Summary1. The formulation of conservation policy relies heavily on demographic, biological and ecological knowledge that is often elusive for threatened species. Essential estimates of abundance, survival and life-history parameters are accessible through mark and recapture studies given a sufficiently large sample. Photographic identification of individuals is an established mark and recapture technique, but its full potential has rarely been exploited because of the unmanageable task of making visual identifications in large data sets. 2. We describe a novel technique for identifying individual whale sharks Rhincodon typus through numerical pattern matching of their natural surface 'spot' colourations. Together with scarring and other markers, spot patterns captured in photographs of whale shark flanks have been used, in the past, to make identifications by eye. We have automated this process by adapting a computer algorithm originally developed in astronomy for the comparison of star patterns in images of the night sky. 3. In tests using a set of previously identified shark images, our method correctly matched pairs exhibiting the same pattern in more than 90% of cases. From a larger library of previously unidentified images, it has to date produced more than 100 new matches. Our technique is robust in that the incidence of false positives is low, while failure to match images of the same shark is predominantly attributable to foreshortening in photographs obtained at oblique angles of more than 30 ° . 4. We describe our implementation of the pattern-matching algorithm, estimates of its efficacy, its incorporation into the new ECOCEAN Whale Shark Photo-identification Library, and prospects for its further refinement. We also comment on the biological and conservation implications of the capability of identifying individual sharks across wide geographical and temporal spans. 5. Synthesis and applications. An automated photo-identification technique has been developed that allows for efficient 'virtual tagging' of spotted animals. The pattern-matching software has been implemented within a Web-based library created for the management of generic encounter photographs and derived data. The combined capabilities have demonstrated the reliability of whale shark spot patterns for long-term identifications, and promise new ecological insights. Extension of the technique to other species is anticipated, with attendant benefits to management and conservation through improved understanding of life histories, population trends and migration routes, as well as ecological factors such as exploitation impact and the effectiveness of wildlife reserves.

Capture-mark-recapture (CMR) data from Ningaloo Marine Park (NMP) in Western Australia have recently been used to study the population dynamics of the local whale shark aggregation. Because nascent research efforts at other aggregation points look to NMP as a model, further analysis of existing modeling approaches is important. We have expanded upon previous studies of NMP whale sharks by estimating CMR survival and recruitment rates as functions of average total length (TL). Our analysis suggests a decline in reported values of TL coincident with marginally increasing abundance among sharks sighted in more than one year ('returning') from 1995 to 2008. We found a positive, average returning recruitment rate (λ) of 1.07 yr -1 (0.99 to 1.15, 95% CI); smaller individuals contributed in larger numbers to recruitment, allowing for population growth accompanied by a decline in median size. We subsequently explored intraseasonal population dynamics with the Open Robust Design (ORD) model structure. Our best-fit model estimated modestly increasing annual abundances between 107 (95% CI = 90 to 124) and 159 (95% CI = 127 to 190) for 2004 to 2007, suggesting a short-term increase in total annual abundance. The ORD also estimated an average residency time of 33 d (95% CI = 31 to 39) and biweekly entry profiles into the study area. Overall, our techniques demonstrate how large aggregations of the species can be modeled to better understand short-and long-term population trends. These results also show the direct scientific benefit from the development of an online, collaborative data management system to increase collection of sighting data for a rare species in conjunction with ecotourism activity.

The formulation of conservation policy for species that are rare and migratory requires broad cooperation to ensure that adequate levels of standardized data collection are achieved and that the results of local analyses are comparable. Estimates of apparent survival rate, relative change in abundance, and proportions of newly marked and returning individuals can inform local management decisions while highlighting corresponding changes at other linked research stations. We have applied computer-assisted photo-identification and mark-recapture population modeling to whale sharks Rhincodon typus at Ningaloo Marine Park (NMP), Western Australia, to create a baseline trend for comparison with other regional aggregations of the species. We estimate several ecological parameters of interest, including an average apparent survival rate of 0.55 yr(-1) for sharks newly marked (new) and 0.83 yr(-1) for sharks captured in multiple seasons (philopatric). The average proportion of philopatric sharks is found to be 0.65 of the total population, and we derive an average population growth rate of 1.12 yr(-1) for them. Our analysis uncovered significant heterogeneity in capture and survival probabilities in this study population; our chosen model structures and data analysis account for these influences and demonstrate a good overall fit to the time-series data. The results show good correspondence between capture probability and an available measure of recapture effort, suggesting that unmodeled systematic effects contribute insignificantly to the model fits. We find no evidence of a decline in the whale shark population at NMP, and our results provide metrics of value to their future management. Overall, our study suggests an effective approach to analyzing and modeling mark-recapture data for a rare species using computer-assisted photo-identification and opportunistic data collection from ecotourism to ensure the quality and volume of data required for population analysis.

The whale shark is an ideal flagship species for 'citizen science' projects because of its charismatic nature, regular presence at numerous coastal aggregation sites and a growing number of ecotourism ventures focusing on the species. An online database of Whale Shark encounters, identifying individuals based on their unique skin patterning from 1992 to 2014 captured almost 30,000 whale shark encounter reports, comprising more than 6000 individuals identified from 54 countries. In this time the number of known whale shark aggregation sites increased from 13 to 20. Examination of encounters revealed a skewed sexratio bias towards males (overall >66%), high site fidelity amongst individuals with limited movements of sharks between neighbouring countries/regions but no records confirming 2 large, ocean basin-scale migrations. Citizen science has been vital in amassing large spatial and temporal datasets to elucidate key aspects of whale shark life-history and demographics and will continue to provide substantial long-term value.

There were 479 reported whale shark Rhincodon typus encounters between 1999 and 2011 at the island of Utila, which forms part of the Meso-American Barrier Reef System (MBRS) in the western Caribbean Sea. The majority of R. typus were found to feed on small bait fish associated with various tuna species. Ninety-five individual R. typus, ranging from 2 to 11 m total length (LT ), were identified through their unique spot patterns. A significant male bias (65%) was present. There was no significant difference between the mean ± s.d. LT of female (6·66 ± 1·65 m) and male (6·25 ± 1·60 m) R. typus. Most R. typus were transient to Utila, with 78% sighted only within a single calendar year, although some individuals were sighted in up to 5 years. Mean residency time was modelled to be 11·76 days using maximum likelihood methods.