Integrating Multiple Data Sources Improves Abundance Estimation in Spatial Capture-Recapture Models

Reliable estimates of abundance are fundamental to conservation assessments and measuring the success of conservation actions, especially where populations are in decline or recovery. Here we present an approach that improves statistical precision and biological value of estimates of population size by integrating multiple data sources in a marine system. Using the eastern North Pacific blue whale (Balaenoptera musculus) as a case study, we apply spatial capture-recapture methods to estimate abundance using photo-identification data from surveys with known effort. We then integrate opportunistic photo-identification data and telemetry data, separately and then together, to quantify the value of auxiliary data for generating more robust estimates. The telemetry data sampled a broader area and revealed a very different pattern of space use in the population when compared to the photo-identification data, which were predominantly collected in higher whale density areas. Thus, the inclusion of these data provides a more representative sample leading to more robust estimates of abundance. Integrated models also improved precision in abundance estimates. The best improvement came from the model integrating all three datasets, but that was largely driven by the addition of the opportunistic photo-identification data. Addition of opportunistic photo-identification data led to a 46% reduction in the coefficient of variation when compared to the photo-identification data from surveys alone. Integrating multiple data streams improves precision of population size estimates, which is critical to accurately track the impact of stressors and to guide management strategies. Making observations to provide robust estimates of abundance can be challenging, especially in wide-ranging and/or cryptic species where data are often limited despite allocation of significant resources. This study identified data integration as a useful tool to maximise inferences from existing datasets and guide future data collection, informing management planning and conservation policy in the marine setting.