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Tag: Dawn Barlow
The ecologist and the economist: Exploring parallels between disciplines
By Dawn Barlow1 and Johanna Rayl2
1PhD Candidate, Oregon State University Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab
2PhD Student, Northwestern University Department of Economics
The Greek word "oikos" refers to the household and serves as the root of the words ecology and economics. Although perhaps surprising, the common origin reflects a shared set of basic questions and some shared theoretical foundations related to the study of how lifeforms on earth use scarce resources and find equilibrium in their respective "households". Early ecological and economic theoretical texts drew inspiration from one another in many instances. Paul Samuelson, fondly referred to as "the father of modern economics," observed in his defining work Foundations of Economic Analysis that the moving equilibrium in a market with supply and demand is "essentially identical with the moving equilibrium of a biological or chemical system undergoing slow change." Likewise, early theoretical ecologists recognized the strength of drawing on theories previously established in economics (Real et al. 1991). Similar broad questions are central to researchers in both fields; in a large and dynamic system (termed "macro" in economics) scale, ecologists and economists alike work to understand where competitive forces find equilibrium, and an in individual (or micro) scale, they ask how individuals make behavior choices to maximize success given constraints like time, energy, wealth, or physical resources.
The central model economists have in mind when trying to understand human choices involves "constrained optimization": what decision will maximize a person, family, firm, or other agent's objectives given their limitations? For example, someone that enjoys relaxing but also seeks a livable income must choose how much time to devote to working versus relaxing, given the constraint of having just 24 hours in the day, and given the wage they receive from working. An economist studying this decision may want to learn about how changes in the wage will affect that person's choice of working hours, or how much they dislike working relative to relaxing. Along similar lines, early ecologists theorized that organisms could be selected for one of two optimization strategies: minimizing the time spent acquiring a given amount of energy (i.e., calories from food), or maximizing total energy acquisition per unit of time (Real et al. 1991). Foundational work in the field of economics clarified numerous technical details about formulating and solving such optimization problems. Returning to the example of the leisure time decision, economic theory asks: does it matter if we model this decision as maximizing income given wages and limited time, or as minimizing hours spent working given a desired lifetime income?; can we formulate a "utility function" that describes how well-off someone is with a given income and amount of leisure?; can we solve for the optimal amount of leisure with pen and paper? The toolkit arising from this work serves as a jumping off point for all contemporary economic research, and the kinds of choices understood under this framework is vast, from, where should a child attend school?; to, how should a government allocate its budget across public resources?
Early work in ecology drew from foundational concepts in economics, following the realization that the strategies by which organisms exploit resources most efficiently also involve optimization. This parallel was articulated by MacArthur and Pianka in their foundational 1966 paper Optimal Use of a Patchy Environment, in which they state: "In this paper we undertake to determine in which patches a species would feed and which items would form its diet if the species acted in the most economical fashion. Hopefully, natural selection will often have achieved such optimal allocation of time and energy expenditures." Subsequently, this idea was refined into what is known in ecology as the marginal value theorem, which states that an animal should remain in a prey patch until the rate of energy gain drops below the expected energy gain in all remaining available patches (Charnov 1976). In other words, if it is more profitable to switch prey patches than to stay, an animal should move on. These optimization models therefore allow ecologists to pose specific evolutionary and behavioral hypotheses, such as examining energy acquisition over time to understand selective forces on foraging behavior.
As the largest animals on the planet, blue whales have massive prey requirements to meet energy demands. However, they must balance their need to feed with costs such as oxygen consumption during breath-holding, the travel time it takes to reach prey patches at depth, the physiological constraints of diving, and the necessary recuperation time at the surface. It has been demonstrated that blue whales forage selectively to optimize this energetic budget. Therefore, blue whales should only feed on krill aggregations when the energetic gain outweighs the cost (Fig. 1), and this pattern has been empirically demonstrated for blue whale populations in the Gulf of St. Lawrence, Canada (Doniol-Valcroze et al. 2011), in the California Current, (Hazen et al. 2015) and in New Zealand (Torres et al. 2020).
Figure 1. Figure reprinted from Hazen et al. 2015, illustrating how a blue whale should theoretically optimize foraging success in two scenarios. Energy gained from feeding is shown by the blue lines, whereas the cost of foraging in terms of declining oxygen stores during a dive is illustrated by the red lines. On the left (panel B), the whale maximizes its energy gain by increasing the number of feeding lunges (shown by black circles) at the expense of declining oxygen stores when prey density is high. On the right (panel C), the whale minimizes oxygen use by reducing the number of feeding lunges when prey density is low.
The notion of the marginal value theorem is likewise at work in countless economic settings. Economic theory predicts that a farmer cultivating two crops would allocate resources into each crop such that the returns to adding more resources into each crop are the same. If not, she should move resources from the less productive crop to the one where marginal gains are larger. A fisherman, according to this notion, continues to fish longer into the season until the marginal value of one additional day at sea equals the marginal cost of their time, effort, and expenses. These predictions are intuitive by the same logic as the blue whale choosing where to forage, and derive from the mathematics of constrained and unconstrained optimization. Reassuringly, empirical work finds evidence of such profit-maximizing behavior in many settings. In a recent working paper, Burlig, Preonas, and Woerman explore how farmers' water use in California responds to changes in the price of electricity, which effectively makes groundwater irrigation more expensive due to electric pumping. They find that farmers are very responsive to these changes in marginal cost. Farmers achieve this reduction in water use predominantly by switching to less water-intensive crops and fallowing their land (Burlig, Preonas, and Woerman 2020).
Undoubtedly there are fundamental differences between an ecosystem with interacting biotic and abiotic components and the human-economic environment with its many social and political structures. But for certain types of questions, the parallels across the shared optimization problems are striking. The foundational theories discussed here have paved the way for subsequent advances in both disciplines. For example, the field of behavioral ecology explores how competition and cooperation between and within species affects fitness of populations. Reflecting on early seminal work lends some perspective on how an area of research has evolved. Likewise, exploring parallels between disciplines sheds light on common threads, in turn revealing insights into each discipline individually.
Burlig, Fiona, Louis Preonas, and Matt Woerman (2020). Groundwater, energy, and crop choice. Working Paper.
Charnov EL (1976) Optimal foraging: The marginal value theorem. Theoretical Population Biology 9:129–136.
Doniol-Valcroze T, Lesage V, Giard J, Michaud R (2011) Optimal foraging theory predicts diving and feeding strategies of the largest marine predator. Behavioral Ecology 22:880–888.
Hazen EL, Friedlaender AS, Goldbogen JA (2015) Blue whales (Balaenoptera musculus) optimize foraging efficiency by balancing oxygen use and energy gain as a function of prey density. Science Advces 1:e1500469–e1500469.
MacArthur RH, Pianka ER (1966) On optimal use of a patchy environment. The American Naturalist 100:603–609.
Real LA, Levin SA, Brown JH (1991) Part 2: Theoretical advances: the role of theory in the rise of modern ecology. In: Foundations of ecology: classic papers with commentaries.
Samuelson, Paul (1947). Foundations of Economic Analysis. Harvard University Press.
Torres LG, Barlow DR, Chandler TE, Burnett JD (2020) Insight into the kinematics of blue whale surface foraging through drone observations and prey data. PeerJ 8:e8906.
Posted on January 18, 2021 January 18, 2021 Author Dawn BarlowCategories UncategorizedTags blue whale, Dawn Barlow, ecology, economics, foraging ecology, Johanna Rayl, optimal foraging theoryLeave a comment on The ecologist and the economist: Exploring parallels between disciplines
Boundaries in the dynamic ocean
By Dawn Barlow, PhD student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab
The ocean is vast, ever-changing, and at first glance, seemingly featureless. Yet, we know that the warm, blue tropics differ from icy polar waters, and that temperate kelp forests are different from coral reefs. In the connected fluid environment of the global oceans, how do such different habitats exist, and what separates them? On a smaller scale, you may observe a current mixing line at the ocean surface, or dive down from the surface and feel the temperature drop sharply. In a featureless ocean, what boundaries exist, and how can we delineate between different environments?
These questions have been on my mind recently as I study for my PhD Qualifying Exams, an academic milestone that involves written and oral exams prepared by each committee member for the student. The subject matter spans many different areas, including ecological theory, underwater acoustics, oceanography, zooplankton dynamics, climate change and marine heatwaves, and protected area design. Yet, in my recent studying, I was struck by a realization: since when did my PhD involve so much physics? Atmospheric pressure differences generate wind, which drive global ocean circulation patterns. Density properties of seawater create structure in the ocean, and these physical features influence productivity and aggregate prey for predators such as whales. Sound propagates through the fluid ocean as a pressure wave, and its transmission is influenced by physical characteristics of the sound and the medium it moves through. Many of these examples can be distilled and described with equations rooted in physics. Physics doesn't behave, it simply… is. In considering the vast and dynamic ocean, there is something quite satisfying in that simple notion.
Circling back to boundaries in the ocean, there are changes in physical properties of the oceans that create boundaries, some stark and some nuanced. These physical features structure and partition the marine environment through differences in properties such as temperature, salinity, density, and pressure. Geographic partitions can occur in both horizontal and vertical dimensions of the water column, and on scales ranging from less than a kilometer to thousands of kilometers [1,2].
