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Research article2020Peer reviewedOpen access

Estimating hunting harvest from partial reporting: a Bayesian approach

Lindstrom, Tom; Bergqvist, Goran

Abstract

Quantifying hunting harvest is essential for numerous ecological topics, necessitating reliable estimates. We here propose novel analytical tools for this purpose. Using a hierarchical Bayesian framework, we introduce models for hunting reports that accounts for different structures of the data. Focusing on Swedish harvest reports of red fox (Vulpes vulpes), wild boar (Sus scrofa), European pine marten (Martes martes), and Eurasian beaver (Castor fiber), we evaluated predictive performance through training and validation sets as well as Leave One Out Cross Validation. The analyses revealed that to provide reliable harvest estimates, analyses must account for both random variability among hunting teams and the effect of hunting area per team on the harvest rate. Disregarding the former underestimated the uncertainty, especially at finer spatial resolutions (county and hunting management precincts). Disregarding the latter imposed a bias that overestimated total harvest. We also found support for association between average harvest rate and variability, yet the direction of the association varied among species. However, this feature proved less important for predictive purposes. Importantly, the hierarchical Bayesian framework improved previously used point estimates by reducing sensitivity to low reporting and presenting inherent uncertainties.

Published in

Scientific Reports
2020, Volume: 10, number: 1, article number: 21113Publisher: NATURE RESEARCH

    UKÄ Subject classification

    Probability Theory and Statistics
    Fish and Wildlife Management

    Publication identifier

    DOI: https://doi.org/10.1038/s41598-020-77988-x

    Permanent link to this page (URI)

    https://res.slu.se/id/publ/110898