Petchey Owen L, Pontarp Mikael, Massie Thomas M, Kéfi Sonia, Ozgul Arpat, Weilenmann Maja, Palamara Gian Marco, Altermatt Florian, Matthews Blake, Levine Jonathan M, Childs Dylan Z, McGill Brian J, Schaepman Michael E, Schmid Bernhard, Spaak Piet, Beckerman Andrew P, Pennekamp Frank, Pearse Ian S
Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
Department of Aquatic Ecology, Eawag: Swiss Federal Institute of Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland.
Ecol Lett. 2015 Jul;18(7):597-611. doi: 10.1111/ele.12443. Epub 2015 May 7.
Forecasts of ecological dynamics in changing environments are increasingly important, and are available for a plethora of variables, such as species abundance and distribution, community structure and ecosystem processes. There is, however, a general absence of knowledge about how far into the future, or other dimensions (space, temperature, phylogenetic distance), useful ecological forecasts can be made, and about how features of ecological systems relate to these distances. The ecological forecast horizon is the dimensional distance for which useful forecasts can be made. Five case studies illustrate the influence of various sources of uncertainty (e.g. parameter uncertainty, environmental variation, demographic stochasticity and evolution), level of ecological organisation (e.g. population or community), and organismal properties (e.g. body size or number of trophic links) on temporal, spatial and phylogenetic forecast horizons. Insights from these case studies demonstrate that the ecological forecast horizon is a flexible and powerful tool for researching and communicating ecological predictability. It also has potential for motivating and guiding agenda setting for ecological forecasting research and development.
预测不断变化的环境中的生态动态变得越来越重要,并且可以针对大量变量进行预测,例如物种丰度和分布、群落结构以及生态系统过程。然而,对于能够做出有用的生态预测的未来时间跨度,或者其他维度(空间、温度、系统发育距离),以及生态系统的特征如何与这些距离相关,人们普遍缺乏了解。生态预测范围是能够做出有用预测的维度距离。五个案例研究说明了各种不确定性来源(例如参数不确定性、环境变化、种群统计随机性和进化)、生态组织水平(例如种群或群落)以及生物体特性(例如体型或营养联系数量)对时间、空间和系统发育预测范围的影响。这些案例研究的见解表明,生态预测范围是研究和交流生态可预测性的灵活而强大的工具。它还有助于推动和指导生态预测研究与开发的议程设定。