![]() Curvilinear relationships for mortality and foraging. A polynomial fit (R 2 0992) was used for dance durations of 035 s and a linear fit (R 2 0994) for dance durations greater than 35 s. This graph was divided into two parts: 035 s and greater than 35 s. opacum predation risk in these experiments (qua-dratic regression, R2 0.68 F 2,7 7.5 P 0.018). To calculate the foraging distance, the duration to distance curve of von Frisch was used (von Frisch 1967 Fig. Substantial variation in population foraging means was explained by A. Pure exploration degrades the agent’s learning but increases the. Foraging rates were higher for larvae originating from ponds with greater A. The agents have to explore in order to improve the state which potentially yields higher rewards in the future or exploit the state that yields the highest reward based on the existing knowledge. Overall, however, our results suggest that a simple and effective search rule for many landscape-explicit models would involve straight or nearly straight movements. The explorationexploitation dilemma has been an unresolved issue within the framework of multi-agent reinforcement learning. We used the foraging box assay to assess bat per-sonality. For all conditions examined, the “average distance rule,” a hybrid search rule incorporating both straight-line and systematic search, was best. Urban and rural pups consistently differ in behavioral traits In total, we carried out behavioral assays on 86 bat pups with no self-experience outdoors, across three years including 61 pups from four urban colonies and 25 from three rural colonies (Methods, Additional file 1: Table S1). 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. Only under high mortality and low energy reserves in a uniform landscape did absolutely straight-line search perform better than any random walk. However, increasing patch density decreased the degree of correlation that maximized dispersal success. Nearly straight correlated random walks always produced better dispersal success than relatively uncorrelated random walks. 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. Ecologists need a better understanding of how animals make decisions about moving across landscapes.
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