Field biology, food web models, and management: challenges of context and scale

Authors
Citation
Me. Power, Field biology, food web models, and management: challenges of context and scale, OIKOS, 94(1), 2001, pp. 118-129
Citations number
90
Categorie Soggetti
Environment/Ecology
Journal title
OIKOS
ISSN journal
00301299 → ACNP
Volume
94
Issue
1
Year of publication
2001
Pages
118 - 129
Database
ISI
SICI code
0030-1299(200107)94:1<118:FBFWMA>2.0.ZU;2-#
Abstract
Managers are increasingly aware of the need for science to inform the stewa rdship of natural lands and resources. If ecologists are to address this ne ed. we must increase the scope of our inferences, while maintaining suffici ent resolution and realism to predict trajectories of specific populations or ecosystem variables. Food chain and simple food web models. used either as core or component hypotheses, can help us to meet this challenge. The si mple mass balance logic of dynamic food chain or food web models can organi ze our thinking about a range of applied problems, such as evaluating contr ols over populations of concern, or of biotic assemblages that affect impor tant ecosystem properties. In other applications. a food chain or web may b e incorporated as one element in models of regional mass balances affecting resources or environments. Specific predictions of food web models will of ten fail because of inadequate resolution (e.g., of functionally significan t differences among taxa within "trophic levels") or insufficient scope (e. g., of spatio-temporal variation over scales relevant to management). Incre asing use of tracers to delimit spatial scales of food web interactions wil l reduce, but not eliminate, this limitation. If used with skepticism and v igilance to local natural history, however. food chain or simple food web m odels can promote the iterative feedback between prediction, falsification by observation, and new prediction central to hypothetico-deductive science and adaptive management. Experience argues that this stepwise path is the fastest towards better understanding and control of our impacts on nature.