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

Abstract

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.

Diffusion modeling reveals effects of multiple release sites and human activity on a recolonizing apex predator

“…We also found that sea otters additionally concentrate in areas where commercial fisheries are closed, as well as in protected areas, where subsistence harvest of sea otters is not permitted (i.e., Glacier Bay; Table 1), which suggests greater prey availability, foraging habitat, and mortality risk are strong drivers of sea otter distribution and abundance. Our finding that sea otters exhibit higher residence time closer to human communities is seemingly inconsistent with previous findings of exposure to subsistence hunting influencing sea otter movement (Hoyt, 2015) and population growth (Raymond et al, 2019). However, the diffusion model is likely capturing what happens during the initial colonization of areas closer to communities, and, in the longer-term, areas closer to communities where harvest is common may act as population sinks (Raymond et al, 2019).…”

“…Our finding that sea otters exhibit higher residence time closer to human communities is seemingly inconsistent with previous findings of exposure to subsistence hunting influencing sea otter movement (Hoyt, 2015) and population growth (Raymond et al, 2019). However, the diffusion model is likely capturing what happens during the initial colonization of areas closer to communities, and, in the longer-term, areas closer to communities where harvest is common may act as population sinks (Raymond et al, 2019).…”

“…Given that subsistence harvest of sea otters (Raymond et al, 2019) and human activities (e.g., disturbance from vessel traffic; Ballachey and Bodkin, 2015) influence sea otter population dynamics, we added a covariate of cumulative distance to the nearest incorporated city, town, or village. This was the sum of the shortest swimmable paths from each city, town, or village, to any location s.…”

“…Recent studies of the sea otter population in Southeast Alaska used integrated data models to investigate regional population trends and density-dependent effects (Tinker et al, 2019) and influence of subsistence harvest (Raymond et al, 2019). While the approaches applied in these studies accounted for movements between discrete sub regions within Southeast Alaska (i.e., immigration and emigration), they assumed a known intrinsic growth rate and did not explicitly incorporate a mechanistic model of population spread that would naturally capture movements of recolonizing individuals throughout this continuous geographic area.…”

Diffusion modeling reveals effects of multiple release sites and human activity on a recolonizing apex predator

“…Recent studies of the sea otter population in Southeast Alaska used integrated data models to investigate regional population trends and density-dependent effects [ 19 ] and influence of subsistence harvest [ 22 ]. While the approaches applied in these studies accounted for movements between discrete sub-regions within Southeast Alaska (i.e., immigration and emigration), they assumed a known intrinsic growth rate and did not explicitly incorporate a mechanistic model of population spread that would naturally capture movements of recolonizing individuals throughout this continuous geographic area.…”