Lingering questions on the potential to bring sea otters back to Oregon

By Dominique Kone, Masters Student in Marine Resource Management

By now, I’m sure you’re aware of recent interests to reintroduce sea otters to Oregon. To inform this effort, my research focuses on predicting suitable sea otter habitat and investigating the potential ecological effects if sea otters are reintroduced in the future. This information will help managers gain a better understanding of the potential for sea otters to reestablish in Oregon, as well as how Oregon’s ecosystems may change via top-down processes. These analyses will address some sources of uncertainties of this effort, but there are still many more questions researchers could address to further guide this process. Here, I note some lingering questions I’ve come across in the course of conducting my research. This is not a complete list of all questions that could or should be investigated, but they represent some of the most interesting questions I have and others have in Oregon.

Credit: Todd Mcleish

The questions, and our associated knowledge on each of these topics:

Is there enough available prey to support a robust sea otter population in Oregon?

Sea otters require approximately 30% of their own body weight in food every day (Costa 1978, Reidman & Estes 1990). With a large appetite, they not only need to spend most of their time foraging, but require a steady supply of prey to survive. For predators, we assume the presence of suitable habitat is a reliable proxy for prey availability (Redfern et al. 2006). Whereby, quality habitat should supply enough prey to sustain predators at higher trophic levels.

In making these habitat predictions for sea otters, we must also recognize the potential limitations of this “habitat equals prey” paradigm, in that there may be parcels of habitat where prey is unavailable or inaccessible. In Oregon, there could be unknown processes unique to our nearshore ecosystems that would support less prey for sea otters. This possibility highlights the importance of not only understanding how much suitable habitat is available for foraging sea otters, but also how much prey is available in these habitats to sustain a viable otter population in the future. Supplementing these habitat predictions with fishery-independent prey surveys is one way to address this question.

Credit: Suzi Eszterhas via Smithsonian Magazine

How will Oregon’s oceanographic seasonality alter or impact habitat suitability?

Sea otters along the California coast exist in an environment with persistent Giant kelp beds, moderate to low wave intensity, and year-round upwelling regimes. These environmental variables and habitat factors create productive ecosystems that provide quality sea otter habitat and a steady supply of prey; thus, supporting high densities of sea otters. This environment contrasts with the Oregon coast, which is characterized by seasonal changes in bull kelp and wave intensity. Summer months have dense kelp beds, calm surf, and strong upwellings. While winter months have little to no kelp, weak upwellings, and intense wave climates. These seasonal variations raise the question as to how these temporal fluctuations in available habitat could impact the number of sea otters able to survive in Oregon.

In Washington – an environment like Oregon – sea otters exhibit seasonal distribution patterns in response to intensifying wave climates. During calm summer months, sea otters primarily forage along the outer coast, but move into more protected areas, such as the Strait of Juan de Fuca, during winter months (Laidre et al. 2009). If sea otters were reintroduced to Oregon, we may very well observe similar seasonal movement patterns (e.g. dispersal into estuaries), but the degree to which this seasonal redistribution and reduction in foraging habitat could impact sea otter reestablishment and recovery is currently unknown.

Credit: Oregon Coast Aquarium

In the event of a reintroduction, do northern or southern sea otters have a greater capacity to adapt to Oregon environments?

In the early 1970’s, Oregon’s first sea otter translocation effort failed (Jameson et al. 1982). Since then, hypotheses on the potential ecological differences between northern and southern sea otters have been proposed as potential factors of the failed effort, potentially due to different abilities to exploit specific prey species. Studies have demonstrated that northern and southern sea otters have slight morphological differences – northern otters having larger skulls and teeth than southern otters (Wilson et al. 1991). This finding has created the hypothesis that the northern otter’s larger skull and teeth allow it to consume prey with denser exoskeletons, and thereby can exploit a greater diversity of prey species. However, there appears to be a lack of evidence to suggest larger skulls and teeth translate to greater bite force. Based on morphology alone, either sub-species could be just as successful in exploiting different prey species.

A different direction to address questions around adaptability is to look at similarities in habitat and oceanographic characteristics. Sea otters exist along a gradient of habitat types (e.g. kelp forests, estuaries, soft-sediment environments) and oceanographic conditions (e.g. warm-temperature to cooler sub-Arctic waters) (Laidre et al. 2009, Lafferty et al. 2014). Yet, we currently don’t know how well or quickly otters can adapt when they expand into new habitats that differ from ones they are familiar with. Sea otters must be efficient foragers and need to acquire skills that allow them to effectively hunt specific prey species (Estes et al. 2003). Hypothetically, if we take sea otters from rocky environments where they’ve developed foraging skills to hunt sea urchins and abalones, and place them in a soft-sediment environment, how quickly would they develop new foraging skills to exploit soft-sediment prey species? Would they adapt quickly enough to meet their daily prey requirements?

Credit: Eric Risberg/Associated Press via The Columbian

In Oregon, specifically, how might climate change impact sea otters, and how might sea otters mediate climate impacts?

Climate change has been shown to directly impact many species via changes in temperature (Chen et al. 2011). Some species have specific thermal tolerances, in which they can only survive within a specified temperature range (i.e. maximum and minimum). Once the temperature moves out of that range, the species can either move with those shifting water masses, behaviorally adapt or perish (Sunday et al. 2012). It’s unclear if and how changing temperatures will impact sea otters, directly. However, sea otters could still be indirectly affected via impacts to their prey. If prey species in sea otter habitat decline due to changing temperatures, this would reduce available food for otters. Ocean acidification (OA) is another climate-induced process that could indirectly impact sea otters. By creating chemical conditions that make it difficult for species to form shells, OA could decrease the availability of some prey species, as well (Gaylord et al. 2011).

Interestingly, these pathways between sea otters and climate change become more complex when we consider the potentially mediating effects from sea otters. Aquatic plants – such as kelp and seagrass – can reduce the impacts of climate change by absorbing and taking carbon out of the water column (Krause-Jensen & Duarte 2016). This carbon sequestration can then decrease acidic conditions from OA and mediate the negative impacts to shell-forming species. When sea otters catalyze a tropic cascade, in which herbivores are reduced and aquatic plants are restored, they could increase rates of carbon sequestration. While sea otters could be an effective tool against climate impacts, it’s not clear how this predator and catalyst will balance each other out. We first need to investigate the potential magnitude – both temporal and spatial – of these two processes to make any predictions about how sea otters and climate change might interact here in Oregon.

Credit: National Wildlife Federation

In Summary

There are several questions I’ve noted here that warrant further investigation and could be a focus for future research as this potential sea otter reintroduction effort progresses. These are by no means every question that should be addressed, but they do represent topics or themes I have come across several times in my own research or in conversations with other researchers and managers. I think it’s also important to recognize that these questions predominantly relate to the natural sciences and reflect my interest as an ecologist. The number of relevant questions that would inform this effort could grow infinitely large if we expand our disciplines to the social sciences, economics, genetics, so on and so forth. Lastly, these questions highlight the important point that there is still a lot we currently don’t know about (1) the ecology and natural behavior of sea otters, and (2) what a future with sea otters in Oregon might look like. As with any new idea, there will always be more questions than concrete answers, but we – here in the GEMM Lab – are working hard to address the most crucial ones first and provide reliable answers and information wherever we can.

References:

Chen, I., Hill, J. K., Ohlemuller, R., Roy, D. B., and C. D. Thomas. 2011. Rapid range shifts of species associated with high levels of climate warming. Science. 333: 1024-1026.

Costa, D. P. 1978. The ecological energetics, water, and electrolyte balance of the California sea otter (Enhydra lutris). Ph.D. dissertation, University of California, Santa Cruz.

Estes, J. A., Riedman, M. L., Staedler, M. M., Tinker, M. T., and B. E. Lyon. 2003. Individual variation in prey selection by sea otters: patterns, causes and implications. Journal of Animal Ecology. 72: 144-155.

Gaylord et al. 2011. Functional impacts of ocean acidification in an ecologically critical foundation species. Journal of Experimental Biology. 214: 2586-2594.

Jameson, R. J., Kenyon, K. W., Johnson, A. M., and H. M. Wight. 1982. History and status of translocated sea otter populations in North America. Wildlife Society Bulletin. 10(2): 100-107.

