Global synthesis of sea turtle von Bertalanffy growth parameters through Bayesian hierarchical modeling

Md Ramirez,T Popovska,Ea Babcock

doi:10.3354/meps13544

Abstract

Knowledge of sea turtle demographic rates is central to modeling their population dynamics, but few studies have quantitatively synthesized existing data globally. Here, we used a Bayesian hierarchical model to conduct a meta-analysis of published von Bertalanffy growth curve parameters (growth coefficient, K; asymptotic length, L∞) for chelonid sea turtles. We identified 34 studies for 5 of 6 extant chelonids that met minimum selection criteria. We implemented a suite of models that included a multivariate normal likelihood on the log-transformed values of the 2 parameters to evaluate the influence of species, population (regional management unit, RMU), parameter estimation method (mark-recapture, skeletochronology, length-frequency analysis), latitude, and sampled body size range (all sizes, no large, no small, no large or small) on growth parameter estimates. According to information criteria, the best model included a random effect of species. The second best model also included latitude as a fixed effect, but RMU, parameter estimation method, latitude, and sampled body size ultimately did not strongly influence the means or variances of K and L∞ among studies. The apparent lack of RMU effect on parameter estimates within species may be an artifact of the small number of RMUs with published growth parameter estimates. The species-specific, and in some cases RMU-specific, posterior means and standard deviations of K and L∞ from this study would be appropriate priors for future studies of growth in chelonid sea turtles or for models of population dynamics. We highlight the need for expanded study and synthesis of sea turtle somatic growth rates.

Highlights

A central feature of protected species conservation and management is the use of demographic models to evaluate population status and trends and inform management decisions
Sea turtles are characterized by rapid growth during the first few years of life followed by monotonically decreasing growth rates until maturity, at which point energy is redirected to reproduction and growth becomes negligible (Omeyer et al 2017, 2018)
We modeled the parameters of the von Bertalanffy growth curve, which is the most commonly used growth function for sea turtles, by integrating data from these studies with a series of Bayesian hierarchical models to characterize means and variances of these parameters for each species and population

Summary

Introduction

A central feature of protected species conservation and management is the use of demographic models to evaluate population status and trends and inform management decisions. Mar Ecol Prog Ser 657: 191–207, 2021 age at maturation, remain high-priority research areas in sea turtle population ecology (Hamann et al 2010, National Research Council 2010, Rees et al 2016). Such information is fundamental to accurately understanding and predicting the dynamics of sea turtle populations in addition to prescribing management targets, quantifying sustainable biological removal levels, and evaluating likely responses to management actions or environmental change scenarios (Crouse et al 1987, Crowder et al 1994, Casale & Heppell 2016, Piacenza et al 2019). Given the variable recovery trajectories of sea turtle populations globally and poor understanding of underlying drivers (Wallace et al 2011, Mazaris et al 2017, Valdivia et al 2019), there is a critical need to synthesize existing data to identify data gaps, aid parameterization of population models, illuminate mechanisms underpinning trends, and set research priorities

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