Tag: ecology

The Seascape of Fear: What are the ecological implications of being afraid in the marine environment?

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

In the GEMM Lab, our research focuses largely on the ecology of marine top predators. Inherent in our work are often assumptions that our study species—wide-ranging predators including whales, dolphins, otters, or seabirds—will distribute themselves relative to their prey. In order to make a living in the highly patchy and dynamic marine environment, predators must find ways to predictably locate and exploit prey resources.

But what about the prey? How do the prey structure themselves relative to their predators? This question is explored in depth in a paper titled “The Landscape of Fear: Ecological Implications of Being Afraid” (Laundre et al. 2010), which we discussed in our most recent lab meeting. When wolves were re-introduced in Yellowstone, the elk increased their vigilance and altered their grazing patterns. As a result, the plant community was altered to reflect this “landscape of fear” that the elk move through, where their distribution not only reflected opportunities for the elk to eat but also the risk of being eaten.

Translating the landscape of fear concept to the marine environment is tricky, but a fascinating exercise in ecological theory. We grappled with drawing parallels between the example system of wolves, elk, and vegetation and baleen whales, zooplankton, and phytoplankton. Relative to grazing mammals like elk, the cognitive abilities of zooplankton like krill, copepods, and mysid might pale in comparison. How could we possibly measure “fear” or “vigilance” in zooplankton? The swarming behavior of mysid and krill into dense patches is a defense mechanism—the strategy they have evolved to lessen the likelihood that any one of them will be eaten by a predator. I would posit that the diel vertical migration (DVM) of zooplankton is a manifestation of fear, at least on some level. DVM occurs over the course of each day, with plankton in pelagic ecosystems migrating vertically in the water column to avoid predators by hiding at depth during the daylight hours, and then swimming upward to feed on phytoplankton under the cover of darkness. I won’t speculate any further on the intelligence of zooplankton, but the need to survive predation has driven them to evolve this effective evolutionary strategy of hiding in the ocean’s twilight zone, swimming upward to feed only after dark so that they’re less likely to linger in spaces occupied by predators.

  • A swarm of mysid along the Oregon Coast. How does fear of predation by gray whales or rockfish drive the distribution of zooplankton in this shallow, nearshore system? Photo by Dawn Barlow.
  • A rock covered in purple urchin on the Oregon Coast. In the absence of their major predators (sea otters and sea stars), are urchins able to thrive in an environment with less fear of predation? Photo by Dawn Barlow.
  • A blue whale lunges on a dense aggregation of krill in New Zealand. In this image, you can actually see the krill “fleeing”, jumping out of the water in an attempt to escape the whale’s mouth. Drone piloted by Todd Chandler.

Laundre et al. (2010) present a visual representation of the landscape of fear (Fig. 1, reproduced below), where as an animal moves through space (represented as distance in meters or kilometers, for example), they also move through varying levels of predation risk. Environmental gradients (temperature, for example) tend to be much more stable across space in terrestrial ecosystems such as in the Yellowstone example from the paper. I wonder whether the same concept and visual depiction of a landscape of fear could be translated as risk across various environmental gradients, rather than geographic distances? In this proposed illustration, a landscape of fear would vary based on gradients of environmental conditions rather than geographic space. Such a shift in spatial reference —from geographic to environmental space—might make the model more applicable in the dynamic ocean ecosystems that we study.

What about cases when the predators we study become prey? One example we discussed was gray whales migrating from breeding lagoons in Mexico to feeding grounds in the Bering Sea. Mother-calf pairs hug the coastline tightly, by no means taking the most direct route between locations and adding considerable travel distance to their migration. The leading hypothesis is that mother gray whales take the coastal route to minimize the risk that their calves will fall prey to killer whale attacks. Are there other cases where the predators we study operate in a seascape of fear that we do not yet understand? Likely so, and the predators’ own seascape of fear may account for cases when we cannot explain predator distribution simply by their prey and their environment. To take this a step further, it might be beneficial not only to think of predation risk as only the potential to be eaten, but expand our definition to include human disturbance. While humans may not directly prey on marine predators, the disturbance from human activity in the ocean likely creates a layer of fear which animals must navigate, even in the absence of actual predation.

