Journal article

Informing management decisions for ecological networks, using dynamic models calibrated to noisy time-series data

Matthew P Adams, Scott A Sisson, Kate J Helmstedt, Christopher M Baker, Matthew H Holden, Michaela Plein, Jacinta Holloway, Kerrie L Mengersen, Eve McDonald-Madden

ECOLOGY LETTERS | WILEY | Published : 2020

Abstract

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 rap..

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University of Melbourne Researchers

Grants

Awarded by Australian Research Council (ARC)


Awarded by ARC


Funding 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.