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

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 the 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 sub-regions.  Consistent
with simulation results, this led to over-estimation 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 datasets and has applicability for
the estimation of population parameters in other ecological models as
well.