Trends and Carrying Capacity of Sea Otters in Southeast Alaska

Tinker, M. Tim; Gill, Verena A.; Esslinger, George G.; Bodkin, James L.; Monk, Melissa; Mangel, Marc; Monson, Daniel H.; Raymond, Wendel W.; Kissling, Michelle L.

doi:10.1002/jwmg.21685

Cited by 33 publications

(147 citation statements)

References 59 publications

(91 reference statements)

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“…For SEAK as a whole and the three focal subregions, we summarized annual reported number of harvested sea otters, the age/sex class of harvested sea otters, and the annual harvest rate using the estimated preharvest population abundance for that year from Tinker et al. (, b; Eq. ).…”

Section: Methodsmentioning

confidence: 99%

“…

Harvest {rate}_{y, i} = \frac{{harvest}_{y, i}}{{population}_{y, i}^{'}}

where harvest y , i is the number of sea otters harvest in subregion, i in year y , and

{population}_{y, i}^{'}

is the estimated preharvest sea otter population from Tinker et al. (). We also calculated the mean annual percent contribution to total harvest for each subregion (Eq.…”

Section: Methodsmentioning

confidence: 99%

“…(1) Our model incorporates spatial structure at a scale that is meaningful for tracking demographic processes in sea otter populations (Bodkin , Tinker , Tinker et al. ). (2) The model allows for density dependence, demographic stochasticity, and environmental stochasticity in age‐ and sex‐specific vital rates.…”

Section: Methodsmentioning

confidence: 99%

“…Specifically, if N j , t −1 represents the abundance for subregion j at time t − 1, K j is the estimated carrying capacity for subregion j , and σ e is the standard error of log(λ) across years (estimates of K j and σ e were based on Tinker et al. ), we calculate

{\overset{false^}{normalλ}}_{j, t}

\begin{matrix} {\overset{λ}{true}}_{j, t} = \exp (r_{\max} ()(1 - \frac{N_{j, t - 1}}{K_{j}}) + ε_{j, t}), \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

“…Tinker et al. () recently estimated population trends and carrying capacity at multiple spatial scales, but to date, SEAK sea otter harvest data have not been analyzed for spatial and temporal trends or for potential effects to the sea otter population.…”

Section: Introductionmentioning

confidence: 99%

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Location‐specific factors influence patterns and effects of subsistence sea otter harvest in Southeast Alaska

et al. 2019

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To better understand the spatial context of population dynamics of sea otters (Enhydra lutris) in Southeast Alaska (SEAK), we investigated the spatial and temporal patterns of subsistence sea otter harvest and assessed the effect of harvest on population growth. U.S. federal law permits subsistence harvest of sea otters and sale of clothing and handicrafts made by coastal Alaska Natives. Hunters are required to self‐report these harvests along with information on date, location, age class, and sex. Using harvest data collected from 1988 to 2015, we developed a spatially explicit, age‐structured, density‐dependent population simulation model to explore the potential impacts of harvest on sea otter population dynamics. We examined patterns of harvest and simulation model results at two spatial scales: the SEAK stock and three smaller subregions that vary in sea otter occupation time and carrying capacity: Sitka Sound, Keku Strait, and the Maurelle Islands. Annual sea otter harvest in SEAK increased from 55 animals in 1988 to a reported maximum of 1449 animals in 2013. Estimated mean annual harvest rate was 2.8% at the SEAK stock scale, but ranged from 0% to 39.3% across the three focal subregions described above. Across all subregions (n = 55), annual sea otter harvest rate was strongly influenced by time since recolonization, sea otter population density, and proximity to communities with sea otter hunters. The simulation model predicted population trends and per capita harvest rates similar to those estimated from aerial survey data, providing a reasonable approximation of population dynamics. Results of the simulation model suggested that current harvest levels can reduce population size at both the SEAK and subregional scales. Variation in harvest impacts was a function of subregion‐specific factors, including time since recolonization and population status with respect to carrying capacity. We found that subsistence harvest and its population effects were scale‐ and location‐dependent, indicating that higher spatial and temporal resolution of sea otter population and hunting data could help address emerging sea otter management and conservation concerns in this region.

