Abstract
Recently, statistical population models using age-at-harvest data have seen increasing use for monitoring of harvested wildlife populations. Even more recently, detailed evaluation of model performance for long-lived, large game animals indicated that the use of random effects to incorporate unmeasured environmental variation, as well as second-stage Horvitz-Thompson-type estimators of abundance, provided more reliable estimates of total abundance than previous models. We adapt this new modeling framework to small game, age-at-harvest models with only young-of-the-year and adult age classes. Our Monte Carlo simulation results indicate superior model performance for the new modeling framework, evidenced by lower bias and proper confidence interval coverage. We apply this method to male wild turkey harvest in the East Ozarks turkey productivity region, Missouri, USA, where statistical population reconstruction indicates a relatively stationary population for 1996–2010.
Highlights
The wildlife literature has seen models for age-atharvest data in the past [1,2], models for these types of data entered the forefront of monitoring population status and trends only recently [3,4,5,6,7,8,9]
The corresponding fixed-effects model, HTFE, showed low bias
The analysis of age-at-harvest data for small game animals presents a number of challenges in addition to those presented by large game data
Summary
The wildlife literature has seen models for age-atharvest data in the past [1,2], models for these types of data entered the forefront of monitoring population status and trends only recently [3,4,5,6,7,8,9]. Models for statistical population reconstruction (SPR) of harvested large game animals have been developed that utilize the same likelihood-based inference techniques, but instead consider estimating animal abundance following optimization, outside of the likelihood framework with a Horvitz-Thompson-type estimator, which adjusts the observed harvest count by the estimated probability of harvest in accordance with the assumption of a binomial sampling scheme [9]. These recent developments constitute improvements over previous models of this nature, when stochastic environmental factors may affect population dynamics [9]. Skalski et al [10] found little loss in precision when big game population reconstruction was based on pooling adult harvest information for age classes 3+
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