Our lively lab meeting discussion prompted me to look into how the landscape of fear model has been applied to the highly dynamic and intricate marine environment. In a study examining predator-prey dynamics of three species of marine mammals—bottlenose dolphins, harbor seals, and dugongs—Wirsing et al. (2007) found that in all three cases, the study species spent less time in more desirable prey patches or decreased riskier behavior in the presence of predators. Most studies in marine ecology are observational, as we rarely have the opportunity to manipulate our study system for experimental design and hypothesis testing. However, a study of coral reefs in the Florida Keys conducted by Catano et al. (2015) used fabricated predators—decoys of black grouper, a predatory fish—to investigate the influence of fear of predation on the reef system. What they found was that herbivorous fish consumed significantly less and fed at a much faster rate in the presence of this decoy predator. The grouper, even in decoy form, created a “reefscape of fear”, altering patterns in herbivory with potential ramifications for the entire ecosystem.

My takeaway from our discussion and my musings in this week’s blog post is that predator and prey distribution and behavior is highly interconnected. While predators distribute themselves to maximize their ability to find a meal, their prey respond accordingly by balancing finding a meal of their own with minimizing the risk that they will be eaten. Ecology is the study of an ecosystem, which means the questions we ask are complicated and hierarchical, and must be considered from multiple angles, accounting for biological, environmental, and behavioral elements to name a few. These challenges of studying ecosystems are simultaneously what make ecology fascinating, and exciting.

References:

Laundré, J. W., Hernández, L., & Ripple, W. J. (2010). The landscape of fear: ecological implications of being afraid. Open Ecology Journal, 3, 1-7.

Catano, L. B., Rojas, M. C., Malossi, R. J., Peters, J. R., Heithaus, M. R., Fourqurean, J. W., & Burkepile, D. E. (2016). Reefscapes of fear: predation risk and reef hetero‐geneity interact to shape herbivore foraging behaviour. Journal of Animal Ecology, 85(1), 146-156.

Wirsing, A. J., Heithaus, M. R., Frid, A., & Dill, L. M. (2008). Seascapes of fear: evaluating sublethal predator effects experienced and generated by marine mammals. Marine Mammal Science, 24(1), 1-15.

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.

Understanding sea otter effects through complexity

By Dominique Kone, Masters Student in Marine Resource Management

Species reintroductions are a management strategy to augment the reestablishment or recovery of a locally-extinct or extirpated species into once native habitat. The potential for reestablishment success often depends on the species’ ecological characteristics, habitat requirements, and relationship and effects to other species in the environment[1]. While the science behind species reintroductions is continuously evolving and improving, reintroductions are still inherently risky and uncertain in nature. Therefore, every effort should be made to fully assess ecological factors before a reintroduction takes place. As Oregon considers a potential sea otter reintroduction, understanding these ecological factors is an important piece of my own graduate research.

Sea otters are oftentimes referred to as keystone species because they can have wide-reaching effects on the community structure and function of nearshore marine environments. Furthermore, relative to other marine mammals or top predators, several papers have documented these effects – partially due to the ease in observing their foraging and social behaviors, which typically take place close to shore. In many of these studies, a classic paradigm repeatedly appears: when sea otters are present, prey densities (e.g., sea urchins) are significantly reduced, while macroalgae (e.g., kelp, seagrass) densities are high.

Source: Belleza.

While this paradigm is widely-accepted amongst researchers, a few key studies have also demonstrated that the effects of sea otters may be more variable than we once thought. The paradigm does not necessarily hold true everywhere sea otters exist, or at least not to the same degree. For example, after observing benthic communities along islands with varying sea otter densities in the Aleutian archipelago, Alaska, researchers found that islands with abundant otter populations consistently supported low sea urchin densities and high, yet variable, kelp densities. In contrast, islands without otters consistently had low kelp densities and high, yet variable, urchin densities[2]. This study demonstrates that while the classic paradigm generally held true, the degree to which the ecosystem belonged to one of two dominant states (sea otters, low urchins, and high kelp or no sea otters, high urchins, and low kelp) was less obvious.

