Intricate covariation between exploration and avoidance learning in a generalist predator

Chi-Yun Kuo, Hao-En Chin and Yu-Zhe Wu

Behavioral Ecology, 2023, vol. 34, issue 4, 708-717

Abstract: Many predators avoid unprofitable prey by learning to use visual features of the prey as reliable indicators of quality. However, individual variation in avoidance learning is rarely examined in detail. It has been hypothesized that better avoidance learning ability might correlate with faster exploration tendency, but available data are limited in both quantity and scope. In this study, we examined the covariation between exploration, foraging decisions, and avoidance learning in a generalist lizard Eutropis multifasciata to test the prediction that faster explorers are also better avoidance learners. We also examined how sex, population, and color of unpalatable prey might mediate the exploration-avoidance learning covariation. We collected data on exploration and foraging behavior in individuals from two allopatric populations and quantified changes in foraging decisions over five daily learning trials, in which individuals were presented with normal- and bitter-tasting prey that differed consistently in color. Even though bitter prey elicited strong negative responses, lizards overall did not avoid consuming fewer such prey with learning. Instead, they learned to prioritize on palatable prey as the experiment progressed. In concordance with our prediction, we found that faster explorers were generally better avoidance learners, even though sex, population, and prey color were also important. Our study represents a rare experimental test of the exploration-avoidance learning covariation, especially in non-avian systems. Our results suggest that unpalatability might be an ineffective defense against generalist predators such as E. multifasciata and that faster explorers might impose stronger selection for the evolution of warning signals in unprofitable prey.

Keywords: animal personality; aposematism; behavioral syndrome; warning signals (search for similar items in EconPapers)
Date: 2023
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