Ecologists combine an understanding of current conditions with models to predict the effects of future environmental conditions on populations and communities. Many of the models used are incomplete, hindering these important predictions. This project improves predictions of where species will occur as environments change by including new data on the similarity, or degree of dependency, among observations that are closely associated in space. It will significantly advance fundamental research in ecology. Simultaneously, the research will result in a clearer general understanding of how species will respond to increasing habitat degradation, invasive species, disease, and climate change, thereby contributing directly to biodiversity conservation.
The goal of this research is to investigate the relationship between stream flow variability, spatial relatedness, and fish species occurrence across sites within the Big River watershed in East Central Missouri while developing statistical techniques to account for the effects of spatial autocorrelation on species distribution predictions. Spatial autocorrelation is the positive association between the proximity of sample locations and the similarity of data at each location. It reduces the accuracy of current species distribution models. Stream fish assemblages at 50 sites across the watershed will be sampled and predicted based on spatial relatedness to other sampling sites and flow variability data estimated from high resolution in-stream depth gauges. The Big River watershed is a primary focus of collaborative conservation efforts by the Missouri Department of Conservation and The Nature Conservancy in Missouri. The data and results generated from this study will be of practical use by these agencies during their attempts to balance human activities and the conservation of biodiversity in this unique aquatic ecosystem. The project will significantly enhance ongoing dissertation research by providing field tests of model predictions, broadening and strengthening graduate student training.
