  430 1114 0.05  1.30  31.5  79  168 Small  -28.7519  36.5177  3174  294  612 0.03  1.61  37.8  88  169 Small  -25.9880  36.5761  2591.  843  867 0.06 '  3.80 '  29.1  7  170 Small  -24.9777  36.6680  2352  939  500 0.09  5.68  40.4 171  171 Small  -24.6186  36.7264  2903  584  669 0.05  2.99  40.4 170  172 Small  -30.1797  36.7515  2788  341  574 0.04  1.55  17.0  81  173 Small  -28.1172  36.8851  2774  626  303 0.08  3.24  15.9  83  174 Small  -31.6410  37.0939  935  774  592 0.10  4.05  15,0  92  175 Small  -27.0401  37.1940  2225  336  593 0.03  1.82  46.6 101  176 Small  -31.7245  37.2776  1140  832  490 0.09  4:52  22.4 174  177 Small  -28.1089  37.4445  2105  315  734 0.04  1.96  18.2 103  178 Small  -30.7141  37.5281  923  811 1045 0.07  2.13  25.5  179 Small  -31.7161  37.7118  1265  776  593 0.10  4.61  13.9 105  180 Small  -24.7355  37.7118  1564  826  701 0.10  4.06  45.2  15  181 Small  -31.3821  37.8621  1090  862  900 0.10  4.04  13.5  21  182 Small  -28.8020  38.0625  623  670  670 0.07  3.41  23.4  23  183 Small  -29.8206  38.1209  1570  542  590 0.06  2.51  19.7 188  184 Small  -28.5682  38.1209  930  641  479 0.06  3.49  19.9 113  185 Small  -30.8895  38.1376  1350  842  351 0.11  4.66  25.2  186 Small  -27.0568  38.1293  941  901  701 0.08  4.91  13.7 114  187 Small  -31.2402  38.2044  1434  682  933 0.09  3.75  27.4  31  188 Small  -29.9542  38.2378  1261  777  656 0.10  3.95  17.1  27  189 Small  -28.9523  38.3547  488  797  451 0.10  4.79  13.9 120  190 Small  -27.8834  38.3714  376 1011  774 0.11  6.22  16.4  29  191 Small  -30.0962  38.6804  819  855 1155 0.07  3.71  13.3  36  192 Small  -29.9960  38.9141  787  644  805 0.08  3.21  14.7  37  193 Small  -30.4553  39.0227  1520  346  151 0.07  2.01  19.7 127  194 Small  -27.7498  39.1396  1052  566  584 0.06  3.00  27.6 130  \u2022  18  31  \u000CN  Category  Location  Depth  h  \u00AB/&gt;  Longitude Latitude  (m)  (m)  Km2  \u00A3  &lt;t&gt; degrees  dL km  N  d  N  km  195 Small  -30.3634  39.1814  1330  608  725 0.06  3.67  20.4 193  196 Small  -29.4199  39.2231  1472  572  915 0.06  1.83  13.3 136  197 Small  -28.8604  39.2315  1358  566  868 0.04  2.80  38.3 137  198 Small  -29.8457  39.3316  1218  672  569 0.07  3.71  10.6 135  199 Small  -29.6870  39.3734  1136  812  724 0.11  5.06  10.5 135  200 Small  -27.0234  39.4319  1001  689  889 0.05  3.52  32.3  201 Small  -30.0377  39.5320  1154  797  297 0.10  5.43  19.6 142  202 Small  -30.1797  39.6489  1309  561  537 0.10  3.10  13.5 143  203 Small  -29.5785  39.7659  1245  654  992 0.09  4.18  12.1 140  204 Small  -26.7645  39.9579  1807  555  614 0.05  1.88  30.6 146  205 Small  -29.8791  39.9663  1381  623  822 0.13  1.68  21.0 150  206 Small  -27.1821  40.0414  1420  548  746 0.05  2.83  18.8 146  207 Small  -29.3865  40.1082  1227  682  747 0.09  2.09  15.8 147  208 Small  -28.0087  40.1667  1852  242  795 0.02  1.30  16.4 153  209 Small  -30.0043  40.2752  1089  585  580 0.06  2.96  18.6 150  210 Small  -30.8978  40.4255  1874  350  773 0.03  1.46  31.2 161  211 Small  -28.8604 .40.4338  1392  824 , 976 0.08  2.97  12.6  212 Small  -30.5555  40.6426  2009  394  742 0.05  2.05  14.1 161  213 Small  -29.0942  40.7511  1552  797  794 0.08  2.44  23.7 159  214 Small  -25.6540  40.8513  3009  382  679 0.03  1.98  46.3 361  215 Small  -24.4522  33.7374  4727  660  664 0.07  2.82  28.2 216  216 Small  -24.6384  33.9103  4722  522  834 0.07  2.04  11.9 218  217 Small  -26.8462  33.9635  3775  749  393 0.10  3.92  36.2 219  218 Small  -24.5852  34.0034  4683  547  871 0.07  2.19  11.9 216  219 Small  -26.5270  34.0300  4266  467  313 0.07  1.91  36.2 217  220 Small  -24.0000  34.0300  4624  551  948 0.06  2.18  34.5 221  221 Small  -24.2660  34.1896  4688  611  803 0.06  2.57  33.0 230  222 Small  -22.8562  34.2428  4713  591  800 0.09  3.71  58.0 237  223 Small  -24.8512  34.2960  4843  539  528 0.08  3.64  29.1 232  6  46  \u000C224 Small  -26.2477  34.3359  3959  742  708 0.08  4.30  46.0 219  225 Small  -25.3965  34.3625  4501  794  966 0.07  4.19  28.0 228  226 Small  -24.5586  34.4290  4596  762  498 0.11  4.06  23.8 232  227 Small  -23.6276  34.4157  5005  241  541 0.03  1.29  18.3 229  228 Small  -25.1571  34.4423  4953  398  555 0.05  3.02  28.0 225  229 Small  -23.4680  34.4556  4722  507  606 0.06  2.49  18.3 227  230 Small  -24.2128  34.4822  4428  785  926 0.08  3.12  14.0 233  231 Small  -24.0000  34.5088  4751  474  781 0.07  3.19  23.8 230  232 Small  -24.7448  34.5354  4664  618  644 0.08  3.49  23.8 226  233 Small  -24.3325  34.5221  4714  558  618 0.07  3.30  14.0 230  234 Small  -23.2818  34.5487  4502  702  736 0.09  3.51  23.1 229  235 Small  -26.7930  34.6152  3760  541  512 0.06  2.60  68.1 224  236 Small  -25.8620  34.6684  4313  651  449 0.09  3.58  