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

Autor: Adams MP; School of Earth and Environmental Sciences, The University of Queensland, St Lucia, Qld, 4072, Australia.; Centre for Biodiversity and Conservation Science, The University of Queensland, St Lucia, Qld, 4072, Australia.; ARC Centre of Excellence for Mathematical and Statistical Frontiers, The University of Queensland, St Lucia, Qld, 4072, Australia., Sisson SA; School of Mathematics and Statistics, The University of New South Wales, Sydney, NSW, 2052, Australia., Helmstedt KJ; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Qld, 4001, Australia.; ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Qld, 4001, Australia., Baker CM; Centre for Biodiversity and Conservation Science, The University of Queensland, St Lucia, Qld, 4072, Australia.; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Qld, 4001, Australia.; School of Biological Sciences, The University of Queensland, St Lucia, Qld, 4072, Australia.; CSIRO Ecosystem Sciences, Ecosciences Precinct, Dutton Park, Qld, 4102, Australia.; Centre of Excellence for Environmental Decisions, The University of Queensland, St Lucia, Qld, 4072, Australia., Holden MH; Centre for Biodiversity and Conservation Science, The University of Queensland, St Lucia, Qld, 4072, Australia.; School of Biological Sciences, The University of Queensland, St Lucia, Qld, 4072, Australia.; Centre of Excellence for Environmental Decisions, The University of Queensland, St Lucia, Qld, 4072, Australia.; Centre for Applications in Natural Resource Mathematics, School of Mathematics and Physics, The University of Queensland, St Lucia, Qld, 4072, Australia., Plein M; School of Earth and Environmental Sciences, The University of Queensland, St Lucia, Qld, 4072, Australia.; Centre for Biodiversity and Conservation Science, The University of Queensland, St Lucia, Qld, 4072, Australia.; Administration de la Nature et des Forêts, 6, rue de la Gare, 6731, Grevenmacher, Luxembourg., Holloway J; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Qld, 4001, Australia.; ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Qld, 4001, Australia., Mengersen KL; School of Mathematical Sciences, Queensland University of Technology, Brisbane, Qld, 4001, Australia.; ARC Centre of Excellence for Mathematical and Statistical Frontiers, Queensland University of Technology, Brisbane, Qld, 4001, Australia., McDonald-Madden E; School of Earth and Environmental Sciences, The University of Queensland, St Lucia, Qld, 4072, Australia.; Centre for Biodiversity and Conservation Science, The University of Queensland, St Lucia, Qld, 4072, Australia.; Centre of Excellence for Environmental Decisions, The University of Queensland, St Lucia, Qld, 4072, Australia.
Jazyk: angličtina
Zdroj: Ecology letters [Ecol Lett] 2020 Apr; Vol. 23 (4), pp. 607-619. Date of Electronic Publication: 2020 Jan 27.
DOI: 10.1111/ele.13465
Abstrakt: 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.
(© 2020 John Wiley & Sons Ltd/CNRS.)
Databáze: MEDLINE
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