In the horizontal dimension, currents, fronts, and eddies mark transition zones between environments. In the time of industrial whaling, observations of temperature and salinity were made at the surface from factory whaling ships and examined to understand where the most whales were available for hunting. These early measurements identified temperature contour lines, or isotherms, and led to observations that whales were found in areas of stark temperature change and places where isotherms bent into "tongues" of interacting water masses [3,4] (Fig. 1). These areas where water masses of different properties meet are often areas of high productivity. Today, we understand that shelf break fronts, river plumes, tidal fronts, and eddies are important horizontal structures that drive elevated nutrient availability, phytoplankton production, and prey availability for mobile marine predators, including whales.
Figure 1. Surface temperature and salinity contour lines from measurements taken aboard a factory whaling ship in the Antarctic, reproduced from Nasu (1959).
In the vertical dimension, the water column is also structured into distinct layers. Surface waters are warmed by the sunlight and are often lower in salinity due to freshwater input from rain and runoff. Below this distinct surface portion of the water column, the temperature drops sharply in a layer known as the thermocline, and below which pressure and density increase with depth. The surface layer is subject to mixing from wind input, which can draw nutrients from below up into the photic zone and spur productivity. The alternation between stratification—a water column with distinctive layers—and mixing drives optimal conditions for entire food webs to thrive [1,2].
While I began this blog post by writing about boundaries that partition different ocean environments, I have continued to learn that those boundary zones are often critically important in their own right. I started by thinking about boundaries in terms of their importance for separation, but now understand that the leaky points between them actually spur ocean productivity. Features such as fronts, currents, mixed layers, and eddies separate water masses of different properties. However, they are not truly complete and rigid boundaries, and precisely for that reason they are uniquely important in promoting productive marine ecosystems.
Figure 2. Left: Some of the materials I am studying for my qualifying exams. Right: A blue whale surfaces in New Zealand's South Taranaki Bight, the subject of my PhD and the lens through which I consider the concepts I am reading about (photo by L. Torres).
Many thanks to my PhD Committee members who continue to guide me through this degree and who I am lucky to learn from. In particular, the contents of this blog post were inspired by materials recommended by, and discussions with, Dr. Daniel Palacios.
1. Mann, K.H., and Lazier, J.R.N. (2006). Dynamics of Marine Ecosystems 3rd ed. (Blackwell Publishing).
2. Longhurst, A.R. (2007). Ecological Geography of the Sea 2nd ed. (Academic Press).
3. Nasu, K. (1959). Surface water conditions in the Antarctic whaling pacific area in 1956-57.
4. Machida, S. (1974). Surface temperature fields in the Crozet and Kerguelen whaling grounds. Sci. Reports Whales Res. Inst. 26, 271–287.
Posted on November 9, 2020 November 14, 2020 Author Dawn BarlowCategories UncategorizedTags blue whale, Dawn Barlow, Graduate School, marine ecology, Marine Mammals, oceanography, qualifying examsLeave a comment on Boundaries in the dynamic ocean
Pretty science
By Solène Derville, Postdoc, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab
Ever since I was a teenager, I have been drawn to both arts and sciences. When I decided to go down the path of marine biology and research, I never thought I would one day be led to exploit my artistic skills as well as my scientific interests.
Processing data, coding, analyzing, modeling… these tasks form the core of my everyday work and are what generates my excitement and passion for research. But once a new result has come up, or a new hypothesis has been formed, how boring would it be to keep it for myself? Science is all about communication, exchanges with our peers, with stakeholders, and with the general public. Graphical representations have always been supported in research throughout the history of sciences, and particularly the life sciences (Figure 1).
I have come to realize how much I enjoy this aspect of my work, and also how much I wish I was better prepared for it! In this blogpost I will talk about visual communication in science, and tackle the question of how to make our plots, diagrams, powerpoints, figures, maps, etc. convey information that goes beyond any spoken language? I have compiled a few tips from the design and infographics fields that I think could be reinvested in our scientific communication material.
Figure 1. Illustration from anonymous biology book (credit: Katie Garrett)
Plan, order, design
This suggestion may appear like a rather simplistic piece of advice, but any form of communication should start with a plan. What is the name of my project, the goal, and the audience? A scientific conference poster will not be created with the same design as a flyer aimed at the general public, nor will the same tools be used. Libre office powerpoint, canva, inkscape, scribus, R, plotly, GIMP… these are the open-source software I use on a regular basis but there so many more possibilities!
For whatever the type of visual you want to create, there are two major rules that need to be considered. First, embrace the empty space! You may think that you are wasting space that could be filled by all sorts of extremely valuable pieces of information… but this empty space has a purpose all by itself. The empty space brings forward the central elements of your design and will help focus the attention of the viewer toward them (top panel of Figure 2). Second, keep it neat and aligned. Whether you choose to anchor elements to each other or to an invisible grid, pay attention to details so that all images and text in the design from a harmonious whole (bottom panel in Figure 2).
Figure 2. Empty spaces and alignment principles of design – examples presented by Kingcom (http://kingkom.net/12-criteres-hierarchie-visuelle/)
Alignment is also an essential aspect to consider when editing images. More than any text, images will provide the first impression to the viewer and may subjectively communicate ideas in an instant. To make them most effective, images may follow the 'rule of thirds'. Imagine breaking the image down into thirds, hence creating four directive lines over it (Figure 3). Placing the points of interest of the image at the intersections or along the lines will provide balance and attract the viewer's attention. In marine mammal science where we often use pictures of animals with the ocean as a background, aligning the horizon along one of these horizontal lines may be a good technique (which I have not followed in Figure 3 though!).
Figure 3. Rule of thirds example applied to a photo of a humpback whale calf (South Lagoon New Caledonia, credit: Opération Cétacés – Solène Derville). Notice how the tip of the calf's jaw is at the intersection of two lines.
When adding text to images, it is important to not overwhelm illustrations with text by trying to use extensive written material (which happens much too often). I try to keep the text to the strict minimum and let the visuals speak for themselves. When including text over or next to an image, I place the text in the empty spaces, where the eye is drawn to (Figure 4). When using dark or contrasted images, I add a semi-transparent layer in between the text and the image to make my text pop out.
Figure 4. Text embedding example applied to a photo of a humpback whale calf (South Lagoon New Caledonia, credit: Opération Cétacés – Solène Derville). Notice how I placed the text in the empty space so that the nose of the calf would point to it.
Tired of using Arial, Times and Calibri but don't know which other font to pick? One good piece of advice I found online was to choose a font that complements the purpose of the design. To do so, it is necessary to choose the message before picking the font. There are three categories of fonts (show in Image 1):
– Serif (classic style designed for books as the little feet at the extremities of the letters guide the eye along the lines of text)
– Sans serif (designed to look clean on digital screen)
– Display (more personality, but to be used in small doses!)
Image 1. Examples of each font category
I have also learned that pairing fonts together is often about using opposites (Figure 5). Contrasting fonts are complementary. For instance, it is visually appealing to combine a very bold font with a very light font, or a round font with something tall. And if you need more font choices than the ones provided by your usual software, here is a web repository to freely download thousands of different fonts: https://www.dafont.com
Figure 5. Paired fonts example applied to a photo of a humpback whale calf (South Lagoon New Caledonia, credit: Opération Cétacés – Solène Derville). Notice how I combined a rounded font with a smaller sans serif font.
Colors have inherent meaning that depends on individual cultures. Whether we want it or not, any plot, photo, or diagram that we present to an audience will carry a subliminal message depending on its color palette. So better make it fit with the message!
Let us go passed the boring blue shades we have used for all of our marine science presentations so far, and instead open ourselves up to an infinite choice of colors! Color nuances are defined by three things: hue (the color itself), saturation (intensity, whether the color looks more subtle or more vibrant), and value (how dark or light a color is, ranging from white to black). The color wheel helps us visualize the relationships between hues and pick the best associations (Figure 6).
Figure 6. The color wheel helps us visualize the relationships between hues and pick the best associations. Any of the principles above should work, from the simple monochromatic schemes to the more complex triad or tetradic schemes.
First, pick the main color, the hero color for your design. Choose a cool color (blues and greens) if you want to provide a calming impression or a warm color (reds and yellows) for something more energizing. This basic principle of color theory made me think back on the black/blue dark shaded presentations that I might have attended in the past and had trouble staying awake!
Now, create your color palette, which are the three to four colors that will compose your design, ideally combining some vibrant and some more neutral colors for contrast. For instance, in a publication, a color palette may be used consistently in all plots or figures to represent a set of variables, study areas, or species . Now how do you pick the right complementary colors? The color wheel provides you with a few basic principles that should help you choose a palette (Figure 6). From monochromatic to tetradic schemes, the choice is up to you:
– monochromatic colors: varying values or saturation of a given color picked in the wheel
– analogous colors: colors sitting next to each other in the wheel
– complementary color: colors sitting opposite to each other
If you are an R user, there are a myriad of color palettes available to produce your visuals. One of the most comprehensive list I have found was compiled by Emil Hvitfeldt in github (https://github.com/EmilHvitfeldt/r-color-palettes). For discrete color palettes, I enjoy using the Canva palettes, which are available both in the Canva designs and in R using the 'canva' library in combination with the 'ggplot2' library (https://www.canva.com/learn/100-color-combinations/).
In practice, this means I can produce R plots or maps with color codes that match those I use in my canva presentations or posters. And finally, thumbs up to Dawn and Clara for creating our very own GEMM lab color palette based on whale photos collected in the field (Figure 7: https://github.com/dawnbarlow/musculusColors)!
Figure 7: Example of a R plot colored with the musculusColors package using the blue whale "Bmlunge" palette (credit: Dawn Barlow & Clara Bird)
I hope these few tips help you make your science as look as pretty as it is in your mind!
