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


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.