Fish–Habitat Relationships in Lakes: Gaining Predictive and Explanatory Insight by Using Artificial Neural Networks

—Understanding and predicting the impacts of habitat modification and loss on fish populations are among the main challenges confronting fisheries biologists in the new millennium. Statistical models play an important role in this regard, providing a means to quantify how environmental conditions shape contemporary patterns in fish populations and communities and formulating this knowledge in a framework where future patterns can be predicted. Developing fish–habitat models by traditional statistical approaches is problematic because species often exhibit complex, nonlinear responses to environmental conditions and biotic interactions. We demonstrate the value of a robust statistical technique, artificial neural networks, relative to more traditional regression techniques for modeling such complexities in fish–habitat relationships. Using artificial neural networks, we provide both explanatory and predictive insight into the whole-lake and withinlake habitat factors shaping species occurrence and abundance in lakes from southcentral Ontario, Canada. The results show that species presence or absence is highly predictable based on wholelake measures of habitat, and that these fish–habitat models show good generality in predicting occurrence in other lakes from an adjacent drainage. Detailed evaluation of these models shows that partitioning the predictive performance of the models into measures such as sensitivity (ability to predict species presence) and specificity (ability to predict species absence) allows assessment of the strengths, weaknesses, and applicability of the models more readily. We show that artificial neural networks are a useful approach for examining the interactive effects of habitat and biotic factors that shape species occurrence, abundance, and spatial occupancy within lakes. Finally, using simulated and empirical examples, we show that artificial neural networks provide greater predictive power than do traditional regression approaches for modeling species occurrence and abundance. In recent years several broad-scale studies have identified modification and loss of aquatic habitat as primary factors threatening the conservation of freshwater fish populations and communities (Williams et al. 1989; Allen and Flecker 1993; Richter et al. 1997). Consequently, efforts to understand the linkage between habitat, its use by fish, and associated productivity have become increasingly important and currently are central issues in the aquatic sciences (Hughes and Noss 1992; Harig and Bain 1998). Anthropogenic activity has altered many components of riparian areas and nearshore habitats (Jennings et al. 1999). Modifications include changes in the composition and density of macrophytes (Bryan and Scarnecchia 1992), quantity and diversity of shoreline habitat such as woody material (Christensen et al. 1996), and substrate composition (Beauchamp et al. 1994; Jennings et al. 1996). Alterations to littoral-zone hab* Corresponding author: [email protected] 1 Current address: Graduate Degree Program in Ecology, Department of Biology, Colorado State University, Fort Collins, Colorado 80523–1878, USA. Received June 2, 2000; accepted March 23, 2001 itat can have dramatic and persistent impacts on fish assemblages because this habitat ultimately provides the template on which lentic ecosystems are organized (Jackson and Harvey 1989; Tonn et al. 1990; Hinch et al. 1991; Jackson et al. 2001;