A lot of the material in this blog post was inspired by the free tutorials provided by Canva: https://designschool.canva.com/courses/graphic-design-basics/?lesson=design-to-communicate
About the rule of thirds: https://digital-photography-school.com/rule-of-thirds/
About alignment: https://blog.thepapermillstore.com/design-principles-alignment/
Posted on November 1, 2020 November 1, 2020 Author Clara BirdCategories UncategorizedTags Clara Bird, communication, Dawn Barlow, graphing, scicomm, Science communication, Scientific illustration, scientific writing, Solene DervilleLeave a comment on Pretty science
Marine mammals of the Northern California Current, 2020 edition
Clara and I have just returned from ten fruitful days at sea aboard NOAA Ship Bell M. Shimada as part of the Northern California Current (NCC) ecosystem survey. We surveyed between Crescent City, California and La Push, Washington, collecting data on oceanography, phytoplankton, zooplankton, and marine mammals (Fig. 1). This year represents the third year I have participated in these NCC cruises, which I have come to cherish. I have become increasingly confident in my marine mammal observation and species identification skills, and I have become more accepting of the things out of my control – the weather, the sea state, the many sightings of "unidentified whale species". Careful planning and preparation are critical, and yet out at sea we are ultimately at the whim of the powerful Pacific Ocean. Another aspect of the NCC cruises that I treasure is the time spent with members of the science team from other disciplines. The chatter about water column features, musings about plankton species composition, and discussions about what drives marine mammal distribution present lively learning opportunities throughout the cruise. Our concurrent data collection efforts and ongoing conversations allow us to piece together a comprehensive picture of this dynamic NCC ecosystem, and foster a collaborative research environment.
Figure 1. Data collection effort for the NCC September 2020 cruise, between Crescent City, CA, and La Push, WA. Red points represent oceanographic sampling stations, and black lines show the track of the research vessel during marine mammal survey effort.
Every time I head to sea, I am reminded of the patchy distribution of resources in the vast and dynamic marine environment. On this recent cruise we documented a stark contrast between expansive stretches of warm, blue, stratified, and seemingly empty ocean and areas that were plankton-rich and supported multi-species feeding frenzies that had marine mammal observers like me scrambling to keep track of everything. This year, we were greeted by dozens of blue and humpback whales in the productive waters off Newport, Oregon. Off Crescent City, California, the water was very warm, the plankton community was dominated by gelatinous species like pyrosomes, salps, and other jellies, and the marine mammals were virtually absent except for a few groups of common dolphins. To the north, the plume of water flowing from the Columbia River created a front between water masses, where we found ourselves in the midst of pacific white-sided dolphins, northern right whale dolphins, and humpback whales. These observations highlight the strength of ecosystem-scale and multi-disciplinary data collection efforts such as the NCC surveys. By drawing together information on physical oceanography, primary productivity, zooplankton community composition and abundance, and marine predator distribution, we can gain a nearly comprehensive picture of the dynamics within the NCC over a broad spatial scale.
Common dolphins
Humpback whale breaching
Risso's dolphins
Pacific white-sided dolphins
A few images of marine mammals of the NCC. Click a photo to explore gallery. All photos by Dawn Barlow.
This year, the marine mammals delivered and kept us observers busy. We lucked out with good survey conditions and observed many different species throughout the NCC (Table 1, Fig. 2).
Table 1. Summary of all marine mammal sightings from the NCC September 2020 cruise.
Figure 2. Maps showing kernel densities of four frequently observed and widely distributed species seen during the cruise. Black lines show the track of the research vessel during marine mammal survey effort, white points represent sighting locations, and colors show kernel density estimates weighted by group size at each sighting.
This year's NCC cruise was unique. We went to sea as a global pandemic, wildfires, and political tensions continue to strain this country and our communities. This cruise was the first NOAA Fisheries cruise to set sail since the start of the pandemic. Our team of scientists and the ship's crew went to great lengths to make it possible, including a seven-day shelter-in-place period and COVID-19 tests prior to cruise departure. As a result of these extra challenges and preparations, I think we were all especially grateful to be on the water, collecting data. At-sea fieldwork is always challenging, but morale was up, spirits were high, and laughs were frequent despite smiles being concealed by our masks. I am grateful for the opportunity to participate in this ongoing valuable data collection effort, and to be part of this team. Thanks to all who made it such a memorable cruise.
Figure 3. The NCC September 2020 science team at the end of a successful research cruise! Fieldwork in the time of COVID-19 presents many logistical challenges, but this team rose to the occasion and completed a safe and fruitful survey despite the circumstances.
Posted on October 5, 2020 October 5, 2020 Author Dawn BarlowCategories Current Projects, Oregon whale distribution, UncategorizedTags Collaborative Research, data collection, Dawn Barlow, Distribution, dolphins, Fieldwork, Marine Mammals, NOAA Fisheries, Northern California Current, oceanography, whales, zooplanktonLeave a comment on Marine mammals of the Northern California Current, 2020 edition
What makes a good meal for a hungry whale?
In the vast and dynamic marine environment, food is notoriously patchy and ephemeral [1]. Predators such as marine mammals and seabirds must make a living in this dynamic environment by locating and capturing those prey patches. Baleen whales such as blue and humpback whales have a feeding strategy called "lunge feeding", whereby they accelerate forward and open their massive jaws, engulf prey-laden water in their buccal pouch that expands like an accordion, and filter the water out through baleen plates so that they are left with a mouthful of food (Fig. 1) [2]. This approach is only efficient if whales can locate and target dense prey patches that compensate for the energetic costs of diving and lunging [3]. Therefore, not only do these large predators need to locate enough food to survive in the expansive and ever-changing ocean, they need to locate food that is dense enough to feed on, otherwise they actually lose more energy by lunging than they gain from the prey they engulf.
Figure 1. Schematic of a humpback whale lunge feeding on a school of fish. Illustration by Alex Boersma.
Why do baleen whales rely on such a costly feeding approach? Interestingly, this tactic emerged after the evolution of schooling behavior of prey such as zooplankton and forage fish (e.g., herring, anchovy, sand lance) [4]. Only because the prey aggregate in dense patches can these large predators take advantage of them by lunge feeding, and by engulfing a whole large patch they efficiently exploit these prey patches. Off the coast of California, where krill aggregations are denser in deeper water, blue whales regularly dive to depths of 100-300 m in order to access the densest krill patches and get the most bang for their buck with every lunge [5]. In New Zealand, we have found that blue whales exploit the dense krill patches near the surface to maximize their energetic gain [6], and have documented a blue whale bypassing smaller krill patches that presumably were not worth the effort to feed on.
By now hopefully I have convinced you of the importance of dense prey patches to large whales looking for a meal. It is not necessarily only a matter of total prey biomass in an area that is important to a whale, it is whether that prey biomass is densely aggregated. What makes for a dense prey patch? Recent work has shown that forage species, namely krill and anchovies, swarm in response to coastal upwelling [7]. While upwelling events do not necessarily change the total biomass of prey available to a whale over a spatial area, they may aggregate prey to a critical density to where feeding by predators becomes worthwhile. Forage species like zooplankton and small fish may school because of enhanced food resources, for predator avoidance, or reproductive grouping. While the exact behavioral reason for the aggregation of prey may still only be partially understood, the existence of these dense patches allows the largest animals on the planet to survive.
Another big question is, how do whales actually find their food? In the vast, seemingly featureless, and ever-changing ocean environment, how does a whale know where to find a meal, and how do they know it will be worthwhile before they take a lunge? In a review paper written by GEMM Lab PI Dr. Leigh Torres, she suggests it is all a matter of scale [8]. On a very large scale, baleen whales likely rely on oceanographic stimuli to home in on areas where prey are more likely to be found. Additionally, recent work has demonstrated that migrating blue whales return to areas where foraging conditions were best in previous years, indicating a reliance on memory [9,10]. On a very fine scale, visual cues may inform how a blue whale chooses to lunge [6,8,11].
What does it matter what a blue whale's favorite type of meal is? Besides my interest in foundational research in ecology such as predator-prey dynamics, these questions are fundamental to developing effective management approaches for reducing impacts of human activities on whales. In the first chapter of my PhD, I examined how oceanographic features of the water column structure krill aggregations, and how blue whale distribution is influenced by oceanography and krill availability [12]. Currently, I am deep into my second chapter, analyzing the pathway from wind to upwelling to krill to blue whales in order to better understand the links and time lags between each step. Understanding the time lags will allow us to make more informed models to forecast blue whale distribution in my third chapter. Environmental managers in New Zealand plan to establish a protected area to conserve the population of blue whales that I study [13] on their foraging grounds. Understanding where blue whales will be distributed, and consequently how their distribution patterns might shift with environmental conditions or overlap with human activities, comes down the fundamental question I started this blog post with: What makes a good meal for a hungry whale?
