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On the evolution of conditional dispersal under environmental and demographic stochasticity

Bach, Lars LU ; Ripa, Jörgen LU and Lundberg, Per LU (2007) In Evolutionary Ecology 9(4). p.663-673
Abstract
Questions: How will density-dependent and costly dispersal evolve in populations subject to local density regulation and environmental stochasticity? What type of density response will evolve, a strong threshold type response or a soft response gradually increasing dispersal?

Method: An individual-based model including density dependence, environmental fluctuations, and population variation was used to simulate evolution of dispersal behaviour.

Key assumptions and variables: Individuals can assess the instantaneous difference between habitat densities and base their dispersal behaviour thereon. However, future density and thus future quality of a chosen habitat patch remain uncertain due to behavioural variation and... (More)
Questions: How will density-dependent and costly dispersal evolve in populations subject to local density regulation and environmental stochasticity? What type of density response will evolve, a strong threshold type response or a soft response gradually increasing dispersal?

Method: An individual-based model including density dependence, environmental fluctuations, and population variation was used to simulate evolution of dispersal behaviour.

Key assumptions and variables: Individuals can assess the instantaneous difference between habitat densities and base their dispersal behaviour thereon. However, future density and thus future quality of a chosen habitat patch remain uncertain due to behavioural variation and density fluctuations. Local density regulation was given by the Beverton-Holt map, affected by stochastic environmental forcing. An individual’s dispersal decision is a sigmoid function of the density ratio between patch densities. The half-saturation point and steepness of the dispersal

reaction norm were allowed to evolve.

Conclusions: Conditional dispersal evolves from a state of random behaviour, yet we do not observe threshold dispersal as the evolutionary endpoint (as found in previous models). Among a heterogeneous set of dispersal strategies, the most successful respond softly to density differences but require a large density advantage to trigger emigration. Although threshold dispersal might be evolutionarily stable, we propose that such an endpoint may not be attainable if the evolutionary trajectory becomes less affected by selection and more by drift. The variability in

dispersal behaviour within populations leads to unpredictability in the potential benefit of dispersal and hence may select for conservative emigration criteria. Other evolving life-history traits, such as phenological traits, subject to density- and frequency-dependent effects may show similar evolutionary patterns. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
conditional dispersal, density dependence, environmental noise, evolutionary trajectory, stochasticity, individual-based
in
Evolutionary Ecology
volume
9
issue
4
pages
663 - 673
publisher
Springer
external identifiers
  • wos:000247437000007
  • scopus:34347257014
ISSN
1573-8477
language
English
LU publication?
yes
id
f23f9fc6-0c1d-460e-b1d6-4c96baa25102 (old id 629214)
alternative location
http://www.evolutionary-ecology.com/issues/v09n04/iiar2086.pdf
date added to LUP
2007-11-28 12:44:05
date last changed
2017-08-13 04:15:07
@article{f23f9fc6-0c1d-460e-b1d6-4c96baa25102,
  abstract     = {Questions: How will density-dependent and costly dispersal evolve in populations subject to local density regulation and environmental stochasticity? What type of density response will evolve, a strong threshold type response or a soft response gradually increasing dispersal?<br/><br>
Method: An individual-based model including density dependence, environmental fluctuations, and population variation was used to simulate evolution of dispersal behaviour.<br/><br>
Key assumptions and variables: Individuals can assess the instantaneous difference between habitat densities and base their dispersal behaviour thereon. However, future density and thus future quality of a chosen habitat patch remain uncertain due to behavioural variation and density fluctuations. Local density regulation was given by the Beverton-Holt map, affected by stochastic environmental forcing. An individual’s dispersal decision is a sigmoid function of the density ratio between patch densities. The half-saturation point and steepness of the dispersal<br/><br>
reaction norm were allowed to evolve.<br/><br>
Conclusions: Conditional dispersal evolves from a state of random behaviour, yet we do not observe threshold dispersal as the evolutionary endpoint (as found in previous models). Among a heterogeneous set of dispersal strategies, the most successful respond softly to density differences but require a large density advantage to trigger emigration. Although threshold dispersal might be evolutionarily stable, we propose that such an endpoint may not be attainable if the evolutionary trajectory becomes less affected by selection and more by drift. The variability in<br/><br>
dispersal behaviour within populations leads to unpredictability in the potential benefit of dispersal and hence may select for conservative emigration criteria. Other evolving life-history traits, such as phenological traits, subject to density- and frequency-dependent effects may show similar evolutionary patterns.},
  author       = {Bach, Lars and Ripa, Jörgen and Lundberg, Per},
  issn         = {1573-8477},
  keyword      = {conditional dispersal,density dependence,environmental noise,evolutionary trajectory,stochasticity,individual-based},
  language     = {eng},
  number       = {4},
  pages        = {663--673},
  publisher    = {Springer},
  series       = {Evolutionary Ecology},
  title        = {On the evolution of conditional dispersal under environmental and demographic stochasticity},
  volume       = {9},
  year         = {2007},
}