Sea otter (&lt;I&gt;Enhydra lutris&lt;/I&gt;) foraging habitat use in a heterogeneous environment in Kachemak Bay off Alaska

Stewart, Nathan D.; Konar, Brenda; Doroff, Angela M.

doi:10.5343/bms.2014.1002

Cited by 7 publications

(8 citation statements)

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“…We observed negative relationships between otter group abundance for all underwater substrates except mud (Table 2). Others have found that otters tend to be more abundant in rocky areas (Laidre et al 2002) and show higher selection for these areas (Stewart et al 2014). Similarly, Stewart et al (2014) did not observe any otters using areas with mud as a substrate, even though we identified the presence of muddy substrate as positively related to otter abundance.…”

Section: Discussionsupporting

confidence: 54%

“…Others have found that otters tend to be more abundant in rocky areas (Laidre et al 2002) and show higher selection for these areas (Stewart et al 2014). Similarly, Stewart et al (2014) did not observe any otters using areas with mud as a substrate, even though we identified the presence of muddy substrate as positively related to otter abundance. Other studies, however, have shown that sea otters forage in areas with soft sediment Oliver 1988, Gilkinson et al 2011).…”

Section: Discussionsupporting

confidence: 54%

“…where β are regression coefficients, which we gave vague priors of β~Normal(0, 0.01) and x are observed environmental conditions at the centroid of each grid. We chose covariates for the estimated number of otters in a grid cell based on factors that have been shown to be important for describing sea otter distributions (Bodkin et al 2004, Gilkinson et al 2011, Stewart et al 2014. Specifically, we included a covariate for ocean depth, sea floor substrates (i.e., probabilities of gravel, mud, or rock presence), and the probability of kelp presence in the grid.…”

Section: Hierarchical Distance Samplingmentioning

confidence: 99%

“…Fish and Wildlife Service 2013). Previous research has identified environmental conditions predictive of sea otter presence such as the presence of kelp (Bodkin et al 2004), shallow water (Gilkinson et al 2011), and various underwater substrate conditions (Stewart 2011, Stewart et al 2014), but research has not been conducted to relate these preferences into otter density (U.S. Fish and Wildlife Service 2013, but see Coletti 2006).…”

Section: Introductionmentioning

confidence: 99%

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A hierarchical distance sampling model to estimate spatially explicit sea otter density

2021

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Wildlife managers often rely on analyses conducted prior to the widespread adoption of hierarchical models which can lead to questions about the accuracy of previous inferences. Hierarchical models allow observed data to be partitioned into factors that influenced the collection of the data such as detectability of animals (i.e., observation processes) and factors that influence the ecology of a population such as features that affect the distribution of animals (i.e., ecological processes). Population surveys for sea otters in the Aleutian Islands, Alaska, have historically been conducted by conflating the observation and ecological processes potentially leading to inaccurate population estimates. Based on boat and planebased sea otter survey data collected in 2017, we sought to overcome many problems of previous sea otter surveys in southwestern Alaska. We developed a spatially explicit hierarchical distance sampling model to estimate the abundance of sea otters in the Eastern Aleutians Management Unit while explicitly accounting for factors that affect the ability to detect sea otters during surveys (i.e., group size, ocean conditions). We also sought to account for the environmental factors leading to the non-uniform distribution of sea otter groups by identifying relationships between otter group abundance and environmental attributes (i.e., ocean depth, presence of kelp, underwater substrate). Detection of sea otter groups was related to group size, and ocean conditions (e.g., ocean swell size). After accounting for detection, we estimated a mean population size of 8593 individual sea otters (95% CI: 7450-9984) which is considerably higher than previous estimates, although comparisons are difficult given divergent methodologies. Sea otter group density was negatively related to ocean depth and the presence of rock and gravel as underwater substrates. Conversely, sea otter group density was positively related to the presence of kelp and mud as an underwater substrate. Our hierarchical distance sampling model accounted for the observation process which allowed better estimates of the environmental attributes affecting otter abundance. Our research can serve as a template for other study systems requiring spatially explicit density estimates from a distance sampling framework and can help provide a new baseline for managers to gauge future population changes in sea otters.

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Section: Discussionsupporting

confidence: 54%

Section: Discussionsupporting

confidence: 54%

Section: Hierarchical Distance Samplingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hierarchical distance sampling model to estimate spatially explicit sea otter density

2021

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“…[4822]-12 availability is likely the most important determinant of sea otter distribution, abiotic factors also play an important role (Stewart, Konar & Doroff, 2014;Tarjan & Tinker, 2016). As is often the case for large or remote areas inhabited by sea otters, prey survey data are not available for the Kuril chain.…”

mentioning

confidence: 99%

Sea otter (Enhydra lutris) abundance assessment for the Kuril Island population in Far Eastern Russia

Ovsyanikova¹,

Altukhov

Carswell

et al. 2020

Marine Mammal Science

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Species distribution modeling of northern sea otters (Enhydra lutris kenyoni) in a data‐limited ecosystem

Hasan,

Gorman,

Coletti

et al. 2024

Ecology and Evolution

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Species distribution models (SDMs) are used to map and predict the geographic distributions of animals based on environmental covariates. Typically, SDMs require high‐resolution habitat data and time series information on animal locations. For data‐limited regions, defined as having scarce habitat or animal survey data, modeling is more challenging, often failing to incorporate important environmental attributes. For example, for sea otters (Enhydra lutris), a federally protected keystone species with variable population trends across the species' range, predictive modeling of distributions has been successfully conducted in areas with robust sea otter population and habitat data. We used open‐access data and employed a presence‐only model, maximum entropy (MaxEnt), to investigate subtidal habitat associations (substrate and algal cover, bathymetry, and rugosity) of northern sea otters (E. lutris kenyoni) for a data‐limited ecosystem, represented by Kachemak Bay, Alaska. Habitat association results corroborated previous findings regarding the importance of bathymetry and understory kelp as predictors of sea otter presence. Novel associations were detected as filamentous algae and shell litter were positively and negatively associated with northern sea otter presence, respectively, advancing existing knowledge of sea otter benthic habitat associations useful for predicting habitat suitability. This study provides a quantitative framework for conducting species distribution modeling with limited temporal and spatial animal distribution and abundance data. Utilizing drop camera information, our novel approach allowed for a better understanding of habitat requirements for a stable northern sea otter population, including bathymetry, understory kelp, and filamentous algae as positive predictors of sea otter presence in Kachemak Bay, Alaska.

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Sea otter (<I>Enhydra lutris</I>) foraging habitat use in a heterogeneous environment in Kachemak Bay off Alaska

Cited by 7 publications

References 0 publications

A hierarchical distance sampling model to estimate spatially explicit sea otter density

A hierarchical distance sampling model to estimate spatially explicit sea otter density

Sea otter (Enhydra lutris) abundance assessment for the Kuril Island population in Far Eastern Russia

Species distribution modeling of northern sea otters (Enhydra lutris kenyoni) in a data‐limited ecosystem

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