A hierarchical framework for estimating abundance and population growth from imperfectly observed removals. Issue 5 (19th May 2020)

• Record Type: Journal Article
• Authors: Stevens, Bryan S.; Bence, James R.; Luukkonen, David R.; Porter, William F.
• Is Part Of: Ecosphere. Volume 11:Issue 5(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-05-19
• Subjects: catch‐effort -- depletion sample -- harvest management -- hierarchical model -- Meleagris gallopavo -- removal sample -- wildlife monitoring -- wild turkey; Ecology -- Periodicals; Ecology; Periodicals; 577.05
• DOI: 10.1002/ecs2.3131
• Languages: English
• ISSNs: 2150-8925

Abstract

Abstract: Estimating abundance and growth of animal populations are central tasks in ecology and natural resource management. Removal models for estimating abundance have a long history in applied ecology, and recent developments provided hierarchical extensions that account for spatially replicated sampling and heterogeneous capture probabilities. Measurement error is common to removal data collected from many broad‐scale monitoring programs, however, and a general framework for population assessment using removal data in the presence of measurement error is lacking. We developed a hierarchical framework for estimating abundance and population trends from removal experiments that are replicated in space and time that accommodates measurement error, as well as heterogeneity in capture probability and animal density. We describe the model for variable‐effort removal sampling and use it to estimate region‐specific abundance and population trends for wild turkeys ( Meleagris gallopavo ) in Michigan, USA. We used a Bayesian approach for estimation and inference and fit models using daily hunter harvest and effort estimates collected over 5 management regions for 14 annual hunting seasons. Our analyses provide evidence for spatially heterogeneous capture probabilities among regions and turkey densities that were heterogeneous in both space and time, and show that populations increased slightly over the study. Our framework provides a general approach for population assessment using removal data that are collected over broad scales in resource management contexts (e.g., animal harvesting), facilitating formal abundance estimation instead of reliance on unverified indices for tracking populations of managed species. Thus, we provide a useful tool for monitoring programs to assess populations over broad scales, and therefore inform decision makers about population status at spatial scales similar to those for which regulatory decisions are made.