FORAGING DEPTHS OF SEA OTTERS AND IMPLICATIONS TO COASTAL MARINE COMMUNITIES

We visually observed 1,251 dives, of 14 sea otters instrumented with TDRs in southeast Alaska, and used attribute values from observed dives to classify 180,848 recorded dives as foraging (0.64), or traveling (0.36). Foraging dives were significantly deeper, with longer durations, bottom times, and postdive surface intervals, and greater descent and ascent rates, compared to traveling dives. Most foraging occurred in depths between 2 and 30 m (0.84), although 0.16 of all foraging was between 30 and 100 m. Nine animals, including all five males, demonstrated bimodal patterns in foraging depths, with peaks between 5 and 15 m and 30 and 60 m, whereas five of nine females foraged at an average depth of 10 m. Mean shallow foraging depth was 8 m, and mean deep foraging depth was 44 m. Maximum foraging depths averaged 61 m (54 and 82 for females and males, respectively) and ranged from 35 to 100 m. Female sea otters dove to depths ≤20 m on 0.85 of their foraging dives while male sea otters dove to depths ≥45 m on 0.50 of their foraging dives. Less than 0.02 of all foraging dives were >55 m, suggesting that effects of sea otter foraging on nearshore marine communities should diminish at greater depths. However, recolonization of vacant habitat by high densities of adult male sea otters may result in initial reductions of some prey species at depths >55 m.     

Evaluating Cementum to Determine Past Reproduction in Northern Sea Otters

Abstract: Age at first reproduction (AFR) has been difficult to quantify in mammals, as the most commonly used methods require reproductive tracts or direct observations. However, work in several large mammal species suggests that the width of cementum light bands in teeth decline once females begin to reproduce, suggesting that teeth structures might provide a new tool to examine AFR. To determine if changes in cementum light band width could be used to calculate AFR for the northern sea otter (Enhydra lutris kenyoni), we measured cementum light band widths on sectioned premolar teeth and compared them to reproductive tracts. We classified otters as parous if any single light band was narrower than a threshold value, selected as the value that minimized error rates. At a threshold value of 0.32, we correctly identified otters as parous or nulliparous in 83% of cases (n = 92) as compared to reproductive tracts, and the AFR estimated from teeth samples (3.52 ± 0.032 yr) was not different from that determined by reproductive tract analysis (3.45 ± 0.031 yr; t-test, P > 0.05). These data support the use of cementum as an indicator of past reproduction in individual female otters, which can then be used to estimate average AFR. Given that declines in cementum width have been described for other mammal species, the same quantitative approach used here could be applied to other species. (JOURNAL OF WILDLIFE MANAGEMENT 72(3):618–624; 2008)     

Loss of genetic diversity in sea otters (Enhydra lutris) associated with the fur trade of the 18th and 19th centuries

Sea otter (Enhydra lutris) populations experienced widespread reduction and extirpation due to the fur trade of the 18th and 19th centuries. We examined genetic variation within four microsatellite markers and the mitochondrial DNA (mtDNA) d-loop in one prefur trade population and compared it to five modern populations to determine potential losses in genetic variation. While mtDNA sequence variability was low within both modern and extinct populations, analysis of microsatellite allelic data revealed that the prefur trade population had significantly more variation than all the extant sea otter populations. Reduced genetic variation may lead to inbreeding depression and we believe sea otter populations should be closely monitored for potential associated negative effects.     

STOCK STRUCTURE OF SEA OTTERS (ENHYDRA LUTRIS KENYONI) IN ALASDA

Sea otters in Alaska are recognized as a single subspecies ( Enhydra lutris kenyoni ) and currently managed as a single, interbreeding population. However, geographic and behavioral mechanisms undoubrably constrain sea otter movements on much smaller scales. This paper applies the phylogeographic method (Dizon et al . 1992) and considers distribution, population response, phenotype and genotype data to identify stocks of sea otters within Alaska. The evidence for separate stock identity is genotypic (all stocks), phenotypic (Southcentral and Southwest stocks), and geographic distribution (Southeast stock), whereas population response data are equivocal (all stocks). Differences in genotype frequencies and the presence of unique genotypes among areas indicate restricted gene flow. Genetic exchange may be limited by little or no movement across proposed stock boundaries and discontinuities in distribution at proposed stock boundaries. Skull size differences (phenotypic) between Southwest and Southcentral Alaska populations further support stock separation. Population response information was equivocal in either supporting or refuting stock identity. On the basis of this review, we suggest the following: (1) a Southeast stock extending from Dixon Entrance to Cape Yakataga; (2) a Southcentral stock extending from Cape Yakataga to Cape Douglas including Prince William Sound and Kenai peninsula coast; and (3) a Southwest stock including Alaska Peninsula coast, the Aleutians to Attu Island, Barren, Kodiak, Pribilof Islands, and Bristol Bay.     

