Integrating modelling of biodiversity composition and ecosystem function

Mokany, Karel, Ferrier, Simon, Connolly, Sean R., Dunstan, Piers K., Fulton, Elizabeth A., Harfoot, Michael B., Harwood, Thomas D., Richardson, Anthony J., Roxburgh, Stephen H., Scharlemann, Jörn P.W., Tittensor, Derek P., Westcott, David A., and Wintle, Brendan A. (2016) Integrating modelling of biodiversity composition and ecosystem function. Oikos, 125 (1). pp. 10-19.

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Abstract

There is increasing reliance on ecological models to improve our understanding of how ecological systems work, to project likely outcomes under alternative global change scenarios and to help develop robust management strategies. Two common types of spatiotemporally explicit ecological models are those focussed on biodiversity composition and those focussed on ecosystem function. These modelling disciplines are largely practiced separately, with separate literature, despite growing evidence that natural systems are shaped by the interaction of composition and function. Here we call for the development of new modelling approaches that integrate composition and function, accounting for the important interactions between these two dimensions, particularly under rapid global change. We examine existing modelling approaches that have begun to combine elements of composition and function, identifying their potential contribution to fully integrated modelling approaches. The development and application of integrated models of composition and function face a number of important challenges, including biological data limitations, system knowledge and computational constraints. We suggest a range of promising avenues that could help researchers overcome these challenges, including the use of virtual species, macro-ecological relationships and hybrid correlative-mechanistic modelling. Explicitly accounting for the interactions between composition and function within integrated modelling approaches has the potential to improve our understanding of ecological systems, provide more accurate predictions of their future states and transform their management.

Item ID: 43219
Item Type: Article (Research - C1)
ISSN: 1600-0706
Funders: Australian Research Council (ARC)
Projects and Grants: ARC Future Fellowship Grant FT0991722
Date Deposited: 09 Mar 2016 07:32
FoR Codes: 05 ENVIRONMENTAL SCIENCES > 0502 Environmental Science and Management > 050202 Conservation and Biodiversity @ 50%
05 ENVIRONMENTAL SCIENCES > 0501 Ecological Applications > 050102 Ecosystem Function @ 50%
SEO Codes: 96 ENVIRONMENT > 9605 Ecosystem Assessment and Management > 960599 Ecosystem Assessment and Management not elsewhere classified @ 50%
97 EXPANDING KNOWLEDGE > 970105 Expanding Knowledge in the Environmental Sciences @ 50%
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