Harvesting wildlife affected by climate change: a modelling and management approach for polar bears

Abstract

Summary The conservation of many wildlife species requires understanding the demographic effects of climate change, including interactions between climate change and harvest, which can provide cultural, nutritional or economic value to humans.We present a demographic model that is based on the polar bear Ursus maritimus life cycle and includes density‐dependent relationships linking vital rates to environmental carrying capacity (K). Using this model, we develop a state‐dependent management framework to calculate a harvest level that (i) maintains a population above its maximum net productivity level (MNPL; the population size that produces the greatest net increment in abundance) relative to a changing K, and (ii) has a limited negative effect on population persistence.Our density‐dependent relationships suggest that MNPL for polar bears occurs at approximately 0·69 (95% CI = 0·63–0·74) of K. Population growth rate at MNPL was approximately 0·82 (95% CI = 0·79–0·84) of the maximum intrinsic growth rate, suggesting relatively strong compensation for human‐caused mortality.Our findings indicate that it is possible to minimize the demographic risks of harvest under climate change, including the risk that harvest will accelerate population declines driven by loss of the polar bear's sea‐ice habitat. This requires that (i) the harvest rate – which could be 0 in some situations – accounts for a population's intrinsic growth rate, (ii) the harvest rate accounts for the quality of population data (e.g. lower harvest when uncertainty is large), and (iii) the harvest level is obtained by multiplying the harvest rate by an updated estimate of population size. Environmental variability, the sex and age of removed animals and risk tolerance can also affect the harvest rate. Synthesis and applications. We present a coupled modelling and management approach for wildlife that accounts for climate change and can be used to balance trade‐offs among multiple conservation goals. In our example application to polar bears experiencing sea‐ice loss, the goals are to maintain population viability while providing continued opportunities for subsistence harvest. Our approach may be relevant to other species for which near‐term management is focused on human factors that directly influence population dynamics within the broader context of climate‐induced habitat degradation.

Interrelated ecological impacts of climate change on an apex predator

“…), and the potential for environmental conditions to affect these classes differently (Regehr et al. ). We hypothesized that single bears (i.e., AMs and AFs without dependent young) would have the highest BCS, and that BCS for subadult bears would be most sensitive to environmental fluctuations.…”

Searching for snakes: ball python hunting in southern Togo, West Africa

“…increasing frequency and duration of droughts; see e.g., Reading et al 2010). However, these effects are not isolated, and habitat loss, for example, can increase a populations' vulnerability to overutilization if the population becomes smaller or less resilient as a result and harvest levels remain the same (Regehr et al 2017; see also Schai-Braun et al 2019 for discussion of necessary adjustments to harvest quotas in response to population fluctuations caused by climatic events).…”

Hierarchical Bayesian model reveals the distributional shifts of Arctic marine mammals

“…With the covariate responses, we can estimate past changes in the expected relative densities. Arctic wide vulnerability assessments do not consider region specific distributional changes which may actually support species relocation inside the area instead of disappearance (Stirling & Derocher, ; Wilson, Regehr et al., ). We assume that the shrinking ice cover has caused the decrease of polar bears’ relative density in coastal regions (Figure ).…”

“…The predictions can be utilized in planning conservation actions and in assessing the risks of different species‐human interactions (Wilson, Regehr et al., ; Wilson, Trukhanov et al., ), whereas habitat utilization functions can underpin spatially explicit demographic analysis for better population assessments (Kearney & Porter, ; Lunn et al., ). In areas with high survey costs and little designed survey data, flawed population assessments may lead to improper conservation or harvest actions (Regehr, Wilson, Rode, Runge, & Stern, ).…”

“…Despite earlier estimates of the population sizes of polar bears (3,200 ± 1,100 individuals [Matishov et al., ]), ringed seals (90,000–150,000 [Kelly et al., ]) and walruses (<500 [Born, Gjertz, & Reeves, ]) in the Kara Sea, there are no rigorous distribution estimates for these species. Previous studies on polar bear distributions (Durner et al., ; Lone, Merkel, Lydersen, Kovacs, and Aars (); Matishov et al., ; Wilson, Horne, Rode, Regehr, & Durner, ; Wilson, Regehr et al., ), have not provided a spatiotemporally explicit fine scale prediction about the density of polar bears, whereas estimates for densities of seals and walruses are mostly missing in the entire Arctic.…”