Krause-Jensen, D., and C. M. Duarte. 2016. Substantial role of macroalgae in marine carbon sequestration. Nature Geoscience. 9: 737-742.

Lafferty, K. D., and M. T. Tinker. 2014. Sea otters are recolonizing southern California in fits and starts. Ecosphere.5(5).

Laidre, K. L., Jameson, R. J., Gurarie, E., Jeffries, S. J., and H. Allen. 2009. Spatial habitat use patterns of sea otters in coastal Washington. Journal of Marine Mammalogy. 90(4): 906-917.

Redfern et al. 2006. Techniques for cetacean-habitat modeling. Marine Ecology Progress Series. 310: 271-295.

Reidman, M. L. and J. A. Estes. 1990. The sea otter (Enhydra lutris): behavior, ecology, and natural history. United States Department of the Interior, Fish and Wildlife Service, Biological Report. 90: 1-126.

Sunday, J. M., Bates, A. E., and N. K. Dulvy. 2012. Thermal tolerance and the global redistribution of animals. Nature: Climate Change. 2: 686-690.

Wilson, D. E., Bogan, M. A., Brownell, R. L., Burdin, A. M., and M. K. Maminov. 1991. Geographic variation in sea otters, Ehydra lutris. Journal of Mammalogy. 72(1): 22-36.

Eyes from Space: Using Remote Sensing as a Tool to Study the Ecology of Blue Whales

By Christina Garvey, University of Maryland, GEMM Lab REU Intern

It is July 8th and it is my 4th week here in Hatfield as an REU intern for Dr. Leigh Torres. My name is Christina Garvey and this summer I am studying the spatial ecology of blue whales in the South Taranaki Bight, New Zealand. Coming from the east coast, Oregon has given me an experience of a lifetime – the rugged shorelines continue to take my breath away and watching sea lions in Yaquina Bay never gets old. However, working on my first research project has by far been the greatest opportunity and I have learned so much in so little time. When Dr. Torres asked me to contribute to this blog I was unsure of how I would write about my work thus far but I am excited to have the opportunity to share the knowledge I have gained with whoever reads this blog post.

The research project that I will be conducting this summer will use remotely sensed environmental data (information collected from satellites) to predict blue whale distribution in the South Taranaki Bight (STB), New Zealand. Those that have read previous blogs about this research may remember that the STB study area is created by a large indentation or “bight” on the southern end of the Northern Island. Based on multiple lines of evidence, Dr. Leigh Torres hypothesized the presence of an unrecognized blue whale foraging ground in the STB (Torres 2013). Dr. Torres and her team have since proved that blue whales frequent this region year-round; however, the STB is also very industrial making this space-use overlap a conservation concern (Barlow et al. 2018). The increasing presence of marine industrial activity in the STB is expected to put more pressure on blue whales in this region, whom are already vulnerable from the effects of past commercial whaling (Barlow et al. 2018) If you want to read more about blue whales in the STB check out previous blog posts that talk all about it!

Figure 1. A blue whale surfaces in front of a floating production storage and offloading vessel servicing the oil rigs in the South Taranaki Bight. Photo by D. Barlow.
Figure 2. South Taranaki Bight, New Zealand, our study site outlined by the red box. Kahurangi Point (black star) is the site of wind-driven upwelling system.

The possibility of the STB as an important foraging ground for a resident population of blue whales poses management concerns as New Zealand will have to balance industrial growth with the protection and conservation of a critically endangered species. As a result of strong public support, there are political plans to implement a marine protected area (MPA) in the STB for the blue whales. The purpose of our research is to provide scientific knowledge and recommendations that will assist the New Zealand government in the creation of an effective MPA.

In order to create an MPA that would help conserve the blue whale population in the STB, we need to gather a deeper understanding of the relationship between blue whales and this marine environment. One way to gain knowledge of the oceanographic and ecological processes of the ocean is through remote sensing by satellites, which provides accessible and easy to use environmental data. In our study we propose remote sensing as a tool that can be used by managers for the design of MPAs (through spatial and temporal boundaries). Satellite imagery can provide information on sea surface temperature (SST), SST anomaly, as well as net primary productivity (NPP) – which are all measurements that can help describe oceanographic upwelling, a phenomena that is believed to be correlated to the presence of blue whales in the STB region.

Figure 3. The stars of the show: blue whales. A photograph captured from the small boat of one animal fluking up to dive down as another whale surfaces close by. (Photo credit: L. Torres)

Past studies in the STB showed evidence of a large upwelling event that occurs off the coast of Kahurangi Point (Fig. 2), on the northwest tip of the South Island (Shirtcliffe et al. 1990). In order to study the relationship of this upwelling to the distribution of blue whales, I plan to extract remotely sensed data (SST, SST anomaly, & NPP) off the coast of Kahurangi and compare it to data gathered from a centrally located site within the STB, which is close to oil rigs and so is of management interest. I will first study how decreases in sea surface temperature at the site of upwelling (Kahurangi) are related to changes in sea surface temperature at this central site in the STB, while accounting for any time differences between each occurrence. I expect that this relationship will be influenced by the wind patterns, and that there will be changes based on the season. I also predict that drops in temperature will be strongly related to increases in primary productivity, since upwelling brings nutrients important for photosynthesis up to the surface. These dips in SST are also expected to be correlated to blue whale occurrence within the bight, since blue whale prey (krill) eat the phytoplankton produced by the productivity.

Figure 4. A blue whale lunges on an aggregation of krill. UAS piloted by Todd Chandler.

To test the relationships I determine between remotely sensed data at different locations in the STB, I plan to use blue whale observations from marine mammal observers during a seismic survey conducted in 2013, as well as sightings recorded from the 2014, 2016, and 2017 field studies led by Dr. Leigh Torres. By studying the statistical relationships between all of these variables I hope to prove that remote sensing can be used as a tool to study and understand blue whale distribution.

I am very excited about this research, especially because the end goal of creating an MPA really gives me purpose. I feel very lucky to be part of a project that could make a positive impact on the world, if only in just a little corner of New Zealand. In the mean time I’ll be here in Hatfield doing the best I can to help make that happen.

References: 

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.

Shirtcliffe TGL, Moore MI, Cole AG, Viner AB, Baldwin R, Chapman B (1990) Dynamics of the Cape Farewell upwelling plume, New Zealand. New Zeal J Mar Freshw Res 24:555–568.

Torres LG (2013) Evidence for an unrecognised blue whale foraging ground in New Zealand. New Zeal J Mar Freshw Res 47:235–248.

Species distribution modeling: Part statistics, part philosophy, and there is no “right answer”

By Dawn Barlow, PhD student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

Just like that, I have wrapped up year 1 of my PhD in Wildlife Science. For my PhD, I am investigating the ecology and distribution of blue whales in New Zealand across multiple spatial and temporal scales. In a region where blue whales overlap with industrial activity, there is considerable interest from managers to be able to reliably forecast when and where blue whales are most likely to be in the area. In a series of five chapters and utilizing multiple different data sources (dedicated boat surveys, oceanographic data, acoustic recordings, remotely sensed environmental data, opportunistic blue whale sightings information), I will attempt to describe, quantify, and predict where blue whales are found in relation to their environment. Each chapter will evaluate the distribution of blue whales relative to the environment at different scales in space (ranging from 4 km to 25 km resolution) and time (ranging from daily to seasonal resolution). One overarching method I am using throughout my PhD is species distribution modeling. Having just completed my research review with my doctoral committee last week, I’ll share this aspect of my research proposal that I’ve particularly enjoyed reading, writing, and thinking about.

A pair of blue whales surfacing in the South Taranaki Bight region of New Zealand. Drone piloted by Todd Chandler during the 2017 field season.

