Enhancing the ecological realism of evolutionary mismatch theory. (March 2022)
- Record Type:
- Journal Article
- Title:
- Enhancing the ecological realism of evolutionary mismatch theory. (March 2022)
- Main Title:
- Enhancing the ecological realism of evolutionary mismatch theory
- Authors:
- Pollack, Lea
Munson, Amelia
Savoca, Matthew S.
Trimmer, Pete C.
Ehlman, Sean M.
Gil, Michael A.
Sih, Andrew - Abstract:
- Abstract : Following rapid environmental change, why do some animals thrive, while others struggle? We present an expanded, cue–response framework for predicting variation in behavioral responses to novel situations. We show how signal detection theory can be used when individuals have three behavioral options (approach, avoid, or ignore). Based on this theory, we outline predictions about which animals are more likely to make mistakes around novel conditions (i.e., fall for a trap or fail to use an undervalued resource) and the intensity of that mismatch (i.e., severe versus moderate). Explicitly considering three options provides a more holistic perspective and allows us to distinguish between severe and moderate traps, which could guide management strategies in a changing world. Highlights: Anthropogenic change is causing novel environmental conditions; this creates mismatches between cues and resources, leading many animals to make suboptimal choices. Despite the growing number of examples of these 'evolutionary mismatches', why certain animals fall victim to these mistakes while others do not remains poorly understood. We present a framework that conceptually unifies two kinds of evolutionary mismatch: evolutionary traps and undervalued resources. Guided by signal detection theory – a set of models that have long been used to understand errors in response to cues – we argue that variation both within and among species in their responses to novelty depends on differencesAbstract : Following rapid environmental change, why do some animals thrive, while others struggle? We present an expanded, cue–response framework for predicting variation in behavioral responses to novel situations. We show how signal detection theory can be used when individuals have three behavioral options (approach, avoid, or ignore). Based on this theory, we outline predictions about which animals are more likely to make mistakes around novel conditions (i.e., fall for a trap or fail to use an undervalued resource) and the intensity of that mismatch (i.e., severe versus moderate). Explicitly considering three options provides a more holistic perspective and allows us to distinguish between severe and moderate traps, which could guide management strategies in a changing world. Highlights: Anthropogenic change is causing novel environmental conditions; this creates mismatches between cues and resources, leading many animals to make suboptimal choices. Despite the growing number of examples of these 'evolutionary mismatches', why certain animals fall victim to these mistakes while others do not remains poorly understood. We present a framework that conceptually unifies two kinds of evolutionary mismatch: evolutionary traps and undervalued resources. Guided by signal detection theory – a set of models that have long been used to understand errors in response to cues – we argue that variation both within and among species in their responses to novelty depends on differences in their cue–response thresholds, previously calibrated by their evolutionary or developmental histories. … (more)
- Is Part Of:
- Trends in ecology & evolution. Volume 37:Number 3(2022)
- Journal:
- Trends in ecology & evolution
- Issue:
- Volume 37:Number 3(2022)
- Issue Display:
- Volume 37, Issue 3 (2022)
- Year:
- 2022
- Volume:
- 37
- Issue:
- 3
- Issue Sort Value:
- 2022-0037-0003-0000
- Page Start:
- 233
- Page End:
- 245
- Publication Date:
- 2022-03
- Subjects:
- signal detection theory -- evolutionary traps -- undervalued resources -- evolutionary mismatch
Ecology -- Periodicals
Evolution (Biology) -- Periodicals
576.8 - Journal URLs:
- http://www.sciencedirect.com/science/journal/01695347 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.tree.2021.10.011 ↗
- Languages:
- English
- ISSNs:
- 0169-5347
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 9049.569000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 20672.xml