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Section: Methodsmentioning

confidence: 99%

“…

Harvest {rate}_{y, i} = \frac{{harvest}_{y, i}}{{population}_{y, i}^{'}}

where harvest y , i is the number of sea otters harvest in subregion, i in year y , and

{population}_{y, i}^{'}

is the estimated preharvest sea otter population from Tinker et al. (). We also calculated the mean annual percent contribution to total harvest for each subregion (Eq.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

{\overset{false^}{normalλ}}_{j, t}

\begin{matrix} {\overset{λ}{true}}_{j, t} = \exp (r_{\max} ()(1 - \frac{N_{j, t - 1}}{K_{j}}) + ε_{j, t}), \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Location‐specific factors influence patterns and effects of subsistence sea otter harvest in Southeast Alaska

et al. 2019

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The role of spatial structure in at‐risk metapopulation recoveries

Wilson

Sawyer

Potapova

et al. 2023

Ecological Applications

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Metapopulations are often managed as a single contiguous population despite the spatial structure underlying their local and regional dynamics. Disturbances from human activities can also be spatially structured with mortality impacts concentrated to just a few local populations among the aggregate. Scale transitions between local and regional processes can generate emergent properties whereby the whole system can fail to recover as quickly as expected for an equivalent single population. Here, we draw on theory and empirical case studies to ask: what is the consequence of spatially structured ecological and disturbance processes on metapopulation recoveries? We suggest that exploring this question could help address knowledge gaps for managing metapopulations including: Why do some metapopulations recover quickly while others remain collapsed? And, what risks are unaccounted for when metapopulations are managed at aggregate scales? First, we used model simulations to examine how scale transitions among ecological and disturbance conditions interact to generate emergent metapopulation recovery outcomes. In general, we found that the spatial structure of disturbance was a strong determinant of recovery outcomes. Specifically, disturbances that unevenly impacted local populations consistently generated the slowest recoveries and highest conservation risks. Ecological conditions that dampened metapopulation recoveries included low dispersal, variable local demography, sparsely connected habitat networks, and spatially and temporally correlated stochastic processes. Second, we illustrate the unexpected challenges of managing metapopulations by examining the recoveries of three USA federally listed endangered species: Florida Everglade snail kites, California and Alaska sea otters, and Snake River Chinook salmon. Overall, our results show the pivotal role of spatial structure in metapopulation recoveries whereby the interplay between local and regional processes shapes the resilience of the whole system. With this understanding, we provide guidelines for resource managers tasked with conserving and managing metapopulations and identify opportunities for research to support the application of metapopulation theory to real‐world challenges.

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Macronutrient composition of sea otter diet with respect to recolonization, life history, and season in southern Southeast Alaska

LaRoche

King

Fergusson

et al. 2023

Ecology and Evolution

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The sea otter ( Enhydra lutris ) population of Southeast Alaska has been growing at a higher rate than other regions along the Pacific coast. While good for the recovery of this endangered species, rapid population growth of this apex predator can create a human‐wildlife conflict, negatively impacting commercial and subsistence fishing. Previous foraging studies throughout the sea otter range have shown they will reduce invertebrate prey biomass when recolonizing an area. The goal of this study was to examine and quantify the energy content of sea otter diets through direct foraging observations and prey collection. Our study area, Prince of Wales Island in southern Southeast Alaska, exhibits a gradient of sea otter recolonization, thus providing a natural experiment to test diet change in regions with different recolonization histories. Sea otter prey items were collected in three seasons (spring, summer, and winter) to measure caloric value and lipid and protein content. We observed 3523 sea otter dives during the spring and summer. A majority of the sea otter diet consisted of clams. Sea otters in newly recolonized areas had lower diet diversity, higher energetic intake rates (EIR, kcal/min), and prey had higher energy content (kcal/g). Females with pups had the highest diet diversity and the lowest EIR. Sea otter EIR were higher in the fall and winter vs. spring and summer. Sea cucumber energy and lipid content appeared to correspond with times when sea otters consumed the highest proportion of sea cucumbers. These caloric variations are an important component of understanding ecosystem‐level effects sea otters have in the nearshore environment.

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Trends and Carrying Capacity of Sea Otters in Southeast Alaska

Cited by 33 publications

References 59 publications

Location‐specific factors influence patterns and effects of subsistence sea otter harvest in Southeast Alaska

Location‐specific factors influence patterns and effects of subsistence sea otter harvest in Southeast Alaska

The role of spatial structure in at‐risk metapopulation recoveries

Macronutrient composition of sea otter diet with respect to recolonization, life history, and season in southern Southeast Alaska

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