SEARCH STRATEGIES FOR LANDSCAPE-LEVEL INTERPATCH MOVEMENTS

Abstract

Ecologists need a better understanding of how animals make decisions about moving across landscapes. To this end, we developed computer simulations that contrast the effectiveness of various search strategies at finding habitat patches in idealized landscapes (uniform, random, or clumped patches), where searchers have different energy reserves and face different mortality risks. Nearly straight correlated random walks always produced better dispersal success than relatively uncorrelated random walks. However, increasing patch density decreased the degree of correlation that maximized dispersal success. Only under high mortality and low energy reserves in a uniform landscape did absolutely straight-line search perform better than any random walk. With low mortality risks and high energy reserves, exhaustive systematic search was superior to the best correlated random walk; an increase in the perceptual range of the searcher (i.e., patch detectability) also favored exhaustive search over relatively straight random walks. For all conditions examined, the “average distance rule,” a hybrid search rule incorporating both straight-line and systematic search, was best. Overall, however, our results suggest that a simple and effective search rule for many landscape-explicit models would involve straight or nearly straight movements.