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Continuous covariates in mark-recapture-recovery analysis: a comparison of methods
Simon J Bonner1, Byron J T Morgan, Ruth King
1Department of Statistics, University of British Columbia, Vancouver, BC, Canada. s.bonner@stat.ubc.ca
Biometrics
|February 19, 2010
Summary
Analyzing mark-recapture-recovery data with time-varying covariates is challenging. Both the trinomial model and Bayesian imputation methods offer effective solutions for estimating survival probabilities in marked animal experiments.
Area of Science:
- Ecology and Evolutionary Biology
- Wildlife Population Dynamics
- Statistical Modeling in Biology
Background:
- Time-varying, individual covariates present analytical challenges in mark-recapture-recovery studies, as they are typically only observable during capture events.
- Accurate estimation of survival probabilities is crucial for understanding population dynamics and the impact of individual traits.
Purpose of the Study:
- To evaluate three distinct statistical methods for incorporating time-varying, individual covariates into the analysis of mark-recapture-recovery data.
- To compare the performance of deterministic imputation, Bayesian imputation, and a conditional (trinomial) model in estimating survival probabilities.
Main Methods:
- Deterministic imputation: A straightforward approach to fill in missing covariate data.
- Bayesian imputation: A sophisticated method modeling the joint distribution of covariates and capture histories.
- Trinomial model: A conditional approach analyzing only data where covariates are fully observed.
Main Results:
- The trinomial model yields precise, unbiased survival estimators when capture and recovery probabilities are high, without requiring assumptions on covariate distribution.
- The Bayesian imputation method demonstrates superior performance with low capture and recovery probabilities, contingent on an accurate covariate model.
- Simulations confirm the differential performance of the methods under varying data conditions.
Conclusions:
- Both the trinomial model and Bayesian imputation are valuable tools for analyzing mark-recapture-recovery data with time-varying covariates, each suited to different scenarios.
- The choice of method depends critically on the estimated capture/recovery probabilities and the quality of the covariate model.
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