Maturation Schedules of Walleye Populations in the Great Lakes Region: Comparison of Maturation Indices and Evaluation of Sampling‐Induced Biases

Abstract

AbstractMaturation schedules, key determinants of fish stocks' harvest potential and population dynamics, are influenced by both plastic and adaptive processes. Various indices are used to describe maturation schedules, and these have differential advantages for discriminating between plastic and adaptive processes. However, potential sampling‐related biases associated with different maturation indices have not been fully evaluated. We analyzed three maturation indices for walleyes Sander vitreus in Lake Erie; Saginaw Bay, Lake Huron; and Oneida Lake, New York: age and length at 50% maturity, midpoint of age‐specific maturity ogives (age‐specific length at which probability of maturity = 0.50), and midpoints of probabilistic maturation reaction norms (PMRNs; age‐specific length at which probability of maturing in the following year = 0.50). We then compared estimated maturation indices to evaluate sensitivity of different maturation indices to sampling‐induced biases and to assess the relative importance of plastic versus adaptive processes in structuring interstock and temporal variation in maturation schedules. Our findings suggest that although small changes in sampling month, gear, and agency‐related effects can bias estimates of age and length at 50% maturity and midpoints of maturity ogives, PMRN estimates appear to be robust to these biases. Furthermore, PMRN estimates are suggestive of potential adaptive variation in maturation schedules among walleye stocks and over time. For instance, Oneida Lake walleyes (which had relatively slow growth and low mortality rates) matured at a smaller size for a given age (smaller midpoints of PMRNs) than the other stocks. Temporally, walleyes in the western basin of Lake Erie matured at a larger size in recent years, as evidenced by increasing midpoints of PMRNs (1978–1989 versus 1990–2006 for Ohio Department of Natural Resources data and 1990–1996 versus 1997–2006 for Ontario Ministry of Natural Resources data). Our study highlights the necessity of monitoring maturation schedules via multiple maturation indices and the need to account for sampling‐induced biases when comparing maturation schedules.