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Causes and consequences of animal dispersal strategies: relating individual behaviour to spatial dynamics
Author(s) -
Bowler Diana E.,
Benton Tim G.
Publication year - 2005
Publication title -
biological reviews
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.993
H-Index - 165
eISSN - 1469-185X
pISSN - 1464-7931
DOI - 10.1017/s1464793104006645
Subject(s) - biological dispersal , population , ecology , interdependence , emigration , biology , geography , demography , archaeology , sociology , political science , law
Knowledge of the ecological and evolutionary causes of dispersal can be crucial in understanding the behaviour of spatially structured populations, and predicting how species respond to environmental change. Despite the focus of much theoretical research, simplistic assumptions regarding the dispersal process are still made. Dispersal is usually regarded as an unconditional process although in many cases fitness gains of dispersal are dependent on environmental factors and individual state. Condition‐dependent dispersal strategies will often be superior to unconditional, fixed strategies. In addition, dispersal is often collapsed into a single parameter, despite it being a process composed of three interdependent stages: emigration, inter‐patch movement and immigration, each of which may display different condition dependencies. Empirical studies have investigated correlates of these stages, emigration in particular, providing evidence for the prevalence of conditional dispersal strategies. Ill‐defined use of the term ‘dispersal’, for movement across many different spatial scales, further hinders making general conclusions and relating movement correlates to consequences at the population level. Logistical difficulties preclude a detailed study of dispersal for many species, however incorporating unrealistic dispersal assumptions in spatial population models may yield inaccurate and costly predictions. Further studies are necessary to explore the importance of incorporating specific condition‐dependent dispersal strategies for evolutionary and population dynamic predictions.