Making ecological models adequate. (27th December 2017)

• Record Type: Journal Article
• Authors: Getz, Wayne M.; Marshall, Charles R.; Carlson, Colin J.; Giuggioli, Luca; Ryan, Sadie J.; Romañach, Stephanie S.; Boettiger, Carl; Chamberlain, Samuel D.; Larsen, Laurel; D'Odorico, Paolo; O'Sullivan, David
• Editors: Coulson, Tim
• Is Part Of: Ecology letters. Volume 21:Number 2(2018)
• Page Start: 153
• Page End: 166
• Publication Date: 2017-12-27
• Subjects: appropriate complexity modelling -- coarse graining -- disease modelling -- ecosystems restoration models -- environmental management models -- extinction risk assessment -- hierarchical modelling; Ecology -- Periodicals; 577
• DOI: 10.1111/ele.12893
• Languages: English
• ISSNs: 1461-023X

Abstract

Abstract: Critical evaluation of the adequacy of ecological models is urgently needed to enhance their utility in developing theory and enabling environmental managers and policymakers to make informed decisions. Poorly supported management can have detrimental, costly or irreversible impacts on the environment and society. Here, we examine common issues in ecological modelling and suggest criteria for improving modelling frameworks. An appropriate level of process description is crucial to constructing the best possible model, given the available data and understanding of ecological structures. Model details unsupported by data typically lead to over parameterisation and poor model performance. Conversely, a lack of mechanistic details may limit a model's ability to predict ecological systems' responses to management. Ecological studies that employ models should follow a set of model adequacy assessment protocols that include: asking a series of critical questions regarding state and control variable selection, the determinacy of data, and the sensitivity and validity of analyses. We also need to improve model elaboration, refinement and coarse graining procedures to better understand the relevancy and adequacy of our models and the role they play in advancing theory, improving hind and forecasting, and enabling problem solving and management.