Abstract
Age and growth information is essential for stock assessment of fish, and growth model selection may influence the accuracy of stock assessment and subsequent fishery management decision making. Previous descriptions of the age and growth of elasmobranchs relied mainly on the von Bertalanffy growth model (VBGM). However, it has been noted that sharks, skates and rays exhibit significant variety in size, shape, and life history traits. Given this variation, the VBGM may not necessarily provide the best fit for all elasmobranchs. This study attempts to improve the growth estimates by using multi-model approach to test four growth models—the VBGM, the two-parameter VBGM, the Robertson (Logistic) and the Gompertz models to fit observed or simulated length-at-age data for 38 species (44 cases) of elasmobranchs. The best-fit growth model was selected based on the bias corrected Akaike’s Information Criterion (AICc), the AICc difference, the AICc weight, the Bayesian Information Criterion (BIC), and the Leave-one-out cross-validation (LOOCV). The VBGM and two-parameter VBGM provide the best fit for species with slow growth and extended longevity (L∞ > 100 cm TL, 0.02 < k < 0.25 yr–1), such as pelagic sharks. For fast-growing small sharks (L∞ < 100 cm TL, kr or kg > 0.2 yr–1) in deep waters and for small-sized demersal skates/rays, the Robertson and the Gompertz models provide the best fit. The best-fit growth models for small sharks in shallow waters are the two-parameter VBGM and the Robertson model. Although it was found that the best-fit growth models for elasmobranchs were associated with their life history trait, exceptions were also noted. Therefore, a multi-model approach incorporating with the best-fit model selected for each group in this study was recommended in growth estimation for elasmobranchs.
Highlights
Elasmobranch life histories and assessments are different than those of teleosts for a number of reasons
Some deep-water species are difficult to age such as spiny dogfish (Squalus spp.) (Campana et al, 2006), and the periodicity of band pair deposition may vary in different life stages such as shortfin mako shark Isurus oxyrinchus (Kinney et al, 2016) which may lead to the uncertainty in age estimation
Two species were from Hemiscylliidae and Rhincodontidae (Orectolobiformes), 3 species were from Odontaspididae, Lamnidae, and Alopiidae (Lamniformes), 19 species were from Triakidae, Carcharhinidae, and Sphyrnidae (Carcharhiniformes), 2 species were from Etmopteridae (Squaliformes), 1species was from Rhinobatidae, 10 species were from Rajiformes (Rajidae), and 1 species was from Dasyatidae (Myliobatiformes)
Summary
Elasmobranch life histories and assessments are different than those of teleosts for a number of reasons. Age and growth study of elasmobranchs is more challenging compared with that of teleost species for several reasons. Ageing based on hard parts of elasmobranchs is typically underestimated in larger and older individuals based on bomb radiocarbon analysis (Passerotti et al, 2010; Harry, 2018; Natanson et al, 2018). These studies showed that vertebrae may not be useful for age estimation in some species once asymptotic length is reached. The underestimated age may lead to an underestimation of longevity and biased estimation of mortality which may affect the result of stock assessment and management measures
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