Hierarchical Multi-Dimensional Maturation Modeling to Isolate the Effects of Commercial Closure on a Great Lakes Fishery

Abstract

As anthropogenic disturbances rapidly change natural environments, species must respond to new selective pressures shaping rates of reproduction, growth, and mortality. One example is intense fisheries harvest, which can drive the evolution of heavily fished populations toward maturation at smaller sizes and younger ages. Changes in maturation have often been measured using probabilistic maturation reaction norms (PMRNs), which were originally designed to control for phenotypic plasticity while allowing for the detection of the evolution of maturation. However, multiple studies have highlighted issues with PMRN estimation, particularly with respect to their accuracy when parameterized with sparse data or when applied to populations experiencing myriad environmental stressors. We used a three-decade time series of Laurentian Great Lakes yellow perch (Perca flavescens Mitchill) data to develop a novel, hierarchical Bayesian PMRN estimation method that can explicitly account for these conceptual issues. Our results indicate that commercial fishing was a primary driver of maturation change in this population, and that the relaxation of harvest pressure via the closure of the commercial fishery in the late 1990s resulted in adaptation toward older ages and larger sizes at maturation within 2–3 generations. Future pairing of hierarchical Bayesian PMRN methods with genome-wide data will help reveal the genetic underpinnings of maturation, and could lead to new avenues for integrating PMRNs into fisheries management and policy.