Species distribution models (SDMs), which are sometimes referred to as habitat models or ecological niche models, are mathematical algorithms that combine observations of a species with environmental conditions at their observed locations, to gain ecological insight and predict spatial distributions of the species (Elith and Leathwick, 2009; Redfern et al., 2006). Any model is just one description of what is occurring in the natural world. Just as there are many ways to describe something with words and many languages to do so, there are many options for modeling frameworks and approaches, with stark and nuanced differences. My labmate and friend Solene Derville has equated the number of choices one has for SDMs to the cracker section in an American grocery store. When navigating all of these choices and considerations, it is important to remember that no model will ever be completely correct—it is our best attempt at describing a complex natural system—and as an analyst we need to do the best that we can with the data available to address the ecological questions at hand. As it turns out, the dividing line between quantitative analysis and philosophy is thin at times. What may seem at first like a purely objective, statistical endeavor requires careful consideration and fundamental decision-making on the part of the analyst.

Ecosystems are multifaceted, complex, and hierarchical. They are comprised of multiple physical and biological components, which operate at multiple scales across space and time. As Dr. Simon Levin stated in at 1989 MacArthur Award lecture on the topic of scale in ecology:

“A good model does not attempt to reproduce every detail of the biological system; the system itself suffices for that purpose as the most detailed model of itself. Rather, the objective of a model should be to ask how much detail can be ignored without producing results that contradict specific sets of observations, on particular scales of interest” (Levin, 1992).

The question of scale is central to ecology. As many biology students learn in their first introductory classes, parsimony is “The principle that the most acceptable explanation of an occurrence, phenomenon, or event is the simplest, involving the fewest entities, assumptions, or changes” (Oxford Dictionary). In other words, the best explanation is the simplest one. One challenge in ecological modeling, including SDMs, is to select spatial and temporal scales as coarse as possible for the most parsimonious—the most straightforward—model, while still being fine enough to capture relevant patterns. Another critical consideration is the scale of the question you are interested in answering. The scale of the analysis must match the scale at which you want to make inferences about the ecology of a species.

Similarly, the issue of complexity is central to distribution modeling. Overly simple models may not be able to adequately describe the relationship between species occurrence and the environment. In contrast, highly complex models may have very high explanatory power, but risk ascribing an ecological pattern to noise in the data (Merow et al., 2014), in other words, finding patterns that aren’t real. Furthermore, highly complex models tend to have poorer predictive capacity than simpler models (Merow et al., 2014). There is a trade-off between descriptive and predictive power in SDMs (Derville et al., 2018). Therefore, a key component in the SDM process is establishing the end goal of the model with respect to the region of interest, scale, explanatory power, predictive capacity, and in many cases management need.

Finally, any model is ultimately limited by the data available and the scale at which it was collected (Elith and Leathwick, 2009; Guillera-Arroita et al., 2015; Redfern et al., 2006). Prior knowledge of what environmental features are important to the species of interest is often limited at the time of the data collection effort, and data collection is constrained by when it is logistically feasible to sample. For example, we collect detailed oceanographic data during the summer months when it is practical to get out on the water, satellite imagery of sea surface temperature might be unavailable during times of cloud cover, and people are more likely to report blue whale sightings in areas where there is more human activity. Therefore, useful SDMs that address both ecological and management needs typically balance the scale of analysis and model complexity with the limitations of the data.

Managers and politicians within the New Zealand government are interested in a tool to predict when and where blue whales are most likely to be, based on sound ecological analysis. This is one of the end-goals of my PhD, but in the meantime, I am grappling with the appropriate scales of analysis, and attempting to balance questions of model complexity, explanatory power, and predictive capacity. There is no single, correct answer, and so my process is in part quantitative analysis, part philosophy, and all with the goal of increased ecological understanding and conservation of a species.

A blue whale breaks the surface. As I grapple with questions of model complexity and scale of analysis, I sometimes need a reminder that behind each data point is a blue whale, and what a privilege it is to study them. Photo by Leigh Torres.

References:

Derville, S., Torres, L. G., Iovan, C., and Garrigue, C. (2018). Finding the right fit: Comparative cetacean distribution models using multiple data sources and statistical approaches. Divers. Distrib. 24, 1657–1673. doi:10.1111/ddi.12782.

Elith, J., and Leathwick, J. R. (2009). Species Distribution Models: Ecological Explanation and Prediction Across Space and Time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697. doi:10.1146/annurev.ecolsys.110308.120159.

Guillera-Arroita, G., Lahoz-Monfort, J. J., Elith, J., Gordon, A., Kujala, H., Lentini, P. E., et al. (2015). Is my species distribution model fit for purpose? Matching data and models to applications. Glob. Ecol. Biogeogr. 24, 276–292. doi:10.1111/geb.12268.

Levin, S. A. (1992). The problem of pattern and scale. Ecology 73, 1943–1967.

Merow, C., Smith, M. J., Edwards, T. C., Guisan, A., Mcmahon, S. M., Normand, S., et al. (2014). What do we gain from simplicity versus complexity in species distribution models? Ecography (Cop.). 37, 1267–1281. doi:10.1111/ecog.00845.

Redfern, J. V., Ferguson, M. C., Becker, E. A., Hyrenbach, K. D., Good, C., Barlow, J., et al. (2006). Techniques for cetacean-habitat modeling. Mar. Ecol. Prog. Ser. 310, 271–295. doi:10.3354/meps310271.

Zooming in: A closer look at bottlenose dolphin distribution patterns off of San Diego, CA

By: Alexa Kownacki, Ph.D. Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

Data analysis is often about parsing down data into manageable subsets. My project, which spans 34 years and six study sites along the California coast, requires significant data wrangling before full analysis. As part of a data analysis trial, I first refined my dataset to only the San Diego survey location. I chose this dataset for its standardization and large sample size; the bulk of my sightings, over 4,000 of the 6,136, are from the San Diego survey site where the transect methods were highly standardized. In the next step, I selected explanatory variable datasets that covered the sighting data at similar spatial and temporal resolutions. This small endeavor in analyzing my data was the first big leap into understanding what questions are feasible in terms of variable selection and analysis methods. I developed four major hypotheses for this San Diego site.

The study species: common bottlenose dolphin (Tursiops truncatus) seen along the California coastline in 2015. Image source: Alexa Kownacki.

Hypotheses:

H1: I predict that bottlenose dolphin sightings along the San Diego transect throughout the years 1981-2015 exhibit clustered distribution patterns as a result of the patchy distributions of both the species’ preferred habitats, as well as the social nature of bottlenose dolphins.

H2: I predict there would be higher densities of bottlenose dolphin at higher latitudes spanning 1981-2015 due to prey distributions shifting northward and less human activities in the northerly sections of the transect.

H3: I predict that during warm (positive) El Niño Southern Oscillation (ENSO) months, the dolphin sightings in San Diego would be distributed more northerly, predominantly with prey aggregations historically shifting northward into cooler waters, due to (secondarily) increasing sea surface temperatures.

H4: I predict that along the San Diego coastline, bottlenose dolphin sightings are clustered within two kilometers of the six major lagoons, with no specific preference for any lagoon, because the murky, nutrient-rich waters in the estuarine environments are ideal for prey protection and known for their higher densities of schooling fishes.

Data Description:

The common bottlenose dolphin (Tursiops truncatus) sighting data spans 1981-2015 with a few gap years. Sightings cover all months, but not in all years sampled. The same transect in San Diego was surveyed in a small, rigid-hulled inflatable boat with approximately a two-kilometer observation area (one kilometer surveyed 90 degrees to starboard and port of the bow).

I wanted to see if there were changes in dolphin distribution by latitude and, if so, whether those changes had a relationship to ENSO cycles and/or distances to lagoons. For ENSO data, I used the NOAA database that provides positive, neutral, and negative indices (1, 0, and -1, respectively) by each month of each year. I matched these ENSO data to my month-date information of dolphin sighting data. Distance from each lagoon was calculated for each sighting.

Figure 1. Map representing the San Diego transect, represented with a light blue line inside of a one-kilometer buffered “sighting zone” in pale yellow. The dark pink shapes are dolphin sightings from 1981-2015, although some are stacked on each other and cannot be differentiated. The lagoons, ranging in size, are color-coded. The transect line runs from the breakwaters of Mission Bay, CA to Oceanside Harbor, CA.

