Monday, August 2, 2010: 2:50 PM
334, David L Lawrence Convention Center
Background/Question/Methods
Many organisms face human-induced novel circumstances often involving rapid environmental change; e.g., habitat alterations, exotic species, or climate change. Some species are doing well in the face of environmental change (e.g., invasive or urbanized pests), while others are doing poorly (e.g., species of conservation concern). A key first step for explaining this variation is to understand variation in behavioral responses to novel environments. In an evolutionarily novel situation, there is little reason to assume that organisms will behave optimally, as most models assume, nor can we simply assume that they will exhibit no adaptive plasticity. Instead, organismal responses might often depend on their sensory/cognitive mechanisms for detecting, evaluating and responding to uncertain, novel environments. Here, we outline a unified theoretical/conceptual/empirical framework for evaluating these behavioral mechanisms, and for explaining the resulting behavioral outcomes.
Results/Conclusions
Theoretical behavioral ecologists have known for decades that Signal Detection Theory (SDT), a set of models widely used in the psychology literature, makes useful predictions about optimal decisions under information constraints. What is much less well known among ecologists is that SDT also provides a rich and flexible statistical framework for inferring details about animals’ sensory and decision-making processes from easily collected behavioral data. In combination with fitness or other performance assays, SDT methods can enable ecologists to detect deviations from optimality in novel environments and also to assess whether those deviations are likely due to sensory limitations, faulty cost-benefit analyses, or other factors. The close link between SDT’s statistical methods and its theoretical predictions will thus allow ecologists to design better experiments, test more detailed hypotheses, and to make extrapolations to novel environments with greater confidence. We demonstrate the power of SDT experimental designs by showing how it can yield new insights about snails' behavioral responses to novel crayfish predators.
Many organisms face human-induced novel circumstances often involving rapid environmental change; e.g., habitat alterations, exotic species, or climate change. Some species are doing well in the face of environmental change (e.g., invasive or urbanized pests), while others are doing poorly (e.g., species of conservation concern). A key first step for explaining this variation is to understand variation in behavioral responses to novel environments. In an evolutionarily novel situation, there is little reason to assume that organisms will behave optimally, as most models assume, nor can we simply assume that they will exhibit no adaptive plasticity. Instead, organismal responses might often depend on their sensory/cognitive mechanisms for detecting, evaluating and responding to uncertain, novel environments. Here, we outline a unified theoretical/conceptual/empirical framework for evaluating these behavioral mechanisms, and for explaining the resulting behavioral outcomes.
Results/Conclusions
Theoretical behavioral ecologists have known for decades that Signal Detection Theory (SDT), a set of models widely used in the psychology literature, makes useful predictions about optimal decisions under information constraints. What is much less well known among ecologists is that SDT also provides a rich and flexible statistical framework for inferring details about animals’ sensory and decision-making processes from easily collected behavioral data. In combination with fitness or other performance assays, SDT methods can enable ecologists to detect deviations from optimality in novel environments and also to assess whether those deviations are likely due to sensory limitations, faulty cost-benefit analyses, or other factors. The close link between SDT’s statistical methods and its theoretical predictions will thus allow ecologists to design better experiments, test more detailed hypotheses, and to make extrapolations to novel environments with greater confidence. We demonstrate the power of SDT experimental designs by showing how it can yield new insights about snails' behavioral responses to novel crayfish predators.
