Continuous, Time-Varying Covariates in Mark-Recapture-Recovery Analyses: A Comparison of Methods

Speaker: Ruth King (CREEM)

Abstract

Time-varying, individual covariates are problematic in experiments with marked animals since the covariate is typically only observed when the animal is captured. We consider three methods to incorporate time-varying, individual covariates in capture-recapture or mark-recapture-recovery experiments: simple imputation, a Bayesian approach based on modelling the joint distribution of the covariate and the capture-history, and a conditional approach considering only events dependent on the observed data. We will describe each method in turn and use a simulation study to compare the three approaches under different scenarios. We discuss the pros and cons of each method before finally applying the methods to real data (Soay sheep).