44.7 240  237 Small  -23.0291  34.7349  4203  999  539 0.12  \u2022 4.35  34.9 234  238 Small  -22.0050  34.8280  4587  573 1002 0.09  3.62  39.8 244  239 Small  -27.8437  34.8679  1945  878  905 0.08  2.50  37.1  240 Small  -25.5295  34.8945  4125  574 1002 0.06  1.92  44.7 236  241 Small  -22.6434  34.9344  4528  519  836 0.05  1.94  48.3 237  242 Small  -24.5320  35.0408  4036  841 1014 0.07  3.37  60.9 232  243 Small  -23.8670  35.0275  4146  737  697 0.11  4.15  47.3 248  244 Small  -21.7656  35.0940  4604  505  948 0.06  3.28  39.8 238  245 Small  -25.1970  35.2802  4021  576  831 0.05  2.44  29.1 249  246 Small  -26.8595  35.3201  3664  501  747 0.05  2.70  19.3 251  247 Small  -22.1114  35.3334  4620  368  919 0.03  1.62  23.8 254  248 Small  -23.6010  35.3600  3971  895 1038 0.08  4.53  47.3 243  249 Small  -24.9576  35.3866  3838  745  755 0.08  4.06  24.5 257  250 Small  -22.6567  35.4265  4218  668  514 0.08  3.02  44.4 263  251 Small  -26.9526  35.4664  3440  635  665 0.07  2.79  19.3 246  252 Small  -29.6658  35.5196  2912  564  385 .0.08  3.20  46.0 272  53  \u000C253 Small  -26.4073  35.5329  3290  709  729 0.08  3.30  20.9 259  254 Small  -22.1380  35.5462  4541  427  591 0.05  2.21  23.8 247  255 Small  -26.7531  35.5595  3255  757  603 0.09  4.36  21.5 260  256 Small  -26.0748  35.5994  3688  403  943 0.04  1.68  37.7 253  257 Small  -24.8645  35.5861  3710  636  964 0.06  2.40  24.5 249  258 Small  -27.5378  35.6526  3075  484  810 0.07  2.17  27.3 265  259 Small  -26.4339  35.7191  3333  767  657 0.12  3.66  10.5 261  260 Small  -26.6999  35.7457  3136  749  522 0.10  4.38  21.5 255  261 Small  -26.5004  35.7856  3105  883  653 0.12  4.26  10.5 259  262 Small  -23.0823  35.7723  3686  924  546 0.10  4.84  19.9 271  263 Small  -22.6833  35.8255  4258  383  406 0.06  1.88  23.4 275  264 Small  -29.4131  35.8521  2779  515  924 0.05  2.31  25.3 272  265 Small  -27.4846  35.8920  3004  672  520 0.07  2.73  27.3 258  266 Small  -27.1388  35.8920  3156  765  499 0.11  3.61  24.5  267 Small  -24.5320  35.8920  3810  555  599 0.09  2.26  22.2 269  268 Small  -25.1172  35.9186  3561  615  910 0.06  2.69  35.6  269 Small  -24.3325  35.9053  3963  527  703 0.07  2.35  22.2 267  270 Small  -23.5212  35.9186  4037  487  916 0.06  2.11  31.1 274  271 Small  -23.2020  35.9053  3976  549  802 0.08  2.56  19.9 262  272 Small  -29.6259  35.9319  2800  497  884 0.05  1.92  25.3 264  273 Small  -24.0665  35.9319  3950  599 1018 0.06  2.66  29.6 274  274 Small  -23.8005  35.9319  3955  644  644 0.08  3.16  29.6 273  275 Small  -22.7498  36.0250  3782  903  921 0.08  3.54  23.4 263  276 Small  -22.9626  36.0782  4088  498  639 0.06  2.30  24.4 275  277 Small  -21.8720  36.0782  4026  767  762 0.08  3.45  33.7 281  278 Small  -21.1006  36.1314  4137  791  878 0.11  3.32  22.5 280  279 Small  -32.2726  36.1580  2179  455  596 0.07  2.55  58.9  280 Small  -21.2868  36.2112  4422 ^ 386 1010 0.04  2.10  22.5 278  281 Small  -21.6060  36.2245  4296  2.45  33.7 277  461  406 0.06  54 55  68  \u000C282 Small  -32.9243  36.3309  1823  383  646 0.06  1.39  51.6  56  283 Small  -23.6276  36.3309  3682  732  568 0.09  3.82  27.0 284  284 Small  -23.3882  36.3708  3866  486  671 0.11  1.97  23.4 287  285 Small  -23.1488  36.3841  3750  703  8.43- 0.07  2.9.7 ,  26.6 284  286 Small  -22.7498  36.4506  3841  657 ' 897 0.07  2.98  44.9 285  287 Small  -23.4547  36.5703  3840  347  494 0.06  1.84  23.4 284  288 Small  -23.6941  36.6501  3454  717  900 0.07  2.61  28.0 287  289 Small  -23.1488  36.7964  3691  627  632 0.07  3.20  37.3 294  290 Small  -22.4971  36.9161  3742  679  563 0.11  3.69  56.8 296  291 Small  -21.8720  36.9294  3448  748  730 0.09  4.49  24.4 292  292 Small  -21.6592  36.9826  3443  651  805 0.08  2.57  24.4 291  293 Small  -20.9410  37.0092  3595  708  932 0.08  2.87  25.8 297  294 Small  -23.3350  37.0757  2898  898  788 0.09  3.42  37.1 295  295 Small  -23.6675  37.1023  2956  959  890 0.17  1.23  37.1 294  296 Small  -22.0981  37.2353  3627  525  851 0.05  1.88  29.6 298  297 Small  -20.8878  37.2353  3086  935  671 0.11  4.20  25.8 293  298 Small  -22.2577  37.4481  3890  463  698 0.07  2.49  29.6 296  299 Small  -32.7115  1196  689  702 0.10  3.12  50.2  300 Small  37.6875 \u2022s. -34.2144 .37.8604  2202  862 1158 0.07  2.70  45.2 302  301 Small  -21.9784  37.8870  3641  775  548 0.09  4.45  36.1 306  302 Small  -34.6134  37.9402  2451  892  821 0.09  4.11  28.4 304  303 Small  -33.6558  37.9402  2098  481  636 0.06  2.74  50.5 312  304 Small  -34.8661  37.9801  2756  959  392 0.17  7.17  28.4 302  305 Small  -23.2552  38.0200  3551  419  756 0.06  1.83  27.2 307  306 Small  -21.7124  38.0732  3866  592  720 0.06  2.86  36.1 301  307 Small  -23.4813  38.1131  3423  360  507 0.05  1.46  27,2 