text:
Global Change and the Future Ocean
Mark R. Payne
Technical University of Denmark, Denmark
Marco Zavatarelli
University of Bologna, Italy
Nova Mieszkowska
University of Liverpool, United Kingdom
Open Supplemental Data
Explanatory Power of Human and Environmental Pressures on the Fish Community of the Grand Bank before and after the Biomass Collapse
Danielle P. Dempsey1*, Wendy C. Gentleman1, Pierre Pepin2 and Mariano Koen-Alonso2
1Department of Engineering Mathematics, Dalhousie University, Halifax, NS, Canada
2Northwest Atlantic Fisheries Centre, Fisheries and Oceans Canada, St. John's, NL, Canada
Ecosystem based fisheries management will benefit from assessment of how various pressures affect the fish community, including delayed responses. The objective of this study was to identify which pressures are most directly related to changes in the fish community of the Grand Bank, Northwest Atlantic. These changes are characterized by a collapse and partial recovery of fish biomass and shifting trophic structure over the past three decades. All possible subsets of nine fishing and environmental pressure indicators were evaluated as predictors of the fish community structure (represented by the biomasses of six fish functional-feeding groups), for periods Before (1985–1995) and After (1996–2013) the collapse, and the Full time series. We modeled these relationships using redundancy analysis, an extension of multiple linear regression that simultaneously evaluates the effect of one or more predictors on several response variables. The analysis was repeated with different lengths (0–5 years) and types (moving average vs. lags) of time delays imposed on the predictors. Both fishing and environmental indicators were included in the best models for all types and length of time delays, reinforcing that there is no single type of pressure impacting the fish community in this region. Results show notable differences in the most influential pressures Before and After the collapse, which reflects the changes in harvester behavior in response to the groundfish moratoria in the mid-1990s. The best models for Before the collapse had strikingly high explanatory power when compared to the other periods, which we speculate is because of changes in the relationships among and within the pressures and responses. Moving average predictor sets generally had higher explanatory power than lagged sets, implying that trends in pressures are important for predicting changes in the fish community. Assigning a carefully chosen delay to each predictor further improved the explanatory power, which is indicative of the complexity of interactions between pressures and responses. Here we add to the current understanding of this ecosystem, while demonstrating a method for selecting pressures that could be useful to scientists and managers in other ecosystems.
Marine fisheries collapses worldwide have important socio-economic and ecological consequences, highlighting the need for ecosystem based fisheries management (EBFM; e.g., Misund and Skjoldal, 2005; DFO, 2007). EBFM supplements conventional single species approaches by explicitly considering interactions among species (target and non-target) in the context of changing human activities and environmental conditions. Implementation of EBFM requires information about the whole ecosystem, which can be provided in part by data-based indicators, i.e., measured or derived proxies of biological status and ecological pressures (Larkin, 1996; Jennings, 2005). Biological indicators include measures of the fish community structure (e.g., biomass, mean length, and trophic level of the community). Both fishing and the environment are external pressures on the fish community, and can be quantified by a range of indicators (e.g., Link et al., 2010b; Shannon et al., 2010). Fishing indicators can refer to metrics of landings (e.g., total or species aggregates), effort (e.g., hours fished), and fishing mortality (e.g., landings/community biomass), while environmental indicators can include large-scale metrics of atmospheric forcing, such as the North Atlantic Oscillation (NAO), and region-specific features such as annual mean temperature and salinity. Managers can regulate (at least partially) fishing pressures, but not environmental pressures (on relevant timescales; Elliott, 2011), and yet their decisions must account for future changes in the environment. It is generally accepted that a suite of indicators from several categories (e.g., biological, fishing, and environmental) is required for successful EBFM (e.g., Jennings, 2005; Link et al., 2010b). Considerable effort has focussed on determining which of the hundreds of proposed biological indicators are the most informative (Rice, 2003; Jennings, 2005; Rice and Rochet, 2005; Shin et al., 2010), but there remains a pressing need to determine which sets of pressures are best predictors of change (e.g., Ojaveer and Eero, 2011; Large et al., 2015).
Improving scientific understanding of multivariate pressure-response relationships can contribute to implementation of EBFM. Identifying which pressures are most directly related to changes in the fish community can help focus investigations of thresholds, guide modeling, plan management scenarios, and direct monitoring efforts. Determining which pressures are the most informative is challenging because of the range ways to quantify them, and because the mechanistic relationships are currently not well-defined. For example, there has been fierce debate about whether fishing or poor environmental conditions caused the infamous collapse of cod and other species on the Grand Banks in the 1990s (e.g., Myers et al., 1996; Bundy, 2001; Halliday and Pinhorn, 2009). Furthermore, changes in pressures can have both immediate and delayed effects on fish communities (e.g., Greenstreet et al., 2011; Gröger and Fogarty, 2011; Dempsey et al., 2017), adding an additional layer of complexity to the analysis of pressures and biological responses. For example, immediate effects of fishing include the removal of biomass, while delayed effects include changes in the size structure of the community (Daan et al., 2005; Devine et al., 2007). While previous studies have acknowledged delays (e.g., Chen and Ware, 1999; Large et al., 2015), more investigation into appropriate types and lengths of delays is warranted (Large et al., 2015).
Our objective here was to identify sets of pressures most directly related to three decades of changes in the structure of the fish community of the Grand Bank, Northwest Atlantic (Figure 1). This will add to the current understanding of this ecosystem, while demonstrating a method that could be useful to scientists and managers for other areas. This region provides a valuable case study because it experienced complex ecological changes resulting in two distinct periods, which are spanned by our suite of indicators (Dempsey et al., 2017). We identify pressure indicators from within this suite that can best predict fish community state over the full time series as well as for the two periods, and use our analysis to examine the past and present dynamics of this ecosystem. We also investigate models with different delay lengths (0–5 years) and types (moving average and lags) to determine which delays have the best explanatory power.
Figure 1. Map of the study area, showing the Grand Bank (NAFO division 3LNO), Station 27, and the Canadian exclusive economic zone (dashed lines).
Here we provide historical context for our study area to highlight some of the ecological changes that have occurred in the region over our period of interest. The Grand Bank, within the Northwest Atlantic Fisheries Organization (NAFO) statistical division 3LNO, and the adjacent southern Labrador and northeast Newfoundland shelf (NAFO Division 2J3K) are recognized as major subunits within the Newfoundland-Labrador shelf in the Northwest Atlantic (Figure 1; NAFO, 2014). The shelf is partly within the Canadian exclusive economic zone (EEZ) established in 1977, but extends into international waters (Figure 1).
For centuries, this region was one of the most productive fishing grounds in the world, with global fisheries for many species including Atlantic cod, flounder, and capelin (Rose, 2007). Fisheries management in the region is the responsibility of Fisheries and Oceans Canada (DFO) within the EEZ, and NAFO in international waters. Throughout the 1980s the primary management strategy for both DFO and NAFO was to set independent quotas for each fish stock; however, these measures were largely ineffective because the limits were ignored by some vessels, and there was error in calculating sustainable exploitation rates (e.g., Rose, 2007). In the 1990s prolonged heavy fishing pressure combined with an environmental regime shift precipitated complex ecological changes, characterized by a collapse of fish biomass. This is commonly referred to as "the collapse of the cod" even though many other species were also impacted (e.g., Atkinson, 1994; NAFO, 2010b). In response to the low biomass of many stocks, groundfish moratoria were enforced for 2J3KL in 1994 and the southern Grand Bank in 1994. Harvesters adapted by targeting different species (e.g., shrimp and crab), retiring from fishing, or leaving the province to find other employment (e.g., Hamilton and Butler, 2001). Over 20 years after imposition of these moratoria, many remain in place (see Annex I.A in NAFO, 2017). The total fish biomass has recovered slowly, although different species are recovering at different rates such that the structure of the ecosystem has shifted from piscivore dominated to include more species at lower trophic levels (Figure 2; Pedersen et al., 2017).
Figure 2. Fish functional group biomass in NAFO division 3LNO, illustrating the collapse of total biomass and changing structure of the community. All functional groups except shellfish are included as response variables. The Before period is from 1985 to 1995, the After period is from 1996 to 2013, and the Full period includes 1985–2013 (Before + After).
Both DFO and NAFO are working toward ecosystem approaches to management (Oceans Act, 1996; DFO, 2009; NAFO, 2010a,b). Current management strategies include the At-Sea Observer Program (DFO, 2014), National Vessel Monitoring System (DFO, 2016), restrictions on total allowable catches (e.g., for redfish and yellowtail flounder; NAFO, 2017), gear restrictions, and restricted entry programs.
The indicator time series used here were synthesized and presented in Dempsey et al. (2017). Fish community indicators are annual values for 1985–2013, and pressures used to predict them additionally extend to 1980–1984 for use with our time delay analysis. Below we present a general overview of these indicators to facilitate the reader's appreciation of our current multivariate analysis, and refer interested readers to Dempsey et al. (2017) for more details on indicator trends and data sources.
Indicators of Fish Community Status
The structure of the fish community was represented by the mean annual biomass indices of six fish functional feeding groups (aggregated species), which have been analyzed by Dempsey et al. (2017) and others (e.g., NAFO, 2010b, 2014; Table 1). Such functional groups are meaningful units to fisheries scientists and managers (NAFO, 2014), are compatible with modern ecosystem models (e.g., Link et al., 2010a; Heymans et al., 2016), and benefit the present analysis by reducing the number of response variables when compared to the use of individual species (Fogarty, 2014). The annual biomass index of each functional group was calculated from DFO spring scientific bottom trawl surveys for NAFO area 3LNO by summing the average catch per tow of the species included in the group. In 1996, the survey gear changed from a commercial Engels to a finer-meshed Campelen trawl, such that the biomass indices cannot be directly compared before and after the gear change due to differing capture efficiencies (McCallum and Walsh, 1997; Belgrano and Fowler, 2011). Scaling factors have been developed to coarsely compare the Engels and Campelen biomasses for most species; however, it was not possible to scale the biomasses of invertebrate species (i.e., shellfish) because they were not sampled consistently by the Engels trawl (Koen-Alonso, unpublished data).
Table 1. Functional groups used to represent the fish community structure of the Grand Bank, Newfoundland.
The mid-1990s also correspond to the minimum total fish biomass in the region, marking the end of the rapid collapse of biomass and the beginning of the recovery. These have been characterized as two ecologically different periods (e.g., Buren et al., 2014; Dempsey et al., 2017). Here we analyzed three time periods: the Full period (1985–2013; using the scaled Engels data) as well as Before (1985–1995) and After (1996–2013) the collapse. "Before" and "After" also correspond to the survey gear change to eliminate the reliance on the coarse scaling factors. Note that because there is no appropriate biomass index for shellfish prior to 1996 (NAFO, 2010b), this functional group had to be excluded from the Full and Before analyses. We do not expect this exclusion to affect results because even though the build-up of shrimp (the major species by biomass in this group) is believed to have started in the mid 1980s, it only peaked on the Grand Bank in the 2000s (Lilly et al., 2000; NAFO, 2014). As discussed later, some After analyses were completed with and without the shellfish index to determine the effect of including this functional group.