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2. Goldbogen JA, Cade DE, Calambokidis J, Friedlaender AS, Potvin J, Segre PS, Werth AJ. 2017 How Baleen Whales Feed: The Biomechanics of Engulfment and Filtration. Ann. Rev. Mar. Sci. 9, 367–386. (doi:10.1146/annurev-marine-122414-033905)
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4. Cade DE, Carey N, Domenici P, Potvin J, Goldbogen JA. 2020 Predator-informed looming stimulus experiments reveal how large filter feeding whales capture highly maneuverable forage fish. Proc. Natl. Acad. Sci. U. S. A. (doi:10.1073/pnas.1911099116)
5. Hazen EL, Friedlaender AS, Goldbogen JA. 2015 Blue whales (Balaenoptera musculus) optimize foraging efficiency by balancing oxygen use and energy gain as a function of prey density. Sci. Adv. 1, e1500469–e1500469. (doi:10.1126/sciadv.1500469)
6. Torres LG, Barlow DR, Chandler TE, Burnett JD. 2020 Insight into the kinematics of blue whale surface foraging through drone observations and prey data. PeerJ (doi:10.7717/peerj.8906)
7. Benoit-Bird KJ, Waluk CM, Ryan JP. 2019 Forage Species Swarm in Response to Coastal Upwelling. Geophys. Res. Lett. 46, 1537–1546. (doi:10.1029/2018GL081603)
8. Torres LG. 2017 A sense of scale: Foraging cetaceans' use of scale-dependent multimodal sensory systems. Mar. Mammal Sci. 33, 1170–1193. (doi:10.1111/mms.12426)
9. Abrahms B et al. 2019 Memory and resource tracking drive blue whale migrations. Proc. Natl. Acad. Sci. U. S. A. (doi:10.1073/pnas.1819031116)
10. Szesciorka AR, Ballance LT, Širovi A, Rice A, Ohman MD, Hildebrand JA, Franks PJS. 2020 Timing is everything: Drivers of interannual variability in blue whale migration. Sci. Rep. 10, 1–9. (doi:10.1038/s41598-020-64855-y)
11. Friedlaender AS, Herbert-Read JE, Hazen EL, Cade DE, Calambokidis J, Southall BL, Stimpert AK, Goldbogen JA. 2017 Context-dependent lateralized feeding strategies in blue whales. Curr. Biol. (doi:10.1016/j.cub.2017.10.023)
12. Barlow DR, Bernard KS, Escobar-Flores P, Palacios DM, Torres LG. 2020 Links in the trophic chain: Modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes. Mar. Ecol. Prog. Ser. (doi:https://doi.org/10.3354/meps13339)
13. Barlow DR et al. 2018 Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger. Species Res. 36, 27–40. (doi:https://doi.org/10.3354/esr00891)
Posted on September 7, 2020 September 7, 2020 Author Dawn BarlowCategories New Zealand blue whalesTags blue whale, Dawn Barlow, ecology, forage fish, foraging ecology, Humpback whale, lunge feeding, Marine Mammals, zooplanktonLeave a comment on What makes a good meal for a hungry whale?
Inference, and the intersection of ecology and statistics
Recently, I had the opportunity to attend the International Statistical Ecology Conference (ISEC), a biennial meeting of researchers at the interface of ecology and statistics. I am a marine ecologist, fascinated by the interactions between animals and the dynamic ocean environment they inhabit. If you had asked me five years ago whether I thought I would ever consider myself a statistician or a computer programmer, my answer would certainly have been "no". Now, I find myself studying the ecology of blue whales in New Zealand using a variety of data streams and methodologies, but a central theme for my dissertation is species distribution modeling. Species distribution models (SDMs) are mathematical algorithms that correlate observations of a species with environmental conditions at their observed locations to gain ecological insight and predict spatial distributions of the species (Fig. 1; Elith and Leathwick 2009). I still can't say I would identify as a statistician, but I have a growing appreciation for the role of statistics to gain inference in ecology.
Figure 1. A schematic of a species distribution model (SDM) illustrating how the relationship between mapped species and environmental data (left) is compared to describe "environmental space" (center), and then map predictions from a model using only environmental predictors (right). Note that inter-site distances in geographic space might be quite different from those in environmental space—a and c are close geographically, but not environmentally. The patterning in the predictions reflects the spatial autocorrelation of the environmental predictors. Figure reproduced from Elith and Leathwick (2009).
Before I continue, let's take a look at just a few definitions from Merriam-Webster's dictionary:
Statistics: a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data
Ecology: a branch of science concerned with the interrelationship of organisms and their environments
Inference: a conclusion or opinion that is formed because of known facts or evidence
Ecological data are notoriously noisy, messy, and complex. Statistical tests are meant to help us understand whether a pattern in the data is different from what we would expect through random chance. When we study how organisms interact with one another and their environment, it is impossible to completely capture all elements of the ecosystem. Therefore, ecology is a field ripe with challenges for statisticians. How do we quantify a meaningful biological signal amidst all the noise? How can we gain inference from ecological data to enhance knowledge, and how can we use that knowledge to make informed predictions? Marine mammals are notoriously difficult to study. They inhabit an environment that is relatively inaccessible and inhospitable to humans, they occur in low numbers, they are highly mobile, and they are rarely visible. All ecological data are difficult and noisy and riddled with small sample sizes, but counting trees presents fewer logistical challenges than counting moving whales in an ever-changing open-ocean setting. Therefore, new methodologies in areas like species distribution modeling are often developed using large, terrestrial datasets and eventually migrate to applications in the marine environment (Robinson et al. 2011).
Many presentations I attended at the conference were geared toward moving beyond correlative SDMs. SDMs were developed to correlate species occurrence patterns with features of the environment they inhabit (e.g. temperature, precipitation, terrain, etc.). However, those relationships do not actually explain the underlying mechanism of why a species is more likely to occur in one environment compared to another. Therefore, ecological statisticians are now using additional information and modeling approaches within SDMs to incorporate information such as species co-occurrence patterns, population demographic information, and physiological constraints. Building SDMs to include such process-explicit information allows us to make steps toward understanding not just when and where a species occurs, but why.
Machine learning is an area that continues to advance and open doors to new applications in ecology. Machine learning approaches differ fundamentally from classical statistics. In statistics, we formulate a hypothesis, select the appropriate model to test that hypothesis (for example, linear regression), then test how well the data fit the model ("Is the relationship linear?"), and test the strength of that inference ("Is the linear pattern different from what we would expect due to random chance?"). Machine learning, on the other hand, does not use a predetermined notion of relationships between variables. Rather, it tries to create an algorithm that fits the patterns in the data. Statistics asks how well the data fit a model, and machine learning asks how well a model fits the data.
Machine learning approaches allow for very complex relationships to be included in models and can be excellent for making predictions. However, sometimes the relationships fitted by a machine learning algorithm are so complex that it is not possible to infer any ecological meaning from them. As one ISEC presenter put it, in machine learning "the computer learns but the scientist does not". The most important thing when selecting your methodology is to remember your question and your goal. Do you want to understand the mechanism of why an animal is where it is? Or do you not need to understand the driver, but rather want to make the best predictions of where an animal will be? In my case, the answer to that question differs from one of my PhD chapters to the next. We want to understand the functional relationships between oceanography, krill availability, and blue whale distribution (Barlow et al. 2020), and subsequently we want to develop forecasting models that can reliably predict blue whale distribution to inform conservation efforts (Fig. 2).
Figure 2. An example predictive map of where we expect blue whales to be distributed based on environmental conditions. Warmer colors represent areas with a higher probability of blue whale occurrence, and the blue crosses represent locations where blue whales were observed.
ISEC was an excellent opportunity for me to break out of my usual marine mammal-centered bubble and get a taste of what is happening on the leading edge of statistical ecology. I learned about the latest approaches and innovations in species distribution modeling, and in the process I also learned about trees, koalas, birds, and many other organisms from around the world. A fun bonus of attending a methods-focused conference is learning about completely new study species and systems. There are many ways of approaching an ecological question, gaining inference, and making predictions. I look forward to incorporating the knowledge I gained through ISEC into my own research, both in my doctoral work and in applications of new methods to future research projects.
Figure 3. The virtual conference photo of all who attended the biennial International Statistical Ecology Conference. Thank you to the organizers, who made it a truly excellent and engaging conference experience!
Barlow, D.R., Bernard, K.S., Escobar-Flores, P., Palacios, D.M., and Torres, L.G. 2020. Links in the trophic chain: Modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes. Mar. Ecol. Prog. Ser. doi:https://doi.org/10.3354/meps13339.
Elith, J., and Leathwick, J.R. 2009. Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 40(1): 677–697. doi:10.1146/annurev.ecolsys.110308.120159.
Robinson, L.M., Elith, J., Hobday, A.J., Pearson, R.G., Kendall, B.E., Possingham, H.P., and Richardson, A.J. 2011. Pushing the limits in marine species distribution modelling: Lessons from the land present challenges and opportunities. doi:10.1111/j.1466-8238.2010.00636.x.
Posted on July 6, 2020 July 6, 2020 Author Dawn BarlowCategories New Zealand blue whales, UncategorizedTags Blue whales, Conference, Data Analysis, Dawn Barlow, ecology, inference, International Statistical Ecology Conference, machine learning, species distribution models, statisticsLeave a comment on Inference, and the intersection of ecology and statistics
What we know now about New Zealand blue whales
For my PhD, I am using a variety of data sources and analytical tools to study the ecology and distribution of blue whales in New Zealand. I live on the Oregon Coast, across the world and in another season from the whales I study. I love where I live and I am passionate about my work, but I do sometimes feel removed from the whales and the ecosystem that are the focus of my research.
A pair of blue whales surface in the South Taranaki Bight region of New Zealand. Drone piloted by Todd Chandler during the 2017 field season.
Recently, I have turned my attention to processing acoustic data recorded in our study region in New Zealand between 2016 and 2018. In the fall, I developed detector algorithms to identify possible blue whale vocalizations in the recording period, and now I am going through each of the detections to validate whether it is indeed a blue whale call or not. Looking closely at spectrograms for hours and hours is a change of pace from the analysis and writing I have been doing recently. Namely, I am looking at biological signals – not lines of code and numbers on a screen, but depictions of sounds that blue whales produced. I have to say, it is the "closest" I have felt to these whales in a long time. Scrolling through thousands of spectrograms of blue whale calls leaves room for my mind to wander, and I recently had the realization that those whales have absolutely no idea that on the other side of the Pacific Ocean, there are a few scientists dedicating years of their lives to understand and protect them. Which led me to another realization: we know so much more about blue whales in New Zealand now than we did 10 years ago. In fact, we know so much more than we did even a year ago.