Results: 

H1: True, dolphins are clustered and do not have a uniform distribution across this area. Spatial analysis indicated a less than a 1% likelihood that this clustered pattern could be the result of random chance (Fig. 1, z-score = -127.16, p-value < 0.0001). It is well-known that schooling fishes have a patchy distribution, which could influence the clustered distribution of their dolphin predators. In addition, bottlenose dolphins are highly social and although pods change in composition of individuals, the dolphins do usually transit, feed, and socialize in small groups.

Figure 2. Summary from the Average Nearest Neighbor calculation in ArcMap 10.6 displaying that bottlenose dolphin sightings in San Diego are highly clustered. When the z-score, which corresponds to different colors on the graphic above, is strongly negative (< -2.58), in this case dark blue, it indicates clustering. Because the p-value is very small, in this case, much less than 0.01, these results of clustering are strongly significant.

H2: False, dolphins do not occur at higher densities in the higher latitudes of the San Diego study site. The sightings are more clumped towards the lower latitudes overall (p < 2e-16), possibly due to habitat preference. The sightings are closer to beaches with higher human densities and human-related activities near Mission Bay, CA. It should be noted, that just north of the San Diego transect is the Camp Pendleton Marine Base, which conducts frequent military exercises and could deter animals.

Figure 3. Histogram comparing the latitudes with the frequency of dolphin sightings in San Diego, CA. The x-axis represents the latitudinal difference from the most northern part of the transect to each dolphin sighting. Therefore, a small difference would translate to a sighting being in the northern transect areas whereas large differences would translate to sightings being more southerly. This could be read from left to right as most northern to most southern. The y-axis represents the frequency of which those differences are seen, that is, the number of sightings with that amount of latitudinal difference, or essentially location on the transect line. Therefore, you can see there is a peak in the number of sightings towards the southern part of the transect line.

H3: False, during warm (positive) El Niño Southern Oscillation (ENSO) months, the dolphin sightings in San Diego were more southerly. In colder (negative) ENSO months, the dolphins were more northerly. The differences between sighting latitude and ENSO index was significant (p<0.005). Post-hoc analysis indicates that the north-south distribution of dolphin sightings was different during each ENSO state.

Figure 4. Boxplot visualizing distributions of dolphin sightings latitudinal differences and ENSO index, with -1,0, and 1 representing cold, neutral, and warm years, respectively.

H4: True, dolphins are clustered around particular lagoons. Figure 5 illustrates how dolphin sightings nearest to Lagoon 6 (the San Dieguito Lagoon) are always within 0.03 decimal degrees. Because of how these data are formatted, decimal degrees is the easiest way to measure change in distance (in this case, the difference in latitude). In comparison, dolphins at Lagoon 5 (Los Penasquitos Lagoon) are distributed across distances, with the most sightings further from the lagoon.

Figure 5. Bar plot displaying the different distances from dolphin sighting location to the nearest lagoon in San Diego in decimal degrees. Note: Lagoon 4 is south of the study site and therefore was never the nearest lagoon.

I found a significant difference between distance to nearest lagoon in different ENSO index categories (p < 2.55e-9): there is a significant difference in distance to nearest lagoon between neutral and negative values and positive and neutral years. Therefore, I hypothesize that in neutral ENSO months compared to positive and negative ENSO months, prey distributions are changing. This is one possible hypothesis for the significant difference in lagoon preference based on the monthly ENSO index. Using a violin plot (Fig. 6), it appears that Lagoon 5, Los Penasquitos Lagoon, has the widest variation of sighting distances in all ENSO index conditions. In neutral years, Lagoon 0, the Buena Vista Lagoon has multiple sightings, when in positive and negative years it had either no sightings or a single sighting. The Buena Vista Lagoon is the most northerly lagoon, which may indicate that in neutral ENSO months, dolphin pods are more northerly in their distribution.

Figure 6. Violin plot illustrating the distance from lagoons of dolphin sightings under different ENSO conditions. There are three major groups based on ENSO index: “-1” representing cold years, “0” representing neutral years, and “1” representing warm years. On the x-axis are lagoon IDs and on the y-axis is the distance to the nearest lagoon in decimal degrees. The wider the shapes, the more sightings, therefore Lagoon 6 has many sightings within a very small distance compared to Lagoon 5 where sightings are widely dispersed at greater distances.

 

Bottlenose dolphins foraging in a small group along the California coast in 2015. Image source: Alexa Kownacki.

Takeaways to science and management: 

Bottlenose dolphins have a clustered distribution which seems to be related to ENSO monthly indices, and likely, their social structures. From these data, neutral ENSO months appear to have something different happening compared to positive and negative months, that is impacting the sighting distributions of bottlenose dolphins off the San Diego coastline. More research needs to be conducted to determine what is different about neutral months and how this may impact this dolphin population. On a finer scale, the six lagoons in San Diego appear to have a spatial relationship with dolphin sightings. These lagoons may provide critical habitat for bottlenose dolphins and/or for their preferred prey either by protecting the animals or by providing nutrients. Different lagoons may have different spans of impact, that is, some lagoons may have wider outflows that create larger nutrient plumes.

Other than the Marine Mammal Protection Act and small protected zones, there are no safeguards in place for these dolphins, whose population hovers around 500 individuals. Therefore, specific coastal areas surrounding lagoons that are more vulnerable to habitat loss, habitat degradation, and/or are more frequented by dolphins, may want greater protection added at a local, state, or federal level. For example, the Batiquitos and San Dieguito Lagoons already contain some Marine Conservation Areas with No-Take Zones within their reach. The city of San Diego and the state of California need better ways to assess the coastlines in their jurisdictions and how protecting the marine, estuarine, and terrestrial environments near and encompassing the coastlines impacts the greater ecosystem.

This dive into my data was an excellent lesson in spatial scaling with regards to parsing down my data to a single study site and in matching my existing data sets to other data that could help answer my hypotheses. Originally, I underestimated the robustness of my data. At first, I hesitated when considering reducing the dolphin sighting data to only include San Diego because I was concerned that I would not be able to do the statistical analyses. However, these concerns were unfounded. My results are strongly significant and provide great insight into my questions about my data. Now, I can further apply these preliminary results and explore both finer and broader scale resolutions, such as using the more precise ENSO index values and finding ways to compare offshore bottlenose dolphin sighting distributions.

What areas on the landscape do you value? Application of Human Ecology Mapping in Oregon

By: Jackie Delie, M.S. Student, OSU Department of Fisheries and Wildlife, Human Dimensions Lab (Dr. Leigh Torres, committee member providing spatial analysis guidance)

 

Mapping sociocultural data for ecosystem-based planning, like people’s values or cultural land use practices, has gained importance in conservation science, as reflected in the use of terms such as social-ecological systems (Lischka et al. 2018). The emergence of the geospatial revolution – where data have a location associated with it – has changed how scientists analyze, visualize, and scale their perceptions of landscapes and species. However, there is a limited collection of spatial sociocultural data compared to biophysical data.

To address the restricted spatial sociocultural data available, scientists (such as social scientists), community leaders, and indigenous groups have used various mapping methods for decision-making in natural resources planning to capture people’s uses, values, and interaction between people and landscapes. Some mapping methods are termed community values mapping (Raymond et al. 2009), landscape values mapping (Besser et al. 2014), public participation GIS (Brown & Reed 2009), and social values mapping (Sherrouse et al. 2011). Mclain et al. (2013) applies the umbrella term Human Ecology Mapping (HEM) to refer to all these mapping approaches that span across academic disciplines and sub-disciplines. HEM focuses on understanding human-environmental interactions, intending to gather spatial data on aspects of human ecology that can potentially be important to ecosystem-based management and planning. As an early career scientist, I embraced the opportunity to incorporate a HEM approach, more specifically the mapping of landscape values, into my thesis.

My research explores the human-black bear relationship in Oregon. The American black bear (Ursus americanus) is one species identified by the Oregon Department of Fish and Wildlife with a stable or increasing population (25,000 to 35,000 individuals) where many human-black bear interactions occur (ODFW 2012). One component of my research incorporates understanding how recreationists use the landscape and the values they associate with different places. For 18 days in the summer of 2018, I was at various trailheads throughout Oregon, approaching people to request their interest in taking my survey (Image 1 & 2). The consenting participants were asked to identify on the digital map of Oregon the primary places they use or visit on the landscape. Participants had the option to draw a point, line, or polygon to identify up to three places within the state (Image 3). Then, participants were asked to choose the type of activity they prefer at each primary location from a list of 17 recreational activities (e.g., hiking, hunting, fishing, camping, etc.). Finally, they were asked to select one primary value they associate with each identified place from a list of five standardized landscape values (Brown & Reed 2009; Besser et al. 2014). The most important values for my study are aesthetic, economic, intrinsic, subsistence, and social. An example of an aesthetic value statement: “I value this area for its scenic qualities”.

