Abstract
Capture-mark-recapture methods (CMR) are a commonly used tool in species conservation and management for the estimation of demographic parameters in a population. However, biases in these estimates can occur due to the heterogeneity in processes influencing recapture data or the experimental design. We outline an approach to quantify and identify ways to reduce this bias through both experimental design and data analysis, using individual based modelling (IBMs). By using an IBM that includes key sources of heterogeneity that are believed to exist in the system, the release and recapture of marked individuals can be simulated under differing experimental, behavioural or landscape scenarios. Using this IBM as a data-generator, we compare a simulated population of individuals with a subset of that population that represent those marked and recaptured in a CMR experiment. Parameter estimates from the data generated by the marked subset are then compared to ‘true’, realised parameters from the wider, unmarked population. We demonstrate this with an application of ‘simulated tagging’ of Pacific skipjack tuna (Katsuwonus pelamis), using scenarios of different release locations of marked individuals, alternative band-recovery models, and differing assumptions that define the spatial extent of the unmarked population. We quantify the error in the estimated survival and fishing mortality parameters and examine how these can be reduced by following differing release and analysis strategies. We show that spatiotemporal heterogeneity of individual dispersion and recovery effort (i.e. fishing pressure) led to severe bias in recapture probability estimates, up to 361%, regardless of experimental design. However, when the baseline of the unmarked population of which marked animals were assumed to be representative was defined by spatial coverage of recaptures, rather than a fixed, spatial management area, bias was reduced to 25%. Our results show that the use of IBM frameworks exploring alternative hypotheses, either in the design of or post-hoc analyses of CMR experiments, support maximising the information that can be derived and quantify the degree of potential bias due to analysis model mis-specification. Furthermore, these methods can benefit other sampling and monitoring programmes in systems with high levels of spatial or ecological heterogeneity.