305  308 Small  -21.0607  38.1397  3973  658  588 0.07  3.44  39.9 313  309 Small  -35.1986  38.2860  3420  635  730 0.09  2.56  50.2 304  310 Small  -34.1346  38.2860  2294  873  878 0.09  3.45  23.7 311  63  \u000C311 Small  -34.3474  38.2993  2551  807  980 0.08  2.10  22.4  312 Small  -33.8154  38:3658  2260  484  859 0.06  1.78  36.6 310  313 Small  -21.3400  38.3658  3900  739  791 0.08  3.31  39.9 308  314 Small  -23.4680  38.4190  3480  368  697 0.05  1.67  34.0 307  315 Small  -21.6725  38.6451  3757  706  951 0.09  2.92  48.3 313  316 Small  -23.9601  38.6717  3437  344  503 0.05  1.25  15.8 124  317 Small  -22.2976  38.8313  3846  415  960 0.05  1.65  40.4 320  318 Small  -35.1321  38.9111  3605  307  910 0.03  1.02  43.1 326  319 Small  -23.6010  38.9643  3175  684  632 0.08  3.34  28.7 322  320 Small  -22.6301  38.9776  3575  710 1158 0.08  3.16  34.1 328  321 Small  -34.3873  39.0175  2935  562  699 0.07  2.63  65.6 326  322 Small  -23.8537  39.0175  3311  481  714 0.07  2.16  28.4 327  323 Small  -22.9892  39.1106  3399  697  741 0.07  2.70  34.5 328  324 Small  -32.8046  39.1904  1617  422 \u2022 458 0.05  2.19  71.2 332  325 Small  -23.4813  39.2037  3314  648  686 0.08  3^70  28.4 327  326 Small  -34.9326  39.2436  3456  486  828 0.09  2.02  43.1 318  327 Small  -23.7340  39.2436  3305.  601  797 0.06  2.94  28.4 322  328 Small  -22.7232  39.2702  3393  766  750 0.07  3.34  34.1 320  329 Small  -22.1646  39.4830  3832  645  795 0.08  3.30  25.9 331  330 Small  -35.4646  39.6160  3890  355  831 0.03  1.44  33.7 335  331 Small  -21.9651  39.6027  4009  415  476 0.06  2.47  25.9 329  332 Small  -32.3790  39.6692  1523  419 1078 0.04  1.92  63.7  333 Small  -34.1213  39.7357  3314  383  700 0.04  1.72  334 Small  -22.4040  39.7357  3591  821  681 0.12  3.32  15.9 336  335 Small  -35.2252  39.8022  3682  618  738 0.07  2.97  33.7 330  336 Small  -22.2710  39.7889  3627  801  483 0.10  4.67  9.0 337  337 Small  -22.1912  39.8022  3647  878  169 0.22  4.52  9.0 336  338 Small  -33.5627  39.8421  2909  615  572 0.07  2.60  27.3 340  339 Small  -22.0582  39.8554  3791  708  718 0.10  4.42  15.9 337  '.'  59  38  63.2 338  \u000C340 Small  -33.3233  39.8953  3071  351  448 0.05  1.55  27.3 338  341 Small  -23.6409  39.9485  3237  590  916 0.07  3.48  51.6 350  342 Small  -22.6301  40.0150  3747  539  959 0.06  2.33  13.4 343  343 Small  -22.5104  40.0017  3770  476  843 0.05  1.87  13.4 342  344 Small  -23.0424  40.0549  3646  563  977 0.11  2.81  19.2 345  345 Small  -22.8828  40.1214  3468  780  625 0.10  3.93  19.2 344  346 Small  -33.6159  40.1347  3070  589  621 0.07  3.48  33.0 338  347 Small  -33.3366  40.3608  2831  592  781 0.07  2.35  39.9 346  348 Small  -32.8844  40.3608  2514  569  943 0.05  2.44  50.2 347  349 Small  -34.7730  40.3874  3779  423  968 0.04  1.78  38.9 351  350 Small  -23.7473  40.4007  3665  521  832 0.05  2.25  51.6 341  351 Small  -35.1188  40.4406  3690  674  589 0.07  3.50  38.9 349  352 Small  -34.5868  40.7066  3533  471  844 0.05  2.15  41.1 349  353 Small  -33.3366  40.7598  3055  491  608 0.05  2.70  44.3 347  354 Small  -31.3948  41.0258  2031  456  614 0.05  2.18  42.3 363  355 Small  -24.0798  41.0258  3472  338  905 0.04  1.32  78.7 350  356 Small  -24.8911  41.0657  3200  384  840 0.04  1.21  69.8 157  357 Small  -28.2294  41.1056  2096  655  670 0.08  3.06  31.1 358  358 Small  -27.9501  41.0923  2029  639 1034 0.06  2.21  31.1 357  359 Small  -30.5702  41.1854  2109  491  457 0.07  1.76  38.6 164  360 Small  -29.5860  41.2120  1429  599  905 0.08  1.90  45.9 362  361 Small  -25.7689  41.2519  2937  551  905 0.05  1.94  46.3 214  362 Small  -29.1870  41.3184  1414  645  859 0.08  3.56  45.9 360  363 Small  -31.1687  41.3317  1612  723  832 0.07  3.47  33.5  364 Small  -26.3408  41.4780  2598  439  456 0.05  2.01  41.9 370  365 Small  -30.6766  41.5578  1506  766  894 0.08  3.55  29.7  366 Small  -34.6134  41.6110  3667  412  789 0.04  2.41  24.5 369  367 Small  -25.6758  41.6642  3055  422  802 0.05  2.85  43.5 370  368 Small  -28.3092  41.6908  2046  553  849 0.06  2.62  50.3 375  60 60  \u000C369 Small  -34.4139  41.7041  3612  347  723 0.04  1.44  24.5 366  370 Small  -26.0615  41.7307  2316  841  925 0.08  3.75  40.6 376  371 Small  -30.4638  41.7573  2004  519  565 0.06  2.70  30.5 374  372 Small  -28.8279  41.8371  1699  545  202 0.12  3.01  20.6 373  373 Small  -29.0008  41.9036  1602  501  953 0.06  1.87  20.6 372  374 Small  -30.6367  41.9701  2072  534  627 0.06  2.58  30.5 371  375 Small  -28.6018  42.0366  1670  739  591 0.10  3.66  23.6 379  376 Small  -26.2610  42.0366  2303  776  438 0.10  3.01  40.6 370  377 Small  -28.8678  42.1696  1645  650  500 0.12  3.80  30.9 379  378 Small  -26.8595  42.1696  2514  511  792 0.05  2.59  68.1 