Now that my data is collected, I am creating GIS layers of the various ways recreationists uses the landscape, and the values they assign to those places, showing the distribution of aggregated uses (Image 4) and their relationship to known human-black bear interaction areas. The approach I employed to collect social-spatial data is just one strategy out of many, and it is recognized that maps are never fully objective representations of reality. However, mapping landscape values is a useful tool for identifying and visualizing human-environment relations. The geographically referenced data can be used to map areas of high value (density) or associated with different types of values (diversity). Further, these maps can be overlaid with other biophysical and land use layers to help land managers understand the variety of landscape values and activities.

 

Southern Oregon in August 2018. Lots of fires in the area during this time and that had an impact on where I could collect data as certain forest areas were closed to the public.

 

Me collecting data at Upper Table Rock Trailhead in Southern Oregon

 

Use of an Ipad and the software Mappt to collect socio-spatial data while at trailheads in Oregon. Participants used the digital map to identify up to three places they primarily use the landscape.

 

Preliminary map displaying all the areas of preferred landscape use (orange) marked by survey participants.

 

References:

Besser, D., McLain, R., Cerveny, L., Biedenweg, K. and Banis, D. 2014. Environmental Reviews and Case Studies: Mapping Landscape Values: Issues, Challenges and Lessons Learned from Field Work on the Olympic Peninsula, Washington, Environmental Practice, 16(2): 138–150.

Brown, G., and Reed, P. 2009. Public Participation GIS: A New Method for Use in National Forest Planning. Forest Science, 55(2): 162-182.

Lischka, S., Teel, T., Johnson, H., Reed, S., Breck, S., Don Carlos, A., Crooks, K. 2018. A conceptual model for the integration of social and ecological information to understand human-wildlife interactions. Biological Conservation 225: 80-87.

McLain, R., Poe, M., Biedenweg, K., Cerveny, L., Besser, D., and Blahna, D. 2013. Making sense of human ecology mapping: An overview of approaches to integrating socio-spatial data into environmental planning. Human Ecology, 41(1).

Oregon Department of Fish and Wildlife (ODFW). 2012. Oregon Black Bear Management Plan.

Raymond, M., Bryan, A., MacDonald, H., Cast, A., Strathearn, S., Grandgirard, A., and Kalivas, T. 2009. Mapping Community Values for Natural Capital and Ecosystem Services. Ecological Economics 68: 1301–1315.

Sherrouse, B. C., Clement, J. M., and Semmens, D. J. 2011. A GIS Application for Assessing, Mapping, and Quantifying the Social Values of Ecosystem Services. Applied Geography, 31: 748–760.

Our GEM(M), Ruby, is back in action!

By Lisa Hildebrand, MSc student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

Every season, or significant period of time, usually has a distinct event that marks its beginning. For example, even though winter officially begins when the winter solstice occurs sometime between December 20 and December 23, many people often associate the first snowfall as the real start of winter. To mark the beginning of schooling, when children start 1stgrade in Germany (which is where I’m from), they receive something called a “Zuckertüte”, which translated means “sugar bag”. It is a large (sometimes as large as the child) cone-shaped container made of cardboard filled with toys, chocolates, sweets, school supplies and various other treats topped with a large bow.

Receiving my Zuckertüte in August of 2001 before starting 1st grade. Source: Ines Hildebrand.

I still remember (and even have) mine – it was almost as tall as I was, had a large Barbie printed on it (and a real one sitting on top of it) and was bright pink. And of course, while at a movie theatre, once the lights dim completely and the curtain surrounding the screen opens just a little further, members of the audience stop chit-chatting or sending text messages, everyone quietens down and puts their devices away – the film is about to start. There are hundreds upon thousands of examples like these – moments, events, days that mark the start of something.

In the past, the beginning of summer has always been tied to two things for me: the end of school and the chance to be outside in the sun for many hours and days. This reality has changed slightly since moving to Oregon. While I don’t technically have any classes during the summer, the work definitely won’t stop. There are still dozens of papers to read, samples to run in the lab, and data points to plot. For anyone from Oregon or the Pacific Northwest (PNW), it’s pretty well known that the weather can be a little unpredictable and variable, meaning that summer might not always be filled with sunny days. Despite somewhat losing these two “summer markers”, I have found a new event to mark the beginning of summer – the arrival of the gray whales.

Their propensity for coastal waters and near-shore feeding is part of what makes gray whales so unique and arguably “easier” to study than some other baleen whale species. Image captured under NOAA/NMFS permit #21678. Source: Leigh Torres.

 

It’s official – the gray whale field season is upon us! As many of you may already know, the GEMM Lab has two active gray whale research projects: investigating the impacts of ocean noise on gray whale physiology and exploring potential individual foraging specialization among the Pacific Coast Feeding Group (PCFG) gray whales. Both projects involve field work, with the former operating out of Newport and the latter taking place in Port Orford, both collecting photographs and a variety of samples and tracklines to study the PCFG, which is a sub-group of the larger Eastern North Pacific (ENP) population. June 1st is the widely accepted “cut-off date” for the PCFG whales, whereby gray whales seen after June 1st along the PNW coastline (specifically northern California, Oregon, Washington and British Columbia) are considered members of the PCFG. While this date is not the only qualifying factor for an individual to be considered a PCFG member, it is a good general rule of thumb. Since last week happened to be the first week of June, PI Leigh Torres, field technician Todd Chandler and myself launched out onto the Pacific Ocean in our trusty RHIB Ruby twice looking for gray whales, and it sure was a successful start to the season!

Even though I have done small boat-based field work before, every project and field team operates a little differently, which is why I was a little nervous at first. There are a lot of components to the Newport-based project as Leigh & co. assess gray whale physiology by collecting fecal samples, drone imagery and taking photographs, observing behavior patterns, as well as assessing local prey through GoPro footage and light traps. I wasn’t worried about the prey components of the research, since there is plenty of prey sampling involved in my Port Orford research, however I was worried about the whale side of things. I wasn’t sure whether I would be able to catch the drone as it returned back home to Ruby, fearing I might fumble and let it slip through my fingers. I also experienced slight déjà vu when handling the net we use to collect the fecal samples as I was forced to think back to some previous field work that involved collecting a biopsy dart with a net as well. During that project, I had somehow managed to get the end of the net stuck in the back of the boat and as I tried to scoop up the biopsy dart with the net-end, the pole became more and more stuck while the water kept dragging the net-end down and eventually the pole ended up snapping in my hands. On top of all this anxiety and work, trying to find your footing in a small RHIB like Ruby packed with lots of gear and a good amount of swell doesn’t make any of those tasks any easier.

However, as it turned out, none of my fears came to fruition. As soon as Todd fired up Ruby’s engine and we whizzed out and under the Newport bridge, I felt exhilarated. I love field work and was so excited to be out on the water again. During the two days I was able to observe multiple individuals of a species of whale that I find unique and fascinating.

Markings and pigmentation on the flukes are also unique to individuals and allow us to perform photo identification to track individuals over months and years. Image captured under NOAA/NMFS permit #21678. Source: Leigh Torres.

I felt back in my natural element and working with Leigh and Todd was rewarding and fun, as I have so much to learn from their years of experience and natural talent in the field dealing with stressful situations and juggling multiple components and gear. Even though I wasn’t out there collecting data for my own project, some of my observations did get me thinking about what I hope to focus on in my thesis – individualization. It is always interesting to see how differently whales will behave, whether due to the substrate we find them over, the water depths we find them in, or what their surfacing patterns are like. Although I still have six weeks to go until my field season starts and feel lucky to have the opportunity to help Leigh and Todd with the Newport field work, I am already looking forward to getting down to Port Orford in mid-July and starting the fifth consecutive gray whale field season down there.