Indicators of Fishing and Environmental Pressures
The nine pressure indicators chosen as predictors for this analysis were based on the results of Dempsey et al. (2017; Figure 3). Note that we do not distinguish "pressures" from "drivers," as some authors do (e.g., in the DPSIR framework, see Gari et al., 2015 and references therein), because they both ultimately influence the fish community and as such it would be a matter of semantics for this analysis.
Figure 3. Pressure indicators used as predictors in this analysis. Fishing indicators: (A–D); Environmental indicators: (E–I). The thick dashed line indicates the beginning of the After period.
Four fishing indicators were included (Figure 3): total, pelagic, and shellfish landings, as well as the marine trophic index (MTI; denoted "MTILand" in Dempsey et al., 2017). Total landings are the sum of groundfish, pelagic, shellfish, and "other" species landings, and provide a metric of fishing pressure on the entire community. Pelagic landings are dominated by capelin, which is a key forage species in the system, and shellfish landings are dominated by shrimp and queen crab (also known as snow crab). MTI is the mean trophic level of the landings weighted by the biomass of species landed, including only species with trophic level ≥ 3.25 (e.g., Atlantic cod, haddock). MTI reflects evolving fishing practices as fishers target different species to adapt to changes in fishing technologies, the ecosystem, and end markets (Caddy and Garibaldi, 2000).
In general, the fishing indicators were highly correlated Before the collapse and for the Full period (Pearson correlation coefficient > 0.60, not shown), but not After the collapse (not shown). Total and pelagic landings both increased in the early 1980s, and then decreased from the late 1980s until the mid-1990s, and to this day remain lower than in the late 1970s and 1980s. MTI also decreased throughout the Before period, but has no trend in the After. Shellfish landings increased over the Full period due to the proliferation of shrimp and crab stocks in the 1980s and a shift in target species after the groundfish moratoria (Schrank, 2005; Dempsey et al., 2017), but have been declining since the mid-2000s (Figure 3; Department of Fisheries Aquaculture, 2014; Dempsey et al., 2017).
Five environmental indicators were included as predictors (Figure 3): the North Atlantic Oscillation (NAO), sea surface salinity (SSS), salinity at 150 m (S150), sea surface temperature (SST), and the timing of the sea ice melt (TimeIce). The NAO represents basin scale atmospheric circulation patterns that influence winds, salinity, temperature, and sea ice (Hurrell, 1995; Petrie, 2007). The remaining indicators characterize physical environmental factors local to the Grand Bank, which are thought to have played a critical role in the collapse of fish biomass in the 1990s (e.g., Halliday and Pinhorn, 2009). TimeIce was included as a proxy of the timing of the spring phytoplankton bloom (Wu et al., 2007) because there are no other suitable measures of phytoplankton biomass or productivity over the required historical time frame.
In general, the environmental pressures were not highly correlated with each other or the fishing pressures for any period (not shown). SST was the only environmental pressure with clear trends: it decreased until 1991, and has generally increased since. The remaining indicators were characterized by inter-annual variability. Notably, the NAO was well above its average in the early 1990s, leading to cooler and fresher water at this time, which has been characterized as a regime shift (Buren et al., 2014). The salinity indicators had higher variability Before the collapse, with extreme minimum values in the mid-1990s. In contrast, TimeIce had higher variability After the collapse (Figure 3).
Method of Data Analysis
Redundancy Analysis
Redundancy analysis (RDA) was used to assess how well different sets of the pressures described above simultaneously modeled the biomasses of all six functional groups over a period of n years. RDA is a multivariate extension of linear regression that uses p predictors in matrix X[n x p] to model r responses in matrix Y[n x r]. The explanatory power is characterized using goodness of fit metrics related to variances in the responses, the model, and/or the residuals. Commonly used metrics are the coefficient of determination (R2) and the adjusted-R2. The R2 measures the fraction of the total variance in the response(s) that is explained by the variance in predictor matrix X. The adjusted-R2 is a modification to R2, which enables comparison among models using different numbers of predictors (see below; Legendre and Legendre, 2012).
The first step in RDA is a multiple linear regression of each response variable (r multiple regressions), which are typically done simultaneously for computational efficiency. The modeled values are stored in the matrix Ŷ[n x r], while matrix β^[p x r] holds the coefficients for linear combinations of the columns of X, and result in the smallest sum of squares of the residuals for each response:
Y^=Xβ^=X(XTX)-1XTY (1)
where the superscript symbols "T" and "−1" respectively denote the matrix transpose and inverse. It is assumed that the columns of X are linearly related to the columns of Y, and so appropriate transformations must be applied if necessary. Commonly, predictors and responses are normalized by centering and scaling them by their respective mean and standard deviation to minimize numerical error in solving for Y^ (Legendre and Legendre, 2012). Due to the skewed nature of the fish biomass data, in this analysis responses were the log-transformed, normalized biomass indicators, and the predictors were normalized pressure indicators. A goodness of fit value for each multiple regression can be calculated. R2 for a single response is given by
R2=SSMSST= ∑i=1n(y^i-y¯)2∑i=1n(yi-y¯)2 (2)
where SSM is the sum of squares of the model, and SST is the sum of squares of the response. The adjusted-R2 "penalizes" models with more predictors (for models with the same number of predictors, the relative change in adjusted-R2 is the same for that of the R2; Equation 3).
adjusted - R2= 1-(1- R2)n-1n-p-1 (3)
The second step of RDA as it is used here is to calculate a single goodness of fit metric that simultaneously evaluates the r separate regressions. RTotal2 (also called the "bivariate redundancy statistic"; Legendre and Legendre, 2012) is given by:
RTotal2= SSM, TotalSST, Total= ∑j=1r∑i=1n(y^i- y¯)2∑j=1r∑i=1n(yi- y¯)2 (4)
where SSM,Total is the total sum of squares of the model, and SST,Total is the total sum of squares of Y. This is also equal to the average of the R2's for each multiple regression when the responses are normalized (Legendre et al., 2011). The RTotal2 can be modified as in Equation 3 to give adjusted-RTotal2. The adjusted-R2 metrics were used to evaluate models in this analysis because we examined subsets of different numbers of indicators. We designated changes in adjusted-R2 >0.05 as "notable."
All Possible Models
An RDA model was fit for each possible combination of our nine predictors so that a total of 511 models were evaluated for each period (Full, Before, and After). Models were ranked by their adjusted-RTotal2, with a rank of 1 indicating the predictor set with the highest explanatory power. We chose to evaluate all possible models vs. stepwise methods to avoid sensitivity of our results to the selection algorithm (e.g., forward/backward; see Whittingham et al., 2006 and references therein). Additionally, identifying and focussing on a single model ignores the potential that other subsets of predictors may have similar explanatory power (Whittingham et al., 2006). Here, we are not interested in one "best" model; rather, we evaluated a range of models that include different types and numbers of predictors to see if there are multiple sets with similarly high explanatory power.
We repeated the analysis using different lengths and types of time delays in the predictors. We considered two types of delays: lags and moving averages, for delay length from k = 1 to k = 5 years (Figure 4). For the lag analysis predictors were shifted forward k years to simulate a delayed response. For an analysis of the Before period, the response time series was from 1985 – 1995, and the predictor time series was from 1984 to 1994 for k = 1, 1983–1993 for k = 2, etc. For the moving average analysis, the predictor value at year i was the average of the current year and the previous k years (resulting in a k+1 year moving average). We were therefore able to compare the explanatory power of past predictor values (lags) and low-pass filter (moving average). All predictors were normalized after the delay was applied, which resulted in minor differences in the values of the lag predictors, and less damping of the moving average predictors as seen in Figure 4. These methods of incorporating time delays did not reduce the length of the time series because the predictors have longer historical data records than the responses. For clarity, "zero delay" (ZD) refers to the original predictors, while Lagk and Avgk refer to lagged and moving average predictors, respectively, that incorporate k years of past data.
Figure 4. Illustration of the different types of time delays for surface temperature (SST) at k = 3: (A) moving average; (B) forward lag shift.
We first used a simple approach of incorporating delays by evaluating all combinations of predictors with the same type and length of delay (e.g., all predictors either Avgk or Lagk; Chen and Ware, 1999). Because pressures may manifest in the fish community biomass on different timescales (i.e., fishing acts immediately, environment generally takes longer), we repeated the analysis using strategically chosen delay types and lengths for each predictor.
Zero Delay Models
The results for the three time periods using the ZD predictors are illustrated in Figure 5. Tables S1–S3 show which pressures were included in the top 50 models (i.e., top 10%) for each period. Inclusion of shellfish as a response in the After models had minimal effects, increasing the explanatory power only slightly, and highlighting the same number and most frequent indicators.
Figure 5. (A) Adjusted-RTotal2for zero-delay indicators of the three time periods; (B) The range of adjusted-RTotal2 for a given number of predictors for each time period; (C) Proportion of times each predictor appeared in the top 50 models of each time period. ("After_shell" indicates analysis that included the community shellfish biomass index as a response).
In general, the Before models had notably higher adjusted-RTotal2 than the After models, and the Full models had intermediate values (Figure 5). The best Before models had strikingly high explanatory power when compared to the other periods (adjusted-RTotal2= 0.94 vs. ~0.60); however, lower-ranked (smaller adjusted-RTotal2) Before and Full models had similar explanatory power. The high adjusted-RTotal2 of the best Before models highlights the common signal among functional groups and the external pressures during this period. Most of the functional group biomasses decreased (Figure 2), as did total landings and MTI, while shellfish landings increased (Figure 3). The environmental pressures were more variable, but SST had a strong decreasing trend from the late 1980s until the early 1990s (Figure 3; Dempsey et al., 2017).
The adjusted-RTotal2 of the top 50 models and the corresponding adjusted-R2 for each functional group further highlighted differences between the two periods (Figure 6). The high explanatory power for large benthivores and piscivores (average adjusted-R2 of 0.95 and 0.91, respectively) contributed to the remarkably high overall explanatory power for the Before period. The adjusted-R2 for medium and small benthivores was also notably higher than the adjusted-RTotal2 for this period, while planktivores and plank-piscivores had the lowest average explanatory power. In contrast, plank-piscivores and medium benthivores had the highest explanatory power for the After period. Piscivores and small benthivores, which were among the best predicted in the Before period, had the lowest average adjusted-R2 for the top 50 After models.
