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Phenotypic Plasticity, Behavioral Syndromes and Their Evolutionary Implications in Pacific Field Cricket Females

Abstract

Two models have been suggested to predict the presence of behavioral syndromes (correlations between different behaviors or the same behavior in different contexts). According to "genetic constraint" models, such correlations, where present, should persist regardless of environmental changes, while "adaptive divergence" models posit that syndromes will exist only where beneficial, and one such case is a high predation environment. We examined the plasticity and potential correlation between two behaviors - female responsiveness to male calls (responsiveness) and latency to emerge from shelter after a simulated predator threat (boldness). The subjects were female Pacific field crickets (Teleogryllus oceanicus) from populations with different predation pressures, and as an additional, experimental proxy for predation pressure, they were reared in an environment simulating either high or low population density. As expected, females reared in incubators where low population density was simulated, were more responsive to male calls, since such environments signal less mating opportunities. However, regardless of rearing conditions, females from the population where parasitoid predation pressure was strongest, were more responsive to male calls and also less bold, which suggests that there are genetic differences between the populations as well. As predicted by divergence models, the only population in which a behavioral syndrome (bold/more responsive) was detected, suffered the highest rate of parasitoid larvae infestation. If predation has driven the formation of bold/more responsive and shy/less responsive coping styles in females in this population, such strategies could be adaptive due to a tradeoff mechanism. A boldness/responsiveness syndrome was also present in both the high and low population density environments, which could be explained by both genetic constraint and adaptive divergence models - different experiments, and in some instances larger sample sizes, are needed to clarify this result.

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