This example demonstrates the danger in applying this one-size-fits-all paradigm to sea otter effects. Hence, we want to achieve a better understanding of potential sea otter effects so that managers may anticipate how Oregon’s nearshore environments may be affected if sea otters were to be reintroduced. Yet, how can we accurately anticipate these effects given these potential variations and deviations from the paradigm? Interestingly, if we look to other fields outside ecology, we find a possible solution and tool for tackling these uncertainties: a systematic review of available literature.

Two ecosystem states as predicted by the classic paradigm (left: kelp-dominated; right: urchin-dominated). Source: SeaOtters.com.

For decades, medical researchers have been conducting systematic reviews to assess the efficacy of treatments and drugs by combining several studies to find common findings[3]. These findings can then be used to determine any potential variation between studies (i.e. instances where the results may conflict or differ from one another) and even test the influence and importance of key factors that may be driving that variation[4]. While systematic reviews are quite popular within the medical research field, they have not been applied regularly in ecology, but recognition of their application to ecological questions is growing[5]. In our case of achieving a better understanding of the drivers of ecological impacts of sea otter, a systematic literature review is an ideal tool to assess variable effects. This review will be the focus of my second thesis chapter.

In conducting my review, there will be three distinct phases: (1) review design and study collection, (2) meta-analysis, and (3) factor testing. In the first phase (review design and study collection), I will search the existing literature to collect studies that explicitly compare the availability of key ecosystem components (i.e. prey species, non-prey species, and macroalgae species) when sea otters are absent and present in the environment. By only including studies that make this comparison, I will define effects as the proportional change in each species’ or organism group’s availability (e.g. abundance, biomass, density, etc.) with and without sea otters. In determining these effects, it’s important to recognize that sea otters alter ecosystems via both direct and indirect pathways. Direct effects can be thought of as any change to prey availability via sea otter predation directly, while indirect effects can be thought of an any alteration to the broader ecosystem (i.e. non-prey species, macroalgae, habitat features) as an indirect result from sea otter predation on prey species. I will record both types of effects.

General schematic of a meta-analysis in a systematic review. A meta-analysis is the process of taking multiple datasets (i.e. Data 1, Data 2 etc.) from literature sources, calculating summary statistics or effects (i.e. Summary 1, Summary 2, etc.) for each dataset, running statistical procedures (e.g. SMA = sequential meta-analysis) to relate summary effects and investigate between study variation, and identifying important features driving variation. Source: MediCeption.

In phase two, I will use meta-analytical procedures (i.e. statistical analyses specific to systematic reviews) to calculate one standardized metric to represent sea otter effects. These effects will be calculated and averaged across all collected studies. As previously discussed, there may be key factors – such as sea otter density – that influence these effects. Therefore, in phase three (factor testing), effects will also be calculated separately for each a priori factor to test their influence on the effects. Such factors may include habitat type (i.e. hard or soft sediment), prey species (i.e. sea urchins, crabs, clams, etc.), otter density, depth, or time after otter recolonization.

In statistical terms, the goal of testing factors is to see if the variation between studies is impacted by calculating sea otter effects separately for each factor versus across all studies. In other words, if we find high variation in effects between studies, there may be important factors driving that variation. Therefore, in systematic reviews, we recalculate effects separately for each factor to try to explain that variation. If, however, after testing these factors, variation remains high, there may be other factors that we didn’t test that could be driving that remaining variation. Yet, without a priori knowledge on what those factors could be, such variation should be reported as a major source of uncertainty.

Source: Giancarlo Thomae.

