Comparison of Bayesian Models to Estimate Survival From Dead-Recovery Alone and Together With Live-Encounter Data: Challenges and Opportunities.

The recovery of dead marked individuals, either alone or in combination with encounters of these individuals while alive, is an important source of data for estimating survival in birds, mammals, and fish. Various models have been developed to analyze such data in a Bayesian framework, including single-state and multistate state-space models, marginalized state-space models, and multinomial models. An overview of the different formulations, together with an assessment of their parameter accuracy, computational efficiency, and flexibility in covariate modeling, is lacking so far. We assessed 13 models based on data simulation and analysis with the widely used R-based software NIMBLE and JAGS. We found that all the models evaluated produced accurate parameter estimates, with the exception of the multistate state-space models, which produced biased parameter estimates. This is because the standard MCMC samplers required for Bayesian inference do not work properly for this model. Although such multistate models work correctly in the frequentist framework, they should not be used in the Bayesian framework unless specially developed samplers are used. Instead, single-state state-space models, marginalized multistate state-space models, multinomial multistate models, or reparameterized multistate models should be used. The marginalized state-space and multinomial models were the most computationally efficient. The models evaluated do not differ in their ability to model temporal covariates but do differ for individual continuous covariates. The latter can be modeled in state-space models but not in multinomial models. We also show that single-state models can be formulated for the joint analysis of dead-recovery and live encounter data, which are usually modeled with multistate models. This facilitates the inclusion of further auxiliary data and results in a computationally efficient model. We expect our overview to help ecologists decide which model to use when estimating survival from dead-recovery data in the Bayesian framework.