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Hunting quotas, selectivity and stochastic population dynamics challenge the management of wild reindeer

Bart Peeters*, Åshild Ønvik Pedersen, Vebjørn Veiberg, Brage Bremset Hansen

*Corresponding author:

ABSTRACT: Wildlife managers adjust harvest quotas based on population changes and specific management goals, like controlling population size and demography, or minimizing human-wildlife interactions. However, establishing quotas that best meet these goals can be challenging due to, e.g., population fluctuations, climate change, and bias or variation in harvest effort. High-arctic Svalbard reindeer (Rangifer tarandus platyrhynchus) experience strong interannual variation in population size and demographic structure due to environmental stochasticity. Here, we analyzed demographic and spatial bias of reindeer harvest in relation to quota regulations, hunter preferences, and population dynamics. Despite the protective management goal to avoid demographic impacts, thirty years of data revealed that the harvest was consistently biased towards yearlings and male adults. This hunting selectivity resulted from both hunter preferences and the coarse license categories separating ‘calf’, ‘yearling or female adult’, and ‘free choice’ licenses. We developed Bayesian multinomial likelihood models to account for hunting selectivity and optimize annual quota distributions among license categories using population monitoring data. Optimized annual quota distributions varied strongly due to demographic fluctuations associated with strong climate variability, whereas simulated harvest offtakes showed that the protective management goal is inherently challenged by the coarse license categories. Although, on average, only 7% of the hunted population was harvested annually, we found strong spatial variation in harvest pressure, with potential implications for spatial population dynamics. Our adaptive management approach accounting for hunting selectivity and demographic fluctuations can be of general relevance for harvested species in stochastic environments.