Predicting or anticipating the effects of reintroduced species is no easy feat. In instances where the ecological role of a species is well known – and there is adequate data – researchers can develop and use ecosystem models to predict with some certainty what these effects may be. Yet, in other cases where the species’ role is less studied, has less data, or is more variable, researchers must look to other tools – such as systematic reviews – to gain a better understanding of these potential effects. In this case, a systematic review on sea otter effects may prove particularly useful in helping managers understand what types of ecological effects of sea otters in Oregon are most likely, what the important factors are, and, after such review, what we still don’t know about these effects.

References:

[1] Seddon, P. J., Armstrong, D. P., and R. F. Maloney. 2007. Developing the science of reintroduction biology. Conservation Biology. 21(2): 303-312.

[2] Estes, J. A., Tinker, M. T., and J. L. Bodkin. 2009. Using ecological function to develop recovery criteria for depleted species: sea otters and kelp forests in the Aleutian Archipelago. Conservation Biology. 24(3): 852-860.

[3] Sutton, A. J., and J. P. T. Higgins. 2008. Recent developments in meta-analysis. Statistics in Medicine. 27: 625-650.

[4] Arnqvist, G., and D. Wooster. 1995. Meta-analysis: synthesizing research findings in ecology and evolution. TREE. 10(6): 236-240.

[5] Vetter, D., Rucker, G., and I. Storch. 2013. Meta-analysis: a need for well-defined usage in ecology and conservation biology. Ecosphere. 4(6): 1-13.

Can we talk about how cool sea otters are?

By Dominique Kone, Masters Student in Marine Resource Management

A couple of months ago, I wrote a blog introducing our new project, and my thesis, on the potential to reintroduce sea otters to the Oregon coast. In that blog, I expressed that in order to develop a successful reintroduction plan, scientists and managers need to have a sound understanding of sea otter ecology and the current state of Oregon’s coastal ecosystems. As a graduate student conducting a research-based thesis in a management program, I’m constantly fretting over the applicability of my research to inform decision-making processes. However, in the course of conducting my research, I sometimes forget just how COOL sea otters are. Therefore, in this blog, I wanted to take the opportunity to nerd out and provide you with my top five favorite facts about these otterly adorable creatures.

Photo Credit: Point Lobos Foundation

Without further ado, here are my top five favorite facts about sea otters:

  1. Sea otters eat a lot. Previous studies show that an individual sea otter eats up to 30% of its own body weight in food each day[1][2]. With such high caloric demands, sea otters spend a great deal of their time foraging the seafloor for a variety of prey species, and have been shown to decrease prey densities in their local habitat significantly. Sea otters are famously known for their taste for sea urchins. Yet, these voracious predators also consume clams, sea stars, crabs, and a variety of other small invertebrate species[3][4].

    Photo Credit: Katherine Johns via www.listal.com
  2. Individuals are specialists, but can change their diet. Sea otters typically show individual foraging specialization – which means an individual predominantly eats a select few species of prey. However, this doesn’t mean an otter can’t switch or consume other types of prey as needed. In fact, while individuals tend to be specialists, on a population or species level, sea otters are actually generalist predators[5][6]. Past studies that looked at the foraging habits of expanding sea otter populations show that as populations expand into unoccupied territory, they typically eat a limited number of prey. But as populations grow and become more established, the otters will start to diversify their diet, suggesting intra-specific competition[3][7].
  3. Sea otters exert a strong top-down force. Top-down forcing is one of the most important concepts we must acknowledge when discussing sea otter ecology. With top-down forcing, consumers at the top of the food chain depress the trophic level on which they feed, and this feeding indirectly increases the abundance of the next lower trophic level, resulting in a cascading effect[8]. The archetype example of this phenomenon is the relationship between sea otters, sea urchins, and kelp forests. This relationship goes as follows: sea otters consume sea urchin, and sea urchins graze on kelp. Therefore, sea otters reduce sea urchin densities by direct predation, thereby mediating grazing pressure on kelp. This indirect effect allows kelp to grow more abundantly, which is why we often see relatively productive kelp forests when sea otters are present[9]. This top-down forcing also has important implications for the whole ecosystem, as I’ll explain in my next fact.