Screenshot of the process of reviewing blue whale D call detections in the acoustic analysis program Raven.
Nine years ago, Dr. Leigh Torres had a cup of coffee with a colleague who recounted observer reports of several blue whales during a seismic survey of the South Taranaki Bight region (STB) of New Zealand. This conversation sparked her curiosity, and led to the formulation of a hypothesis that the STB was in fact an unrecognized feeding ground for blue whales in the southern hemisphere (Torres 2013).
A blue whale surfaces in front of an oil rig in the South Taranaki Bight. Compiling opportunistic sightings like this one was an important step in realizing the importance of the region for blue whales. Photo by Deanna Elvines.
After three field seasons and several years of dedicated work, the hypothesis that the STB region is important for blue whales was validated. By drawing together multiple data streams and lines of evidence, we now know that New Zealand is home to a unique population of blue whales, which are genetically distinct from all other documented populations in the Southern Hemisphere. Furthermore, they use the STB for multiple critical life history functions such as feeding, nursing and calf raising, and they are present there year-round (Barlow et al. 2018).
Once we documented the New Zealand population, we were left with perhaps even more questions than we started with. Where do they feed, and why? Are they feeding and breeding there? Does their distribution change seasonally? What is the health of the population? Are they being impacted by industrial activity and human impacts such as noise in the region? We certainly do not have all the answers, but we have been piecing together an increasingly comprehensive story about these whales and their ecology.
For example, we now know that blue whales in New Zealand average around 19 meters in length, which we calculated by measuring images taken via drones and using an analysis program developed in the GEMM Lab (Burnett et al. 2018). The use of drones has opened up a whole new world for studying health and behavior in whales, and we recently used video footage to better understand the movement and kinematics of how blue whales engulf their krill prey. Furthermore, we know that blue whales may preferentially feed on dense krill aggregations at the surface, and that this surface feeding strategy may be an energetically favorable strategy in this part of the world (Torres et al. 2020).
We have also assessed one aspect of the health of blue whales by describing their skin condition. By analyzing thousands of photographs, we now know that nearly all blue whales in New Zealand bear the scars of cookie cutter shark bites, which they seem more likely to acquire at more northerly latitudes, and that 80% are affected by blister lesions (Barlow et al. 2019). Next, we are beginning to draw together multiple data streams such as body condition and hormone analysis, paired with skin condition, to form a detailed understanding about the health of this population.
Most recently we have produced a study describing how oceanography, prey and blue whales are connected within this region of New Zealand. The STB region is home to a wind-driven upwelling system that drives productivity and leads to aggregations of krill, which in turn provide sustenance for blue whales to feed on. By compiling data on oceanography and water column structure, krill availability, and blue whale distribution, we now have a solid understanding of this trophic pathway: how oceanography structures prey, and how blue whales respond to both prey and oceanography (Barlow et al. 2020). Furthermore, we are beginning to understand how those relationships may look under changing ocean conditions, with global sea temperatures rising and the increasing frequency and intensity of marine heatwaves.
The knowledge we have accumulated better enables managers to make informed decisions for the conservation of these blue whales and the ecosystem they inhabit. To me, there are several take-away messages from the story that continues to unfold about these blue whales. One is the importance of following a hunch, and then gathering the necessary tools and team to explore and tackle challenging questions. An idea that started over a cup of coffee and many years of hard work and dedication have led to a whole new body of knowledge. Another message is that the more questions you ask and the more questions you try to answer, the more questions you are often left with. That is a beautiful truth about scientific inquiry – the questions we ask drive the knowledge we uncover, but that process is never complete because new questions continue to emerge. Finally, it is easy to get swept up in details, outputs, timelines, and minutia, and every now and then it is important to take a step back. I have appreciated taking a step back and musing on the state of our knowledge about these whales, about how much we have learned in less than 10 years, and mostly about how many answers and new questions are still waiting to be uncovered.
A victorious field team celebrates a successful end to the 2017 field season with an at-sea sunset dance party. A good reminder of sunny, salty days on the water and where the data come from!
Barlow DR, Bernard KS, Escobar-Flores P, Palacios DM, Torres LG (2020) Links in the trophic chain: Modeling functional relationships between in situ oceanography, krill, and blue whale distribution under different oceanographic regimes. Mar Ecol Prog Ser.
Barlow DR, Pepper AL, Torres LG (2019) Skin Deep: An Assessment of New Zealand Blue Whale Skin Condition. Front Mar Sci.
Barlow DR, Torres LG, Hodge KB, Steel D, Baker CS, Chandler TE, Bott N, Constantine R, Double MC, Gill P, Glasgow D, Hamner RM, Lilley C, Ogle M, Olson PA, Peters C, Stockin KA, Tessaglia-hymes CT, Klinck H (2018) Documentation of a New Zealand blue whale population based on multiple lines of evidence. Endanger Species Res 36:27–40.
Burnett JD, Lemos L, Barlow DR, Wing MG, Chandler TE, Torres LG (2018) Estimating morphometric attributes on baleen whales using small UAS photogrammetry: A case study with blue and gray whales. Mar Mammal Sci.
Torres LG (2013) Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zeal J Mar Freshw Res 47:235–248.
Torres LG, Barlow DR, Chandler TE, Burnett JD (2020) Insight into the kinematics of blue whale surface foraging through drone observations and prey data. PeerJ.
Posted on May 25, 2020 May 25, 2020 Author Dawn BarlowCategories New Zealand blue whalesTags blue whale, Dawn Barlow, discovery, ecology, Leigh Torres, New ZealandLeave a comment on What we know now about New Zealand blue whales
"Do Dolphins Get Hives?": The Skinny on Allergies in Cetaceans
By: Alexa Kownacki, Ph.D. Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab
While sitting on my porch and watching the bees pollinate the blooming spring flowers, I intermittently pause to scratch the hives along my shoulders and chest. In the middle of my many Zoom calls, I mute myself and stop my video because a wave of pollen hits my face and I immediately have to sneeze. With this, I'm reminded: Welcome to prime allergy season in the Northern Hemisphere. As I was scratching my chronic idiopathic urticaria (hives caused by an overactive immune system), I asked myself "Do dolphins get hives?" I had no idea. I know most terrestrial mammals can and do—just yesterday, one of the horses in the nearby pasture was suffering from a flare of hives. But, what about aquatic and marine mammals?
Springtime flowers blooming on the Central California Coast 2017. (Image Source: A. Kownacki)
As with most research on marine mammal health, knowledge is scare and is frequently limited to studies conducted on captive and stranded animals. Additionally, most of the current theories on allergic reactions in marine mammals are based on studies from terrestrial wildlife and humans. Because nearly all research on histamine pathways centers on terrestrial animals, I wanted to see what information exists the presence of skin allergies in marine mammals.
Allergic reactions trigger a cascade within the body, beginning with the introduction of a foreign body, which for many people is pollen. The allergen binds to antibodies that are produced to fight potentially harmful substances. Once this allergen binds to different types of cells, including mast cells, chemicals like histamines are released. Histamines cause the production of mucus and constriction of blood vessels, and thus are the reason your eyes water, your nose runs, or you start coughing.
Basic cartoon of an allergic reaction from exposure to the allergen to the reaction from the animal. (Image Source: Scientific Malaysian)
As you probably can tell just by looking at a marine mammal, they have thicker skin and fewer mucus membranes that humans, due to the fact that they live in the water. However, mast cells or mast cell-like cells have been described in most vertebrate lineages including mammals, birds, reptiles, amphibians, and bony fishes (Hellman et al. 2017, Reite and Evenson 2006). Mast cell-like cells have also been described in an early ancestor of the vertebrates, the tunicate, or sea squirt (Wong et al. 2014). Therefore, allergic-reaction cascades that may present as hives, red and itchy eyes or nose in humans, also exist in marine mammals, but perhaps cause different or less visible symptoms.
Skin conditions in cetaceans are gathering interest within the marine mammal health community. Even our very own Dawn Barlow, Dr. Leigh Torres, and Acacia Pepper assessed the skin conditions in New Zealand blue whales in their recent publication. Most visible skin lesions or markings on cetaceans are caused by parasites, shark bits, fungal infections, and fishery or boat interactions (Leone et al. 2019, Sweeney and Ridgway 1985). However, there is very little scientific literature about allergic reactions in marine mammals, let alone cetaceans. That being said, I managed to find a few critical pieces of information supporting the theory that marine mammals do in fact have allergies that can produce dermal reactions similar to hives in humans.
In one study, three captive bottlenose dolphins developed reddened skin, sloughing, macules, and wheals on their ventral surfaces (Monreal-Pawlowsky et al. 2017). The medical staff first noticed this atopic dermatitis in 2005 and observed the process escalate over the next decade. Small biopsy samples from the affected areas on the three dolphins coincided with the appearance of four pollens in the air within the geographic region: Betula, Pistacia, Celtis, and Fagus (Monreal-Pawlowsky et al. 2017). Topical prednisone treatments were applied to the affected areas at various dosages that slowly resolved the skin irritations. Researchers manufactured an allergy vaccine using a combination of the four pollens in hopes that it would prevent further seasonal outbreaks, but it was unsuccessful. In the coming years, the facility intends to adjust the dosages to create a successful vaccine.
