globalchange  > 气候变化与战略
DOI: 10.1111/ele.13465
论文题名:
Informing management decisions for ecological networks, using dynamic models calibrated to noisy time-series data
作者: Adams M.P.; Sisson S.A.; Helmstedt K.J.; Baker C.M.; Holden M.H.; Plein M.; Holloway J.; Mengersen K.L.; McDonald-Madden E.
刊名: Ecology Letters
ISSN: 1461023X
出版年: 2020
卷: 23, 期:4
起始页码: 607
结束页码: 619
语种: 英语
中文关键词: Conservation ; decision science ; ecological forecasting ; ecological modelling ; food webs ; interaction network ; population dynamics ; predator–prey interactions ; prediction ; uncertainty propagation
英文关键词: biological model ; ecology ; ecosystem ; population dynamics ; Ecology ; Ecosystem ; Models, Biological ; Population Dynamics
英文摘要: 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
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/166574
Appears in Collections:气候变化与战略

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作者单位: 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; School of Mathematics and Statistics, The University of New South Wales, Sydney, NSW 2052, Australia; 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; 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; Centre for Applications in Natural Resource Mathematics, School of Mathematics and Physics, The University of Queensland, St Lucia, QLD 4072, Australia; Administration de la Nature et des Forêts, 6, rue de la Gare, Grevenmacher, 6731, Luxembourg

Recommended Citation:
Adams M.P.,Sisson S.A.,Helmstedt K.J.,et al. Informing management decisions for ecological networks, using dynamic models calibrated to noisy time-series data[J]. Ecology Letters,2020-01-01,23(4)
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