Habitat Features Predict Carrying Capacity of a Recovering Marine Carnivore

The recovery of large carnivore species from over-exploitation can have socioecological effects; thus, reliable estimates of potential abundance and distribution represent a valuable tool for developing management objectives and recovery criteria. For sea otters (Enhydra lutris), as with many apex predators, equilibrium abundance is not constant across space but rather varies as a function of local habitat quality and resource dynamics, thereby complicating the extrapolation of carrying capacity (K) from one location to another. To overcome this challenge, we developed a state-space model of density-dependent population dynamics in southern sea otters (E. l. nereis), in which K is estimated as a continuously varying function of a suite of physical, biotic, and oceanographic variables, all described at fine spatial scales. We used a theta-logistic process model that included environmental stochasticity and allowed for density-independent mortality associated with shark bites. We used Bayesian methods to fit the model to time series of survey data, augmented by auxiliary data on cause of death in stranded otters. Our model results showed that the expected density at K for a given area can be predicted based on local bathymetry (depth and distance from shore), benthic substrate composition (rocky vs. soft sediments), presence of kelp canopy, net primary productivity, and whether or not the area is inside an estuary. In addition to density-dependent reductions in growth, increased levels of shark-bite mortality over the last decade have also acted to limit population expansion. We used the functional relationships between habitat variables and equilibrium density to project estimated values of K for the entire historical range of southern sea otters in California, USA, accounting for spatial variation in habitat quality. Our results suggest that California could eventually support 17,226 otters (95% CrI = 9,739–30,087). We also used the fitted model to compute candidate values of optimal sustainable population abundance (OSP) for all of California and for regions within California. We employed a simulation-based approach to determine the abundance associated with the maximum net productivity level (MNPL) and propose that the upper quartile of the distribution of MNPL estimates (accounting for parameter uncertainty) represents an appropriate threshold value for OSP. Based on this analysis, we suggest a candidate value for OSP (for all of California) of 10,236, which represents 59.4% of projected K. © 2021 The Authors. The Journal of Wildlife Management published by Wiley Periodicals LLC on behalf of The Wildlife Society.     

Status,trends, and equilibrium abundance estimates of the translocated sea otter population in Washington State

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 simulations of future population abundance and geographic range help determine population status in relation to population recovery targets and identify the most influential parameters affecting future population growth and range expansion for sea otters in Washington State.     

Trends and Carrying Capacity of Sea Otters in Southeast Alaska

Sea otter populations in Southeast Alaska, USA, have increased dramatically from just over 400 translocated animals in the late 1960s to >8,000 by 2003. The recovery of sea otters to ecosystems from which they had been absent has affected coastal food webs, including commercially important fisheries, and thus information on expected growth and equilibrium abundances can help inform resource management. We compile available survey data for Southeast Alaska and fit a Bayesian state-space model to estimate past trends and current abundance. Our model improves upon previous analyses by partitioning and quantifying sources of estimation error, accounting for over-dispersion of aerial count data, and providing realistic measurements of uncertainty around point estimates of abundance at multiple spatial scales. We also provide estimates of carrying capacity (K) for Southeast Alaska, at regional and sub-regional scales, and analyze growth rates, current population status and expected future trends. At the regional scale, the population increased from 13,221 otters in 2003 to 25,584 otters in 2011. The average annual growth rate in southern Southeast Alaska (7.8%) was higher than northern Southeast Alaska (2.7%); however, growth varied at the sub-regional scale and there was a negative relationship between growth rates and the number of years sea otters were present in an area. Local populations vary in terms of current densities and expected future growth; the mean estimated density at K was 4.2 ± 1.58 sea otters/km² of habitat (i.e., the sub-tidal benthos between 0 m and 40 m depth) and current densities correspond on average to 50% of projected equilibrium values (range = 1–97%) with the earliest-colonized sub-regions tending to be closer to K. Assuming a similar range of equilibrium densities for currently un-occupied habitats, the projected value of K for all of Southeast Alaska is 74,650 sea otters. Future analyses can improve upon the precision of K estimates by employing more frequent surveys at index sites and incorporating environmental covariates into the process model to generate more accurate, location-specific estimates of equilibrium density. © 2019 The Authors. The Journal of Wildlife Management Published by Wiley Periodicals, Inc.     

Summer activity pattern and Field Metabolic Rate of adult male sea otters (Enhydra lutris) in a soft sediment habitat in Alaska

Individual follows and instantaneous sampling were used to examine the behavior of adult male sea otters (Enhydra lutris) in Simpson Bay, Prince William Sound, Alaska, during the summer (May to August) of 2005 and 2006. Six behaviors (foraging, grooming, interacting with other otters, patrolling, resting, and swimming at the surface) were observed during four time periods (dawn, day, dusk, and night) to create 24-h activity budgets. Adult male sea otters were observed during 190 focal follows, representing 98 h of observation. Male otters allocated 27% of their time (over a 24-h period) to resting, 26% to swimming, 19% to grooming, 14% foraging, 9% to patrolling and 5% to interacting with other otters. Field Metabolic Rate (FMR) was estimated by combining the energetic costs for foraging, grooming, resting, and swimming behavior of captive otters from Yeates et al. (2007) with our activity budgets. Our study considered ‘patrolling’ to be energetically similar to ‘swimming’ and therefore the two categories were combined. This combined category accounted for the greatest percentage (43%) of energy expended each day followed by grooming (23%), resting (15%), feeding (13%) and other (5%). The estimated weight specific FMR for all activities was 686.7 kJ day^− 1 kg^− 1 and the total FMR was 19.04 MJ day^− 1. The FMR was 6.6 times the resting metabolic rate and 2.2 times greater than the allometric prediction for terrestrial mammals of similar size but similar to other marine mammals. With a peak summer sea otter density in 5.6 otters km^− 2, the low percentage of time spent foraging (even after correction for possible sampling biases) indicates that Simpson Bay is still below equilibrium density.     

Influence of occupation history and habitat on Washington sea otter diet

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.