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.