Exploring the Influence of Stock–Recruitment Relationships and Environmental Variables on Black Bass and Crappie Recruitment Dynamics in Missouri Reservoirs

Abstract

Large reservoirs provide important sport fisheries, but managing these fisheries is difficult because of the multiple biological and environmental variables that interact to shape them. Understanding how sport fish recruitment responds to parental stock and environmental influences would improve our management capabilities. We compiled long-term datasets for 15 Missouri reservoirs and used them to examine the influence of parental stock abundance and a suite of environmental variables on the recruitment of Largemouth Bass Micropterus salmoides, Spotted Bass M. punctulatus, White Crappie Pomoxis annularis, and Black Crappie P. nigromaculatus. Comparisons of log-linear, Ricker, and Beverton–Holt stock–recruit models revealed that the log-linear model was the most parsimonious for Largemouth Bass, White Crappie, and Black Crappie, but the Ricker model was best for Spotted Bass. However, stock–recruit models alone explained less than 13% of the variation in Largemouth Bass, Spotted Bass, and White Crappie recruitment while explaining about 33% of the variability in recruitment of Black Crappie. Largemouth Bass recruitment was positively related to spring rises in water levels, summer water levels, and average summer air temperatures, whereas Spotted Bass recruitment was positively related to spring air temperatures. White Crappie recruitment was positively related to total phosphorus levels and drops in spring water levels. Black Crappie recruitment was best explained by the model positively relating recruitment to spawning stock abundance, spring water levels, and summer water levels, while negatively relating recruitment to rapid drops in spring water levels. With the exception of models for Black Crappie, the best models did not include spawning stock abundance, and environmental variables appeared more important in explaining variation in recruitment. However, environmental variables in these models explained only a modest amount of variation (27–44%) in recruitment, revealing the complexity of recruitment processes in large reservoirs.

Received February 21, 2012; accepted August 9, 2012

ACKNOWLEDGMENTS

We thank M. Colvin for initiating this project and P. Collara and R. Wright for their efforts in collecting and managing the reservoir data set. We thank the many current and past biologists that assisted us in obtaining and understanding the data for their reservoirs and J. Jones, University of Missouri, for supplying water quality data. M. Roell provided helpful comments on the manuscript and S. Gao provided statistical support. We thank staff from the U.S. Army Corps of Engineers, AmerenUE Company, and Associated Electric Cooperative for providing water level data.