376  379 Small  -28.6018  42.2494  1713  768  607 0.10  3.42  23.6 375  380 Small  -33.9218  42.2760  3582  488  769 0.05  2.41  83.8 369  381 Small  -32.4455  42.2627  3088  260  836 0.02  1.04  32.7 384  382 Small  -29.5993  42.2627  1520  771  679 0.09  4.09  19.3 387  383 Small  -27.5777  42.2627  2347  439  887 0.05  2.27  80.5 378  384 Small  -32.7248  42.3558  3153  428  706 0.05  1.75  32.7 381  385 Small  -31.2352  42.3558  2680  332  932 0.03  1.30  36.1 392  386 Small  -30.2510  42.4356  2049  567  937 0.06  2.31  67.2 374  387 Small  -29.5860  42.4356  1461  827  912 0.09  2.68  19.3 382  388 Small  -28.3624  42.4356  1791  886  688 0.10  4.56  33.7 379  389 Small  -28.7880  42.4888  1705  619  380 0.10  4.08  18.0 390  390 Small  -28.9476  42.5154  1531  602  457 0.15  3.74  13.2 393  391 Small  -32.1529  42.5287  2957  323  700 0.03  1.64  43.9 381  392 Small  -31.5012  42.5420  2833  322  999 0.03  1.22  36.1 385  393 Small  -29.0008  42.6218  1414  723  401 0.13  3.73  13.2 390  394 Small  -30.7298  42.8346  2502  612  403 0.09  2.96  69.3 386  395 Small  -32.5652  42.8612  3010  381  898 0.03  1.42  58.9 391  \u000CAPPENDIX 3 DETAILS OF THE GENERIC SEAMOUNT MODEL  ECOLOGICAL GROUPS OF THE GENERIC SEAMOUNT MODEL  A model of a theoretical isolated seamount in the North Atlantic was built. The depth of the summit was set to be at around 300 m and the base at around 2000m. The area of the model is assumed as 30 km radius from the summit, in order to include the theoretical area of its influence. As a result the total area under consideration was equal to 2827 km 2 . A total of 37 functional groups were included in the seamount model, stratified by depth of habitat. The models included three marine mammals groups (toothed whales, baleen whales and dolphins), seabirds, turtles, seven invertebrate groups (benthic filter feeders such as corals or gorgonians, benthic scavengers, benthic crustaceans, pelagic crustaceans, seamount resident cephalopods, small and large drifting cephalopods), three zooplankton groups (gelatinous, shallow and deepwater zooplankton), primary producers (phytoplankton), detritus and twenty fish groups. 1 Primary Producers In this model we assumed that the primary production was the same as in the surrounded areas, expecting that the upwelling generated by the model increased phytoplankton biomass over the seamount. Phytoplankton biomass was estimated based on the Azores model (Guenette and Morato, 2001). The authors used information taken at the Azores front (south of Azores), in early October (Li, 1994) and obtained a biomass of 7.16 g-m\"2. Based on the SeaWIFS data set (www.me.sai.jrc.it), Guenette and Morato (2001) estimated a primary productivity of phytoplankton at 2030 g-m\" -year . The P/B was estimated at 283.5 year\" , based on the primary production and phytoplankton biomass.  \u000C2 Zooplankton The shallow and deep-water zooplankton groups included both small and large size organisms. The small zooplankton was defined as small herbivores, while the large zooplankton included, among others, mysids, euphausiids, chaetognaths, decapods' larvae. The gelatinous zooplankton consisted mainly of Thaliacea (salps, pyrosomes), Hydrozoa (siphonophores, hydroids) and Scyphozoa (jellyfish). 2.1 Shallow Zooplankton Huskin et al. (2001) found that about 90% of the small zooplankton of the Subtropical Atlantic near the Azores was composed by copepods, mainly small calanoids. Using the data presented by Huskin et al. (2001), we estimated small zooplankton biomass (until 200 m) of 6.071 t-km 2 and a Q/B of 43.285 year\"1 . Large shallow water zooplankton biomass was estimated from profiles of the zooplankton of the Azores front (Angel, 1989) from 0 to 1100 meters to be equal to 10.613 t-km\"2. Q/B estimate (i.e., 34 year\"1) was taken from the Azores model (Guenette and Morato, 2001; salps were not considered). Total biomass was then equal to the sum of small and large shallow water zooplankton, while for the final Q/B a weighted average was calculated (Q/B= 37.379 year\"1). Finally, production over consumption (P/Q) ratio was.considered equal to 0.3 (Christensen, 1996). 2.2 Deep-water Zooplankton Biomass estimated for deep large zooplankton from 0 to 1100 meters was 4.357 t-km\"2 (based on Angel, 1989). Assuming the same ratio large/small zooplankton as in shallow water, the estimate of deep small zooplankton biomass was equal to 2.492 t-km\"2. In addition, total biomass of deepwater zooplankton was 6.849 t-km\"2. Finally, Q/B value was equal to 29.0 year\"1, while P/Q of 0.3 (Christensen, 1996) was also used to estimate P/B. 2.3 Gelatinous Zooplankton Biomass estimation of gelatinous zooplankton was derived from data presented by Angel (1989). Using a conversion factor of 1 ml of displacement volume to 0.8 g of jellyfish we obtained a total biomass of 14.960 t-km\"2. This value was extremely high when compared to  \u000Cother models, such as the Eastern Bering Sea (0.048 t-km\" ; Trites et al., 1999) and the Barents Sea (6.47 t-km\"2; Dommasnes et al., 2001). We decided to enter an EE of 0.8 and let the model estimate the biomass for this group. P/B (0.85 year\"') and Q/B (2.0 year\"') values were adopted from Trites et al. (1999). 