But back to Newport – over the course of two days, we were able to deploy and retrieve one light trap to collect zooplankton, collect two fecal samples, perform two GoPro drops, fly the drone three times, and take hundreds of photos of whales. Leigh and Todd were both glad to be reunited with an old friend while I felt lucky to be able to meet such a famous lady – Scarback. A whale with a long sighting history not just for the GEMM Lab but for various researchers along the coast that study this population. Scarback is well-known (and easily identified) by the large concave injury on her back that is covered in whale lice, or cyamids. While there are stories about how Scarback’s wound came to be, it is not known for sure how she was injured. However, what researchers do know is that the wound has not stopped this female from reproducing and successfully raising several calves over her lifetime. After hearing her story from Leigh, I wasn’t surprised that both she and Todd were so thrilled to get both a fecal sample and a drone flight from her early in the season. The two days weren’t all rosy; most of day 1 was shrouded in a cloud of mist resulting in a thin but continuous layer of moisture forming on our clothes, while on day 2 we battled with some pretty big swells (up to 6 feet tall) and in typical Oregon coast style we were victims of a sudden downpour for about 10 minutes. We had some excellent sightings and some not-so-excellent sightings. Sightings where we had four whales surrounding our boat at the same time and sightings where we couldn’t re-locate a whale that had popped up right next to us. It happens.

 

A local celebrity – Scarback. Image captured under NOAA/NMFS permit #21678. Source: Lisa Hildebrand.

 

An ecstatic Lisa with wild hair standing in the bow pulpit of Ruby camera at the ready. Source: Leigh Torres.

Field work is certainly one of my favorite things in the world. The smell of the salt, the rustling of cereal bar wrappers, the whipping of hair, the perpetual rosy noses and cheeks no matter how many times you apply and re-apply sunscreen, the awkward hilarity of clambering onto the back of the boat where the engine is housed to take a potty break, the whooshing sound of a blow, the sometimes gentle and sometimes aggressive rocking of the boat, the realization that you haven’t had water in four hours only to chug half of your water in a few seconds, the waft of peanut butter and jelly sandwiches, the circular footprint where a whale has just gracefully dipped beneath the surface slipping away from view. I don’t think I will ever tire of any of those things.

 

 

Current gray whale die-off: a concern or simply the circle of life?

By Leila Lemos, PhD Candidate in Wildlife Sciences, Fisheries and Wildlife Department / OSU

Examination of a dead gray whale found in Pacifica, California, in May 2019.
Source: CNN 2019.

 

The avalanche of news on gray whale deaths this year is everywhere. And because my PhD thesis focuses on gray whale health, I’ve been asked multiple times now why this is happening. So, I thought it was a current and important theme to explore in our blog. The first question that comes to (my) mind is: is this a sad and unusual event for the gray whales that raises concern, or is this die-off event expected and simply part of the circle of life?

At least 64 gray whales have washed-up on the West Coast of the US this year, including the states of California, Oregon and Washington. According to John Calambokidis, biologist and founder of the Cascadia Research Collective, the washed-up whales had one thing in common: all were in poor body condition, potentially due to starvation (Calambokidis in: Paris 2019). Other than looking skinny, some of the whale carcasses also presented injuries, apparently caused by ship strikes (CNN 2019).

Cascadia Research Collective examining a dead gray whale in 9 May 2019, washed up in Washington state. Cause of death was not immediately apparent but appeared consistent with nutritional stress.
Source: Cascadia Research Collective 2019.

To give some context, gray whales migrate long distances while they fast for long periods. They are known for performing the longest migration ever seen for a mammal, as they travel up to 20,000 km roundtrip every year from their breeding grounds in Baja California, Mexico, to their feeding grounds in the Bering and Chukchi seas (Calambokidis et al. 2002, Jones and Swartz 2002, Sumich 2014). Thus, a successful feeding season is critical for energy replenishment to recover from the previous migration and fasting periods, and for energy storage to support their metabolic needsduring the migration and fasting periods that follow. An unsuccessful feeding season could likely result in poor body condition, affecting individual performance in the following seasons, a phenomenon known as the carry-over effect(Harrison et al., 2011).

In addition, environmental change, such as climate variations, might impact shifts in prey availability and thus intensify energetic demands on the whales as they need to search harder and longer for food. These whales already fast for months and spend large energy reserves supporting their migrations. When they arrive at their feeding grounds, they need to start feeding. If they don’t have access to predictable food sources, their fitness is affected and they become more vulnerable to anthropogenic threats, including ship strikes, entanglement in fishery gear, and contamination.

For the past three years, I have been using drone-based photogrammetry to assess gray whale body condition along the Oregon coast, as part of my PhD project. Coincident to this current die-off event, I have observed that these whales presented good body condition in 2016, but in the past two years their condition has worsened. But these Oregon whales are feeding on different prey in different areas than the rest of the ENP that heads up to the Bering Sea to feed. So, are all gray whales suffering from the same broad scale environmental impacts? I am currently looking into environmental remote sensing data such as sea surface temperature, chlorophyll-a and upwelling index to explore associations between body condition and environmental anomalies that could be associated.

Trying to answer the question I previously mentioned “is this event worrisome or natural?”, I would estimate that this die-off is mostly due to natural patterns, mainly as a consequence of ecological patterns. This Eastern North Pacific (ENP) gray whale population is now estimated at 27,000 individuals (Calambokidis in: Paris 2019) and it has been suggested that this population is currently at its carrying capacity(K), which is estimated to be between 19,830 and 28,470 individuals (Wade and Perryman, 2002). Prey availability on their primary foraging grounds in the Bering Sea may simply not be enough to sustain this whole population.

The plot below illustrates a population in exponential growth over the years. The population reaches a point (K) that the system can no longer support. Therefore, the population declines and then fluctuates around this K point. This pattern and cycle can result in die-off events like the one we are currently witnessing with the ENP gray whale population.

Population at a carrying capacity (K)
Source: Conservation of change 2019.

 

According to the American biologist Paul Ehrlich: “the idea that we can just keep growing forever on a finite planet is totally imbecilic”. Resources are finite, and so are populations. We should expect die-off events like this.

Right now, we are early on the 2019 feeding season for these giant migrators. Mortality numbers are likely to increase and might even exceed previous die-off events. The last ENP gray whale die-off event occurred in the 1999-2000 season, when a total of 283 stranded whales in 1999 and 368 in 2000 were found displaying emaciated conditions (Gulland et al. 2005). This last die-off event occurred 20 years ago, and thus in my opinion, it is too soon to raise concerns about the long-term impacts on the ENP gray whale population, unless this event continues over multiple years.

 

References

Calambokidis, J. et al. 2002. Abundance, range and movements of a feeding aggregation of gray whales (Eschrichtius robustus) from California to southeastern Alaska in 1998. Journal of Cetacean research and Management. 4, 267-276.

Cascadia Research Collective (2019, May 10). Cascadia and other Washington stranding network organizations continue to respond to growing number of dead gray whales along our coast and inside waters. Retrieved from http://www.cascadiaresearch.org/washington-state-stranding-response/cascadia-and-other-washington-stranding-networkorganizations?fbclid=Iw AR1g7zc4EOMWr_wp_x39ertvzpjOnc1zZl7DoMbBcjI1Ic_EbUx2bX8_TBw

Conservation of change (2019, May 31). Limits to Growth: the first law of sustainability. Retrieved from http://www.conservationofchange.org/limits

CNN (2019, May 15). Dead gray whales keep washing ashore in the San Francisco Bay area.Retrieved from https://www.cnn.com/2019/05/15/us/gray-whale-deaths-trnd-sci/index.html

Gulland, F. M. D., H. Pérez-Cortés M., J. Urbán R., L. Rojas-Bracho, G. Ylitalo, J. Weir, S. A. Norman, M. M. Muto, D. J. Rugh, C. Kreuder, and T. Rowles. 2005. Eastern North Pacific gray whale (Eschrichtius robustus) unusual mortality event, 1999-2000. U. S. Dep. Commer., NOAA Tech. Memo. NMFS-AFSC-150, 33 p.