    Pictured: sea urchin dominated seascape in habitat without sea otters. Photo Credit: BISHOPAPPS via Ohio State University.
  4. Sea otters help restore ecosystems, and associated ecosystem services. In kelp habitat where sea otters have been removed, we often see high densities of sea urchins and low biomasses of kelp. In this case, sea urchins have no natural predators to keep their populations in check and therefore completely decimate kelp forests. However, what we’ve learned is that when sea otters “reclaim” previously occupied habitats or expand into unoccupied territory, they can have remarkable restorative effects because their predation on sea urchins allows for the regrowth of kelp forest[10]. Additionally, with the restoration of key ecosystems like kelp forests, we can see a variety of other indirect benefits – such as increased biodiversity, refuge for fish nurseries and commercially-important species, and carbon sequestration[11][12][13]. The structure of nearshore ecosystems and communities change drastically with the addition or removal of sea otters, which is why they’re often referred to as keystone species.

    Photo Credit: University of California, Santa Barbara.
  5. Sea otters are most often associated with coastal kelp forests, but they can also exist in other types of habitats and ecosystems. In addition to kelp dominated ecosystems, sea otters are known to use estuaries and bays, seagrass beds, and swim over a range of bottom substrates[14][15]. As evidenced by previous studies, sea otters exert similar top-down forces in non-kelp ecosystems, as they do within kelp forests. One study found that sea otters also had restorative effects on seagrass beds within estuaries, where they consumed different types of prey (i.e., crabs instead of urchins), demonstrating that sea otters play a significant keystone role in seagrass habitats as well [12]. Findings such as these are vitally important to understanding (1) where sea otters are capable of living relative to habitat characteristics, and (2) how recovering or expanding sea otter populations may impact ecosystems and habitats in which they don’t currently exist, such as the Oregon coast.

Pictured: sea otter swimming through eel grass at Elkhorn Slough, California. Photo Credit: Kip Evans Photography.

Well, there you have it – my top five favorite facts about sea otters. This list is by no means exhaustive of all there is to know about sea otter ecology, and isn’t enough information to develop an informative reintroduction plan. However, a successful reintroduction plan will rely heavily on these underlying ecological characteristics of sea otters, in addition to the current state of Oregon’s nearshore ecosystems. As someone who constantly focuses on the relationship between scientific research and management and conservation, it’s nice every now and then to take a step back and just simply appreciate sea otters for being, well, sea otters.

References:

[1] 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.

[2] 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.

[3] Laidre, K.L. and R. J. Jameson. 2006. Foraging patterns and prey selection in an increasing and expanding sea otter population. Journal of Mammology. 87(4): 799-807.

[4] Estes, J. A., Jameson, R.J., and B. R. Rhode. 1982. Activity and prey election in the sea otter: influence of population status on community structure. The American Naturalist. 120(2): 242-258.

[5] Tinker, M. T., Costa, D. P., Estes, J. A., and N. Wieringa. 2007. Individual dietary specialization and dive behavior in the California sea otter: using archival time-depth data to detect alternative foraging strategies. Deep-Sea Research Part II. (54):330-342.

[6] Newsome et al. 2009. Using stable isotopes to investigate individual diet specialization in California sea otters (Enhydra lutris nereis). Ecology. 90(4): 961-974.

[7] Ostfeld, R. S. 1982. Foraging strategies and prey switching in the California sea otter. Oecologia. 53(2): 170-178.

[8] Paine, R. T. 1980. Food webs: linkage, interaction strength and community infrastructure. The Journal of Animal Ecology. 49(3): 666-685.

[9] Estes, J. A. and J.F. Palmisano. 1974. Sea otters: their role in structuring nearshore communities. Science. 185(4156): 1058-1060.

[10] Estes, J. A., and D. O. Duggins. 1995. Sea otters and kelp forests in Alaska: generality and variation in a community ecological paradigm. Ecological Monographs. 65(1): 75-100.

[11] Wilmers, C. C., Estes, J. A., Edwards, M., Laidre, K. L., and B. Konar. 2012. Do trophic cascades affect the storage and flux of atmospheric carbon? An analysis of sea otters and kelp forests. Frontiers in Ecology and the Environment. 10(8): 409-415.

