The management of sustainable harvest of animal populations is of great ecological and conservation importance. Development of formal quantitative tools to estimate and mitigate the impacts of harvest on animal populations has positively impacted conservation efforts. The vast majority of existing harvest models, however, do not simultaneously estimate ecological and harvest impacts on demographic parameters and population trends. Given that the impacts of ecological drivers are often equal to or greater than the effects of harvest, and can covary with harvest, this disconnect has the potential to lead to flawed inference. In this study, we used Bayesian hierarchical models and a 43‐year capture–mark–recovery dataset from 404,241 female mallards We demonstrate that density dependence, habitat conditions and harvest can simultaneously impact demographic parameters of female mallards, and discuss implications for existing and future harvest management models. Our results demonstrate the importance of controlling for multicollinearity among demographic drivers in harvest management models, and provide evidence for multiple mechanisms that lead to partial compensation of mallard harvest. We provide a novel model structure to assess these relationships that may allow for improved inference and prediction in future iterations of harvest management models across taxa.
Bayesian hierarchical models allow ecologists to account for uncertainty and make inference at multiple scales. However, hierarchical models are often computationally intensive to fit, especially with large datasets, and researchers face trade‐offs between capturing ecological complexity in statistical models and implementing these models. We present a recursive Bayesian computing (RB) method that can be used to fit Bayesian models efficiently in sequential MCMC stages to ease computation and streamline hierarchical inference. We also introduce transformation‐assisted RB (TARB) to create unsupervised MCMC algorithms and improve interpretability of parameters. We demonstrate TARB by fitting a hierarchical animal movement model to obtain inference about individual‐ and population‐level migratory characteristics. Our recursive procedure reduced computation time for fitting our hierarchical movement model by half compared to fitting the model with a single MCMC algorithm. We obtained the same inference fitting our model using TARB as we obtained fitting the model with a single algorithm. For complex ecological statistical models, like those for animal movement, multi‐species systems, or large spatial and temporal scales, the computational demands of fitting models with conventional computing techniques can limit model specification, thus hindering scientific discovery. Transformation‐assisted RB is one of the most accessible methods for reducing these limitations, enabling us to implement new statistical models and advance our understanding of complex ecological phenomena.
- Award ID(s):
- 1927177
- PAR ID:
- 10453933
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 12
- Issue:
- 2
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- p. 245-254
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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