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

Abstract
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 within-lake habitat factors shaping species occurrence and...