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Predicting Fish Densities in Lotic Systems: a Simple Modeling Approach
Citation:
MCGARVEY, D. J., J. M. JOHNSTON, AND M. C. BARBER. Predicting Fish Densities in Lotic Systems: a Simple Modeling Approach. JOURNAL OF THE NORTH AMERICAN BENTHOLOGICAL SOCIETY. North American Benthological Society, Lawrence, KS, 29(4):1212-1227, (2010).
Impact/Purpose:
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Description:
Fish density models are essential tools for fish ecologists and fisheries managers. However, applying these models can be difficult because of high levels of model complexity and the large number of parameters that must be estimated. We designed a simple fish density model and tested whether it could predict fish densities in lotic systems with meaningful levels of accuracy and precision. We built our 6-parameter model on 2 key assumptions: 1) fish population density is a power function of mean body mass (i.e., the self-thinning relationship), and 2) energetic resources are transferred from lower to higher trophic levels at a nearly constant rate (i.e., trophic transfer efficiency). We estimated the self-thinning and trophic transfer efficiency parameters by randomly sampling from values reported in the primary literature. Remaining parameters were net primary production, trophic level, the productionbiomass ratio, and mean body mass. We used empirical parameter estimates and fish density estimates to test the model in 4 warm-water and 4 cold-water systems. Model accuracy was high in 3 test systems (deviations between the model-predicted densities and empirically observed densities <30%), moderate in 3 test systems (deviations 75–111%), and low in 2 systems (deviations >150%). Model precision was low (e.g., the interquartile ranges of model-predicted densities encompassed 1 order of magnitude), but appropriate for predicting fish densities at coarse spatial and temporal scales. We concluded that the model is a potentially useful and efficient tool, and we provide recommendations for applying the model. In particular, we emphasize that the model is scalable, and therefore, well-suited for estimating fish densities at large spatial scales. We also point out that the model is a carrying capacity model, and therefore, can be used to predict fish densities in undisturbed systems or to approximate reference conditions.