Harrison, X. A., et al., 2011. Carry-over effects as drivers of fitness differences in animals. Journal of Animal Ecology. 80, 4-18.

Jones, M. L., Swartz, S. L., Gray Whale, Eschrichtius robustus. Encyclopedia of Marine Mammals. Academic Press, San Diego, 2002, pp. 524-536.

Paris (2019, May 27). Gray Whales Wash Up On West Coast At Near-Record Levels.Retrieved from https://www.wbur.org/hereandnow/2019/05/27/gray-whales-wash-up-record-levels

Sumich, J. L., 2014. E. robustus: The biology and human history of gray whales. Whale Cove Marine Education.

Wade, P. R., Perryman, W., An assessment of the eastern gray whale population in 2002. IWC, Vol. SC/54/BRG7 Shimonoseki, Japan, 2002, pp. 16.

 

A Weekend of Inspiration in Marine Science: NWSSMM and Dr. Sylvia Earle!

By Karen Lohman, Masters Student in Wildlife Science, Cetacean Conservation and Genomics Lab, Oregon State University

My name is Karen Lohman, and I’m a first-year student in Dr. Scott Baker’s Cetacean Conservation and Genomics Lab at OSU. Dr. Leigh Torres is serving on my committee and has asked me to contribute to the GEMM lab blog from time to time. For my master’s project, I’ll be applying population genetics and genomics techniques to better understand the degree of population mixing and breeding ground assignment of feeding humpback whales in the eastern North Pacific. In other words, I’ll be trying to determine where the humpback whales off the U.S. West Coast are migrating from, and at what frequency.

Earlier this month I joined the GEMM lab members in attending the Northwest Student Society of Marine Mammalogy Conference in Seattle. The GEMM lab members and I made the trip up to the University of Washington to present our work to our peers from across the Pacific Northwest. All five GEMM lab graduate students, plus GEMM lab intern Acacia Pepper, and myself gave talks presenting our research to our peers. I was able to present preliminary results on the population structure of feeding humpback whales across shared feeding habitat by multiple breeding groups in the eastern North Pacific using mitochondria DNA haplotype frequencies. In the end GEMM lab’s Dawn Barlow took home the “Best Oral Presentation” prize. Way to go Dawn!

A few of the GEMM lab members and me presenting our research at the NWSSMM conference in May 2019 at the University of Washington.

While conferences have a strong networking component, this one feels unique.  It is a chance to network with our peers, who are working through the same challenges in graduate school and will hopefully be our future research collaborators in marine mammal research when we finish our degrees. It’s also one of the few groups of people that understand the challenges of studying marine mammals. Not every day is full of dolphins and rainbows; for me, it’s mostly labwork or writing code to overcome small and/or patchy sample size problems.

All of the CCGL and GEMM Lab members excited to hear Dr. Sylvia Earle’s presentation at Portland State University in May 2019 (from L to R: Karen L., Lisa H., Alexa K., Leila L., Dawn B., and Dom K.) . Photo Source: Alexa Kownacki

On the way back from Seattle we stopped to hear the one and only Dr. Sylvia Earle, talk in Portland. With 27 honorary doctorates and over 200 publications, Dr. Sylvia Earle is a legend in marine science. Hearing a distinguished marine researcher talk about her journey in research and to present such an inspiring message of ocean advocacy was a great way to end our weekend away from normal grad school responsibilities. While the entirety of her talk was moving, one of her final comments really stood out. Near the end of her talk she called the audience to action by saying “Look at your abilities and have confidence that you can and must make a difference. Do whatever you’ve got.” As a first-year graduate student trying to figure out my path forward in research and conservation, I couldn’t think of better advice to end the weekend on.

 

Should scientists engage in advocacy?

By Dominique Kone, Masters Student in Marine Resource Management

Should scientists engage in advocacy? This question is one of the most debated topics in conservation and natural resource management. Some experts firmly oppose researchers advocating for policy decisions because such actions potentially threaten the credibility of their science. While others argue that with environmental issues becoming more complex, society would benefit from hearing scientists’ opinions and preferences on proposed actions. While both arguments are valid, we must recognize the answer to this question may never be a universal yes or no. As an early-career scientist, I’d like to share some of my observations and thoughts on this topic, and help continue this dialogue on the appropriateness of scientists exercising advocacy.

Policymakers are tasked with making decisions that determine how species and natural resources are managed, and subsequently affect and impact society. Scientists commonly play an integral role in these policy decisions, by providing policymakers with reliable and accurate information so they can make better-informed decisions. Examples include using stock assessments to set fishing limits, incorporating the regeneration capacity of forests into the timing of timber harvest, or considering the distribution of blue whales in permitting seafloor mining projects. Importantly, informing policy with science is very different from scientists advocating on policy issues. To understand these nuances, we must first define these terms.

A scientist considering engaging in policy advocacy. Source: Karen Brey.

According to Merriam-Webster, informing means “to communicate knowledge to” or “to give information to an authority”. In contrast, advocating means “to support or argue for (a cause, policy, etc.)” (Merriam-Webster 2019). People can inform others by providing information without necessarily advocating for a cause or policy. For many researchers, providing credible science to inform policy decisions is the gold standard. We, as a society, do not take issue with researchers supplying policymakers with reliable information. Rather, pushback arises when researchers step out of their role as informants and attempt to influence or sway policymakers to decide in a particular manner by speaking to values. This is advocacy.

Dr. Robert Lackey is a fisheries & political scientist, and one of the prominent voices on this issue. In his popular 2007 article, he explains that when scientists inform policy while also advocating, a conflict of interest is created (Lackey 2007). To an outsider, it can be difficult to distinguish values from scientific evidence when researchers engage in policy discussions. Are they engaging in these discussions to provide reliable information as an honest scientist, or are they advocating for decisions or policies based on their personal preferences? As a scientist, I like to believe most scientists – in natural resource management and conservation – do not engage in policy decisions for their own benefit, and they truly want to see our resources managed in a responsible and sustainable manner. Yet, I also recognize this belief doesn’t negate the fact that when researchers engage in policy discussions, they could advocate for their personal preferences – whether they do so consciously or subconsciously – which makes identifying these conflicts of interest particularly challenging.

Examples of actions scientists take in conducting and reporting research. Actions on the left represent actions of policy advocacy, those on the right do not, and the center is maybe. This graphic was adapted from a policy advocacy graphic from Scott et al. 2007. Source: Jamie Keyes.

It seems much of the unease with researchers exercising advocacy has to do with a lack in transparency about which role the researcher chooses to play during those policy debates. A simple remedy to this dilemma – as Lackey suggested in his paper – could be to encourage scientists to be completely transparent when they are about to inform versus advocate (Lackey 2007). Yet, for this suggestion to work, it would require complete trust in scientists to (1) verbalize and make known whether they’re informing or advocating, and (2) when they are informing, to provide credible and unbiased information. I’ve only witnessed a few scientists do this without ensuing some skepticism, which unfortunately highlights issues around an emerging mistrust of researchers to provide policy-neutral science. This mistrust threatens the important role scientists have played in policy decisions and the relationships between scientists and policymakers.

While much of this discussion has been focused on how researchers and their science are received by policymakers, researchers engaging in advocacy are also concerned with how they are perceived by their peers within the scientific community. When I ask more-senior researchers about their concerns with engaging in advocacy, losing scientific credibility is typically at or near the top of their lists. Many of them fear that once you start advocating for a position or policy decision (e.g. protected areas, carbon emission reduction, etc.), you become known for that one cause, which opens you up to questions and suspicions on your ability to provide unbiased and objective science. Once your credibility as a scientist comes into question, it could hinder your career.

How it sometimes feels when researchers conduct policy-relevant science. Source: Justin DeFreitas.

Conservation scientists are faced with a unique dilemma. They value both biodiversity conservation and scientific credibility. Yet, in some cases, risk or potential harm to a species or ecosystem may outweigh concerns over damage to their credibility, and therefore, may choose to engage in advocacy to protect that species or ecosystem (Horton 2015). Horton’s explanation raises an important point that researchers taking a hands-off approach to advocacy may not always be warranted, and that a researcher’s decision to engage in advocacy will heavily depend on the issue at hand and the repercussions if the researcher does not advocate their policy preferences. Climate change is a great example, where climate scientists are advocating for the use of their science, recognizing the alternative could mean continued inaction on carbon emission reduction and mitigation. [Note: this is called science advocacy, which is slightly different than advocating personal preferences, but this example helps demonstrate my point.]