3 Cephalopods We used Nesis (1986) faunal components to originate two cephalopod groups. The Resident (benthic) Cephalopods includes groups 1 and 2 from Nesis (1986), while the group Drifting (pelagic) Cephalopods includes groups 3 and 4. 3.1 Resident (benthic) Cephalopods This group included mainly deep-sea octopus (Order Octopoda) and some species of squids that maintain themselves constantly above seamounts (e.g. Todarodes,  Ornitoteuthis,  Lycoteuthis, etc.). The occurrence of cuttlefish (Order Sepiida) and little cuttlefish like (Order Sepiolida) species over or around seamounts was not confirmed. At this stage these Orders were excluded form the model. P/B and Q/B values for this group were taken from the Azores model (Guenette and Morato, 2001) and were equal to 2.89 year'1 and 10.0 year\"1 respectively. Biomass was estimated by the model assuming an EE of 0.95. 3.2 Drifting (pelagic) Cephalopods This group included mainly squids (Order Teuthida) and vampire squid (Order Vampyromorphida) and was divided in two sub-groups depending on the size of the species. The small drifting cephalopods (mantle length &lt; 50 cm) included species of the families Loliginidae, Histioteuthidae, as well as some small members of the families Onychoteuthidae and Ommastrephidae. Daily feeding rates were estimated at 4.09% of body weight for Loligo forbesi (Porteiro et al., 1995) and from 3.6% to 6.7% for Illex sp. (O'Dor, 1980). An average feeding rate of 4.62% was used to estimate the Q/B for small drifting cephalopods and was found equal to 16.863 year\"'. P/B ratio was inferred from daily growth rate for the same species and was equal to 4.45 year\"'. Biomass was estimated by the model assuming an EE of 0.95.  \u000CThe large drifting; cephalopods (mantle length &gt; 50 cm) included species of the families Architeuthidae and Lepidoteuthidae as well as the large members of the families Onychoteuthidae and Ommastrephidae. P/B and Q/B values for this group were also taken from the Azores model (Guenette and Morato, 2001) and were equal to 2.5 year\"1 and 10.0 I 2 year\" respectively. Biomass was set at a very small value of 0.001 t-km\" . 4 Crustacea Crustaceans were considered separately from the other benthic or pelagic groups because of their importance in the seamount food web. The easiest way of sub-dividing crustaceans is to split them in two groups: pelagic (mainly shrimps) and benthic (mainly crabs). Two main features  have been observed for pelagic crustaceans over and around seamounts  (Vereshchaka, 1994): 1) the rise of lines of equal size, abundances and biomass of the pelagic animals, and 2) the decrease in abundance, biomass and sizes of pelagic animals near the bottom water layer. One of the possible important causes of the decrease in abundance and biomass of pelagic shrimps near the bottom is that they are consumed by benthic and benthopelagic predators. Many fish and invertebrates dwelling over seamounts are known to live mainly on pelagic macroplankton brought there by the ocean currents. Vereshchaka (1996) concluded that the abundance of pelagic animals decreases while the concentration of benthopelagic predators increases near the seafloor and the role of the former in planktonic communities falls in the near-bottom layer. 4.1 Pelagic Crustacea This group included mainly pelagic shrimps, such as Sergestidae, Penaeidae, Oplophoridae, Pandalidae, etc. P/B and Q/B values were taken from Bundy et al. (2000) and were equal to 1.45 year\"1 and 9.667 year\"1, respectively. An EE of 0.95 was used and the biomass was estimate by the model.  \u000C4.2 Benthic Crustacea This group included the benthic crab species (including deep-water species such as Chaceon affmis)  as well as some benthic shrimps. P/B, Q/B and EE parameters were taken from the  Azores model (Guenette and Morato, 2001) and were equal to 1.6 year\"1, 10.0 year\"1 and 0.95 respectively. 5 Benthic invertebrates The seamount benthic environment is typically dominated by corals and other suspension feeders, rather than the deposit feeders that are typical of most of the deep-sea benthos. 5.1 Benthos filter feeders This group consisted of benthic filter feeders, such as hard corals, gorgonian and anthipatharian corals, suspension feeding ophiuroids and polychaetes, etc. P/B and Q/B values were taken from Optiz (1993) and were equal to 0.8 year\"1 and 9.0 year\"1 respectively. These values were estimated based on sponges and corals. An EE of 0.95 was adopted. 