Abstract
Animal behaviors can often be challenging to model and predict, though optimality theory has improved our ability to do so. While many qualitative predictions of behavior exist, accurate quantitative models, tested by empirical data, are often lacking. This is likely due to variation in biases across individuals and variation in the way new information is gathered and used. We propose a modeling framework based on a novel interpretation of Bayes’s theorem to integrate optimization of energetic constraints with both prior biases and specific sources of new information gathered by individuals. We present methods for inferring distributions of prior biases within populations rather than assuming known priors, as is common in Bayesian approaches to modeling behavior, and for evaluating the goodness of fit of overall model descriptions. We apply this framework to predict optimal escape during predator-prey encounters, based on prior biases and variation in what information prey use. Using this approach, we collected and analyzed data characterizing white-tailed deer (Odocoileus virginianus) escape behavior in response to human approaches. We found that distance to predator alone was not sufficient to predict deer flight response and show that the inclusion of additional information is necessary. We also compared differences in the inferred distributions of prior biases across different populations and discuss the possible role of human activity in influencing these distributions.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 321-331 |
| Number of pages | 11 |
| Journal | American Naturalist |
| Volume | 192 |
| Issue number | 3 |
| DOIs | |
| State | Published - Sep 1 2018 |
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Keywords
- Animal behavior
- Antipredator behavior
- Decision-making
- Modeling
- Optimal escape theory
- Predator-prey interactions
ASJC Scopus subject areas
- Ecology, Evolution, Behavior and Systematics
Cite this
Born to run? Quantifying the balance of prior bias and new information in prey escape decisions. / Sutton, Nicholas M.; O'Dwyer, James Patrick.
In: American Naturalist, Vol. 192, No. 3, 01.09.2018, p. 321-331.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Born to run? Quantifying the balance of prior bias and new information in prey escape decisions
AU - Sutton, Nicholas M.
AU - O'Dwyer, James Patrick
PY - 2018/9/1
Y1 - 2018/9/1
N2 - Animal behaviors can often be challenging to model and predict, though optimality theory has improved our ability to do so. While many qualitative predictions of behavior exist, accurate quantitative models, tested by empirical data, are often lacking. This is likely due to variation in biases across individuals and variation in the way new information is gathered and used. We propose a modeling framework based on a novel interpretation of Bayes’s theorem to integrate optimization of energetic constraints with both prior biases and specific sources of new information gathered by individuals. We present methods for inferring distributions of prior biases within populations rather than assuming known priors, as is common in Bayesian approaches to modeling behavior, and for evaluating the goodness of fit of overall model descriptions. We apply this framework to predict optimal escape during predator-prey encounters, based on prior biases and variation in what information prey use. Using this approach, we collected and analyzed data characterizing white-tailed deer (Odocoileus virginianus) escape behavior in response to human approaches. We found that distance to predator alone was not sufficient to predict deer flight response and show that the inclusion of additional information is necessary. We also compared differences in the inferred distributions of prior biases across different populations and discuss the possible role of human activity in influencing these distributions.
AB - Animal behaviors can often be challenging to model and predict, though optimality theory has improved our ability to do so. While many qualitative predictions of behavior exist, accurate quantitative models, tested by empirical data, are often lacking. This is likely due to variation in biases across individuals and variation in the way new information is gathered and used. We propose a modeling framework based on a novel interpretation of Bayes’s theorem to integrate optimization of energetic constraints with both prior biases and specific sources of new information gathered by individuals. We present methods for inferring distributions of prior biases within populations rather than assuming known priors, as is common in Bayesian approaches to modeling behavior, and for evaluating the goodness of fit of overall model descriptions. We apply this framework to predict optimal escape during predator-prey encounters, based on prior biases and variation in what information prey use. Using this approach, we collected and analyzed data characterizing white-tailed deer (Odocoileus virginianus) escape behavior in response to human approaches. We found that distance to predator alone was not sufficient to predict deer flight response and show that the inclusion of additional information is necessary. We also compared differences in the inferred distributions of prior biases across different populations and discuss the possible role of human activity in influencing these distributions.
KW - Animal behavior
KW - Antipredator behavior
KW - Decision-making
KW - Modeling
KW - Optimal escape theory
KW - Predator-prey interactions
UR - http://www.scopus.com/inward/record.url?scp=85049945403&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85049945403&partnerID=8YFLogxK
U2 - 10.1086/698692
DO - 10.1086/698692
M3 - Article
C2 - 30125227
AN - SCOPUS:85049945403
VL - 192
SP - 321
EP - 331
JO - American Naturalist
JF - American Naturalist
SN - 0003-0147
IS - 3
ER -