Calibrating Statistical Population Reconstruction Models Using Catch‐Effort and Index Data

Abstract

This paper illustrates how age‐at‐harvest data, when combined with hunter‐effort information routinely collected by state game management agencies, can be used to estimate and monitor trends in big game abundance. Twenty‐four years of age‐at‐harvest data for black‐tailed deer (Odocoileus hemionus) were analyzed to produce abundance estimates ranging from 1,281 adult females to 3,232 adult females on a 22,079‐ha tree farm in Pierce County, Washington, USA. The annual natural survival probability was estimated to be 0.7293 ( = 0.0097) for this female population. The estimated abundance was highly correlated with an independent browse damage index (r = 0.8131, P < 0.001). A population reconstruction incorporating the browse index did not substantially improve the model fit but did provide an auxiliary model for predicting deer abundance. This population reconstruction illustrates a cost‐effective alternative to expensive big game survey methods.

Spring spotlight counts provide reliable indices to track changes in population size of mountain‐dwelling red deer Cervus elaphus

“…L'applicazione di questo metodo di stima agli Ungulati selvatici è piuttosto contenuta e limitata per lo più a studi nordamericani (ad esempio Skalski et al, 2007). Per una trattazione esaustiva si veda Skalski et al (2005).…”

Performance of hunting statistics as spatiotemporal density indices of moose (Alces alces) in Norway

“…However, as the population abundance is seldom known, an alternative is to compare them with other independent indices or estimates of density (e.g., Skalski et al 2007). For instance, by examining the relationship between the moose, Alces alces, seen per unit effort (SPUE) and moose abundance reconstructed by cohort analysis, concluded that the SPUE may be used for monitoring the temporal variation in moose abundance (see also Rö nnegå rd et al 2008).…”

Random Effects Models and Multistage Estimation Procedures for Statistical Population Reconstruction of Small Game Populations

“…Some of the most recent developments require estimation of initial animal cohort abundance as a parameter [3], [4], [7], [8], or as a latent variable [5] in a frequentist or Bayesian framework, respectively. Recently, 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].…”