Figure 1 We consider a scenario where the distributions of signals under safe or dangerous situations mean that a ... Figure 1 We consider a scenario where.

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Figure 1 We consider a scenario where the distributions of signals under safe or dangerous situations mean that a ... Figure 1 We consider a scenario where the distributions of signals under safe or dangerous situations mean that a threshold should be used to choose behavior, with evasive action (running away) being chosen if a high signal is received. Classical signal detection theory identifies that the optimal threshold, x<sub>T</sub>, should move left (making an individual more ready to run away) as the probability of danger, p, increases. Thus, as danger increases, the actions of an individual should tend to appear less “bold.” However, our analysis shows that if a series of decisions will be made using the threshold, always with the same probability of danger, p, then the optimal threshold will instead move to the right. Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2018. Published by Oxford University Press on behalf of the International Society for Behavioral Ecology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Behav Ecol, Volume 30, Issue 1, 03 November 2018, Pages 16–19, https://doi.org/10.1093/beheco/ary132 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 2 The level of boldness affects the immediate probabilities of increasing or decreasing reserves by 1 unit, and ... Figure 2 The level of boldness affects the immediate probabilities of increasing or decreasing reserves by 1 unit, and the probability of being killed. We are interested in the behavioral strategy that maximizes the long-term probability of reaching the next reserve level without being killed by a predator or through background mortality (i.e., the optimal level of boldness). Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2018. Published by Oxford University Press on behalf of the International Society for Behavioral Ecology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Behav Ecol, Volume 30, Issue 1, 03 November 2018, Pages 16–19, https://doi.org/10.1093/beheco/ary132 The content of this slide may be subject to copyright: please see the slide notes for details.

Figure 3 Contrasting the predictions of boldness with the optimal (multiple decision) calculations. With no background ... Figure 3 Contrasting the predictions of boldness with the optimal (multiple decision) calculations. With no background mortality, as p increases, the optimal threshold (i.e., boldness) increases; that is, the individual forages when they would otherwise have run. The monotonic upward trend is entirely contrary to SDT, which predicts a monotonic decrease in boldness. With nonzero background mortality, the optimal boldness decreases for small predation probabilities but then monotonically increasing as p comes to dominate. The SDT line has been calculated against baseline payoffs relating to p = 0.1, with 1 reserve level to go, using the optimal payoffs (that would be measured by an observer if behavior evolved to be optimal). A similar monotonic downward trend occurs for the SDT line, irrespective of which value of p is chosen. Introducing nonzero background mortality to the SDT case also produces a similar monotonic downward trend, with a slower asymptote to zero (not shown). (Signals normally distributed with unit variance and means μ<sub>N</sub> = 0, μ<sub>D</sub> = 2.) Unless provided in the caption above, the following copyright applies to the content of this slide: © The Author(s) 2018. Published by Oxford University Press on behalf of the International Society for Behavioral Ecology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.comThis article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) Behav Ecol, Volume 30, Issue 1, 03 November 2018, Pages 16–19, https://doi.org/10.1093/beheco/ary132 The content of this slide may be subject to copyright: please see the slide notes for details.