Evaluating population recovery for sea turtles under nesting beach protection while accounting for nesting behaviours and changes in availability.
Sea turtles and sea birds generally have high conservation importance world-wide and are often difficult to survey except when present on nesting grounds. Consequently, many such surveys tag nesting individuals and use tag-resighting models to estimate population size and assess anthropogenic impacts. However, the conventional Cormac-Jolly-Seber (CJS) tag-resighting model is problematic for these species for three reasons: individuals often return to nesting areas in alternating years because of high energetic costs for nesting, estimated detectability confounds changes in survey efficiency with availability on the surveyed beach, and tag loss is confounded with mortality. We develop a robust design model that uses higher-order Markovian transitions to approximate skip-nesting behaviours and incorporates multiple observations for each nesting individual to estimate changes in availability (the probability of returning to the surveyed area rather than alternative nesting areas). We approximate time-varying effects using a flexible spline method and demonstrate the model using data for leatherback sea turtles Dermochelys coriacea and loggerhead sea turtles Caretta caretta in South Africa. The apparent lack of recovery for leatherback sea turtles after implementing beach protection, as observed in nest count data, is likely to be due to declining detectability caused by decreased availability during population recovery (e.g. habitat expansion). By contrast, loggerhead sea turtles have approximately constant detectability and stable abundance since the 1970s. We find that increased fishing effort has no explanatory power regarding changes in survival for either species. Synthesis and applications. Based on study results, we recommend that future tag-resighting programmes for sea turtles and birds are accompanied periodically by count surveys beyond the regularly monitored nesting areas to evaluate evidence of range expansion. However, the identification of range expansion in historical data is only possible using model-based inference and robust design methods such as presented in this study.