In the three top images, visible skin irritation including redness, macules, wheals, and sloughing are present. In the image below, the above animal was treated with methylprednisolone and the skin irritation subsides. (Monreal-Pawlowsky et al. 2017)
In addition to the above study, there is an unpublished case of suspected allergic reaction to another pollen that produces a pruritic reaction on the ventral areas of dolphins on a seasonal basis (Vicente Arribes, personal communication). Although there are only a few documented cases of environmentally-triggered allergic reactions that are visible on the dermal layer of cetaceans, I believe this evidence makes the case that some cetaceans suffer from allergies much like us. So, next time you're enjoying the beautiful blooms and annoyingly scratch your eyes, know that you are not alone.
Image Source: FurEver Family
Barlow DR, Pepper AL and Torres LG (2019) Skin Deep: An Assessment of New Zealand Blue Whale Skin Condition. Front. Mar. Sci. 6:757.doi: 10.3389/fmars.2019.00757
Hellman LT, Akula S, Thorpe M and Fu Z (2017) Tracing the Origins of IgE, Mast Cells, and Allergies by Studies of Wild Animals. Front. Immunol. 8:1749. doi: 10.3389/fimmu.2017.01749
Leone AB, Bonanno Ferraro G, Boitani L, Blasi MF. Skin marks in bottlenose dolphins (Tursiops truncatus) interacting with artisanal fishery in the central Mediterranean Sea. PLoS One. 2019;14(2):e0211767. Published 2019 Feb 5. doi:10.1371/journal.pone.0211767
Monreal-Pawlowsky T, Fernández-Bellon H, Puigdemont A (2017) Suspected Allergic Reaction in Bottlenose Dolphins (Tursiops truncatus). J Vet Sci Ani Husb 5(1): 108. doi: 10.15744/2348-9790.5.108
Reite OB, Evensen O. Inflammatory cells of teleostean fish: a review focusing on mast cells/eosinophilic granule cells and rodlet cells. Fish Shellfish Immunol (2006) 20:192–208. doi:10.1016/j.fsi.2005.01.012
Sweeney, J. C., & Ridgway, S. H. (1975). Common diseases of small cetaceans. J. Am. Vet. Med. Assoc, 167(7), 533-540.
Wong GW, Zhuo L, Kimata K, Lam BK, Satoh N, Stevens RL. Ancient originof mast cells. Biochem Biophys Res Commun (2014) 451:314–8. doi:10.1016/j.bbrc.2014.07.124
Posted on May 11, 2020 May 11, 2020 Author Alexa KownackiCategories Bottlenose Dolphin Population HealthTags Acacia Pepper, Alexa Kownacki, allergens, allergic reaction, allergy, biopsy, bloom, Dawn Barlow, ecology, environmental variables, Evolution, Fisheries and Wildlife, genetics, histamines, Leigh Torres, macules, marine, marine mammal health, Marine Mammals, medical, mucus membranes, Oregon State University, pathways, physiology, pollen, prednisone, skin, skin conditions, sloughing, urticaria, vaccine, whealsLeave a comment on "Do Dolphins Get Hives?": The Skinny on Allergies in Cetaceans
Snacks at the surface: New GEMM Lab publication reveals insights into blue whale surface foraging through drone observations and prey data
As the largest animals on the planet, blue whales have massive prey requirements to meet energy demands. Despite their enormity, blue whales feed on a tiny but energy-rich prey source: krill. Furthermore, they are air-breathing mammals searching for aggregations of prey in the expansive and deep ocean, and must therefore budget breath-holding and oxygen consumption, the travel time it takes to reach prey patches at depth, the physiological constraints of diving, and the necessary recuperation time at the surface. Additionally, blue whales employ an energetically demanding foraging strategy known as lunge feeding, which is only efficient if they can locate and target dense prey aggregations that compensate for the energetic costs of diving and lunging. In our recent paper, published today in PeerJ, we examine how blue whales in New Zealand optimize their energy use through preferentially feeding on dense krill aggregations near the water's surface.
Figure 1. A blue whale lunges on a dense aggregation of krill at the surface. Note the krill jumping away from the mouth of the onrushing whale. UAS piloted by Todd Chandler.
Figure 2. Survey tracklines in 2017 in the South Taranaki Bight (STB) with locations of blue whale sightings, and where surface lunge feeding was observed, denoted. Inset map shows location of the STB within New Zealand. Figure reprinted from Torres et al. 2020.
To understand how predators such as blue whales optimize foraging strategies, knowledge of predator behavior and prey distribution is needed. In 2017, we surveyed for blue whales in New Zealand's South Taranaki Bight region (STB, Fig. 2) while simultaneously collecting prey distribution data using an echosounder, which allowed us to identify the location, depth, and density of krill aggregations throughout the region. When blue whales were located, we observed their behavior from the research vessel, recorded their dive times, and used an unmanned aerial system (UAS; "drone") to assess their body condition and behavior.
Much of what is known about blue whale foraging behavior and energetics comes from extensive studies off the coast of California, USA using accelerometer tags to track fine-scale kinematics (i.e., body movements) of the whales. In the California Current, the krill species targeted by blue whales are denser at depth, and therefore blue whales regularly dive to depths of 300 meters to lunge on the most energy-rich prey aggregations. However, given the reduced energetic costs of feeding closer to the surface, optimal foraging theory predicts that blue whales should only forage at depth when the energetic gain outweighs the cost. In New Zealand, we found that blue whales foraged where krill aggregations were relatively shallow and dense compared to the availability of krill across the whole study area (Fig. 3). Their dive times were quite short (~2.5 minutes, compared to ~10 minutes in California), and became even shorter in locations where foraging behavior and surface lunge feeding were observed.
Figure 3. Density contours comparing the depth and density (Sv) of krill aggregations at blue whale foraging sightings (red shading) and in absence of blue whales (gray shading). Density contours: 25% = darkest shade, 755 = medium shade, 95% = light shade. Blue circles indicate krill aggregations detected within 2 km of the sighting of the UAS filmed surface foraging whale analyzed in this study. Figure reprinted from Torres et al. 2020.
Figure 4. Kinematics of a blue whale foraging dive derived from a suction cup tag. Upper panel shows the dive profile (yellow line), with lunges highlighted (green circles), superimposed on a prey field map showing qualitative changes in krill density (white, low; blue, medium; red, high). The lower panels show the detailed kinematics during lunges at depth. Here, the dive profile is shown by a black line. The orange line shows fluking strokes derived from the accelerometer data, the green line represents speed estimated from flow noise, and the grey circles indicate the speed calculated from the vertical velocity of the body divided by the sine of the body pitch angle, which is shown by the red line. Figure and caption reprinted from Goldbogen et al. 2011.
Describing whale foraging behavior and prey in the surface waters has been difficult due to logistical limitations of conventional data collection methods, such as challenges inferring surface behavior from tag data and quantifying echosounder backscatter data in surface waters. To compliment these existing methods and fill the knowledge gap surrounding surface behavior, we highlight the utility of a different technological tool: UAS. By analyzing video footage of a surface lunge feeding sequence, we obtained estimates of the whale's speed, acceleration, roll angle, and head inclination, producing a figure comparable to what is typically obtained from accelerometer tag data (Fig. 4, Fig. 5). Furthermore, the aerial perspective provided by the UAS provides an unprecedented look at predator-prey interactions between blue whales and krill. As the whale approaches the krill patch, she first observes the patch with her right eye, then turns and lines up her attack angle to engulf almost the entire prey patch through her lunge. Furthermore, we can pinpoint the moment when the krill recognize the impending danger of the oncoming predator—at a distance of 2 meters, and 0.8 seconds before the whale strikes the patch, the krill show a flee response where they leap away from the whale's mouth (see video, below).
Figure 5. Body kinematics during blue whale surface lunge feeding event derived from Unmanned Aerial Systems (UAS) image analysis. (A) Mean head inclination and roll (with CV in shaded areas), (B) relative speed and acceleration, and (C) distance from the tip of the whale's rostrum to the nearest edge of krill patch. Blue line on plots indicate when krill first respond to the predation event, and the purple dashed lines indicate strike at time = 0. The orange lines indicate the time at which the whale's gape is widest, head inclination is maximum, and deceleration is greatest. Figure reprinted from Torres et al. 2020
In this study, we demonstrate that surface waters provide important foraging opportunities and play a key role in the ecology of New Zealand blue whales. The use of UAS technology could be a valuable and complimentary tool to other technological approaches, such as tagging, to gain a comprehensive understanding of foraging behavior in whales.
To see the spectacle of a blue whale surface lunge feeding, we invite you to take a look at the video footage, below:
The publication is led by GEMM Lab Principal Investigator Dr. Leigh Torres. I led the prey data analysis portion of the study, and co-authors include our drone pilot extraordinaire Todd Chandler and UAS analysis guru Dr. Jonathan Burnett. We are grateful to all who assisted with fieldwork and data collection, including Kristin Hodge, Callum Lilley, Mike Ogle, and the crew of the R/V Star Keys (Western Workboats, Ltd.). Funding for this research was provided by The Aotearoa Foundation, The New Zealand Department of Conservation, The Marine Mammal Institute at Oregon State University, Greenpeace New Zealand, OceanCare, Kiwis Against Seabed Mining, The International Fund for Animal Welfare, and The Thorpe Foundation.
Read Oregon State University's press release about the publication here.