Figure 6. The adjusted-RTotal2 of the top 50 models and corresponding adjusted-R2 of each functional group for (A) Before models, ZD predictors; (B) After models, ZD predictors; (C) Before models, Avg1 predictors; (D) After models, Avg1 predictors. The legend is the same for each panel.
There is a clear plateau in the overall explanatory power of the Full models, with the best set of three predictors (total landings, MTI and SST; see Table S1) having only a marginally different adjusted-RTotal2 than sets with more predictors (Figure 5B). Six predictors were used in the best model for the Full period, (adjusted-RTotal2 = 0.60), although all top 50 sets had adjusted-RTotal2 within 0.05 (sets of 3–9 predictors). In contrast, the Before models did not plateau, with the best set requiring all 9 predictors (adjusted-RTotal2 = 0.94). All of the top 50 Before models have higher explanatory power than the best Full and After models. The best After model used 8 predictors (adjusted-RTotal2 = 0.59 including shellfish as a response, and 0.56 excluding shellfish), although there were sets of 6, 7, and 9 predictors with similar explanatory power. The lower adjusted-RTotal2 of the After (and Full) models suggests that there is at least one pressure not included here that could improve the explanatory power for these periods. In general, using less predictors in the Before and After models sacrificed more explanatory power than for the Full models. The range of adjusted-RTotal2 for a given number of predictors is generally smaller for the Full time series compared to the Before and After models (Figure 5B). For example, the range for 6-predictor Before models is over three times larger than that of 6-predictor Full time series models. This suggests that for the Full models, the number of predictors included is more important than which predictors are included. For the other two periods, a specific set of p predictors has high explanatory power, while other sets of p predictors do not.
Several predictors have much different inclusion frequencies Before and After the collapse, suggesting that different pressures were most influential in these two periods (Figure 5C). These differences are obscured by considering only the Full time series, underscoring the importance of selecting an ecologically coherent time frame for indicator analysis (Dempsey et al., 2017). As expected, the frequency of landings indicators reflects the change in target species after the collapse and subsequent groundfish moratoria. Total landings (which are highly correlated with groundfish landings; Dempsey et al., 2017), are more frequent in the Before models, while pelagic and shellfish landings are more frequent in the After models. The MTI was included in almost all of the Full and After models, but less than half of the Before models. This may be explained by the changes in fishing pressures after the collapse and moratoria. Before the collapse, MTI was highly correlated with the other fishing predictors because most of the landings were high trophic level species. After the collapse, MTI was not highly correlated with the other fishing predictors, indicating that it provides different information, which may be why it is included in so many of the best After models. The most notable differences in the environmental predictors were NAO and SST, which were included in all or most of the Before models, respectively, but in only about 60% of the After models. This suggests that environmental conditions were more influential Before the collapse, and/or that relationships between the fish functional groups and these specific environmental pressures were more linear before the collapse.
Delay Models
The above analysis showed that there are ecologic differences between the Before and After periods, and that the relationships for Full period do not represent either. As such, for the delay analysis we focussed on the two periods separately. In general, the moving average predictors improved the explanatory power more than the lag predictors (Figure 7). The delay with the highest explanatory power for both periods was Avg1, a 2-year moving average including the current year, i and year i−1. The Before Avg1 models were marginally better than the top 10 ZD models, and were notably better for most of the remaining models. The other Before moving average models had lower or marginally higher explanatory power than ZD models (until worse ranks). The After Avg1 models were marginally different than the top 25 ZD models, and notably better for the rest of the models.
Figure 7. Adjusted-RTotal2 in decreasing rank for models using predictors with time delays: (A) Before models, moving average predictors; (B) After models, moving average predictors; (C) Before models, lag predictors; (D) After models, lag predictors.
We investigated how delays affect the explanatory power of individual functional groups using the Avg1 predictors (i.e., the best delay type and length). In general, the adjusted-R2 for each functional group increased for the Avg1 predictors compared to the ZD predictors (Figure 6). The most notable improvements were for plank-piscivores and planktivores in the Before period, and large benthivores for the After (adjusted-R2 increased by ~0.30 for each). The explanatory power for several functional groups decreased with the Avg1 predictors (small and medium benthivores Before; small benthivores, plank-piscivores, and planktivores After), but these changes were minor when compared to the improvements of the other groups. Another striking feature of Figure 6 is the reduced variance in the adjusted-R2 for each functional group (except large benthivores) for the Avg1 predictors. This is most noticeable for the adjusted-R2 of the planktivores in the Before period, which had a standard deviation of 0.29 with ZD predictors vs. 0.08 with Avg1 predictors.
The results of the lag analysis were less consistent between the two periods. All the models with lag predictors for the Before period had notably lower explanatory power than with ZD predictors. This could suggest that the major pressures on the fish community during this time were related to fisheries removals, which have immediate primary effects on the ecosystem. In contrast, the After Lag4 models had similar explanatory power as the ZD models, while the Lag5 predictors were similar or marginally better for the top 50 models, and notably better for some models at worse ranks. This suggests that the fish community could also be experiencing indirect effects of fishing (e.g., Daan et al., 2005; Koen-Alonso et al., 2010), or environmental effects that require time to manifest in the system.
For most delays, subsets of the predictors had similar or higher explanatory power than the full suite (Figure 8; Supplementary Figures). In general, the range of adjusted-RTotal2 for a given number of predictors was larger for the Before models than the After, suggesting that which pressures included in the subset was more important Before, and the number of pressures included was more important After. For all delays (except Before Avg1 and Avg3), the adjusted-RTotal2 plateaued or decreased after a certain number of predictors were used.
Figure 8. The range of adjusted-RTotal2 for a given number of predictors all delayed by k for moving average and lag predictors (A) Before models; (B) After models. Select k are shown here; see Figures S1 and S2 for all k.
There are some notable differences in the patterns of most frequent predictors between the two delay types (Figure 9). In the Before models, total landings were most frequent at ZD, which was expected because the ecosystem was heavily exploited during this period, especially for groundfish (represented by total landings, Dempsey et al., 2017). Pelagic and shellfish landings were also expected to be most frequent at ZD, but were more frequent at higher delays, which suggests indirect effects of fishing. Shellfish landings were included in about 80% of the ZD and Avg2 models, and almost all the Lag1, Lag2, and Lag3 models. This was unexpected because there were limited commercial shellfish landings at this time, and because shellfish was not included as a functional group response. One possible interpretation is that because shellfish landings were increasing approximately linearly with time (Figure 3), they were highly negatively correlated with the declines in the other functional groups, and therefore contributed to explaining variance in the responses. Both MTI and NAO were included in almost all of the top Lag3 models, but only about 60% of the Avg3 models. In contrast, SST was included in all of the Avg2 and Avg3 models, but less than half of the lag models at these same k. S150 and TimeIce were also included in notably more Avg models than Lag models.
Figure 9. Proportion of times each predictor appeared in the top 50 models of the Before and After models for each delay type and length: (A) Before models, moving average predictors; (B) After models, moving average predictors; (C) Before models, lag predictors; (D) After models, lag predictors.
In the After models, total landings were more frequent at delays (Avg3 and Lag1; Figure 9), which could indicate delayed effects of fishing on the fish community structure. Pelagic landings were most frequent at ZD (closely followed by Lag2), and shellfish landings were included in almost all the ZD and Lag1 models, which is curious because the community shellfish index was not included as a response for that analysis. MTI was included in most of the ZD, Avg1, and Avg2 models. NAO was included in all the Lag2 models, but only about 80% of the Avg models, while SST was most frequent at delays of 2 and 3 years for both delay types. SSS was not particularly frequent in any of the lag models, but was included in about 70% of of Avg1. S150 and TimeIce were most frequent at the same k for both delay types (k = 1 and k = 3, respectively).
We repeated the analysis using specific delays for each predictor ("Mix" models) based on the idea that fishing pressures have shorter time delays, while environmental pressures take longer to manifest in the fish community. None of the combinations of delay types and lengths we tested had notably higher explanatory power than the best ZD and Avg1 Before models (not shown). In contrast, several combinations of mixed delay pressures were notably better than the best After models (e.g., Mix1 and Mix2, Figure 10). Mix1 and Mix2 both had shorter time delays (ZD, k = 1) for most of the fishing pressures and longer delays (k = 2+) for most of the environmental pressures. The top 50 Mix1 models included sets of 5–9 predictors, with total landings, MTI, and SST being the most frequent. The best set used 7 predictors, but there were other sets of 7–9 with only marginally different explanatory power. The top 50 Mix2 models included sets of 4–9 predictors, with pelagic landings, NAO, SST, and TimeIce the most frequent. The best Mix2 set used 6 predictors, and there were other sets of 5–8 with only marginally different explanatory power. The delays for Mix3 were chosen to provide a counter-example where all pressures have longer delays except NAO, which was assigned ZD. As expected, Mix3 was notably worse than the After ZD models, and NAO was not present in most of these "best" models.
Figure 10. (A) Adjusted-RTotal2 in decreasing rank for After models using predictors with mixed time delays; (B) Proportion of times each predictor appeared in the top 50 models for each set of After mix delays. Refer to table legend for type and length of delay used for each predictor.
Our analysis adds to the literature demonstrating that there is no single type of pressure driving fish community dynamics on the Newfoundland shelf (e.g., Mann and Drinkwater, 1994; Devine et al., 2007; Koen-Alonso et al., 2010). In this study, both fishing and environmental indicators were included in nearly all the top models for all types and lengths of time delays (exceptions are five After models with adjusted-RTotal2≤ 0.40), which highlights that managers in this area should factor both types of pressures into their decisions. The dominant pressures Before the collapse of fish biomass (i.e., those that caused the collapse) in the Northwest Atlantic have been the subject of debate, especially for commercially important species such as Atlantic cod. Some authors conclude that fishing mortality was the sole major cause of the collapse (e.g., Myers et al., 1996, 1997), even though non-commercial species were also impacted (e.g., Gomes et al., 1995 for Division 2J3KL, NAFO, 2010b for Divisions 2K3KL and 3LNO). Others assert the poor environmental conditions were an important driving pressure (e.g., Parsons and Lear, 2001; Rothschild, 2007; Halliday and Pinhorn, 2009), pointing out for example that the community recovered from a similar collapse in the 1970s, when fishing pressure was high, but environmental conditions were more favorable than the early 1990s. Our analysis supports a broader argument that the combination of these two drivers (high fishing and poor environment) were necessary for the extensive and widespread changes that occurred (e.g., Rose, 2004; Devine et al., 2007; Koen-Alonso et al., 2010). Specifically, total landings (here also a proxy for groundfish landings), NAO and SST were the most frequent pressures included the best (top 50) Before models. The best 1-predictor model was total landings (adjusted-RTotal2 = 0.55), while the best 2-predictor model included total landings and NAO (adjusted-RTotal2 = 0.63), and including SST further improved the explanatory power (adjusted-RTotal2 = 0.67). The inclusion of both NAO and SST speaks to the importance of both basin and local environmental effects on the ecosystem, further strengthening the need to have regional information to understand and predict changes in the fish community.
