A Spatial Model for Estimating Mortality Rates, Abundance and Movement Probabilities from Fishery Tag-Recovery Data

Spatial heterogeneity in survival and capture probabilities is a critical issue to consider in tagging experiments. If a non-trivial level of spatial heterogeneity exists and is not accounted for, it can lead to unreliable estimates of mortality rates and abundance, and of the uncertainty in these estimates. Here we present a spatial model for analysing multiyear tag-recovery and fishery catch data that allows for mortality rates and abundance to differ among discrete regions and for fish to move among these regions at discrete time intervals. For a given cohort of fish tagged in consecutive years in all regions, this model can provide year- and region-specific estimates of both natural mortality and fishing mortality, region-specific estimates of abundance at the time of initial tagging, as well as year-specific movement probabilities between regions. The precision of parameter estimates can be poor with such a full model, but can be improved with more restricted model parameterizations. Tagging in some regions may be logistically difficult and/or very expensive. We show that if tagging is conducted in all regions in the first year of the experiment, but only in one region thereafter, accurate and precise parameter estimates can sometimes still be achieved. It is not always the regional estimates of mortality rates and abundance that are of primary interest, but rather the population-wide estimates (over all regions). Such population-wide estimates can be obtained by applying a non-spatial model to the data pooled across regions; however, simulation results suggest that there are many situations for which large biases are incurred by using a non-spatial model. Simulations also suggest that there is almost no loss in precision from using the spatial model to obtain population-wide estimates even when the non-spatial model would suffice.