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Since 1990, walleyes Sander vitreus in Lake Erie have been tagged annually with jaw tags to better understand the population dynamics and ecological characteristics of individual spawning populations. Although the data collected from this tagging program have been used for a variety of management purposes (e.g., estimating migration patterns, stock intermixing, and mortality rates), there has been only cursory examination of the shedding and reporting rates associated with the program. We used double tagging and high-reward tagging experiments to estimate tag shedding and reporting rates for jaw-tagged walleyes in Lake Erie. Double tagging of walleyes with jaw and passive integrated transponder (PIT) tags suggested that the tagging method and tagging agency contributed to the observed variability in both immediate (within 21 d of tagging) retention and chronic jaw tag shedding rates. Agency-specific model-averaged estimates of immediate tag retention ranged from 95% to 99%. For chronic shedding, model-averaged instantaneous rates (annual) ranged from 0.07 to 0.28. Jaw tag reporting rates, estimated via releases of high-reward tags in 1990 and 2000, varied among tagging years, tagging basins, and commercial and recreational fisheries. In general, tag reporting rates were higher for the recreational fishery (range, 33–55%) than for the commercial fishery (10–17%), and the reporting rates for both fisheries and tagging basins were found to have declined between 1990 and 2000. Uncertainty in the tag reporting rates was greater for the recreational fishery than for the commercial fishery. Our findings will benefit management of the economically important Lake Erie walleye fisheries by providing managers with robust tag shedding and reporting rate estimates, providing more reliable estimates of important dynamic rates (e.g., fishing and natural mortality) by correcting the long-term jaw tagging data set for these previously unaccounted for biases. Received September 13, 2011; accepted November 30, 2011 [ABSTRACT FROM AUTHOR]

Maturation 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. [ABSTRACT FROM AUTHOR]