|Multiple prey traits, multiple predators: keys to understanding complex community dynamics|
DeWitt, T.J.; Langerhans, R.B. (2003). Multiple prey traits, multiple predators: keys to understanding complex community dynamics, in: Philippart, C.J.M. et al. (Ed.) Structuring Factors of Shallow Marine Coastal Communities, part II. Journal of Sea Research, 49(2): pp. 143-155
In: Philippart, C.J.M.; Van Raaphorst, W. (Ed.) (2003). Structuring Factors of Shallow Marine Coastal Communities, part II. Journal of Sea Research, 49(2). Elsevier Science: Amsterdam. 81-155 pp., more
In: Journal of Sea Research. Elsevier/Netherlands Institute for Sea Research: Amsterdam; Den Burg. ISSN 1385-1101, more
|Also published as |
- DeWitt, T.J.; Langerhans, R.B. (2003). Multiple prey traits, multiple predators: keys to understanding complex community dynamics. J. Sea Res. 49(2): 143-155, more
|Authors|| || Top |
- DeWitt, T.J.
- Langerhans, R.B.
Natural communities can be complex. Such complexity makes it difficult to discern the mechanisms generating community structure. In this paper we review concepts and issues related to linking functional and community studies while also including greater complexity into the experimental realm. These principles are primarily illustrated with case studies involving predation ecology in a freshwater snail-fish-crayfish model system. The system illustrates how predator impacts on prey are mediated by multiple prey traits, correlations between traits, functional trade-offs in predator defence, interactions between predators, and interactions with other community members. We argue for a pluralistic approach to investigating mechanisms of community structure; that is, an approach that integrates many subdisciplines of ecology and evolution. We discuss four main areas that when used together yield important insights on community structure. First, selection gradient analyses formally link functional and community ecology. This formalisation is shown to help identify targets of selection, estimate environment-specific mortality rates, and identify agents of selection in complex communities. Second, we encourage increased focus on emergent community properties (results not predicted based on pairwise species interactions). Third, we emphasise that a community, rather than a web of species interactions, may more profitably be viewed as a network of trait interactions. This trait-centred view makes clear how indirect community effects arise between species that do not interact physically. This perspective also leads to our fourth topic, the integration of phenotypes. Just as populations evolve co-adapted suites of traits, so too should individuals embody integrated trait correlations, termed `trait integration', rather than randomly assembled collections of phenotypes. All the perspectives mentioned above suggest that investigations should focus on multiple traits and multiple environments simultaneously, rather than singular, atomised components of complex systems.