Posted on April 22, 2020 April 22, 2020 Author Dawn BarlowCategories New Zealand blue whalesTags blue whale, Dawn Barlow, drone, foraging ecology, kinematics, krill, Leigh Torres, lunge feeding, New Zealand, publication2 Comments on Snacks at the surface: New GEMM Lab publication reveals insights into blue whale surface foraging through drone observations and prey data
Marine heatwaves and their impact on marine mammals
In recent years, anomalously warm ocean temperatures known as "marine heatwaves" have sparked considerable attention and concern around the world. Marine heatwaves (MHW) occur when seawater temperatures rise above a seasonal threshold (greater than the 90th percentile) for five consecutive days or longer (Hobday et al. 2016; Fig. 1). With global ocean temperatures continuing to rise, we are likely to see more frequent and more intense MHW conditions in the future. Indeed, the global prevalence of MHWs is increasing, with a 34% rise in frequency, a 17% increase in duration, and a 54% increase in annual MHW days globally since 1925 (Oliver et al. 2018). With sustained anomalously warm water temperatures come a range of ecological, sociological, and economic consequences. These impacts include changes in water column structure, primary production, species composition, marine life distribution and health, and fisheries management including closures and quota changes (Oliver et al. 2018).
Figure 1. Illustration of how marine heatwaves are defined. Source: marineheatwaves.org
The notorious "warm blob" was an MHW event that plagued the northeast Pacific Ocean from 2014-2016. Some of the most notable consequences of this MHW were extremely high levels of domoic acid, extreme changes in the biodiversity of pelagic species, and an unprecedented delay in the opening of the Dungeness crab fishery, which is an important and lucrative fishery for the West Coast of the United States (Santora et al. 2020). The "warm blob" directly impacted the California Current ecosystem, which is typically a highly productive coastal area driven by seasonal upwelling. Yet, as a consequence of the 2014-2016 MHW, upwelling habitat was compressed and constricted to the coastal boundary, resulting in a contraction in available habitat for humpback whales and a shift in their prey (Santora et al. 2020; Fig. 2).
Figure 2. A figure from Santora et al. 2020 illustrating the compression in available upwelling habitat, defined by areas with SST<12°C (delineated by the black line), during the 2014-2016 marine heatwave in the California Current ecosystem.
Shifting to an example from another part of the world, the austral summer of 2015-2016 coincided with a strong regional MHW in the Tasman Sea between Australia and New Zealand, which lasted for 251 days and had a maximum intensity of 2.9°C above the climatological average (Oliver et al. 2017). Subsequently, the conditions were linked to a significant shift in zooplankton species composition and abundance in Australia (Evans et al. 2020). Ocean warming, including MHWs, also appears to decrease primary production in the Tasman Sea and large portions of New Zealand's marine ecosystem (Chiswell & Sutton 2020). In New Zealand's South Taranaki Bight region, where we study the ecology of blue whales, we observed a shift in blue whale distribution in the MWH conditions of February 2016 relative to more typical ocean conditions in 2014 and 2017 (Fig. 3). The first chapter of my dissertation includes a detailed analysis of the impacts of the 2016 MHW on New Zealand oceanography, krill, and blue whales, documenting how the warm, stratified water column of 2016 led to consequences across multiple trophic levels, from phytoplankton, to zooplankton, to whales.
Figure 3. Maps showing monthly sea surface temperature (SST) in the South Taranaki Bight region of New Zealand during our three years of survey effort to document blue whale distribution (February 2014, 2016, and 2017). Vessel tracklines are shown in black, with blue whale sighting locations shown in dark red. Red circles are scaled by the number of blue whales observed at each sighting. The color ramp of SST values is consistent across the three maps, making the dramatically warmer ocean conditions of 2016 evident.
The response of marine mammals is tightly linked to shifts in their environment and prey (Silber et al. 2017). With MHWs and changing ocean conditions, there will likely be "winners" and "losers" among marine predators including large whales. Blue whales are highly selective krill specialists (Nickels et al. 2019), whereas other species of whales, such as humpback whales, have evolved flexible feeding tactics that allow them to switch target prey species when needed (Cade et al. 2020). In California, humpback whales have been shown to switch their primary prey from krill to fish during warm years (Fossette et al. 2017, Santora et al. 2020). By contrast, blue whales shift their distribution in response to changing krill availability during warm years (Fossette et al. 2017), however this strategy comes with increased risk and energetic cost associated with searching for prey in new areas. Furthermore, in instances when a prey resource such as krill becomes increasingly scarce for a multi-year period (Santora et al. 2020), krill specialist predators such as blue whales are at a considerable disadvantage. It is also important to acknowledge that although the humpbacks in California may at first seem to have a winning strategy for adaptation by switching their food source, this tactic may come with unforeseen consequences. Their distribution overlapped substantially with Dungeness crab fishing gear during MHW conditions in the warm blob years, resulting in record numbers of entanglements that may have population-level repercussions (Santora et al. 2020).
While this is certainly not the most light-hearted blog topic, I believe it is an important one. As warming ocean temperatures contribute to the increase in frequency, intensity, and duration of extreme conditions such as MHW events, it is paramount that we understand their impacts and take informed management actions to mitigate consequences, such as lethal entanglements as a result of compressed whale habitat. But perhaps more importantly, even as we do our best to manage consequences, it is critical that we as individuals realize the role we have to play in reducing the root cause of warming oceans, by being conscious consumers and being mindful of the impact our actions have on the climate.
Cade DE, Carey N, Domenici P, Potvin J, Goldbogen JA (2020) Predator-informed looming stimulus experiments reveal how large filter feeding whales capture highly maneuverable forage fish. Proc Natl Acad Sci USA.
Chiswell SM, Sutton PJH (2020) Relationships between long-term ocean warming, marine heat waves and primary production in the New Zealand region. New Zeal J Mar Freshw Res.
Evans R, Lea MA, Hindell MA, Swadling KM (2020) Significant shifts in coastal zooplankton populations through the 2015/16 Tasman Sea marine heatwave. Estuar Coast Shelf Sci.
Fossette S, Abrahms B, Hazen EL, Bograd SJ, Zilliacus KM, Calambokidis J, Burrows JA, Goldbogen JA, Harvey JT, Marinovic B, Tershy B, Croll DA (2017) Resource partitioning facilitates coexistence in sympatric cetaceans in the California Current. Ecol Evol.
Hobday AJ, Alexander L V., Perkins SE, Smale DA, Straub SC, Oliver ECJ, Benthuysen JA, Burrows MT, Donat MG, Feng M, Holbrook NJ, Moore PJ, Scannell HA, Sen Gupta A, Wernberg T (2016) A hierarchical approach to defining marine heatwaves. Prog Oceanogr.
Nickels CF, Sala LM, Ohman MD (2019) The euphausiid prey field for blue whales around a steep bathymetric feature in the southern California current system. Limnol Oceanogr.
Oliver ECJ, Benthuysen JA, Bindoff NL, Hobday AJ, Holbrook NJ, Mundy CN, Perkins-Kirkpatrick SE (2017) The unprecedented 2015/16 Tasman Sea marine heatwave. Nat Commun.
Oliver ECJ, Donat MG, Burrows MT, Moore PJ, Smale DA, Alexander L V., Benthuysen JA, Feng M, Sen Gupta A, Hobday AJ, Holbrook NJ, Perkins-Kirkpatrick SE, Scannell HA, Straub SC, Wernberg T (2018) Longer and more frequent marine heatwaves over the past century. Nat Commun.
Santora JA, Mantua NJ, Schroeder ID, Field JC, Hazen EL, Bograd SJ, Sydeman WJ, Wells BK, Calambokidis J, Saez L, Lawson D, Forney KA (2020) Habitat compression and ecosystem shifts as potential links between marine heatwave and record whale entanglements. Nat Commun.
Silber GK, Lettrich MD, Thomas PO, Baker JD, Baumgartner M, Becker EA, Boveng P, Dick DM, Fiechter J, Forcada J, Forney KA, Griffis RB, Hare JA, Hobday AJ, Howell D, Laidre KL, Mantua N, Quakenbush L, Santora JA, Stafford KM, Spencer P, Stock C, Sydeman W, Van Houtan K, Waples RS (2017) Projecting marine mammal distribution in a changing climate. Front Mar Sci.
Posted on March 9, 2020 March 9, 2020 Author Dawn BarlowCategories New Zealand blue whales, UncategorizedTags blue whale, California current ecosystem, climate change, Dawn Barlow, Humpback whale, krill, marine heatwaves, Marine Mammals, New Zealand1 Comment on Marine heatwaves and their impact on marine mammals


text:
Organising care around patients, Stories from the frontline of the NHS
Alliance Manchester Business School
University staff, Adults, Alumni, General public
In category "Talk"
By Alliance Manchester Business School
To celebrate the launch of Organising care around patients Manchester University Press and Alliance Manchester Business School are hosting a live panel discussion, asking how care can be better organised around the needs of patients. Joining authors Naomi Chambers, Professor of Healthcare Management at AMBS, and Jeremy Taylor, Director for Public Voice at the National Institute for Health Research, the discussion will be chaired by Alastair McLellan, Editor of the Health Service Journal, with Professor Kath Checkland and Dr Habib Naqvi as panellists.
About the book Organising care around patients is not for the fainthearted. Naomi Chambers and Jeremy Taylors have curated twenty-five accounts from people who agreed to tell the story of what happened when they or their loved ones came into contact with the NHS. The authors defy you not to laugh or cry, or hold your breath in disbelief, at some point when reading this book.
In these true and compelling accounts, we learn the experiences - good and bad - of people grappling with birth and death, caring for loved ones, living with mental illness, coping with long-term conditions, and struggling in older age. This book is a call to action aimed at healthcare professionals, managers and politicians: a manifesto for more patient-centred care.
These stories show the NHS at its very best - and also when it falls significantly short. Patients or carers currently battling with the system will derive some hope and encouragement, and clues about what to expect, what to ask for, and from whom.