Our analysis shows that there was a shift in which fishing and environmental pressures were most directly related to the fish community structure Before and After the collapse. The environment can clearly influence fish community dynamics on the Grand Bank, but the most frequently included pressures in the ZD After models are pelagic and shellfish landings and MTI. This set of three pressures has notably higher overall explanatory power Before the collapse; however, its adjusted-RTotal2 represents a higher percentage of the maximum explanatory power for the After period. This suggests that the remaining pressures (e.g., environment) add relatively less predictive information in the After models. This shift reflects the changes in target species after the groundfish moratoria, although it is somewhat surprising that shellfish landings were included in all but two top models, because the community shellfish biomass was not included as a response. Shellfish landings consist mainly of snow crab and Pandalus shrimp, while pelagic landings are mainly capelin. Shrimp and capelin are considered important forage species on the Grand Bank (DFO, 2015a,b), and are managed conservatively (for example there is capelin moratorium in 3NO); however, our analysis suggests landings of these species are impacting the ecosystem. Shellfish landings increased overall in the After period (despite a slight decrease in the last several years), and are negatively correlated with the biomass indices of small benthivores, piscivores, and plank-piscivores, suggesting that they are hindering the recovery of these functional groups. The question remains whether this is indicative of a causal relationship, or is only correlative. One potential mechanistic explanation is given by Koen-Alonso et al. (2010), who speculated that fishing may be reducing food availability for key species on the Grand Banks, and thus hindering their recovery. Another hypothesis is that there are secondary effects on the fish community from shrimp beam trawls. These trawls are considered to have low bycatch rates (for commercial species; NAFO, 2014), but they could be negatively impacting the habitat of other species in the community.
Another striking difference between the two periods is the remarkably high explanatory power of the best Before models compared to the best After models (Figure 5) for any time delay (type or length) or number of predictors. We can speculate that the reasons for this relate to changes in relationships among and within the pressures and responses. For example, there was a strong signal in the responses Before the collapse. The biomasses of many functional groups were relatively highly correlated (Dempsey et al., 2017), such that any set of predictors with high explanatory power for one particular group was also high for several of the others (e.g., small, medium, and large benthivores and piscivores; Figure 6). The weaker signal After the collapse means prediction of all six functional groups may require a broader spectrum of pressures.
We showed the pressures that were driving changes Before the collapse are no longer as influential After, and the relatively lower adjusted-RTotal2 suggests that there may be pressures missing from the After models. Bottom up pressure indicators such as primary production and trophic transfer efficiencies were not included here because there was no suitable data for the required time frame. Our models include the timing of the sea ice melt as a proxy for the timing of phytoplankton spring bloom, but other characteristics of the bloom (e.g., duration and magnitude), or lower trophic level energy transfer may prove better predictors for this period through some mechanism not identified here. Other missing pressures are measures of predation by sea birds and marine mammals that could exert a top-down influence on the fish community. For example, harp seals migrate from the Arctic to northern Newfoundland in the Fall, and prey predominantly on capelin, but also eat other species (e.g., Atlantic herring, Arctic cod, shrimp, and Atlantic cod; Stenson, 2013). The Northwest Atlantic harp seal population has been increasing rapidly since the 1980s (Hammill et al., 2011), and some authors suggest that seal predation has supressed the recovery of fish species on the Newfoundland-Labrador shelf (e.g. Bundy, 2001; Devine et al., 2007). One type of human-related pressure specific to the After period that wasn't considered here is oil production. While oil exploration has occurred on the Grand Banks since the 1960s, active platforms have only been producing oil since 1997. Related pressures could include chemical pollution from regular discharge or accidental spills (Templeman, 2010), which can be especially harmful to early life stages, and can disrupt development, growth, and reproductive rates (etc., see JWL, 2007 and references therein). These pressures were not included here because we did not expect them to significantly influence the Grand Bank fish community; however, our analyses suggest that future investigations into these pressures are warranted.
Finally, non-linearity in the relationships between pressures and responses may have a stronger influence during the After period, through some unidentified mechanism. Predator-prey relationships, population dynamics, environmental changes, and human impacts can all result in non-linearity in marine ecosystems (Liu et al., 2014). Given the significant changes in the fish community structure and related pressures, we can speculate that the relationships among them could be less linear After the collapse, resulting in lower explanatory power because RDA is based on linear regression. This could be tested by comparing this analysis to results from a non-linear model such as neural networks or generalized additive models. These flexible models don't require the user to specify the forms of the relationships between predictors and responses, which affords a potential advantage of being able to capture the nonlinearity when the mechanistic forms of these relationships are unknown.
Our analysis showed that incorporating delays can improve explanatory power, but that the type and length of delay should be carefully considered. In general, moving average predictors had higher explanatory power than lagged predictors for both periods. Since the moving averages incorporate the trend in the predictors, causing the responses to change gradually over time (see Figure A1 in Gentleman and Neuheimer, 2008), our results suggest that trends in these pressures are influencing the fish community. For the models with the same delay type and length for each predictor, Avg1 was the best, notably improving the explanatory power compared to ZD for both periods. Avg1 also increased the explanatory power and reduced the variability in the adjusted-R2 for most of the individual functional groups (Figure 6). Other delays, particularly those with lagged predictors, had notably worse explanatory power than ZD. While some of the improved fit of the moving averages may be an artifact of smoothing, our results still strongly suggest that the rates of change are useful for predicting.
Our examination of Mixed delays illustrated that the explanatory power of the Avg1 After models can be further improved by considering simple relationships when choosing the delays for each predictor (Figure 10). This suggests that including the different timescales of influence for the pressures is important for this period, and could improve the ability to forecast changes in the fish community. Even longer time delays could be beneficial because changes in some pressures may take more than 5 years to manifest in the fish community. For example, Daan et al. (2005) and Greenstreet et al. (2011) found that secondary effects of fishing could impact different size-based metrics of the fish community in the North Sea after lags of 6–12 and 12–20 years, respectively. However, while we showed that different mixed delays among the predictor set resulted in similar overall explanatory power, we also showed it could be worse. We therefore recommend that future investigations into suitable delays—and their mechanisms—should come from analysis of mechanistic models.
For all three periods (Full, Before, and After) and delay types and lengths, there was no one set of pressures that "best" predicted fish community status. Rather there was a range of sets, differing in number and type of indicators that had similar explanatory power (see Tables S1–S5). For example, there are two sets of 6-pressure Avg1 After models with only marginally different explanatory power; however, one includes two fishing indicators (pelagic landings and MTI) and four environmental indicators (all except S150; adjusted-RTotal2 = 0.68), while the other includes all four fishing and two environmental (S150 and TimeIce; adjusted-RTotal2 = 0.70). In many cases the same base indicators were used in most or all of the top sets (Figure 5C; e.g., for ZD After: pelagic and shellfish landings and MTI), while other pressures improved the explanatory power by adding additional information. This is not to say that any set of nine pressures would have high explanatory power—recall that we curated these pressures because we expected them to have measurable impacts on the Grand Bank fish community. The point here is that pressure sets with similar predictive power may be comprised of different types of strategically selected data. This suggests that various direct effects of pressures on fish and lower trophic level production have spread throughout the community such that changes in fish functional group biomass can be directly predicted from different pressures. Therefore, if there was a lack of one type of pressure data, it may be possible to replace it with another type without compromising explanatory power. Furthermore, using a suite of models to predict future changes can provide a measure of uncertainty, much like the model ensembles used to forecast climate changes and associated impacts by the Intergovernmental Panel on Climate Change (Kirtman et al., 2013).
Here we provided one case study of how RDA can be applied to learn about the Grand Bank ecosystem. Ultimately the best predictor set(s) for the Grand Bank and other ecosystems would depend on a number of considerations, including the final application of the model. Other considerations may include the responses of interest (i.e., if structure of the fish community were expanded to also consider length and/or biodiversity measures), the explanatory power for the individual responses (Figure 6), data availability (e.g., types of indicators and length of time series), and costs of monitoring (e.g., data collection, database entry, analysis). As well as potentially varying with application-specific criteria, best sets will likely be ecosystem-specific, and depend on the unique combination of exploitation history, oceanographic conditions, and ecological interactions. Finally, because ecosystems are dynamic and the types and intensity of pressures may change over time, the best predictor sets may differ for different time periods of the same ecosystem. Scientists and managers should be aware of this, and watch for declines in the ability of these sets to predict changes in the ecosystem. Here we offer a statistical approach that can be used with various types of data, and is suggestive of being easily calibrated to different systems. This could serve as a useful complementary tool to help design modeling studies, plan field programs, and direct monitoring efforts. The synergistic use of statistical and mechanistic models to help guide identification of the most informative pressures, their most influential time delays, and their mechanisms are important future research directions that could improve ability to forecast changes in the fish community, and implement appropriate management measures.
DD and WG conceived of and designed the analysis. DD interpreted the results and drafted the manuscript. All authors provided critical input, and gave final approval of the version to be published.
DFO Ecosystem Research Initiative for the Newfoundland and Labrador Region (ERI-NEREUS), DFO International Governance Strategy (IGS), and DFO Strategic Program for Ecosystem-based Research and Advice (SPERA) programs provided logistical and financial support for this work. PP and MK-A were funded partly by DFO's Fisheries Science & Ecosystem Research Program (FSERP). WG and DD were supported by the Natural Science and Engineering Research Council of Canada.
The handling Editor declared a past co-authorship with one of the authors DD.
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmars.2018.00037/full#supplementary-material
Atkinson, D. B. (1994). Some observations on the biomass and abundance of fish captured during stratified- random bottom trawl surveys in NAFO divisions 2J and 3KL, Autumn 1981 – 1991. NAFO Sci. 21, 43–66.
Belgrano, A., and Fowler, C. W. (eds.) (2011). Ecosystem Based Management for Marine Fisheries. New York, NY: Cambridge Unviersity Press.
