  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.