5.2 Benthos scavengers This group consisted mainly of echinoderms (such as brittle stars Ophiuroidea, seastars Asteroidea, sea cucumbers Holothuroidea, and sea urchins Echinoidea), worms (Annelida), bivalves (Bivalvia), etc. P/B and Q/B values were estimated as the average of the groups \"worms\", \"mollusks\", \"echinoderms\" from Okey et al. (2001) and Ainsworth et al. (2001), and were equal to 1.83 year\"1 and 13.57 year\"1 respectively. An EE of 0.95 was adopted. 6 Epipelagic fishes 6.1 Epipelagic Small This group included species small (&lt; 25 cm) Clupeidae (e.g. Sardina pilchardus), Atherinindae (e.g. Atherina  presbyter),  Scomberesocidae (e.g. Nanychthys  simulans),  Exocoetidae (e.g. Exocoetus volitans), Macroramphosidae (e.g. Macroramphosus  scolopax),  Caproidae (Capros aper), etc. These last two species are known as benthopelagic, but since  \u000Cthey are preyed in the water column up to 200 m depth we opted to include them in the epipelagic group. 6.2 Epipelagic Medium This group included epipelagic fishes with more than 25 cm and less than 100 cm of total length (TL), such as the Scomberesocidae (e.g. Scomberesox saurus), Carangidae (e.g. Caranx sp., Trachurus sp., Trachinotus sp.), Scombridae (e.g. Scomber sp.), Centrolophidae (e.g. Schedophilus sp.), Balistidae (e.g. Balistes sp.), etc.  6.3 Epipelagic Large This group included epipelagic fish greater than 100 cm TL, such as Coryphaenidae (e.g. Coryphaena sp.), Carangidae (e.g. Seriola sp., Acanthocybium solandri), Sphyraenidae (e.g. Sphyraena sp.), etc.  The average Q/B values for each group were estimated using the empirical equation of Palomares and Pauly (1998) and were equal to 19.867 year\"1 for small epipelagic fish, 10.750 year\"1 for medium, and 5.095 year\"1 for large. P/B values were taken from the model of the Oceanic ecosystems of the Atlantic (Vasconcellos and Watson, 2004), and were equal to 2.053, 1.080, 0.690 .for small, medium and large epipelagic groups, respectively. The Biomasses for the epipelagic groups were assumed to be same as those estimated for the North Atlantic by Vasconcellos and Watson (2004). The values were 0.859 t-km\" for small epipelagic, 0.112 t-km\"2 for medium, and 0.014 t-km\"2 for the large group. 7 Mesopelagic and Deep-water fish groups Childress et al. (1980) showed that the estimated food intake of migratory fishes was greater than that of non-migratory ones. This food was channelled into activity, development of energy stores, and earlier reproduction. As a result, the growth rate of migratory fish was significantly greater than in non-migratory species. Thus, the Q/B and P/B values of migratory species were greater than in non-migratory species. Demersal deep water species are generally classified as either benthic (Bathybenthic) or benthopelagic (bathydemersal).  \u000CBenthic fishes include those that sit and wait for prey or forage slowly over the bottom, while benthopelagic fishes move to the water column to feed (Childress and Somero, 1990). It has been suggested that benthic fishes have lower metabolic rates than benthopelagic fishes. Those fishes found in association with seamounts and other topographic features form another distinct group (Koslow, 1996). These fishes are typically robust-bodied and capable of strong swimming performances (Koslow et al., 1995). These species are known to have high metabolic rates (Koslow, 1996). Since it is required to maintain position in highly dynamic current regimes, such as at seamounts, with frequent movements and strong locomotory performance, these species require greater metabolic expenditure than benthic or benthopelagic ones. These fishes usually exhibit delayed age of first maturity, low growth, and low mortality (Roff, 1984).  7 Mesopelagic migrating fishes This group contained mesopelagic (200-1000 m depth) species that migrate diurnally into near-surface, and was sub-divided into two groups based upon body size. The small group (&lt; 25 cm TL) consisted of Gonostomatidae (e.g. Bonapartia Gonostoma  sp.),  Sternoptychidae  Melanostomiidae (e.g. Bathophilus hygomii, Lampanyctus  (e.g.  Argyropelecus  pedaliota, sp.,  Cyclothone  Maurolicus  sp.,  muelleri),  sp.), Myctophidae (e.g. Electrona rissoi, Hygophum  sp., Lobianchia  dofleini, Myctophum  sp.), Diretmidae (Diretmus  argenteus). P/B and Q/B values were assumed to be 2.053 year\"1 and 8.0 year\"1 (Koslow, 1996; Williams, et al., 2001) respectively. A biomass of 2.0 t-km\"2 was taken from the Azores model (Guenette and Morato, 2001), which was similar to the biomass estimated (i.e., 1.720 t/km 2 ) by Vasconcellos and Watson (2004). The large (&gt; 25 cm TL) mesopelagic migrating fish group included a large range of sizes (25 up to 250 cm TL) and consists of Melanostomiidae (e.g. Echiostoma barbatum, Eustomias Obscurus, Leptostomias Macroparalepis zugmayeri), glesne),  affinis,  haplocaulus, Photonectes dinema), Paralepididae (e.g.  