TY - JOUR
T1 - Biphasic growth modelling in elasmobranchs based on asymmetric and heavy-tailed errors
AU - Contreras-Reyes, J.E.
AU - Wiff, R.
AU - Soto, J.
AU - Donovan, C.R.
AU - Araya, M.
N1 - Funding: J.E. Contreras-Reyes’ research was fully supported by FONDECYT (Chile) grant No. 11190116. R. Wiff was funded by CAPES Project Conicyt FB 0002 (2014) and by ANID - Programa Iniciativa Científica Milenio - Código ICN2019_015.
PY - 2021/5/29
Y1 - 2021/5/29
N2 - Growth in fishes is usually modelled by a function encapsulating a common growth mechanism across ages. However, several theoretical works suggest growth may comprise two distinct mechanistic phases arising from changes in reproductive investment, diet, or habitat. These models are termed two-state or biphasic, where acceleration in growth typically changes around some transition age. Such biphasic models have already been successfully applied in elasmobranch species, where such transitions are detectable from length-at-age data alone, but where estimation has assumed normally distributed errors, which is inappropriate for such slow-growing and long-lived fishes. Using recent advances in growth parameter estimation, we implement a biphasic growth model with asymmetric and heavy-tailed errors. We use data from six datasets, encompassing four species of elasmobranchs, to compare the performance of the von Bertalanffy and biphasic models under normal, skew-normal, and Student-t error distributions. Conditional expectation maximization estimation proves both effective and efficient in this context. Most datasets analysed here supported asymmetric and heavy-tailed errors and biphasic growth, producing parameter estimates different from previous studies.
AB - Growth in fishes is usually modelled by a function encapsulating a common growth mechanism across ages. However, several theoretical works suggest growth may comprise two distinct mechanistic phases arising from changes in reproductive investment, diet, or habitat. These models are termed two-state or biphasic, where acceleration in growth typically changes around some transition age. Such biphasic models have already been successfully applied in elasmobranch species, where such transitions are detectable from length-at-age data alone, but where estimation has assumed normally distributed errors, which is inappropriate for such slow-growing and long-lived fishes. Using recent advances in growth parameter estimation, we implement a biphasic growth model with asymmetric and heavy-tailed errors. We use data from six datasets, encompassing four species of elasmobranchs, to compare the performance of the von Bertalanffy and biphasic models under normal, skew-normal, and Student-t error distributions. Conditional expectation maximization estimation proves both effective and efficient in this context. Most datasets analysed here supported asymmetric and heavy-tailed errors and biphasic growth, producing parameter estimates different from previous studies.
KW - ECME algorithm
KW - Elasmobranchs
KW - Sharks
KW - Skates
KW - Skew-t distribution
KW - Two-stage growth
KW - von Bertalanffy model
U2 - 10.1007/s10641-021-01100-z
DO - 10.1007/s10641-021-01100-z
M3 - Article
SN - 0378-1909
VL - 104
SP - 615
EP - 628
JO - Environmental Biology of Fishes
JF - Environmental Biology of Fishes
IS - 5
ER -