Incorporating telemetry information into capture-recapture analyses improves precision and accuracy of abundance estimates given spatiotemporally biased recapture effort

Natural populations that are rare, cryptic or inaccessible provide a monumental challenge to monitoring, as adequate data are extremely difficult to collect. Surveys often encompass only a small portion of a population's range due to difficult terrain or inclement weather, especially for populations with extensive ranges. Thus, to maximise encounters, sampling efforts may be largely opportunistic or biased to accessible areas. The resulting sparse and spatially biased data may be difficult to model, standardise across years and incorporate into an assessment or management framework. However, in many monitoring programs, there are usually multiple threads of data that, though each may have its own limitations, can be synthesised to reveal important ecological processes. Here, we demonstrate a simple technique to incorporate two additional streams of data on the same population, telemetry and survey effort data, into capture-recapture analyses to address spatiotemporal sampling bias using simulated data. Utilisation distributions (UDs) computed from telemetry data are overlaid with UDs of survey efforts, providing an 'effort by animal space use' overlap covariate for modelling detection in a Jolly-Seber open population model. Using simulated data, we found that our method resulted in more accurate and precise estimates of abundance than traditional capture-recapture models. We then applied this method to a 16 year photo-identification capture-recapture dataset (n = 143 individuals) along with telemetry data (n = 44 satellite tag deployments) collected from the endangered population of false killer whales resident to the main Hawaiian Islands. Incorporating space use and effort into this analysis improved precision of abundance estimates relative to previous modelling endeavours.