Bundy, A. (2001). Fishing on ecosystems: the interplay of fishing and predation in Newfoundland and Labrador. Can. J. Fish. Aquat. Sci. 58, 1153–1167. doi: 10.1139/f01-063
Buren, A. D., Koen-Alonso, M., Pepin, P., Mowbray, F., Nakashima, B., Stenson, G., et al. (2014). Bottom-up regulation of capelin, a keystone forage species. PLoS ONE 9:e87589. doi: 10.1371/journal.pone.0087589
Caddy, J. F., and Garibaldi, L. (2000). Apparent changes in the trophic composition of world marine harvests: the perspective from the FAO capture database. Ocean Coast. Manag. 43, 615–655. doi: 10.1016/S0964-5691(00)00052-1
Chen, D. G., and Ware, D. M. (1999). A neural network model for forecasting fish stock recruitment. Can. J. Fish. Aquat. Sci. 56, 2385–2396. doi: 10.1139/f99-178
Daan, N., Gislason, H., Pope, J. G., and Rice, J. C. (2005). Changes in the North Sea fish community: evidence of indirect effects of fishing? ICES J. Mar. Sci. 62, 177–188. doi: 10.1016/j.icesjms.2004.08.020
Dempsey, D. P., Koen-Alonso, M., Gentleman, W. C., and Pepin, P. (2017). Compilation and discussion of driver, pressure, and state indicators for the Grand Bank ecosystem, Northwest Atlantic. Ecol. Indic. 75, 331–339. doi: 10.1016/j.ecolind.2016.12.011
Department of Fisheries and Aquaculture (2014). Seafood Industry Year in Review - 2013. Available online at: http://www.fishaq.gov.nl.ca/publications/pdf/SYIR_2013.pdf
Devine, J. A., Zuur, A. F., Ieno, E. N., and Smith, G. M. (2007). "Common trends in demersal communities on the Newfoundland-Labrador shelf," in Analysing Ecological Data, eds A. F. Zuur, E. N. Ieno, and G. M. Smith (New York, NY: Springer), 672.
DFO (2007). A New Ecosystem Science Framework in Support of Integrated Management. Available online at: http://www.dfo-mpo.gc.ca/science/publications/ecosystem/index-eng.htm
DFO (2009). Sustainable Fisheries Framework. Available online at: http://www.dfo-mpo.gc.ca/reports-rapports/regs/sff-cpd/overview-cadre-eng.htm (Accessed on July 20, 2017).
DFO (2014). Corporation Designation Policy and Procedures. Available online at: http://www.dfo-mpo.gc.ca/fm-gp/sdc-cps/nir-nei/obs-dpp-eng.htm (Accessed July 20, 2017).
DFO (2015a). Assessment of Northern Shrimp (Pandalus borealis) in Shrimp Fishing Areas 4-6 (NAFO Divisions 2G-3K) and of Striped Shrimp (Pandalus montagui) in Shrimp Fishing Area 4 (NAFO Division 2G). 2015/018.
DFO (2015b). Assessment of Capelin in Subarea 2 and Divisions 3KL in 2015.
DFO (2016). The National Vessel Monitoring System. Available online at: http://www.nfl.dfo-mpo.gc.ca/e0010178
Elliott, M. (2011). Marine science and management means tackling exogenic unmanaged pressures and endogenic managed pressures – a numbered guide. Mar. Pollut. Bull. 62, 651–655. doi: 10.1016/j.marpolbul.2010.11.033
PubMed Abstract | CrossRef Full Text
Fogarty, M. J. (2014). The art of ecosystem-based fishery management. Can. J. Fish. Aquat. Sci. 71, 479–490. doi: 10.1139/cjfas-2013-0203
Gari, S. R., Newton, A., and Icely, J. D. (2015). A review of the application and evolution of the DPSIR framework with an emphasis on coastal social-ecological systems. Ocean Coast. Manag. 103, 63–77. doi: 10.1016/j.ocecoaman.2014.11.013
Gentleman, W. C., and Neuheimer, A. B. (2008). Functional responses and ecosystem dynamics : how clearance rates explain the influence of satiation, food-limitation and acclimation. J. Plankton Res. 30, 1215–1231. doi: 10.1093/plankt/fbn078
Gomes, M. C., Haedrich, R. L., and Villagarcia, M. G. (1995). Gomes_1995.pdf. Fish. Oceanogr. 4, 85–101. doi: 10.1111/j.1365-2419.1995.tb00065.x
Greenstreet, S. P. R., Rogers, S. I., Rice, J. C., Piet, G. J., Guirey, E. J., Fraser, H. M., et al. (2011). Development of the EcoQO for the North Sea fish community. ICES J. Mar. Sci. 68, 1–11. doi: 10.1093/icesjms/fsq156
Gröger, J. P., and Fogarty, M. J. (2011). Broad-scale climate influences on cod (Gadus morhua) recruitment on Georges Bank. ICES J. Mar. Sci. 68, 592–602. doi: 10.1093/icesjms/fsq196
Halliday, R. G., and Pinhorn, A. T. (2009). The roles of fishing and environmental change in the decline of Northwest Atlantic groundfish populations in the early 1990s. Fish. Res. 97, 163–182. doi: 10.1016/j.fishres.2009.02.004
Hamilton, L. C., and Butler, M. J. (2001). Outport adaptations: social indicators through Newfoundland's Cod crisis. Hum. Ecol. Rev. 8, 1–11. Available online at: https://scholars.unh.edu/soc_facpub/169/
Hammill, M. O., Stenson, G. B., Doniol-Valcroze, T., and Mosnier, A. (2011). Northwest Atlantic Harp Seals Population Trends, 1952-2012. DFO Canadian Science Advisory SecretariatScience Advisory Report 2011/070.
Heymans, J. J., Coll, M., Link, J. S., Mackinson, S., Steenbeek, J., Walters, C., et al. (2016). Best practice in Ecopath with Ecosim food-web models for ecosystem-based management. Ecol. Modell. 331, 173–184. doi: 10.1016/j.ecolmodel.2015.12.007
Hurrell, J. W. (1995). Decadal trends in the North Atlantic Oscillation: regional temperatures and precipitation. Science 269, 676–679. doi: 10.1126/science.269.5224.676
Jennings, S. (2005). Indicators to support an ecosystem approach to fisheries. Fish Fish. 6, 212–232. doi: 10.1111/j.1467-2979.2005.00189.x
JWL (2007). "Chapter 5: potential environmental effects from exploration and production activities," Sydney Basin Strategic Environmental Assessment: Final Report. Prepared for CNLOPB.
Kirtman, B., Power, S., Adedoyin, J., Boer, G., Bojariu, R., Camilloni, F., et al. (2013). "Near-term climate change: projections and predictability," in Climate Change 2013: The Physcial Science Basis, eds T. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. Allen, J. Boschung, et al. (New York, NY; Cambridge: Cambridge Unviersity Press), 959–961.
Koen-Alonso, M., Pepin, P., and Mowbray, F. (2010). Exploring the Role of Environmental and Anthropogenic Drivers in the Trajectories of Core Fish Species of the Newfoundland-Labrador Marine Community.
Large, S. I., Fay, G., Friedland, K. D., and Link, J. S. (2015). Quantifying patterns of change in marine ecosystem response to multiple pressures. PLoS ONE 10:e0119922. doi: 10.1371/journal.pone.0119922
Larkin, P. A. (1996). Concepts and issues in marine ecosystem management. Rev. Fish Biol. Fish. 6, 139–164. doi: 10.1007/BF00182341
Legendre, P., and Legendre, L. (2012). Numerical Ecology, 3rd Edn. Amsterdam: Elsevier, B.V.
Legendre, P., Oksanen, J., and ter Braak, C. J. F. (2011). Testing the significance of canonical axes in redundancy analysis. Methods Ecol. Evol. 2, 269–277. doi: 10.1111/j.2041-210X.2010.00078.x
Lilly, G. R., Parsons, D. G., and Kulka, D. W. (2000). Was the increase in shrimp biomass on the northeast newfoundland shelf a consequence of a release in predation pressure from cod? J. Northwest Atl. Fish. Sci. 27, 45–61. doi: 10.2960/J.v27.a5
Link, J. S., Fulton, E. A., and Gamble, R. J. (2010a). Progress in Oceanography The northeast US application of ATLANTIS : a full system model exploring marine ecosystem dynamics in a living marine resource management context. Prog. Oceanogr. 87, 214–234. doi: 10.1016/j.pocean.2010.09.020
Link, J. S., Yemane, D., Shannon, L. J., Coll, M., Shin, Y. J., Hill, L., et al. (2010b). Relating marine ecosystem indicators to shing and environmental drivers: an elucidation of contrasting responses. ICES J. Mar. Sci. 67, 787–795. doi: 10.1093/icesjms/fsp258
Liu, H., Fogarty, M. J., Hare, J. A., Hsieh, C., Glaser, S. M., Ye, H., et al. (2014). Modeling dynamic interactions and coherence between marine zooplankton and fishes linked to environmental. J. Mar. Syst. 131, 120–129. doi: 10.1016/j.jmarsys.2013.12.003
Mann, K. H., and Drinkwater, K. F. (1994). Environmental influences on fish and shellfish production in the Northwest Atlantic. Environ. Rev. 2, 16–32. doi: 10.1139/a94-002
McCallum, B. R., and Walsh, S. J. (1997). Groundfish survey trawls used at the Northwest Atlantic fisheries centre, 1971 to present. NAFO Sci. Counc. Stud. 29, 93–104.
Misund, O. A., and Skjoldal, H. R. (2005). Implementing the ecosystem approach: experiences from the North Sea, ICES, and the Institute of Marine Research, Norway. Mar. Ecol. Prog. Ser. 300, 260–265. doi: 10.3354/meps300260
Myers, R. A., Hutchings, J. A., and Barrowman, N. J. (1996). Hypotheses for the decline of cod in the North Atlantic. Mar. Ecol. Prog. Ser. 138, 293–308. doi: 10.3354/meps138293
Myers, R. A., Hutchings, J. A., and Barrowman, N. J. (1997). Why do fish stocks collapse? The example of cod in Atlantic Canada. Ecol. Appl. 7, 91–106. doi: 10.1890/1051-0761(1997)007[0091:WDFSCT]2.0.CO;2
NAFO (2010a). Part B: Scientific Council Meeting, 3-16 June 2010.
NAFO (2010b). Report of the NAFO Scientific Council Working Group on Ecosystem Approaches to Fisheries Management (WGEAFM). NAFO SCS Doc. 10/ 19. Serial No. N5815.
NAFO (2014). Report of the 7th Meeting of the NAFO Scientific Council (SC) Working Group on Ecosystem Science and Assessment (WGESA) [Formerly SC WGEAFM]. NAFO SCS Doc. 14/023 Serial No. N6410.
NAFO (2017). Northwest Atlantic Fisheries Organization Conservation and Enforcement Measures. NAFO FC Doc. 17-01. Serial No. N6638.
