Elevation-related difference in serial reversal learning ability in a nonscatter hoarding passerine

Ethan Hermer, Maxime Cauchoix, Alexis S Chaine and Julie Morand-Ferron

Behavioral Ecology, 2018, vol. 29, issue 4, 840-847

Abstract: Environments characterized by scarce and variable food supply, termed “harsh environments,†have been hypothesized to favor cognitive abilities that aid an animal in finding food, remembering where it is located, or predicting its availability. Most studies of the “harsh environment†hypothesis have found that scatter hoarders from harsher environments have better spatial memory abilities, but few studies have looked at this hypothesis in nonscatter hoarders. Here, we present the first comparison of performance on a serial reversal learning task in a nonscatter hoarder from 2 elevations that differ in harshness. Serial reversal learning tasks measure a suite of cognitive abilities that are believed to allow an animal to adjust its foraging behavior to match changes in the availability of food over time. Therefore, performance on this task is predicted to increase with elevation. There was no significant difference between the high and low elevation great tits in initial reversal learning accuracy. While both high and low elevation birds were able to improve their reversal learning accuracy, they did not differ in their rate of improvement over reversals. However, we found that lower elevation birds had higher accuracy across all reversals. Contrary to the “harsh environment†hypothesis, our findings suggest that birds from the less harsh environment at low elevation performed more accurately on the reversal learning task. Overall, our results suggest that the study of the relationship between harshness and cognition in nonhoarders would benefit from taking into account other environmental factors, and trade-offs with other cognitive abilities.

Keywords: altitude; cognitive ecology; intraspecific variation; operant learning; Parus major; winter resident (search for similar items in EconPapers)
Date: 2018
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