Iterative near-term ecological forecasting: Needs, opportunities, and challenges.

Autor:	Dietze MC; Department of Earth and Environment, Boston University, Boston, MA 02215; dietze@bu.edu., Fox A; School of Natural Resources and the Environment, University of Arizona, Tucson, AZ 85721., Beck-Johnson LM; Department of Biology, Colorado State University, Fort Collins, CO 80523., Betancourt JL; National Research Program, Water Mission Area, US Geological Survey, Reston, VA 20192., Hooten MB; Colorado Cooperative Fish and Wildlife Research Unit, US Geological Survey, Fort Collins, CO 80523.; Department of Fish, Wildlife, and Conservation Biology, Colorado State University, Fort Collins, CO 80523.; Department of Statistics, Colorado State University, Fort Collins, CO 80523., Jarnevich CS; Fort Collins Science Center, US Geological Survey, Fort Collins, CO 80523., Keitt TH; Department of Integrative Biology, University of Texas, Austin, TX 78712., Kenney MA; Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 20740., Laney CM; Battelle, National Ecological Observatory Network, Boulder, CO 80301., Larsen LG; Department of Geography, University of California, Berkeley, CA 94720., Loescher HW; Battelle, National Ecological Observatory Network, Boulder, CO 80301.; Institute of Alpine and Arctic Research, University of Colorado Boulder, Boulder, CO 80301., Lunch CK; Battelle, National Ecological Observatory Network, Boulder, CO 80301., Pijanowski BC; Department of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47907., Randerson JT; Department of Earth System Science, University of California, Irvine, CA 92697., Read EK; Office of Water Information, US Geological Survey, Middleton, WI 53562., Tredennick AT; Department of Wildland Resources, Utah State University, Logan, UT 84322.; Ecology Center, Utah State University, Logan, UT 84322., Vargas R; Department of Plant and Soil Sciences, University of Delaware, Newark, DE 19716., Weathers KC; Cary Institute of Ecosystem Studies, Millbrook, NY 12545., White EP; Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL 32603.; Informatics Institute, University of Florida, Gainesville, FL 32603.; Biodiversity Institute, University of Florida, Gainesville, FL 32603.
Jazyk:	angličtina
Zdroj:	Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2018 Feb 13; Vol. 115 (7), pp. 1424-1432. Date of Electronic Publication: 2018 Jan 30.
DOI:	10.1073/pnas.1710231115
Abstrakt:	Two foundational questions about sustainability are "How are ecosystems and the services they provide going to change in the future?" and "How do human decisions affect these trajectories?" Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward. Competing Interests: The authors declare no conflict of interest.
Databáze:	MEDLINE
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