[12] Hughes et al. 2014. Recovery of a top predator mediate negative eutrophic effects on seagrass. Proceedings of the National Academy of Sciences. 110(38): 15313-15318.

[13] Lee, L.C., Watson, J. C., Trebilco, R., and A. K. Salomon. Indirect effects and prey behavior mediate interactions between an endangered prey and recovering predator. Ecosphere. 7(12).

[14] 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 Mammalogy. 90(4): 906-917.

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

 

New Study Looks to Investigate the Potential Reintroduction of Sea Otters to Oregon

By Dominique Kone, Masters Student in Marine Resource Management

As I begin a new chapter as a grad student in the Marine Resource Management program at Oregon State University, the GEMM Lab is also entering into unchartered waters by expanding its focus to a new species outside the lab’s previous research portfolio. This project – which will be the focus of my thesis – will assess the potential reintroduction of sea otters to the Oregon coast through an examination of available habitat and ecological impacts. Before I explain how this project came to fruition, it’s important to understand why sea otter reintroduction to Oregon is relevant, and why this step is important to advance the conservation of these charismatic species.

While exact historical populations are unknown, sea otters were once abundant along the coasts of northern Japan, across Russia and Alaska, and down North America to Baja California, Mexico[1]. In the United States, specifically, sea otters were native to coastal waters along the entire west coast – including Oregon. However, beginning in the 1740’s sea otters were subject to intense and unsustainable hunting pressure from Russian, British, and American entrepreneurs seeking to sell their highly-valuable pelts in the lucrative fur trade[2].  Historical records suggest these hunters did not arrive in Oregon until the 1780’s, but from that point on the sea otter was exploited over the next several decades until the last known Oregon sea otter was killed in 1906 at Otter Rock, OR[3].

Pictured: Sea otter hunters near Coos Bay, OR in 1856. Photo Credit: The Oregon History Project.

After decades of intense pressure, sea otter numbers dropped to critically low levels and were thought to have gone extinct throughout most of their range. Luckily, remnant populations persisted and were later discovered in parts of Alaska, British Columbia, California, and Mexico beginning in the 1910’s. Since then sea otters have been the focus of intense conservation efforts. With the goal of augmenting their recovery, the Alaska Department of Fish and Game lead a series of translocation projects, where groups of sea otters were transported from Alaska to unoccupied habitats in Alaska, British Columbia, Washington, and Oregon (Note: these were not the only sea otter translocations.)1.

Pictured: Sea otters on glacier ice, northern Prince William Sound, Alaska. Photo Credit: Patrick J. Endres/AlaskaPhotoGraphics.com

Fun Fact: For a marine mammal, sea otters have surprisingly little blubber. Luckily, they also have the densest fur of all animals – an estimated 1,000,000 hairs per square inch – that helps to keep them well-insulated from the cold.

Many of these projects are considered successful as sea otter populations grew, and continue to expand today. With a significant exception: sea otters mysteriously disappeared shortly after reintroduction into Oregon waters and the translocation effort failed. Many hypothesized what could have gone wrong – natural mortality, dispersal, conflicts with humans – but few have concrete answers. Aside from occasional reports of strandings and sightings of sea otters in Oregon coastal waters, no resident populations have formed. This is where my thesis project comes in.

Pictured: Cape Arago, OR – one of the unsuccessful translocation sites along the Oregon coast. Photo Credit: TravelOregon.com

With renewed interests from scientists, tribes, and the public, we are now revisiting this idea from a scientific perspective. Over the next two years, we will work to objectively assess the ecological aspects of sea otter reintroduction to Oregon to identify and fill current knowledge gaps, which will help inform decision-making processes by environmental managers. Throughout this process we will give consideration to not just the ecology and biology of sea otters, but the cultural, economic, and political relevance and implications of sea otter reintroduction. Much of this work will involve working with state and federal agencies, tribes, and other scientists to gain their insights and perspectives, which we will use to shape our research questions and analyses.