Oceans Act (1996). Available online at: http://laws-lois.justice.gc.ca/eng/acts/O-2.4/
Ojaveer, H., and Eero, M. (2011). Methodological challenges in assessing the environmental status of a marine ecosystem: case study of the Baltic Sea. PLoS ONE 6:e19231. doi: 10.1371/journal.pone.0019231
Parsons, L. S., and Lear, W. H. (2001). Climate variability and marine ecosystem impacts : a North Atlantic perspective. Progr. Oceanogr. 49, 167–188. doi: 10.1016/S0079-6611(01)00021-0
Pedersen, E. J., Thompson, P. L., Ball, A. R., Fortin, M.-J., Gouhier, T. C., Link, H., et al. (2017). Signatures of the collapse and incipient recovery of an overexploited marine ecosystem. R. Soc. Open Sci. 4:170215. doi: 10.1098/rsos.170215
Petrie, B. (2007). Does the North Atlantic oscillation affect hydrographic properties on the Canadian Atlantic continental shelf? Atmos. Ocean 45, 141–151. doi: 10.3137/ao.450302
Rice, J. C., and Rochet, M.-J. (2005). A framework for selecting a suite of indicators for fisheries management. ICES J. Mar. Sci. 527, 516–527. doi: 10.1016/j.icesjms.2005.01.003
Rice, J. (2003). Environmental health indicators. Ocean Coast. Manag. 46, 235–259. doi: 10.1016/S0964-5691(03)00006-1
Rose, G. A. (2004). Reconciling overfishing and climate change with stock dynamics of Atlantic cod (Gadus morhua) over 500 years. Can. J. Fish. Aquat. Sci. 61, 1553–1557. doi: 10.1139/f04-173
Rose, G. A. (2007). Cod: The Ecological History of the North Atlantic Fisheries. St. John's, NL: Breakwater Books.
Rothschild, B. J. (2007). Coherence of Atlantic cod stock dynamics in the Northwest Atlantic Ocean. Trans. Am. Fish. Soc. 136, 858–874. doi: 10.1577/T06-213.1
Schrank, W. E. (2005). The Newfoundland fishery: ten years after the moratorium. Mar. Policy 29, 407–420. doi: 10.1016/j.marpol.2004.06.005
Shannon, L. J., Coll, M., Yemane, D., Jouffre, D., Neira, S., Bertrand, A., et al. (2010). Comparing data-based indicators across upwelling and comparable systems for communicating ecosystem states and trends. ICES J. Mar. Sci. 67, 807–832. doi: 10.1093/icesjms/fsp270
Shin, Y.-J., Shannon, L. J., Bundy, A., Coll, M., Aydin, K., Bez, N., et al. (2010). Using indicators for evaluating, comparing, and communicating the ecological status of exploited marine ecosystems. 2. Setting the scene. ICES J. Mar. Sci. 67, 692–716. doi: 10.1093/icesjms/fsp294
Stenson, G. B. (2013). Estimating consumption of prey by Harp Seals (Pagophilus groenlandicus) in NAFO Divisions 2J3KL. DFO Can. Sci. Advis. Sec. Res. Doc. 2012/156. iii + 26 p. (Errata: October 2015)
Templeman, N. D. (2010). Ecosystem Status and Trends Report for the Newfoundland and Labrador Shelf.
Whittingham, M. J., Stephens, P. A., Bradbury, R. B., and Freckleton, R. P. (2006). Why do we still use stepwise modelling in ecology and behaviour? J. Anim. Ecol. 75, 1182–1189. doi: 10.1111/j.1365-2656.2006.01141.x
Wu, Y., Peterson, I. K., Tang, C. C. L., Platt, T., Sathyendranath, S., and Fuentas-Yaco, C. (2007). The impact of sea ice on the initiation of the spring bloom on the Newfoundland and Labrador Shelves. J. Plankton Res. 29, 509–514. doi: 10.1093/plankt/fbm035
Keywords: redundancy analysis, ecosystem indicators, moving average, lag, time delay, Northwest Atlantic, regression analysis
Citation: Dempsey DP, Gentleman WC, Pepin P and Koen-Alonso M (2018) Explanatory Power of Human and Environmental Pressures on the Fish Community of the Grand Bank before and after the Biomass Collapse. Front. Mar. Sci. 5:37. doi: 10.3389/fmars.2018.00037
Received: 12 October 2017; Accepted: 24 January 2018;
Mark R. Payne, Technical University of Denmark, Denmark
Nova Mieszkowska, Marine Biological Association of the United Kingdom, United Kingdom
Marco Zavatarelli, Università di Bologna, Italy
Copyright © 2018 Dempsey, Gentleman, Pepin and Koen-Alonso. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Danielle P. Dempsey, Danielle.Dempsey@dal.ca


text:
Login to EEdition
One Tammany
Weather/Traffic
Where NOLA Eats
Ian McNulty
Keith Spera
Will Sutton
Dan Fagan
Statewide Elections
East Jefferson
Former Gov. Kathleen Blanco, a recipient of an honorary doctorate of liberal arts, is hooded by University of Louisiana at Lafayette President Joseph Savoie during the University of Louisiana at Lafayette Fall Commencement General Assembly Friday, December 15, 2017, at the Cajundome in Lafayette, La. ORG XMIT: BAT1712151342212298
file photo by LESLIE WESTBROOK
Former Louisiana Governor Kathleen Blanco at the New Orleans Saints Hall of Fame induction announcement at the team's training facility in Metairie on Wednesday, June 5, 2019. ORG XMIT: BAT1906051537429950
Advocate Staff photo by CHRIS GRANGER
Former Louisiana Governor Kathleen Babineaux Blanco talks with New Orleans Saints owner Gayle Benson during a dedication ceremony for the naming the Gate A Lobby in her honor, prior to the New Orleans Saints vs. Philadelphia game Sunday, Jan. 13, 2019. During her term, Governor Blanco guided the State through its recovery and led the return of the New Orleans Saints back to the Mercedes-Benz Superdome after Hurricane Katrina. (Photo by David Grunfeld, NOLA.com | The Times-Picayune) ORG XMIT: MER15a606c174d96a2a1dada138574be
(Photo by David Grunfeld NOLA.com | The Times-Picayune)
Former Louisiana Governor Kathleen Babineaux Blanco with her husband Raymond "Coach" Blanco, during a dedication ceremony for renaming Gate A in her honor, prior to the New Orleans Saints vs. Philadelphia game Sunday, Jan. 13, 2019. During her term, Governor Blanco guided the State through its recovery and led the return of the New Orleans Saints back to the Mercedes-Benz Superdome after Hurricane Katrina. (Photo by David Grunfeld, NOLA.com | The Times-Picayune) ORG XMIT: MER91adf632849b09b024b08a9b362c5
James Gill: As Louisiana celebrates Kathleen Blanco, it's a good time to be a Cajun
By James Gill
Aug 21, 2019 - 10:30 pm
Cajun power was much in our thoughts last weekend.
Our most recent Cajun governor, Kathleen Blanco, died Sunday in Lafayette, while Warren Perrin was in New Brunswick, where he spoke at the Congres Mondial Acadien. Perrin was the man who, in 2003, secured an apology in the name of the Queen of England for Le Grand Derangement.
Stephanie Grace: Kathleen Blanco gave 'normal' a very good name in Louisiana
Blanco, born a Babineaux, rose to become Louisiana's "Queen Bee" without appearing to make any enemies. When that nickname was bestowed by a disgruntled legislator, she embraced it with relish. As ex Gov. Edwin Edwards observed in a TV interview after her death, she never said a bad word about anybody, which is not how the political game is usually played. Blanco's chances of winning a second term disappeared only when Katrina struck on her watch and she took the blame for the subsequent chaos. Later, her grace in face of terminal cancer was much admired, and her eulogies will be more than usually heartfelt.
Perrin, founder of the Acadian Museum in Erath, is former president of the Council for the Development of French in Louisiana. He was first appointed to that post in 1994 by Edwards, who now, at 92, becomes our only living Cajun ex governor. Perrin remained at CODOFIL under Mike Foster and Blanco.
Perrin, in common with all Cajuns who have read about the expulsion of their forebears from Canada during the French and Indian War from 1755 to 1764, thought the British had committed a crime against humanity. The British, not unreasonably, doubted the loyalty of the resident francophones, but enforced their exodus with a brutality that would never be forgotten. Perrin campaigned for years before the British government added the Cajuns to the list of ethnic groups who were securing apologies at the time for ancient wrongs.
"After the deportation, there was a century of silence. We disappeared," Perrin said in his New Brunswick speech. Individual Cajuns certainly made their mark, however. One of them, Alexandre Mouton, was a U.S. Senator from 1837 until 1842 and Governor of Louisiana from 1843 until 1846. Longfellow's bestseller of a poem Evangeline, about the suffering of fictitious lovers separated after being exiled by the British to Louisiana, was published in 1847. It inspired the Acadians in Canada to embrace their cultural heritage, Perrin said in New Brunswick, but those in Louisiana soon had other fish to fry.
One of them, Alexandre Mouton, grew up speaking French on the family plantation and went on to preside over Louisiana's secession convention in 1861. He later contributed much of his considerable wealth to the Lost Cause. His son Alfred became a Confederate general and died in the Battle of Mansfield.
The preservation of Cajun culture was not on the legislative agenda in Louisiana, where the state constitution was amended in 1921 to ban French from public schools. Many Louisiana kids were punished for speaking their native tongue, and the number of French speakers in Louisiana steadily and significantly declined until the political class finally awoke to the severity of the cultural loss. The legislature established CODOFIL in 1968 and the French language was enthusiastically embraced by the school systems where it used to be verboten.
Our Views: Kathleen Blanco was an underestimated governor who gave it her 'all'
Despite the ban, many Cajun kids grew up speaking French at home. Thus in 1972, Louisiana once again had a bilingual Cajun governor in Edwin Edwards. History will be ambivalent about Edwards, who served four terms as governor and more than 8 years in the federal pen.
Edwards was succeeded as governor by Foster, whose name evokes the perpetrators rather than the victims of Le Grand Derangement, but Cajun rule returned with Blanco, who was running an effective administration before Katrina; it was under her, for instance that teacher pay was finally and briefly boosted to the long-promised Southern average.
Blanco declined to run for reelection when she seemed overwhelmed by Katrina and the public lost faith. It may be that ferocity of the storm, and the feebleness of the federal response, would have overstretched any governor, and history is likely to be kinder to her than her successor, Bobby Jindal, the alleged genius who almost managed to bankrupt the state.
Thank God for the Cajuns.
Email James Gill at Gill1407@bellsouth.net.
Kathleen Blanco
Edwin Edwards
nola.com
840 St. Charles Avenue
© Copyright 2020 NOLA.com, 840 St. Charles Avenue New Orleans, LA | Terms of Use | Privacy Policy

redpajama_set_name: RedPajamaCommonCrawl


A financial services organisation based in Canary Wharf is looking for a Compensation Manager to join the team on a permanent basis.
