Estimating spatially-varying density and time-varying demographics with open population spatial capture-recapture: a photo-ID case study on bottlenose dolphins in Barataria Bay, Louisana, USA

1. From long-term, spatial capture-recapture (SCR) surveys we infer a population's dynamics over time and distribution over space. It is becoming more computationally feasible to fit these open population SCR (openSCR) models to large datasets and include complex model components, e.g., spatially-varying density surfaces and time-varying population dynamics. Yet, there is limited knowledge on how these methods perform. 2. As a case study, we analyze a multi-year, photo-ID survey on bottlenose dolphins (Tursiops truncatus) in Barataria Bay, Louisana, USA. This population has been monitored due to the impacts of the nearby Deepwater Horizon oil spill in 2010. Over 2000 capture histories have been collected between 2010 and 2019. Our aim is to identify the challenges in applying openSCR methods to real data and to describe a workflow for other analysts using these methods. 3. We show that inference on survival, recruitment, and density over time since the oil spill provides insight into increased mortality after the spill, possible redistribution of the population thereafter, and continued population decline. Issues in the application are highlighted throughout: possible model misspecification, sensitivity of parameters to model selection, and difficulty in interpreting results due to model assumptions and irregular surveying in time and space. For each issue, we present practical solutions including assessing goodness-of-fit, model-averaging, and clarifying the difference between quantitative results and their qualitative interpretation. 4. Overall, this case study serves as a practical template other analysts can follow and extend; it also highlights the need for further research on the applicability of these methods as we demand richer inference from them.