The process to move forward with bringing sea otters back to Oregon will no doubt take great effort by a lot of people, consultation, patience, and time. To date, we have been reviewing the relevant literature and meeting with local experts on this topic. Through these activities, we have determined the types of questions and information – suitable habitat and potential ecological impacts – of most need to managers. My goal is to conduct a meaningful, applied project as an objective scientist, and by gaining this type of feedback at the outset, I am to help managers make better-informed decisions. I hope my thesis can serve as a critical starting point to ensure a solid foundation that future Oregon-specific sea otter research can build from.

References:

[1] Jameson et al. 1982. History and status of translocated sea otter populations in North America. Wildlife Society Bulletin. (10) 2: 100-107.

[2] The Oregon History Project: Sea Otter. Accessed September 2017. <https://oregonhistoryproject.org/articles/historical-records/sea-otter/#.WamgT7KGPIU>

[3] The Oregon History Project: Otter Hunting. Accessed September 2017. <https://oregonhistoryproject.org/articles/historical-records/otter-hunting/#.Wa2TCLKGPIU>

 

Understanding How Nature Works

By: Erin Pickett, MS student, Oregon State University

They were climbing on their hands and knees along a high, narrow ridge that was in places only two inches wide. The path, if you could call it that, was layered with sand and loose stones that shifted whenever touched. Down to the left was a steep cliff encrusted with ice that glinted when the sun broke down through the thick clouds. The view to the right, with a 1,000ft drop, wasn’t much better.

–The Invention of Nature by Andrea Wulf

This is a description of Alexander von Humboldt and the two men that accompanied him when attempting to summit Chimborazo, which in 1802 was believed to be the highest mountain in the world. The trio was thwarted about 1,000 ft from the top of the peak by an impassable crevice but set a record for the highest any European had ever climbed. This was a scientific expedition. With them the men brought handfuls of scientific instruments and Humboldt identified and recorded every plant and animal species along the way. Humboldt was an explorer, a naturalist, and an observer of everything. He possessed a memory that allowed him to recount details of nature that he had observed on a mountain in Asia, and find patterns and connections between that mountain and another in South America. His perspective of nature as being interconnected, and theories as to why and how this was so, led to him being called the father of Ecology. In less grandeur terms, Humboldt was a biodiversity explainer.

Humboldt sketched detailed images like this one of Chimborazo, which allowed him to map vegetation and climate zones and identify how these and other patterns and processes were related. Source: http://www.mappingthenation.com/blog/alexander-von-humboldt-master-of-infographics/

In a recent guest post on Carbon Brief, University of Connecticut Professor Mark Urban summarized one of his latest publications in the journal Science, and called on scientists to progress from biodiversity explainers to biodiversity forecasters.  Today, as global biodiversity is threatened by climate change, one of our greatest scientific problems has become accurately forecasting the responses of species and ecosystems to climate change. Earlier this month, Urban and his colleagues published a review paper in Science titled “Improving the forecast for biodiversity under climate change”. Many of our current models aimed at predicting species responses to climate change, the authors noted, are missing crucial data that hamper the accuracy and thus the predictive capabilities of these models. What does this mean exactly?

Say we are interested in determining whether current protected areas will continue to benefit the species that exist inside their boundaries over the next century. To do this, we gather basic information about these species: what habitat do they live in, and where will this habitat be located in 100 years? We tally up the number of species currently inhabiting these protected areas, figure out the number of species that will relocate as their preferred habitat shifts (e.g. poleward, or higher in elevation) and then we subtract those species from our count of those who currently exist within the boundaries of this protected area. Voilà, we can now predict that we will lose up to 20% of the species within these protected areas over the next 100 years*.  Now we report our findings to the land managers and environmental groups tasked with conserving these species and we conclude that these protected areas will not be sufficient and they must do more to protect these species. Simple right? It never is.