This is an excellent opportunity to join a well-regarded organisation where diversity is key on their agenda. To be considered for interview, or for further information, please apply accordingly.


A toxic smog which is blowing over from Europe has sparked a warning from DEFRA for UK residents.
DEFRA has issued an air pollution warning for the UK, with pollution levels in the South East expected to be 'moderate' from today (March 19).
This means adults and children with lung problems, heart problems, and asthma should reduce strenuous physical activity, particularly outdoors.
The pollution levels are expected to improve tomorrow (Friday March 20), and be clear by Saturday.


text:
Moral consistency in a burning world
After watching Freeman Dyson speak about his time with the Royal Air force during World War II in A Glorious Accident, I got to thinking about the notion of criminality.
Ego's, which are often blamed for crimes, start as inherently healthy. Egos become in- or deflated to cope with trauma, and hence are a defence mechanism that kept us alive thus far.
I believe self-interest is not the cause of the crime, but a consequence of something else.
Durkheim argued that crimes could not be avoided, since every individual is socialised with different norms and values. Further, he argued that criminals play a vital part of the living organism of society, showing weak spots that needer further investigation.
Similarly, depression is the disease of taking time too seriously, and bureaucracy is its cause.
Its undeniable that bureaucracy plays a vital part in crime legislation and punishment. Studied bureaucracies involve red tape, conflict, duplication, imperialism, and waste.
Bureaucracy is deadly, and there are countless Netflix documentaries that prove it.
To come back to Dyson's interview, he talked about "remote control killer" Adolf Eichmann. How to punish someone whose defence is 'I was just doing my job and following orders'? If there is no admission of guilt or semblance of remorse, how can a society feel that justice has been served?
Hannah Arendt described Eichman as the 'banality of evil'. I feel like there may be more and more such criminals in today's world. This is because of how removed people feel from their actions and their consequences through the Internet. People become sleeper-agents, helping push agendas and negating all responsibilities.
Out of personal interest I am looking for ways to make sure I am not regretting my part in some genocide further down the line. We must accept all criminals within us, all the horror people have ever blamed us for, all the terror we witnessed in the world.
criminality, criminals, durkheim, essay, ethics, evil, forgiveness, freeman dyson, friendship, good, hannah arendt, journalist, Mental Health, morality, philosphy, relationships, religion, writing
Judge people by how they act to people apologising to them
From the sublime to the ridiculous #poem


text:
College Language Exams and Certificates
Departmental and College Exams
The Slavic Department administers several language exams:
Placement exams are available in Russian, Czech, Polish and Serbian/Croatian. Up to three years of college credit may be awarded for demonstrated knowledge. These exams are generally administered during Orientation Week before the Autumn Quarter begins.
Competency exams in all Slavic languages allow students to satisfy the college language requirement. These exams are generally offered as part of the final exam for the first-year language courses. Students who are not taking these courses, but who would like to take the exam to satisfy the College's language competency requirement, should contact the language coordinator Cori Anderson for scheduling information.
Advanced Proficiency Certificates are available in Slavic languages for students who have
completed the equivalent of three years of Russian or two years of Czech, Polish or Bosnian/Croatian/Serbian;
spent eight or more weeks in the country of interest;
successfully passed an examination in advanced language skills.
For further information regarding any of these exams and certificates contact the language coordinator Erik Houle.


text:
Home » QA » Question: How To Get From Chicago Midway Airport To Millenium Station
Question: How To Get From Chicago Midway Airport To Millenium Station
How do I get from Midway to Millennium Station?
Is there a shuttle at Midway Airport?
How do I get from Midway Airport to downtown Chicago?
How much is an uber from downtown Chicago to Midway?
What time does Chicago train stop running?
What Blue Line stop is downtown Chicago?
How do I get around Midway airport?
How much is a taxi from Midway to downtown Chicago?
Where is uber pickup at Midway airport?
Which Chicago airport is closest to downtown?
How do I get to downtown Chicago from the airport?
Does the Orange Line go into Midway airport?
Can Uber pick me up at Midway Airport?
How far apart are O'Hare and Midway airports?
Does Van Galder go to Midway Airport?
What train stop is Millennium Park?
Is there a subway system in Chicago?
Does the Chicago subway run 24 hours?
How do I get from Millennium Park to airport?
Is the CTA Blue Line Safe?
Where does Amtrak stop in Chicago?
The best way to get from Millennium Station to Chicago Midway Airport (MDW) without a car is to subway which takes 28 min and costs $1 – $5.
There are a number of shuttles and regional buses that service Midway International Airport, making it easy for passengers all over the region to travel to and from the airport.
About the CTA Orange Line 'L' Orange Line train service operates from Midway (on the southwest side of Chicago) to downtown via elevated tracks. All trains from the Midway station go to the downtown Loop. The normal travel time to downtown from Midway is 20-25 minutes.
Grab an Uber/Lyft If you're interested in shared rides, you can also use a rideshare service like Uber or Lyft to get from Midway International Airport to downtown Chicago. This typically costs about $25 from Midway International Airport.
RED LINE Chicago 'L' Route Schedule and Stops (Updated) The RED LINE Chicago 'L' (95th/Dan Ryan) has 31 stations departing from Howard and ending in 95th/Dan Ryan. RED LINE Chicago 'L' time schedule overview for the upcoming week: Starts operating at 12:00 AM and ends at 11:50 PM. Operating days this week: everyday.
There are a number of stations you can get off at downtown, but the closest Blue Line station to Millennium Park is the Washington station. If you're looking to transfer trains or explore more of the Loop, the Clark/Lake station is a good one to hop off at.
MDW consists of only 1 terminal and 3 concourses, which are all interlinked, so there is no transport for getting around. Passengers can walk between the concourses without the need to re-clear security (unless arriving on an international flight).
Leaving Midway All taxis should have a working meter to calculate the fare based on time and mileage. Fares are based on traffic conditions, but an average fare from Midway to downtown Chicago is $35-40. For current fare information, please check the City of Chicago taxi passenger information website.
Picking up your rider Once you accept a trip request, the pickup location at MDW is located at Arrivals – Door 4 (see map below). To meet your rider, enter the center aisle. Pull over prior to picking up a passenger so as to not block the flow of traffic.
Which Airport is Closer to Downtown Chicago? The closest airport to Downtown Chicago is Midway International. Even though O'Hare is farther away with all forms of transportation, Signature Transportation Group is here to help. If you can't change your airport, you can always change your means of transportation.
The Blue Line 'L' train will take you from O'Hare to downtown in about 45 minutes for about $5.00. You can get the Blue Line from Terminals 1, 2, and 3 easily by just following the signs posted at the airport that say "CTA Trains" or "Trains to the City.".
Orange Line 'L' The Orange Line provides rapid transit train service between Midway Airport and downtown (Loop), and provides service to Chicago's Southwest Side.
Top questions from riders Do Uber driver-partners pick up at Midway Airport? Yes. Tap here for a list of airports around the world where you can request a ride with Uber.
Chicago O'Hare is 18 miles from the Bean by car, while Midway is about 12.5 miles away.Distance to downtown and transportation options. Getting downtown O'Hare (ORD) Midway (MDW) Distance (by car) 18 miles 12.5 miles.
This location is for service to/from Chicago Downtown/AMTRAK, Chicago Midway Airport, and connections to South Beloit, IL, Janesville, WI, and Madison, WI. Parking is available for $3.00 per calendar day.
Located in Millennium Park, east of Downtown. From the Red and Blue Line subways, exit at Lake Red Line station or Washington Blue Line station and walk two to three blocks east to Millennium Park.
Chicago's subway system is known as the "L" train, which stands for "elevated" train. Not every train line is elevated, but especially in the Loop, seeing trains rattle by high above the ground is a constant occurrence.
The "L" provides 24-hour service on the Red and Blue Lines and is one of only five rapid transit systems in the United States to do so. The oldest sections of the "L" started operations in 1892, making it the second-oldest rapid transit system in the Americas, after New York City's elevated lines.
How do I travel from Chicago O'Hare Airport (ORD) to Millennium Park without a car? The best way to get from Chicago O'Hare Airport (ORD) to Millennium Park without a car is to subway which takes 55 min and costs $1 – $5.
The Blue Line to/from O'Hare is considered very safe transportation. Not to worry – no matter the time of day/night.
Chicago, Illinois Best known for its majestic Great Hall, often bathed in soft light, Chicago Union Station is the hub for mid-western corridor services and national network trains serving the west.
How Far Is Midway Airport From Union Station Chicago
When Did Midway Airport Close Chicago
What Airlines Fly Into Midway Airport Chicago
How To Get To Chicago Midway Airport
Does Chicago Midway Airport Have Free Wifi
How Far Is Midway Airport From Downtown Chicago
How Far Is Chicago Midway Airport From Downtown
Does Chicago Midway Airport Go Outside The Us
Does American Airlines Fly Into Midway Airport Chicago
Is Chicago Midway Airport Near The Silversmith Hotel
Is There A Hotel In Chicago Midway Airport
How Far Trump Hotel Chicago To Midway Airport
Previous Post: Question: How To Get From Chicago Airport To Madison
Next Post: Question: How To Get From Denver Airport To Car Rental


text:
Case cards
Previous Case card
CEPSA (C-279/06)
Next Case card
Leclerc v. Commission (T-88/92)
Bulk Oil (174/84)
Destination clauses non-member countries
(Judicial) Authority:
Name of parties:
Bulk Oil (Zug) AG ('Bulk') v. Sun International Limited and Sun Oil Trading Company (together referred to as 'Sun')
curia.europa.eu
Information re: proceedings
Type of proceedings:
Preliminary ruling
Instance:
Court (preliminary ruling)
Connected decisions:
Opinion: Advocate General Slynn 10 December 1985
While this dispute was initially referred to arbitration, the award was appealed to the High Court of Justice (United Kingdom ('UK')). It is the High Court which referred a number of questions to the European Court of Justice ('ECJ') for a preliminary ruling.
1. CASE SUMMARY
A. Summary of facts
This case deals mainly with other aspects of EU law. Its relevance from an EU competition law perspective relates to the argumentation and findings regarding a 'destination clause under national policy'.
Ever since January 1979 it has been UK policy to authorize the exportation of oil of UK origin only to Member States of the Community, Member States of the International Energy Agency and countries with which there was before 1979 an 'existing pattern of trade' (specifically, Finland). This policy had never been incorporated into legislation or into any other kind of legal measure. It had, however, been made public on several occasions via government statements.