To revisit the question – should scientists engage in advocacy? Honestly, I don’t have a clear answer, because there is no clear answer. This topic is one that has so many dimensions beyond the few I mentioned in this blog post. In my opinion, I don’t think researchers should have an always yes or always no stance on advocacy. Nor do I think every researcher needs to agree on this topic. A researcher’s decision to engage in advocacy will all depend on context. When faced with this decision, it might be useful to ask yourself the following questions: (1) How much do policymakers trust me? (2) How will my peers perceive me if I choose to engage? (3) Could I lose scientific credibility if I do engage? And (4) What’s at stake if I don’t make my preferences known? Hopefully, the answers to these sub-questions will help you decide whether you should advocate.

References:

Horton, C. C., Peterson, T. R., Banerjee, P., and M. J. Peterson. 2015. Credibility and advocacy in conservation science. Conservation Biology. 30(1): 23-32.

Lackey, R. T. 2007. Science, Scientists, and Policy Advocacy. Conservation Biology. 21(1): 12-17.

Scott et al. (2007). Policy advocacy in science: prevalence, perspectives, and implications for conservation biologists. Conservation Biology. 21(1): 29-35.

Merriam-Webster. 2019. Retrieved from < https://www.merriam-webster.com/ >

Marine Mammal Observing: Standardization is key

By: Alexa Kownacki, Ph.D. Student, OSU Department of Fisheries and Wildlife, Geospatial Ecology of Marine Megafauna Lab

For the past two years, I’ve had the opportunity to be the marine mammal observer aboard the NOAA ship Bell M. Shimada for 10 days in May. Both trips covered transects in the Northern California Current Ecosystem during the same time of year, but things looked very different from my chair on the fly bridge. This trip, in particular, highlighted the importance of standardization, seeing as it was the second replicate of the same area. Other scientists and crew members repeatedly asked me the same questions that made me realize just how important it is to have standards in scientific practices and communicating them.

Northern right whale dolphin porpoising out of the water beside the ship while in transit. May 2019. Image source: Alexa Kownacki

The questions:

  1. What do you actually do here and why are you doing it?
  2. Is this year the same as last year in terms of weather, sightings, and transect locations?
  3. Did you expect to see greater or fewer sightings (number and diversity)?
  4. What is this Beaufort Sea State scale that you keep referring to?

All of these are important scientific questions that influence our hypothesis-testing research, survey methods, expected results, and potential conclusions. Although the entire science party aboard the ship conducted marine science, we all had our own specialties and sometimes only knew the basics, if that, about what the other person was doing. It became a perfect opportunity to share our science and standards across similar, but different fields.

Now, to answer those questions:

  1. a) What do you actually do here and b) why are you doing it?

a) As the only marine mammal observer, I stand watch during favorable weather conditions while the ship is in transit, scanning from 0 to 90 degrees off the starboard side (from the front of the ship to a right angle towards the right side when facing forwards). Meanwhile, an application on an iPad called SeaScribe, records the ship’s exact location every 15 seconds, even when no animal is sighted. This process allows for the collection of absence data, that is, data when no animals are present. The SeaScribe program records the survey lines, along with manual inputs that I add, including weather and observer information. When I spot a marine mammal, I immediately mark an exact location on a hand held GPS, use my binoculars to identify the species, and add information to the sighting on the SeaScribe program, such as species, distance to the sighted animal(s), the degree (angle) to the sighting, number of animals in a group, behavior, and direction if traveling.

b) Marine mammal observing serves many different purposes. In this case, observing collects information about what species are where at what time. By piggy-backing on these large-scale, offshore oceanographic NOAA surveys, we have the unique opportunity to survey along standardized transect lines during different times of the year. From replicate survey data, we can start to form an idea of which species use which areas and what oceanographic conditions may impact species distributions. Currently there is not much consistent marine mammal data collected over these offshore areas between Northern California and Washington State, so our work is aiming to fill this knowledge gap.

Alexa observing on the R/V Shimada in May 2019, all bundled up. Image Source: Alexa Kownacki
  1. What is this Beaufort Sea State scale that you keep referring to?

Great question! It took me a while to realize that this standard measuring tool to estimate wind speeds and sea conditions, is not commonly recognized even among other sea-goers. The Beaufort Sea State, or BSS, uses an empirical scale that ranges from 0-12 with 0 being no wind and calm seas, to 12 being hurricane-force winds with 45+ ft seas. It is frequently referenced by scientists in oceanography, marine science, and climate science as a universally-understood metric. The BSS was created in 1805 by Francis Beaufort, a hydrographer in the Royal Navy, to standardize weather conditions across the fleet of vessels. By the mid-1850s, the BSS was standardized to non-naval use for sailing vessels, and in 1916, expanded to include information specific to the seas and not the sails1. We in the marine mammal observation field constantly collect BSS information while on survey to measure the quality of survey conditions that may impact our observations. BSS data allows us to measure the extent of our survey range, both in the distance that we are likely to sight animals and also the likelihood of sighting anything. Therefore, the BSS scale gives us an important indication of how much absence data we have collected, in addition to presence data.

A description of the Beaufort Sea State Scale. Image source: National Weather Service.

 

  1. Is this year the same as last year in terms of weather, sightings, and transect locations?

The short answer is no. Observed differences in marine mammal sightings in terms of both species diversity and number of animals between years can be normal. There are many potential explanatory variables, from differences in currents, upwelling strength, El Nino index levels, water temperatures, or, what was obvious in this case: sighting conditions. The weather in May 2019 varied greatly from that in May 2018. Last year, I observed for nearly every day because the Beaufort Sea State (BSS) was frequently less than a four. However, this year, more often than not, the BSS greater than or equal to five. A BSS of 5 equates to approximately 17-21 knots of breeze with 6-foot waves and the water appears to have many “white horses” or pronounced white caps with sea spray. Additionally, mechanical issue with winches delayed and altered our transect locations. Therefore, although multiple transects from May 2018 were also surveyed during May 2019, there were a few lines that do not have data for both cruises.

May 2018 with a BSS 1
May 2019 with a BSS 6

 

 

 

 

 

  1. Did you expect to see greater or fewer sightings (number and diversity)?

Knowing that I had less favorable sighting conditions and less amount of effort observing this year, it is not surprising that I observed fewer marine mammals in total count and in species diversity. Even less surprising is that on the day with the best weather, where the BSS was less than a five, I recorded the most sightings with the highest species count. May 2018 felt a bit like a tropical vacation because we had surprisingly sunny days with mild winds, and during May 2019 we had some rough seas with gale force winds. Additionally, as an observer, I need to remove as much bias as possible. So, yes, I had hoped to see beaked whales or orca like I did in May 2018, but I was still pleasantly surprised when I spotted fin whales feeding in May 2019.

Marine Mammal Species Number of Sightings
May 2018 May 2019
Humpback whale 31 6
Northern right whale dolphin 1 2
Pacific white-sided dolphin 3 6
UNID beaked whale 1 0
Cuvier’s beaked whale 1 0
Gray whale 4 1
Minke whale 1 1
Fin whale 4 1
Blue whale 1 0
Transient killer whale 1 0
Dall’s porpoise 2 0
Northern fur seal 1 0
California sea lion 0 1
Pacific white-sided dolphin. Image source: Alexa Kownacki

Standardization is a common theme. Observing between years on standard transects, at set speeds, in different conditions using standardized tools is critical to collecting high quality data that is comparable across different periods. Scientists constantly think about quality control. We look for trends and patterns, similarities and differences, but none of those could be understood without having standard metrics.

The entire science party aboard the R/V Shimada in May 2019, including a marine mammal scientist, phytoplankton scientists, zooplankton scientists, and fisheries scientists, and oceanographers. Image Source: Alexa Kownacki

Literature Cited:

1Oliver, John E. (2005). Encyclopedia of world climatology. Springer.