Including a spatial predictive process in band recovery models improves inference for Lincoln estimates of animal abundance

• Published in: Ecology and evolution 2022-10, Vol.12 (10), p.e9444-n/a
• Main Authors: Gonnerman, Matthew, Linden, Daniel W., Shea, Stephanie A., Sullivan, Kelsey, Kamath, Pauline, Blomberg, Erik
• Format: Article
• Language: English
• Subjects: Abundance; band recovery; Banding; Bias; Configurations; Critical components; Data recovery; Ecological models; Estimates; Harvesting; Heterogeneity; integrated population model; Lincoln estimator; Mathematical models; Parameter estimation; Population; Population number; Population statistics; Random variables; Seasons; Spatial heterogeneity; spatial predictive process; Spatial variations; Telemetry; Wildlife conservation
• ISSN: 2045-7758; 2045-7758
• DOI: 10.1002/ece3.9444

Abundance estimation is a critical component of conservation planning, particularly for exploited species where managers set regulations to restrict harvest based on current population size. An increasingly common approach for abundance estimation is through integrated population modeling (IPM), which uses multiple data sources in a joint likelihood to estimate abundance and additional demographic parameters. Lincoln estimators are one commonly used IPM component for harvested species, which combine information on the rate and total number of individuals harvested within an integrated band‐recovery framework to estimate abundance at large scales. A major assumption of the Lincoln estimator is that banding and recoveries are representative of the whole population, which may be violated if major sources of spatial heterogeneity in survival or harvest rates are not incorporated into the model. We developed an approach to account for spatial variation in harvest rates using a spatial predictive process, which we incorporated into a Lincoln estimator IPM. We simulated data under different configurations of sample sizes, harvest rates, and sources of spatial heterogeneity in harvest rate to assess potential model bias in parameter estimates. We then applied the model to data collected from a field study of wild turkeys (Meleagris gallapavo) to estimate local and statewide abundance in Maine, USA. We found that the band recovery model that incorporated a spatial predictive process consistently provided estimates of adult and juvenile abundance with low bias across a variety of spatial configurations of harvest rate and sampling intensities. When applied to data collected on wild turkeys, a model that did not incorporate spatial heterogeneity underestimated the harvest rate in some subregions. Consistent with simulation results, this led to overestimation of both local and statewide abundance. Our work demonstrates that a spatial predictive process is a viable mechanism to account for spatial variation in harvest rates and limit bias in abundance estimates. This approach could be extended to large‐scale band recovery data sets and has applicability for the estimation of population parameters in other ecological models as well. We developed an approach to account for spatial variation in harvest rates using a spatial predictive process, which we incorporated into a Lincoln estimator IPM. We simulated data to assess potential model bias, then applied the model to da