Code and data from: A hierarchical approach for estimating state-specific mortality and state transition in dispersing animals with incomplete death records

Unbiased mortality estimates are fundamental for testing ecological and
evolutionary theory as well as for developing effective conservation
actions. However, mortality estimates are often confounded by dispersal,
especially in studies where dead-recovery is not possible. In such
instances, missing individuals (i.e. individuals with unobserved time of
death) may have died or permanently emigrated from a study area, making
inferences about their fate difficult. Mortality before and during
dispersal, as well as the decision to disperse, usually depend on a suite
of individual, social, and environmental covariates, which in turn can be
used to draw conclusions about the fate of missing individuals.Here, we
propose a Bayesian hierarchical model that takes into account time-varying
covariates to estimate transitions between life-history states and
mortality in each state using mark-resighting data with missing
individuals. Specifically, our framework estimates mortality rates in two
states (resident and dispersing state) by treating the fate of missing
individuals as a latent (i.e. unobserved) variable that is statistically
inferred based on information from individuals with a known fate and given
the individual, social, and environmental conditions at the time of
disappearance. Our model also estimates rates of state transition (i.e.
emigration) to assess whether a missing individual was more likely to have
died or survived due to unobserved emigration from the study area. We used
simulations to check the validity of our model and assessed its
performance with data of varying degrees of uncertainty. Our modeling
framework provided accurate mortality and emigration estimates for
simulated data of different sample sizes, proportions of missing
individuals, and resighting intervals. Variation in sample size appeared
to affect the precision of estimated parameters the most.Our approach
offers a solution to estimating unbiased mortality of both resident and
dispersing individuals as well as the probability of emigration using
mark-resighting data with incomplete death records. Conditional on the
availability of data on known-fate individuals and relevant time-varying
covariates, our model can reconstruct the fate (death or emigration) of
missing individuals. The modularity of our framework allows mortality
analyses to be tailored to a variety of species-specific life histories.