redpajama_set_name: RedPajamaCommonCrawl


text:
Q: rst2pdf AttributeError: 'PDFWriter' object has no attribute 'custom_cover' Cant' create PDF's anymore, earlier I could but then I guess I messed up my python installation on my mac...
I run this command:
sphinx-build -D language=de -b pdf /Applications/MAMP/htdocs/sakkadentrainer/doc/ /Applications/MAMP/htdocs/sakkadentrainer_medical_doc/pdf/de/

/usr/local/bin/sphinx-build:11: RemovedInSphinx20Warning: sphinx.main()has moved tosphinx.cmd.build.main(). sys.exit(main())

Sphinx v1.8.5 in Verwendung Lade Übersetzungen
[de]…erledigt Lade pickle Umgebung...erledigt
building [mo]: targets for 0 po files that are out of date building [pdf]: targets for 23 source files that are out of date updating environment:
0 added, 0 changed, 0 removed looking for now-outdated files... none found processing Sakkadentrainer_Dokumentation_de_v1.0... index faq medical_product registration payment login pw_forgot general_training car train dot img1 img3 statistics statistics_details settings settings_profile settings_general settings_display settings_exercises settings_change_pwd settings_del eyetracking resolving references... done writing Sakkadentrainer_Dokumentation_de_v1.0...

[ERROR] pdfbuilder.py:149 'PDFWriter' object has no attribute 'custom_cover' Traceback (most recent call last): File "/Users/beatgurtner/Library/Python/2.7/lib/python/site-packages/rst2pdf/pdfbuilder.py", line 146, in write docwriter.write(doctree, destination)
File "/Library/Python/2.7/site-packages/docutils/writers/__init__.py", line 78, in write self.translate() File "/Users/beatgurtner/Library/Python/2.7/lib/python/site-packages/rst2pdf/pdfbuilder.py", line 595, in translate log.error("Can't find cover template %s, using default"%self.custom_cover)
AttributeError: 'PDFWriter' object has no attribute 'custom_cover' FAILED build abgeschlossen.

Description of problem
Don't know the steps to reproduce the problem... (sorry)
I tried reinstalling rst2pdf. This worked without any errors.
I tried reinstalling pdfbuilder:
Could only uninstall it. When I wanted to reinstall it I got this error:
pip install pdfbuilder
ERROR: Could not find a version that satisfies the requirement pdfbuilder (from versions: none)
ERROR: No matching distribution found for pdfbuilder

 Versions
python -V
Python 2.7.10
pip freeze | grep rst2pdf
rst2pdf==0.96
pip freeze | grep reportlab
reportlab==3.5.42
pip freeze | grep Sphinx
Sphinx==1.8.5
Which operating system are you using?
MacOS Mojave 10.14.1 (18B75)

A: I think I solved the problem with those steps:
brew upgrade sphinx-doc
pip3 install -U sphinx
pip3 install sphinx-rtd-theme
pip3 install --user rst2pdf

But now I really am confused, wether now I use python3 or 2 :D
Anyway, my script seems to be running again

redpajama_set_name: RedPajamaStackExchange


text:
Valparaiso Takes Game One of Series 9-2
Valparaiso (12-19) 0 1 1 0 0 1 2 4 0 9 13 0
2012 YSU Baseball
2B: Tanner Vavra; Kyle Schneider
2B: Mike Accardi; Neil Schroth
Valparaiso scored seven runs after the fifth inning and kept Youngstown State from getting big hits in a 9-2 victory over the Penguins on Friday afternoon at Eastwood Field.
The Crusaders had just one more hit than the Penguins, but the visitors also benefitted from seven walks and five errors.
Mike Accardi and Nick DiNello had three hits apiece for YSU, and Neil Schroth and Jason Shirley both had two hits. YSU, which stranded 10 runners, was caught stealing three times and hit into a big inning-ending double play in the second inning.
Andrew Bain led Valpo with three hits, and four Crusaders had two hits.
Valpo took a 1-0 lead in the top of the second on three straight singles from its five through seven hitters. Chris Manning started with a single, and Spencer Mahoney followed with a grounder that snuck through with the shortstop playing at double play depth. Manning was thrown out trying to steal for the first out of the inning, but Kyle Schneider brought in Mahoney on a bloop single.
The Penguins had a chance to take the momentum with a big inning in the bottom half of the second as they loaded the bases with nobody out, but Valpo starter Cole Webb kept them scoreless. Josh White led off with a walk, and Phil Lipari and DiNello followed with singles. Webb struck out Marcus Heath then got Kevin Hix to ground into a 4-6-3 double play to end the inning.
The Crusaders scored another run to go up 2-0 in the third when Tanner Vavra, John Loeffler and Manning registered three straight two-out singles. A one-out double by Schneider in the sixth put Valparaiso up 3-0, and the visitors registered two more runs on two hits and three walks in the seventh.
The Penguins finally got to Webb in the seventh with two runs on three hits. DiNello led off with a single, and Shirley singled him to third with two outs. Accardi then brought in both runners with a double to right. Valpo reliever Ben Mahar came in and walked Schroth to bring the tying run to the plate, but he got Drew Dosch to ground out to end the inning.
Valpo then scored four runs on two hits, two walks and three errors in the eighth to go ahead 9-2.
Webb, who entered the contest with a 1.88 ERA, allowed two runs on nine hits and three walks in 6.2 innings. Two Crusaders relievers held YSU scoreless on three hits over the final 2.1 innings.
YSU starter Blake Aquadro was charged with four earned runs on 11 hits in 6.1 innings in a losing effort. Four of Valpo's nine runs were unearned.
The Penguins and Crusaders will play game two of the series on Saturday at 1 p.m.


Ok so I lost 2 Big Agnes pads last year to leaks I could never find and I'm pretty sure I just lost a Nemo pad to mulitiple leaks. Sealed 2 but she is still leaking.
Pads I have owned that have never sprung a leak.
I've had my NeoAir since 2013 and have slept on it for well over 200 nights - still holds up perfectly. When its finally time to replace it, I'll replace it with another NeoAir.
Prior to that, I had the typical Thermarest self-inflating pad - warmer but not as comfy - and that has been around since 2011 - still no leaks after around 150 nights - only bought the NeoAir cause I wanted to trade warmth for comfort.
I've got the thermarest mondo, well over 8 years old, slept on about 150 nights or so. I travel through my whole tent, am a restless sleeper. Never had a leak yet.
Another vote of confidence for Thermarest. Just a good old fashioned good company. Avoid MEC brand, in my experience.
I had a Nemo pad that leaked. Nemo replaced it. So I guess that's a bad and good experience with the brand.
My original pad is a circa 1998 self-inflating blue thermarest that has never failed me. I bought an identical one as my girlfriend's xmas present and its had no problem. To save space and weight I bought an orange circa 2006 3/4 light thermarest that got a puncture on the first night I used it. I patched it and have not had a problem since, and it's my preferred pad. We bought a neoair as my wife was not comfortable enough on the blue thermarest, due to a back injury, and we've had no problem. I am very happy with thermarest.
The problem with the 3/4 length is that dog always shoves her way onto it through the night so it's not quite big enough to share, so I sometimes use the old blue one. When wife comes along dog often monopolises about half the neoair so we periodically need to tell her to move. Dog swears by the neoair.
I've never bought any "expensive" pads new but usually rolled the dice at the MEC gear exchange.
Over the years I've picked up 3 Big Agnes Insulated Air Cores for about $10 each. The first had a leak which I fixed but it tended to spring a new leak every other time I used it. The second one was garbage with dozens of pinhole leaks down the side. Got lucky with the 3rd one. It had a leak as well but once fixed it was problem free. Still working 4 years in. I think age is an issue with Big Agnes.
My best purchase was a Exped DownMat 9 DLX back in 2013. Didn't know what it was at the time but it looked good for $15. It has a leak right in one of the corners but a little dab of seam sealer fixed it up and it has been problem free since then. Got to say, I was pretty happy with that purchase when I saw what they cost new!
Anyway, I use the Exped canoeing and the BA when I'm backpacking and both get used around the house fairly regularly. I also have an old MEC Apogee 3/4 that I bought new in 2002 which leaked from the start from around the valve. It's the designated dog pad.
Sometimes they are tough to find. I find you often need to fold and squeeze them a lot to really build up the pressure before you see the bubbles coming out.
Thx Everyone ! Lots of positive comments for Thermarest. That mixed with my own experience I decided to order another Neoair. Found a Camper version for $80. It's a little heavier but looks more durable and since it will be used primarily by my kids seemed like a good choice.
I use that pad myself and have been very happy with it. It takes a while to blow up, but it's super comfortable (3 inches thick) and I've never had to repair a leak. This is year 4, maybe 5 with it.
The Therm-a-rest camper mat came today.
Very impressed with it. The 3 inch thickness is super comfy.
Packs much smaller than I was expecting too.
Appears to be quite durable and no crinkle noise of the other neoair mats.
+1 on thermarest brand. I had a bad one (seam) and they replaced it. They also fixed an old guidelite that I had kicking around - they found the leak that I could never find.
Appreciate a company that stands behind its products. Thermarest (owned by Cascades Designs I think) is one such company.
Agreed! Them, and MSR by most accounts.
I have the older model of the MEC Reactor 3.8 and it's lasted 6 years so far and I find it very comfortable and not as noisy as some of the other sleeping pads with thinner materials. It seems pretty tough to me. I've used it directly on granite to cowboy camp when there's no bugs or as a float on the lake when I want to lounge.