Continuous Covariates in Mark-Recapture-Recovery Analysis: A Comparison of Methods
Simon Bonner, Byron Morgan and Ruth King
Universities of British Columbia, Kent and St. Andrews
Summary
Time-varying, individual covariates are problematic in experiments with marked animals because the covariate can typically only be observed when each animal is captured. We examine three methods to incorporate time-varying, individual covariates of the survival probabilities into the analysis of data from mark-recapture-recovery experiments: deterministic imputation, a Bayesian imputation approach based on modelling the joint distribution of the covariate and the capture-history, and a conditional approach considering the events which depend only on the observed
covariate data (the trinomial method). After describing the three methods, we compare results from their application to the analysis of the effect of body mass on the survival of Soay Sheep (Ovis aries) on the Isle of Hirta, Scotland. Simulations based on these results are then used to make further
comparisons. We conclude that both the trinomial and Bayesian imputation methods perform best in different situations. If the capture and recovery probabilities are all high, then the conditional model produces precise, unbiased estimators that do not depend on any assumptions regarding the
distribution of the covariate. In contrast, the Bayesian imputation method performs substantially
better when capture and recovery probabilities are low, provided that the specified model of the
covariate is a good approximation to the true data generating mechanism.