Effects of Including Misidentified Sharks in Life History Analyses: A Case Study on the Grey Reef Shark Carcharhinus amblyrhynchos from Papua New Guinea

Abstract

Fisheries observer programs are used around the world to collect crucial information and samples that inform fisheries management. However, observer error may misidentify similar-looking shark species. This raises questions about the level of error that species misidentifications could introduce to estimates of species’ life history parameters. This study addressed these questions using the Grey Reef Shark Carcharhinus amblyrhynchos as a case study. Observer misidentification rates were quantified by validating species identifications using diagnostic photographs taken on board supplemented with DNA barcoding. Length-at-age and maturity ogive analyses were then estimated and compared with and without the misidentified individuals. Vertebrae were retained from a total of 155 sharks identified by observers as C. amblyrhynchos. However, 22 (14%) of these were sharks were misidentified by the observers and were subsequently re-identified based on photographs and/or DNA barcoding. Of the 22 individuals misidentified as C. amblyrhynchos, 16 (73%) were detected using photographs and a further 6 via genetic validation. If misidentified individuals had been included, substantial error would have been introduced to both the length-at-age and the maturity estimates. Thus validating the species identification, increased the accuracy of estimated life history parameters for C. amblyrhynchos. From the corrected sample a multi-model inference approach was used to estimate growth for C. amblyrhynchos using three candidate models. The model averaged length-at-age parameters for C. amblyrhynchos with the sexes combined were = 159 cm TL and = 72 cm TL. Females mature at a greater length (l50 = 136 cm TL) and older age (A50 = 9.1 years) than males (l50 = 123 cm TL; A50 = 5.9 years). The inclusion of techniques to reduce misidentification in observer programs will improve the results of life history studies and ultimately improve management through the use of more accurate data for assessments.

Evidence for rapid recovery of shark populations within a coral reef marine protected area

“…One reason could be underestimation of the model parameters applied by Smith et al (1998) (age at maturity 7 years, maximum age 18 years, fecundity 2.5 pups yr -1 ) to generate an increase of 5.4% year -1 . Recent research has revised these variables to larger ranges (age at maturity of 9 -11 years; (Robbins 2006;Smart et al 2016), maximum age of 12 -19 years; (Radtke and Cailliet 1984;Robbins 2006;Smart et al 2016), fecundity of 1 -6 pups year -1 ; (Robbins 2006;Stevens and McLoughlin 1991;Wetherbee et al 1997) and C. amblyrhynchos in northern Australia has been found to have on average three pups year -1 (Stevens and McLoughlin 1991) and 3-4 pups year -1 on the Great Barrier Reef (Robbins 2006). The second model applied by Smith et al (1998) increased fecundities by 25%, a value more consistent with observed patterns, however population growth of 7.8% year -1 still remained well below the values required to obtain the rate of recovery we observed.…”

Refining mortality estimates in shark demographic analyses: a Bayesian inverse matrix approach

“…When the same Leslie matrix models were applied to a different population of C. amblyrhynchos using the results of Smart et al. () in the present study, the inherent error within these models was far more apparent. Using life history estimates from the PNG population, the present study predicted negative population growth even if the population was unfished.…”

“…Indirect mortality estimates were more erroneous for the PNG population based on the results of Smart et al. () and were severely overestimated when compared to estimates from the Bayesian analysis. This was corrected by updating the demographic analyses for the PNG population with the Bayesian Z posteriors for the GBR population.…”

“…Age‐specific maturity proportions for the PNG population were estimated from the coefficients of a logistic ogive (Smart et al. ) using the equation: where P a was the mature proportion of age a . Age at maturity ( a mat ) of the PNG population was the inflection point of this regression while a mat of the GBR population was sourced from Robbins ().…”

“…Recently, life history estimates for the PNG population became available (Smart et al. ). However, accurate estimates of M are still lacking for this population, which ultimately prevents accurate population analyses and effective fisheries management from being undertaken.…”