Inferring population trends for the world's largest fish from mark–recapture estimates of survival

Abstract

1. Precise estimates of demographic rates are key components of population models used to predict the effects of stochastic environmental processes, harvest scenarios and extinction probability. 2. We used a 12-year photographic identification library of whale sharks from Ningaloo Reef, Western Australia to construct Cormack-Jolly-Seber (CJS) model estimates of survival within a capture-mark-recapture (CMR) framework. Estimated survival rates, population structure and assumptions regarding age at maturity, longevity and reproduction frequency were combined in a series of age-classified Leslie matrices to infer the potential trajectory of the population. 3. Using data from 111 individuals, there was evidence for time variation in apparent survival (phi) and recapture probability (p). The null model gave a phi of 0.825 (95% CI: 0.727-0.893) and p = 0.184 (95% CI: 0.121-0.271). The model-averaged annual phi ranged from 0.737 to 0.890. There was little evidence for a sex effect on survival. 4. Using standardized total length as a covariate in the CMR models indicated a size bias in phi. Ignoring the effects of time, a 5-m shark has a phi = 0.59 and a 9 m shark has phi = 0.81. 5. Of the 16 model combinations considered, 10 (63%) indicated a decreasing population (lambda < 1). For models based on age at first reproduction (alpha) of 13 years, the mean age of reproducing females at the stable age distribution (A) ranged from 15 to 23 years, which increased to 29-37 years when alpha was assumed to be 25. 6. All model scenarios had higher total elasticities for non-reproductive female survival [E(s(nr))] compared to those for reproductive female survival [E(s(r))]. 7. Assuming relatively slow, but biologically realistic, vital rates (alpha = 25 and biennial reproduction) and size-biased survival probabilities, our results suggest that the Ningaloo Reef population of whale sharks is declining, although more reproductive data are clearly needed to confirm this conclusion. Combining relatively precise survival estimates from CMR studies with realistic assumptions of other vital rates provides a useful heuristic framework for determining the vulnerability of large oceanic predators for which few direct data exist.