According to a contract concluded in 1981, the company Sun had agreed to sell a large quantity of crude oil to another company, Bulk, under the condition that this crude oil was not to be sold to countries other than those approved according to UK policy. As Sun discovered that Bulk, despite the destination clause of the contract, intended to sell the crude oil to Israel, Sun refused to deliver the product.
Bulk argued in its defence that the UK policy authorizes and even requires undertakings to infringe Article 101(1) TFEU.
B. Legal analysis
B.1 - Article 101(1) TFEU – effect on interstate trade
The ECJ held that a destination clause which obliges to sell within the EU, but not to certain third countries has no effect on interstate trade. Therefore, such a destination clause does not fall within the prohibition as set out by Article 101 TFEU.
2. QUOTES
"As has just been stated, a measure such as that in question which is specifically directed at exports of oil to a non-member country is not in itself likely to restrict or distort competition within the common market. It cannot therefore affect trade within the Community and infringe Articles 3 (f), [4(3) TEU] and [101 TFEU]." (§44)
"The Agreement of 20 May 1975 between the European Economic Community and the State of Israel does not prohibit the imposition of new quantitative restrictions or measures having equivalent effect on exports from a Member State to Israel." (§1 of the operative part)
"Regulation No 2603/69 of the Council of 20 December 1969 establishing common rules for exports does not prohibit a Member State from imposing new quantitative restrictions or measures having equivalent effect on its exports of oil to non-member countries." (§2 of the operative part)
"Articles [35 and 101 TFEU], upon their true construction, do not prevent a Member State from adopting a policy restricting or prohibiting exports of oil to a non-member country, on the basis of Article 10 of Regulation No 2603/69." (§3 of the operative part)
"Article 4 of the Council Decision of 9 October 1961, in conjunction with the Council Decision of 25 September 1962 and Article 15 of the Council Decision of 16 September 1969, requires a Member State contemplating a change in the state of liberalization of its exports to non-member countries to give prior notice to the other Member States and the Commission. A Member State which fails to give prior notice, delays in doing so or does so in an inadequate manner fails to fulfil its obligations under the Council decisions referred to; that failure does not, however, create individual rights which national courts must protect." (§4 of the operative part)
"The fact that no Community institution challenges the legality of a policy adopted by a Member State cannot in itself have any effect on the compatibility with Community law of a policy imposing quantitative restrictions on exports of oil to non-member countries or, consequently, on the reply to be given to the questions raised by the national court." (§5 of the operative part)
Article 101 TFEU
Article 8 of Regulation 2015/479 on common rules for export
4. RELEVANT LITERATURE
On the inapplicability of Article 101 TFEU to vertical agreements on exports outside the EU, see F. WIJCKMANS and F. TUYTSCHAEVER, Vertical Agreements in EU Competition Law, Oxford University Press, 2018, §3.123 – 3.145.
5. PRACTICAL SIGNIFICANCE
This case is relevant for practitioners as it provides that a destination clause in supply agreements restricting sales outside the EEA is not in itself distorting competition inside the EEA and hence not caught by Article 101 TFEU.
Belgium, Europe
Save, download or share this article
appreciable effect on trade non-EEA sales restriction (active) sales restriction (passive) destination clause export to non-member countries restriction by effect
More case cards about Europe
Case card
Melia (Holiday Pricing) (AT.40528)
Cross-border sales restrictions
By CONTRAST
Film merchandise (Universal) (AT.40433)
Limitation of out-of-territory sales and of sales to allocated customers by licensees
Guess (AT.40428)
By object restrictions imposed within a selective distribution network
Character merchandise (Sanrio) (AT.40432)
Limitation of out-of-territory sales by licensees
Comment on this case card
Sign in to post comments


text:
Arrest of a Stalkerware-maker in Australia Underscores Link Between Stalkerware and Domestic Abuse
By Karen Gullo
The ease with which bad actors can find a worldwide market for malicious apps that spy on people's digital devices is at the center of an Australian Federal Police case against a man who, starting at the age of 15, wrote a stalkerware application and sold it to 14,500 people in 128 countries.
Australian police last month arrested the man, now 24, and identified at least 201 of his Australian customers, in an investigation that began in 2017 and involved a dozen law enforcement agencies in Europe and Australia, and information provided by Palo Alto Networks and the FBI. The case underscores the sheer scope of the market for stalkerware—the app, costing just $35, was sold for seven years before law enforcement shut it down. Tens of thousands of victims were spied on, police said. Its customers included domestic violence perpetrators and even a child sex offender.
Stalkerware—commercially-available apps that are designed to be covertly installed on another person's device for the purpose of monitoring their activity without their knowledge or consent—continues to be a huge threat to consumers in general and to survivors of domestic abuse in particular. Research indicates that tens of thousands of people around the world are victims of stalkerware each year; the actual number is probably much higher due to underreporting.
Media outlets reported that Australian police arrested Jacob Wayne John Keen, the creator of Imminent Monitor stalkerware, on July 24. The tool, one of thousands of commodity Remote Access Tools, or, aptly, RATs, was designed to spy on computers running Windows. The spyware could be installed remotely on a victim's computer, without their knowledge, though phishing, where a user is duped into opening an email or text message that looks legitimate but then takes control of the computer without the user's knowledge or consent.
Much of the focus in the discussion of stalkerware is on the malicious apps that run on mobile devices. Stalkers can use those apps to track victims' locations, as well as other privacy-invasive uses. But stalkerware that runs on computers is also very dangerous, providing perpetrators access to a lot of sensitive user information, including all passwords and documents. Imminent Monitor, once installed on a victim's computer, could turn on their webcam and microphone, allow perpetrators to view their documents, photographs, and other files, and record all keystrokes entered.
Imminent Monitor's creator tried to maintain that the app was a legitimate remote desktop utility (monitoring apps are often used for spying by stalkers). But Palo Alto Networks report noted that Imminent Monitor hawked nefarious features that kept the presence of the app secret from the user and mined the victim's computer for cryptocurrency.
The law enforcement investigation of the app targeted both sellers and users. The Australian police were able to identify both the Australian offenders who bought the software and the victims they targeted, which they said was a first for any law enforcement agency. Two hundred and one buyers were identified in Australia alone—half of whom were identified through their PayPal records.
Australian police said that a statistically high percentage of those customers were respondents on domestic violence orders. The app was sold to buyers in 128 countries before its web page was taken down in late 2019, when 85 warrants were executed in Australia and Belgium, 434 devices seized, including the app-makers custom-built computer, and 13 of the apps most prolific users were arrested. The investigation involved actions in Colombia, Czechia, the Netherlands, Poland, Spain, Sweden and the United Kingdom.
Imminent Monitor's creator was charged with six counts of committing computer offenses, which together carry a maximum sentence of 20 years in prison.
Every time a stalkerware app is taken down, it's a victory for users everywhere. Unfortunately, we know that those caught are just the tip of the iceberg. Still, the Imminent Monitor investigation and takedown should serve as a deterrent and send a strong message that, while stalkerware app creators are on the hunt for customers, defenders of spyware victims are on the hunt for them.
Cell Tracking
Locational Privacy
Deeplinks Blog by Karen Gullo, Veridiana Alimonti | January 27, 2023
Brazil's biggest internet connection providers made moderate advances in protecting customer data and being transparent about their privacy practices, but fell short on meeting certain requirements for upholding users' rights under Brazil's data protection law, according to InternetLab's 2022 Quem Defende Seus Dados? (Who Defends Your Data?) report. In...
Deeplinks Blog by Karen Gullo | January 9, 2023
EFF and Partners Call Out Threats to Free Expression in Draft Text as UN Cybersecurity Treaty Negotiations Resume
EFF is attending this week and next a new round of negotiations over the proposed UN Cybercrime Treaty to raise concerns that draft provisions now on the table include a long list of content-related crimes that pose serious threats to free expression, privacy, and the legitimate activities of journalists,...
Deeplinks Blog by Katitza Rodriguez, Karen Gullo | January 2, 2023
Global Cybercrime and Government Access to User Data Across Borders: 2022 in Review
Since the new UN cybercrime treaty began to take shape in 2022, EFF has been fighting on behalf of users to make sure content-based crimes are excluded from the Treaty, and robust human rights safeguards and rule of law standards are the basis of any final product.There's a lot...
Deeplinks Blog by Joe Mullin | December 27, 2022
Users Worldwide Said "Stop Scanning Us": 2022 in Review
The online conversations that bring us closer together can help build a world that's more free, fair, and creative. But talking to each other only works when the people talking have their human rights respected, including their right to speak privately. The best tool to defend that right in...
Deeplinks Blog by Veridiana Alimonti | December 25, 2022
Hacking Governments and Government Hacking in Latin America: 2022 in Review
In 2022, cyber-attacks on government databases and systems broke into headlines in several Latin American countries. These attacks have exposed government systems' vulnerabilities—including sometimes basic ones, like failing to keep software updated with critical patches—and shown how attacks can affect government data, services, and infrastructure. On the other hand, they...
Deeplinks Blog by Bill Budington, Cooper Quintin | December 24, 2022
EFF's Threat Lab Sharpens Its Knives: 2022 in Review
EFF's Threat Lab is dedicated to deep-dive investigations that examine technology-enforced power imbalances in society. In 2022 we've sharpened our knives and honed our skills in an effort to bring down the stalkerware industry, taken aim at invasive surveillance by police, raised red flags around the security and privacy...
Deeplinks Blog by Ross Schulman | December 22, 2022
A Roller Coaster for Decentralization: 2022 in Review
This year has been a roller coaster for the movement to decentralize the services and tools that we rely on every day. Decentralizing internet services may help remedy concerns ranging from traditional big business competition to online privacy.In order to devote closer attention to these issues which impact user autonomy...
Deeplinks Blog by Adam Schwartz, Corynne McSherry | December 21, 2022
California Courts Must Protect Data Privacy
If our legal rights to data privacy aren't enforceable, they are just empty promises. One of the best ways to enforce them is to let people sue the companies that violate their data privacy. Unfortunately, the U.S. Supreme Court has been chipping away at private enforcement by rewriting...
Deeplinks Blog by Jason Kelley, Aaron Mackey | December 15, 2022
Dangerous "Kids Online Safety Act" Does Not Belong in Must-Pass Legislation
Every year, Congress must follow through on an enormous and complicated task: agreeing on how to fund the government for the following year. The wrangling over spending often comes down to the wire, and this year, some Senators are considering shoehorning a controversial and unconstitutional bill, the Kids Online...
Deeplinks Blog by Gennie Gebhart | December 9, 2022
A Promising New GDPR Ruling Against Targeted Ads
Targeted advertising's days may be numbered. The Wall Street Journal and Reuters report that the European Data Protection Board has ruled that Meta cannot continue targeting ads based on user's online activity without affirmative, opt-in consent. This ruling is based on the European Union's General Data Protection Regulation...