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Making ecological models adequate
Author(s) -
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
Publication year - 2018
Publication title -
ecology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.852
H-Index - 265
eISSN - 1461-0248
pISSN - 1461-023X
DOI - 10.1111/ele.12893
Subject(s) - computer science , process (computing) , ecological systems theory , management science , risk analysis (engineering) , ecology , set (abstract data type) , determinacy , environmental resource management , environmental science , engineering , business , mathematical analysis , mathematics , biology , programming language , operating system
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.