Sea turtles generally have high conservation importance worldwide, 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 nesting-age female abundance, which would otherwise be difficult for such populations. However, the Cormac-Jolly-Seber tag-resighting model is problematic for nesting beach sea turtle studies for three reasons: individuals often return to nesting areas in alternating years due to 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 therefore develop a robust design model that uses higher-order Markovian transitions to approximate skip-nesting behaviors and incorporates multiple observations for each nesting individual to estimate changes in availability, i.e., 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 45 years of nesting beach data for leatherback and loggerhead sea turtles in Kwa-Zulu Natal, South Africa while comparing results with nesting count data from the same study sight.
Results/Conclusions
An apparent lack of recovery for leatherback sea turtles after implementing beach protection, as observed in nest count data, is likely due to declining detectability caused by decreased availability during population recovery (e.g. habitat expansion). By contrast, loggerhead turtles have approximately constant detectability and stable abundance since the 1970s. Both species have approximately stable detection probabilities for each nesting return, as would be expected given generally consistent sampling effort and design over time.
Based on study results, we recommend that future tag-resighting programs for sea turtles 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 multi-state robust design models.