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

Abstract

Background: Reintroducing predators is a promising conservation tool to help remedy human-caused ecosystem changes. However, the growth and spread of a reintroduced population is a spatiotemporal process that is driven by a suite of factors, such as habitat change, human activity, and prey availability. Sea otters (Enhydra lutris) are apex predators of nearshore marine ecosystems that had declined nearly to extinction across much of their range by the early 20th century. In Southeast Alaska, which is comprised of a diverse matrix of nearshore habitat and managed areas, reintroduction of 413 individuals in the late 1960s initiated the growth and spread of a population that now exceeds 25,000. Methods: Periodic aerial surveys in the region provide a time series of spatially-explicit data to investigate factors influencing this successful and ongoing recovery. We integrated an ecological diffusion model that accounted for spatially-variable motility and density-dependent population growth, as well as multiple population epicenters, into a Bayesian hierarchical framework to help understand the factors influencing the success of this recovery. Results: Our results indicated that sea otters exhibited higher residence time as well as greater equilibrium abundance in Glacier Bay, a protected area, and in areas where there is limited or no commercial fishing. Asymptotic spread rates suggested sea otters colonized Southeast Alaska at rates of 1-8 km/yr with lower rates occurring in areas correlated with higher residence time, which primarily included areas near shore and closed to commercial fishing. Further, we found that the intrinsic growth rate of sea otters may be higher than previous estimates suggested. Conclusions: This study shows how predator recolonization can occur from multiple population epicenters. Additionally, our results suggest spatial heterogeneity in the physical environment as well as human activity and management can influence recolonization processes, both in terms of movement (or motility) and density dependence.

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

“…Several methods for defining range extent have been used for sea otter populations (Bowlby et al 1988, Lubina and Levin 1988, Hatfield et al 2019), and we estimated sea otter range extent in Washington and observed rates of range expansion using a variety of methods (Table , available in Supporting Information; −0.8 km/year to 2.24 km/year). All of these methods resulted in lower estimated rates of range expansion for sea otters in Washington than typical rates from other studies (2–6 km/year; Lubina and Levin 1988, Tinker et al 2008 b , Williams et al 2019, Eisaguirre et al 2021), and each method produced very different estimates for the same range expansion front (north or east front and south front). We lacked reliable estimates of rates of range expansion for sea otters in Washington, so we used a diffusion approximation to simulate future population growth and range expansion under a range of biologically realistic population parameters (including rate of range expansion) and used these forward projections as the basis of a sensitivity analysis to explore how variation in population parameters would influence predictions of future population growth.…”

“…We parametrized v separately for southward and eastward range expansion, thereby allowing for differing rates of range expansion at either end of the range, consistent with previously observed patterns of range expansion (Figure 1). We set maximum v values to 5 km/year, comparable with previous studies (3‒6 km/year; Lubina and Levin 1988, Tinker et al 2008 b , Williams et al 2019, Eisaguirre et al 2021). To allow for the possibility of range expansion stopping, we set the minimum v values to zero.…”

“…The ability to make inferences about small‐scale processes is more limited with discrete space‐time models; however, these models are computationally more tractable over large areas, are simpler to fit if habitat layers or survey data are not spatially resolved at smaller scales, and simplify inclusion of complexities such as environmental stochasticity, age or sex structure, and hierarchical random effects (e.g., unexplained spatial differences in equilibrium density). Both modeling approaches are useful for exploring different aspects of population dynamics and, encouragingly, both approaches have produced similar results for sea otters in Glacier Bay (Williams et al 2017, 2019; Lu et al 2019; Tinker et al 2019 a ) and southeast Alaska (Tinker et al 2019 a , Eisaguirre et al 2021), including overall population trends and rates of range expansion.…”

“…Previous researchers have reported the utility of state‐space models in modeling population abundance and trends (Royle and Dorazio 2008, Cressie et al 2009), and have used Bayesian state‐space models to analyze marine mammal survey data and make inference about abundance, trends, and population parameters (Gerrodette et al 2011, Moore and Barlow 2011). Recently, state‐space models have been used to estimate equilibrium abundance for sea otters in southeast Alaska (Lu et al 2019, Tinker et al 2019 a , Eisaguirre et al 2021) and California (Tinker et al 2021 b ), and Lu et al (2019), Eisaguirre et al (2021), and Tinker et al (2021 b ) estimate equilibrium abundance as a function of a suite of habitat variables. Unlike the earlier habitat‐based K estimates, state‐space models avoid the problematic assumption that certain areas have already reached K (and that these densities are generalizable), and instead infer the statistically most likely value of equilibrium abundance by fitting a density‐dependent process model to the full time series.…”

Range reexpansion after long stasis: Italian otters (Lutra lutra) at their northern edge

“…One of the most studied cases is the range expansion of the sea otter ( Enhydra lutria ) across North Pacific following translocations (Tinker et al, 2008). Mechanistic models of ecological diffusion have been used to estimate spatiotemporal changes in occupancy and abundance of this gregarious carnivore from counts of individuals (Eisaguirre et al, 2021; Lubina & Levin, 1988; Williams et al, 2017, 2019).…”