Paralepis coregonoides  Macrouridae (e.g. Odontomacrurus  Gempylidae (e.g. Gempylus serpens),  borealis), Melanonidae (e.g. Melanomus murrayi),  Regalecidae (e.g. Regalecus  Alepisauridae (Alepisaurus  brevirostris),  \u000CCentrolophidae (Centrolophus niger). P/B and Q/B values were assumed to be 0.600 year\"1 and 3.550 year\"1 respectively. An EE value of 0.95 was used to let the model estimate the biomass of this group. 8 Mesopelagic non-migrating fishes The mesopelagic non-migrating fishes group consisted of species that remain at depth (until 1000 m), including some Gonostomatidae, Myctophidae species. For this group, P/B, Q/B and EE values were assumed to be 0.500, 1.507 (from Williams et al., 2001), and 0.95 respectively. 9 Bathypelagic fishes This group included non-migratory deepwater (&gt; 1000m) pelagic species that remain at depth and have a reduced metabolic rate and \"poor\" condition (Koslow, 1996). This group includes some species of Saccopharyngidae (e.g. Saccopharynx ampullaceus), Alepocephalidae (e.g. Herwigia kreffti,  Photostylus pycnopterus), Gonostomatidae (e.g. Gonostoma bathyphilum),  Melamphaidae (e.g. Melamphaes microps), Trichiuridae (Aphanopus carbo). For this group, P/B and Q/B values were assumed to be 0.5 year\"1 and 1.477 year\"1 respectively.  10 Bathybenthic fishes This group included non-aggregating deep-water (&gt;1000m) benthic species that do not feed in the water column. Some examples are: Apogonidae (e.g. Epigonus Chlorophthalmidae  (e.g. Bathypterois  sp.),  Aphyonidae (e.g. Aphyonus gelatinosus),  Synodontidae  telescopus),  (e.g. Bathysaurus ferox),  Scorpaenidae (e.g. Trachyscorpia cristulata  echinata). For this group, P/B and Q/B values were assumed to be 0.2 year\"1 and 0.5 year\"1 respectively.  11 Bathydemersal (Deep-sea Benthopelagic) This group included non-aggregating deep-water (&gt;1000m) benthopelagic species that feed in the water column, thus having higher metabolic rates. Some of these species are: Alepocephalidae (e.g. Alepocephalus rostratus, A. bardii), Halosauridae (e.g. Halosauropsis  \u000Csp.),  Notacanthidae  Synaphobranchus  (e.g.  Polyacanthonotus  rissoanus),  Synaphobranchidae  (e.g.  kaupi), Bythitidae (e.g. Cataetyx laticeps), Moridae (e.g. Mora moro,  Lepidion guentheri), Macrouridae (e.g. Bathygadus melanobranchus, Chalinura sp., Nezumia sp.,' Trachonurus villosus, Coelorhynchus  Cetonurus labiatus),  globiceps, Gempylidae  (e.g. Nesiarchus nasutus). For this group, P/B and Q/B values were assumed to be 0.2 year\"1 and 0.6 year\"1 respectively. 12 Seamount-associated fishes Seamount associated fishes were divided into three different groups. Two groups containing species that are targeted by the north Atlantic fishery (Hoplostethus  atlanticus and Beryx  spp.), and a third one with the other seamount associated species. Parameters for these three groups were taken from  Bulman (2002; P/B=0.048 year' 1 and Q/B=2.0year~l for  Hoplostethus atlanticus). For the other two groups, \"Beryx spp.\" (B. splendens and B. decadactylus) and \"Other Seamount-associated fishes\" (including some Oreosomatidae species inhabiting the North Atlantic waters, such as Allocyttus verrucosus, Neocyttus helgae, and some aggregating Macrouridae such as Coryphaenoides rupestris), we used the values estimated by Bulman (2002) for oreos (i.e., P/B= 0.06 year\"1 and Q/B= 2.2 year\"1).  13 Shallower Fish groups Shallower water fish groups included those fishes with benthic or benthopelagic affinities occurring between 200 and 1000m depth. This group was divided further into\"Shallow Benthic fishes\", and \"Shallow Benthopelagic fishes\". The first group included benthic fishes, such as Helicolenus dactylopterus, Ponthinus kuhlii, Molva macrophthalma,  Conger  conger Phycis phycis, Lophius piscatorius, etc. The second group included species such as Pagellus bogaraveo. P/B and Q/B values were taken from the Azores model (Guenette and Morato, 2001) and were equal to 0.59 year\"1 and 4.70 year\"1 respectively (based on the group of demersal large predators) for benthic fishes and 0.66 year\"1 and 5.20 year\"1 respectively for benthopelagic fishes (based on P. bogaraveo).  \u000C14 Large Oceanic Planktivores This group consisted of large oceanic planktivorous fish, such as the whale shark (.Rhincondon typus), basking shark (Cetorhinus maximus), manta rays (Mobida spp., Manta spp.), and sunfish (Mola mola, Masturus spp.). There is some anecdotic information (e.g. fishermen and observers onboard of tuna vessels) suggesting that these species may stop over and around seamount to feed during their migrations routes. An average Q/B was estimated from the empirical equation of Palomares and Pauly (1998) and was equal to 2.06 year\"'. The other parameters for this group were taken from Vasconcellos and Watson (2004; P/B= 0.112 year\"1 and EE= 0.1). Biomass was estimated by the model assuming no fishing mortality but allowing Pelagic Sharks to feed upon this group. 15 Tunas Three species are usually caught on the seamounts (Fonteneau, 1991; Holland et al., 1999): yellowfin (Thunnus albacares), skipjack (Katsuwonus pelamis) and bigeye tuna (T. obesus). The Tuna group also includes other species, such as bluefin tuna (T. thynnus) and albacore (T. alalunga). Vasconcellos and Watson (2004) estimated the biomass for these five species in the North Atlantic as being 0.029 t-km\" , similar to that estimated by Guenette and Morato (2001) for the Azores area (0.032 t-km\"2). We used the first value as a general estimation for the North Atlantic. For the same reason P/Band Q/B values were taken from Vasconcellos and Watson (2004), as the weighted mean of P/B and Q/B values for the five tuna species (i.e., P/B= 0.742 year\"' and Q/B= 16.291 year\"'). Comparing these values with the ones used by Guenette and Morato (2001) in the Azores model we found that the estimated P/B were very similar, while Q/B was much greater. This is probably due to that Vasconcellos and Watson (2004) estimated their Q/B values based on daily rations while Guenette and Morato (2001) used the empirical equation from Palomares and Pauly (1998).  16 Billfishes This group consisted of swordfish (Xiphias  gladius) and several billfishes: blue marlin,  (Makaira nigriscans), white marlin (Tetrapturus albidus), and longbill spearfish (T. pfluegeri). P/B value was equal to 0.5 year\"1 based on an average F value of 0.3 year\"1 and an  \u000Caverage M of 0.2 year\"1. The biomass for this group was taken from Guenette and Morato (2001; 0.02 t-km\" ), while an average Q/B was estimated from the empirical equation from Palomares and Pauly (1998; 4.2 year\"1). 17 Rays and Skates This group consisted of all Rajiformes species (except Manta rays) such as Torpedo spp., Raja spp., Dasyatis spp., Myliobatis spp.  18 Pelagic Sharks Pelagic shark consisted of Carcharodon carcharias, Prionace glauca, Isurus  oxyrinchus,  Sphyrna spp., Alopias spp., Lamna nasus. P/B, Q/B and biomass for this group were taken from the Azores model (Guenette and Morato, 2001), based on P. glauca, L. nasus and Galeorhinus galeus.  19 Benthopelagic Sharks Benthopelagic sharks consisted mainly of deep-water species such as Squaliformes (Centroscymnus  spp., Etmopterus spp., Daenia spp., Dalathia spp., Oxynotus spp., etc.) or  Hexanchiformes. The Ecopath parameters came from the Azores model (Guenette and Morato, 2001), based on D. licha and Galeus melastomus. 20 Sea-Turtles Turtles occurring in the North Atlantic included the loggerhead (Caretta caretta), leatherback (Dermochelys coriacea), and green turtles (Chelonia mydas), whereas other species have been considered rare. Juvenile loggerhead turtles are transported by the North Atlantic Gyre current and live a pelagic life for about 8.2 years in the Eastern Atlantic (Bjorndal et al., 2000) where they seem to feed mainly on jellyfish (Bjorndal, 1997). P/B, Q/B and biomass values were taken from the Azores model (Guenette and Morato, 2001) and were equal to 0.15 year\"1, 3.50 year\"1, and 0.001 t-km\"2 respectively. Turtles have been found in sharks stomach (H. Rost Martins, unpublished data) and a considerable by-catch by the pelagic longline swordfish fishery has been reported (Ferreira et al., 2001).  \u000C21 Seabirds Seabirds occurring around this theoretical seamount were assumed to be the same occurring around the Azores (Guenette and Morato, 2001). This is not totally true because we assumed to simulate an isolated seamount without islands nearby. However, the values presented for the Azores were very similar to those presented for the North Atlantic (Vasconcellos and Watson, 2004). P/B, Q/B and biomass were assumed to be 0.04 year\"1, 84.39 year\"1, and 0.0002 t/km 2 respectively. 22 Marine Mammals Marine mammals were separated in three groups based on their diets. Baleen whales that feed  mainly on zooplankton  and small fish  included minke whale (Balenoptera  acutorostrata), sei whale (Balaenoptera borealis), blue whale (Balaenoptera musculus), fin whale (Balaenoptera physalus), and humpback whales (Megaptera  novaeangliae). 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<https://open.library.ubc.ca/cIRcle/collections/ubctheses/831/items/1.0074904>