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Sea otters (Enhydra lutris kenyoni) historically occurred in Washington State, USA, until their local extinction in the early 1900s as a result of the maritime fur trade. Following their extirpation, 59 sea otters were translocated from Amchitka Island, Alaska, USA, to the coast of Washington, with 29 released at Point Grenville in 1969 and 30 released at La Push in 1970. The Washington Department of Fish and Wildlife has outlined 2 main objectives for sea otter recovery: a target population level and a target geographic distribution. Recovery criteria are based on estimates of population abundance, equilibrium abundance (K), and geographic distribution; therefore, estimates of these parameters have important management implications. We compiled available survey data for sea otters in Washington State since their translocation (1977–2019) and fit a Bayesian state‐space model to estimate past and current abundance, and equilibrium abundance at multiple spatial scales. We then used forward projections of population dynamics to explore potential scenarios of range recolonization and as the basis of a sensitivity analysis to evaluate the relative influence of movement behavior, frontal wave speed, intrinsic growth, and equilibrium density on future population recovery potential. Our model improves upon previous analyses of sea otter population dynamics in Washington by partitioning and quantifying sources of estimation error to estimate population dynamics, by providing robust estimates of K, and by simulating long‐term population growth and range expansion under a range of realistic parameter values. Our model resulted in predictions of population abundance that closely matched observed counts. At the range‐wide scale, the population size in our model increased from an average of 21 independent sea otters (95% CI = 13–29) in 1977 to 2,336 independent sea otters (95% CI = 1,467–3,359) in 2019. The average estimated annual growth rate was 12.42% and varied at a sub‐regional scale from 6.42–14.92%. The overall estimated mean K density of sea otters in Washington was 1.71 ± 0.90 (SD) independent sea otters/km2 of habitat (1.96 ± 1.04 sea otters/km2, including pups), and estimated densities within the current range correspond on average to 87% of mean sub‐regional equilibrium values (range = 66–111%). The projected value of K for all of Washington was 5,287 independent sea otters (95% CI = 2,488–8,086) and 6,080 sea otters including pups (95% CI = 2,861–9,300), assuming a similar range of equilibrium densities in currently un‐occupied habitats. Sensitivity analysis of simulations of sea otter population growth and range expansion suggested that mean K density estimates in currently occupied sub‐regions had the largest impact on predicted future population growth (r2 = 0.52), followed by the rate of southward range expansion (r2 = 0.26) and the mean K density estimate of currently unoccupied sub‐regions to the south of the current range (r2 = 0.04). Our estimates of abundance and sensitivity analysis of simulatio...

Habitat characteristics are primary determinants of nearshore marine communities. However, biological drivers like predation can also be important for community composition. Sea otters (Enhydra lutris ssp.) are a salient example of a keystone species exerting top‐down control on ecosystem community structure. The translocation and subsequent population growth and range expansion of the northern sea otter (Enhydra lutris kenyoni) in Washington State over the last five decades has created a spatio‐temporal gradient in sea otter occupation time and density, and acts as a natural experiment to quantify how sea otter population status and habitat type influence sea otter diet. We collected focal observations of sea otters foraging at sites across the gradient in varying habitat types between 2010 and 2017. We quantified sea otter diet composition and diversity, and long‐term rates of energy gain across the gradient. We found that sea otter diet diversity was positively correlated with cumulative sea otter density, while rate of energy gain was negatively correlated with cumulative density. Additionally, we found that habitat type explained 1.77 times more variance in sea otter diet composition than sea otter cumulative density. Long‐term diet studies can provide a broader picture of sea otter population status in Washington State.

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