Illinois Natural History Survey - University of Illinois

Development of an Individual-based Model to Evaluate Growth and Survival of Walleye

Walleye (Stizostedion vitreum) are a popular and economically important sport fish, and populations throughout Illinois are maintained through stocking (Figure 1). Recently, a seven-year research project was completed that examined the factors influencing growth and survival of introduced walleye at 14 Illinois reservoirs. These walleye were followed to determine how factors such as size at stocking, food availability, predator density, and water temperature affect growth and survival. It is especially important to understand the relationships between these factors during the first year, because walleye growth during this period is a good indicator of later survival.

Figure 1. Walleye that had been introduced at different sizes in a local Illinois reservoir.

To help in these efforts, we are integrating information from field data and laboratory experiments about walleye foraging, growth, and mortality into an individual-based model (IBM). Most ecological models group individual organisms into different categories (e.g., size, age, populations), consequently ignoring important individual interactions. An advantage of using an IBM is that it is capable of following individuals and identifying consequences of size-specific processes. For example, larval and juvenile walleyes are "gape limited," meaning that the size of food they can eat is limited by the size of their mouth. Likewise, predators of walleye may also be gape limited. Both foraging and predation may depend on small differences in walleye size. The model will be comprised of three different submodels representing foraging, growth, and mortality (Fig. 2).

Figure 2. Schematic of the submodels used in the walleye individual-based model.

The foraging submodel will determine the amount and type of food the walleye eats. Walleye undergo diet shifts as they grow that include zooplankton, benthic macroinverte-brates, and forage fish as prey. Factors such as walleye size and prey type, size, and densities driving these switches are not understood for young-of-year walleye. Currently, we are completing a series of laboratory experiments to determine how important these factors are in the walleye diets. We also have a field database of zooplankton, benthic macro- invertebrates, and forage fish information. A series of functions based on the laboratory and field data will determine foraging activity by the walleye in the model. Diet information will then be transferred to a growth submodel. A standard bioenergetics equation will be used to determine growth as the difference between energy consumed minus energy lost to metabolism and waste products. Finally, whether the fish survives will be determined in the mortality submodel. Death may be the result of thermal stress, predation, or starvation. Thermal stress occurs during the first 48 hours after stocking and is a function of walleye size and temperatures of the hatchery and reservoir. Alternatively, the walleye could die from predation or cannibalism. Ongoing population estimates and diet analyses of predators like largemouth bass (Micropterus salmoides) will allow development of mortality relationships as a function of predator density and size structure. Cannibalism rates will also be determined from field collections of walleye. Finally, walleye may die of starvation determined by each fish's feeding history and condition in the foraging and growth submodels.

By including factors such as size at stocking, water temperature, thermal stress, predation pressure, and food availability, we will be better able to predict growth and survival of stocked walleye. By integrating laboratory and field data, we are building a database about walleye and identifying knowledge gaps. This IBM will eventually be applied to other systems with different temperatures, walleye sizes, predation pressure, and prey resources from within and outside Illinois. The model may ultimately also be modified to include effects of natural reproduction, different stocking practices, and habitat availability. With this model, fishery managers will be better able to predict future walleye population structures with important ecological and economic implications.

Tracy Galarowicz, David H. Wahl, and Bob Herendeen, Center for Aquatic Ecology

Illinois Natural History Survey

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Champaign, IL 61820

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