Estimating abundance of harvested populations at the management unit scale

Abstract

Management of harvested populations relies on accurate assessment of abundance within management units to reevaluate and set harvest regulations. Several statistical approaches use readily available age-at-harvest data to estimate populations, but these often rely on auxiliary data which can be costly to collect and may not provide reliable estimates at the management unit scale. We developed a Bayesian integrated population model (IPM) relying solely on available harvest data to estimate abundance of white-tailed deer in Tennessee where estimates of abundance were lacking. We fit the IPM to reported harvest data and estimates of total harvest from hunter surveys to estimate abundance statewide and within deer management units (DMUs). Statewide deer harvest in Tennessee from 2005 to 2023 ranged between 132,256 and 181,477 deer annually (mean = 160,050; SD = 16,178). Although the population fluctuated, median population growth rate was 0.99 (90% CRI 0.978-1.003) during the study. Statewide population abundance was estimated at 890,657 (90% CRI 786,627-1,172,514) deer in 2023. Our IPM provided a comprehensive picture of deer population dynamics and allowed us to estimate abundance and demographic rates using only harvest data and informative priors. This model demonstrates the benefits of using informative priors and regularizing parameters in ecological studies. The IPM is a useful, flexible tool to monitor harvested populations at finer spatial scales thereby allowing decisions on harvest regulations to be based on precise estimates of abundance within specific management units.

Fig 1. Study area map.

Study area for the application of an integrated population model to estimate abundance of white-tailed deer statewide and within the 6 deer management units using harvest data from 2005–2023 in Tennessee, USA.

Fig 2. Integrated population model structure.

Directed acyclic diagram for an integrated population model for white-tailed deer in Tennessee, USA based on harvest data (green rectangles). The demographic parameters (blue circles) include abundance ($N$), hunting survival ($HS$), natural survival ($NS$), recruitment rate ($\gamma$), and reporting rate ($Rep$).

Fig 3. Statewide abundance of deer.

Estimated abundance for white-tailed deer in Tennessee from an integrated population model based on harvest data from 2005 to 2023. The black line represents the median estimate, and the shaded areas represent the 66% and 90% Bayesian credible intervals.

Fig 4. Abundance of deer by management units.

Estimated abundance for white-tailed deer in Tennessee, USA by deer management units from an integrated population model based on harvest data from 2005 to 2023. The black line represents the median estimate, and the shaded areas represent the 66% and 90% Bayesian credible intervals.

Fig 5. Demographic rates.

Estimates of baseline demographic rates from an integrated population model for adult white-tailed deer in Tennessee, USA from 2005 to 2023. Estimates are reported as the median with 66% and 90% Bayesian credible intervals for the baseline or grand-mean parameter value. Demographic rates then varied spatiotemporally from the baseline with random effects and by age with a log-hazard ratio offset. It is important to note that recruitment rate does not yet take into account female survival.