Discrimination and classification of foraging paths produced by search-tactic models

Search tactics are cognitive processes, or decision mechanisms, that organisms use to locate available resources such as food, mates, refugia, and high-quality habitats. However, our knowledge of the actual tactics that animals use while searching for resources is limited, and very little empirical evidence has been accumulated. Therefore, we developed a suite of search-tactic models (1) to simulate possible searching behaviors of mobile organisms so that inferences can be made about their decision mechanisms, and (2) to determine the extent to which different models produce paths that approximate a globally optimal solution. The search-tactic models included deterministic and probabilistic searches in attempt to characterize biologically plausible searching behaviors. Classical linear multivariate methods (discriminant function analysis, Mahalanobis distances) and nonlinear artificial neural networks were used to discriminate the paths produced by the different models and to classify "unknown" foraging paths into one of the search-tactic models, based on the geometry of the resulting paths. Both linear and nonlinear analyses suggested that it is possible for animals to use a nearest-neighbor search tactic to search with near-optimum efficiency without having complete knowledge of the specific locations of all available resources. Furthermore, both methods of analyses demonstrated that it might be possible to use characteristics of foraging paths in an experimental setting to make inferences about the actual decision mechanisms animals use while searching for resources. Copyright 2004.