Informing management decisions for ecological networks, using dynamic models calibrated to noisy time-series data
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Description
Well‐intentioned environmental management can backfire, causing unforeseen damage. To avoid this, managers and ecologists seek accurate predictions of the ecosystem‐wide impacts of interventions, given small and imprecise datasets, which is an incredibly difficult task. We generated and analysed thousands of ecosystem population time series to investigate whether fitted models can aid decision‐makers to select interventions. Using these time‐series data (sparse and noisy datasets drawn from deterministic Lotka‐Volterra systems with two to nine species, of known network structure), dynamic model forecasts of whether a species’ future population will be positively or negatively affected by rapid eradication of another species were correct > 70% of the time. Although 70% correct classifications is only slightly better than an uninformative prediction (50%), this classification accuracy can be feasibly improved by increasing monitoring accuracy and frequency. Our findings suggest that models may not need to produce well‐constrained predictions before they can inform decisions that improve environmental outcomes.
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ID Code: | 136700 | ||||||||
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Item Type: | Contribution to Journal (Journal Article) | ||||||||
Refereed: | Yes | ||||||||
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Additional Information: | Acknowledgements: The corresponding author thanks Brodie A. J. Lawson, Chris C. Drovandi, Matias Quiroz and Robert Salomone for fruitful discussions regarding the application of Bayesian inference to differential equation models. Nigel Bean, Phillip Staniczenko and several students and researchers from the School of Earth and Environmental Sciences, The University of Queensland, are thanked for their comments on an earlier version of the manuscript. The authors wish to acknowledge The University of Queensland’s Research Computing Centre (RCC) for its support in this research, and also thank Gloria M. Monsalve-Bravo for her technical assistance with running the simulations. This work was funded by the Australian Research Council (ARC) Linkage Grant LP160100496, and ideas for this study were initiated from a 2017 workshop on Novel Methods for Modelling Complex Dynamic Ecological Systems jointly funded by the ARC Centre of Excellence in Environmental Decisions and the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). SAS is directly supported by ACEMS. CMB is the recipient of a John Stocker Fellowship from the Science and Industry Endowment Fund. EMM’s contribution was funded by an ARC Future Fellowship FT170100140. The authors also thank three anonymous reviewers whose comments greatly improved the manuscript. | ||||||||
Measurements or Duration: | 13 pages | ||||||||
Keywords: | Conservation, decision science, ecological forecasting, ecological modelling, food webs, interaction network, population dynamics, predator–prey interactions, prediction, uncertainty propagation | ||||||||
DOI: | 10.1111/ele.13465 | ||||||||
ISSN: | 1461-023X | ||||||||
Pure ID: | 43021188 | ||||||||
Divisions: | Current > Research Centres > Centre for Data Science Current > Research Centres > Centre for the Environment Past > Institutes > Institute for Future Environments Past > QUT Faculties & Divisions > Science & Engineering Faculty ?? 3232 ?? Current > QUT Faculties and Divisions > Faculty of Science |
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Copyright Owner: | 2020 John Wiley & Sons | ||||||||
Copyright Statement: | This work is covered by copyright. Unless the document is being made available under a Creative Commons Licence, you must assume that re-use is limited to personal use and that permission from the copyright owner must be obtained for all other uses. If the document is available under a Creative Commons License (or other specified license) then refer to the Licence for details of permitted re-use. It is a condition of access that users recognise and abide by the legal requirements associated with these rights. If you believe that this work infringes copyright please provide details by email to qut.copyright@qut.edu.au | ||||||||
Deposited On: | 29 Jan 2020 16:25 | ||||||||
Last Modified: | 25 Apr 2024 08:05 |
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