This predication, like many others, was based on a correlation between these species ranges and climate. So what are we missing? In their review, Urban et al. outline six key factors that are commonly left out of predictive models, and these are: species interactions, dispersal, demography, physiology, evolution and environment (specifically, environment at appropriate spatiotemporal scales) (Figure 1). In fact, they found that more than 75% of models aimed at predicting biological responses to climate change left out these important biological mechanisms. Since my master’s project is centered on species interactions, I will now provide you with a little more information about why this specific mechanism is important, and what we might have overlooked by not including species interactions in the protected area example above.

Figure 1: Six critical biological mechanisms missing from current biodiversity forecasts. Source: Urban et al. 2016
Figure 1: Six critical biological mechanisms missing from current biodiversity forecasts. Source: Urban et al. 2016

I study Adelie and gentoo penguins, two congeneric penguin species whose breeding ranges overlap in a few locations along the Western Antarctic Peninsula. You can read more about my research in previous blog posts like this one. Similar to many other species around the world, both of these penguins are experiencing poleward range shifts due to atmospheric warming. The range of the gentoo penguin is expanding farther south than ever before, while the number of Adelie penguins in these areas is declining rapidly (Figure 2). A correlative model might predict that Adelie penguin populations will continue to decline due to rising temperatures, while gentoo populations will increase. This model doesn’t exactly inform us of the underlying mechanisms behind what we are observing. Are these trends due to habitat shifts? Declines in key prey species? Interspecific competition? If Adelie populations are declining due to increased competition with other krill predators (e.g. gentoo penguins), then any modelling we do to predict future Adelie population trends will certainly need to include this aspect of species interaction.

Figure 2. A subset of the overall range of Adelie and gentoo penguins and their population trends at my study site at Palmer Station 1975-2014. Source: https://www.allaboutbirds.org/on-the-antarctic-peninsula-scientists-witness-a-penguin-revolution/
Figure 2. A subset of the overall range of Adelie and gentoo penguins and their population trends at my study site at Palmer Station 1975-2014. Source: https://www.allaboutbirds.org/on-the-antarctic-peninsula-scientists-witness-a-penguin-revolution/

Range expansion can result in novel or altered species interactions, which ultimately can affect entire ecosystems. Our prediction above that 20% of species within protected areas will be lost due to habitat shifts does not take species interactions into account. While some species may move out of these areas, others may move in. These new species may potentially outcompete those who remain, resulting in a net loss of species larger than originally predicted. Urban et al. outline the type of data needed to improve the accuracy of predictive models. They openly recognize the difficulties of such a task but liken it to the successful, collective effort of climate scientists over the past four decades to improve the predictive capabilities of climate forecasts.

As a passionate naturalist and philosopher, there is no doubt Humboldt would agree with Urban et al.’s conclusion that “ultimately, understanding how nature works will provide innumerable benefits for long-term sustainability and human well-being”. I encourage you to read the review article yourself if you’re interested in more details on Urban et al.’s views of a ‘practical way forward’ in the field of biodiversity forecasting. For a historical and perhaps more romantic account of the study of biodiversity, check out Andrea Wulf’s biography of Alexander von Humboldt, called The Invention of Nature.

 *This is an oversimplified example based off of a study on biodiversity and climate change in U.S. National parks (Burns et al. 2003)

References:

Burns, C. E., Johnston, K. M., & Schmitz, O. J. (2003). Global climate change and mammalian species diversity in US national parks. Proceedings of the National Academy of Sciences, 100(20), 11474-11477.

Urban, M. 14 September 2016. Carbon Brief. Guest post: How data is key to conserving wildlife in a challenging environment. From: https://www.carbonbrief.org/guest-post-data-key-conserving-wildlife-changing-climate (Accessed: 22 September 2016)

Urban, M. C., Bocedi, G., Hendry, A. P., Mihoub, J. B., Pe’er, G., Singer, A., … & Gonzalez, A. (2016). Improving the forecast for biodiversity under climate change. Science, 353(6304), aad8466.

Wulf, A. (2015). The Invention of Nature: Alexander Von Humboldt’s New World. Knopf Publishing Group.