Abstract
Population estimation is essential for the conservation and management of fish and wildlife, but accurate estimates are often difficult or expensive to obtain for cryptic species across large geographical scales. Accurate statistical models with manageable financial costs and field efforts are needed for hunted populations and using age-at-harvest data may be the most practical foundation for these models. Several rigorous statistical approaches that use age-at-harvest and other data to accurately estimate populations have recently been developed, but these are often dependent on (a) accurate prior knowledge about demographic parameters of the population, (b) auxiliary data, and (c) initial population size. We developed a two-stage state-space Bayesian model for a black bear (Ursus americanus) population with age-at-harvest data, but little demographic data and no auxiliary data available, to create a statewide population estimate and test the sensitivity of the model to bias in the prior distributions of parameters and initial population size. The posterior abundance estimate from our model was similar to an independent capture-recapture estimate from tetracycline sampling and the population trend was similar to the catch-per-unit-effort for the state. Our model was also robust to bias in the prior distributions for all parameters, including initial population size, except for reporting rate. Our state-space model created a precise estimate of the black bear population in Wisconsin based on age-at-harvest data and potentially improves on previous models by using little demographic data, no auxiliary data, and not being sensitive to initial population size.
Highlights
Population estimates are essential for making decisions about management and conservation of many species[1,2], but often are difficult or expensive to obtain across large geographical scales[2,3]
Deterministic methods can sometimes be limited in accuracy[11,14], because they rely on assumptions that demographic parameters are stable over time (e.g.,13–15), and can be biased when erroneous or subjective demographic parameter values are used[2,13,14,15,16]
Our study focused on the black bear population for the entire state of Wisconsin (Fig. 2), where the Wisconsin Department of Natural Resources (WDNR) manages bears in 4 hunting zones (Supplementary Material 1)
Summary
Population estimates are essential for making decisions about management and conservation of many species[1,2], but often are difficult or expensive to obtain across large geographical scales[2,3]. Drawbacks of Bayesian models is that they can be more complex and difficult to comprehend and more computationally intensive to implement than simpler models Their implementation, could result in better decision-making about populations and harvest quotas, and lead to more effective monitoring and management, for cryptic species. An independent capture-recapture estimate generated from tetracycline marking found that the current model underestimated the population size by nearly 2/322 This is mainly due to the inability of the deterministic model to account for variation in harvest and population demographics over time and because the model incorrectly assumes a linear relationship between independent bear abundance estimates from bait stations and population abundance[24]. Our goal was to create and evaluate a Bayesian state-space model using age-at-harvest data to estimate the statewide abundance of black bears in Wisconsin. Our objectives were to (1) determine reasonable prior distributions using literature review and harvest data; (2) compare abundance estimates to estimates from the capture-recapture estimates using tetracycline marking from 201126 and the population trend to the trend from catch-per-unit-effort for the state; and (3) analyze the sensitivity of the state-space model’s population estimate to different specifications of the prior distributions for each demographic parameter and initial population size
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