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. ISSN 0030-1299

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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, macroecological 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 Type: Article
Keywords: biodiversity modelling
Schools and Departments: School of Life Sciences > Evolution, Behaviour and Environment
Research Centres and Groups: Sussex Sustainability Research Programme
Subjects: Q Science > QH Natural history > QH0301 Biology
Depositing User: Jorn Scharlemann
Date Deposited: 21 Sep 2015 11:42
Last Modified: 06 Mar 2017 12:44
URI: http://sro.sussex.ac.uk/id/eprint/56835

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