| Abstract | dc.description.abstract | Little is known about how animals acquire and use prior information, particularly for Bayesian patch assessment strategies. Because different patch assessment strategies rely upon distinct capabilities to obtain information, we analyzed whether foragers can alter their foraging strategy when they exploit predictable patches with periodic renewal. For this, we evaluated if learning contribute to increase foraging efficiency by improving patch assessment abilities in degus (Octodon degus), a diurnal caviomorph rodent from central Chile. Single degus exploited pairs of depleting patches that were renewed daily. During the initial two days of the experiment, degus exploited patches in agreement with a fixed-time strategy, i.e. at the population level, giving-up densities (GUD) were not distinguishable from density-independence (i.e. consumption proportional to initial patch densities), and richer patches were under-exploited. After day five, degus improved significantly their assessment strategy, showing agreement with Bayesian information updating. However, on day 15 and afterwards, degus foraged patches in agreement with a prescient strategy, because GUDs across patches indicated positive density-dependence and equalization of GUDs. Although highly variable, the GUD ratio between rich and poor patches decreased significantly throughout time. Within-subject data showed that as subjects learned patch qualities they showed a tendency toward GUD equalization and differentiation from density-independence. By the end of the experiment, degus allocated more time to richer patches during the initial period of each trial, and allocated similar amounts of time by the end of trials. Further, the first visit of a session was significantly biased toward the rich patch by the final days of the experiment. The results suggest that assessment abilities can change when exploiting novel but predictable patches. When degus can incorporate adequate environmental information, prior and current information may become accurate enough to make animals exploit patches efficiently. | en |
