Synthesizing forecasts to inform decision‐making and advance ecological theory
Synthesizing forecasts to inform decision‐making and advance ecological theory
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DOI:
10.1111/2041-210x.14070
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发表时间:
2023-03
影响因子:
6.6
通讯作者:
S. Record;C. Boettiger;C. Rollinson
中科院分区:
文献类型:
--
作者:
S. Record;C. Boettiger;C. Rollinson
Since the inception of the Intergovernmental Panel on Climate Change in 1988, there has been growing scientific consensus that humans have modified the Earth's environment and that human decisions have the potential to mitigate or exacerbate the effects of future global change (Intergovernmental Panel on Climate Change, 2022). In the face of widespread environmental change, society needs information to make sound decisions. Ecological forecasts provide such information about how ecosystems and their services may respond to different environmental conditions before they happen and considering alternative management scenarios. Following the publication of the first IPCC report in 1990 that called for a better understanding of the future effects of climatic change, the number of ecological publications focused on forecasting increased exponentially (Figure 1). The notion of forecasting ecosystem change in ecology is not new (e.g. Clark et al., 2001; Hodgson, 1932). However, as the number of ecological papers focused on forecasting grew in the context of climatic change, ecologists from across subdisciplines became increasingly concerned with various aspects of forecasting. Paleoecologists acknowledged the limits of our ability to forecast into nonanalog climatic conditions when using forecast horizons suggested by the IPCC (e.g. anticipating conditions 60– 80 years into the future; Williams & Jackson, 2007). Applied and theoretical ecologists questioned the utility of longterm forecasts for advancing decisionmaking and ecological theory, respectively (Houlahan et al., 2017; Mouquet et al., 2015). At the same time as these concerns, ecologists recognized the transformative potential of nearterm, iterative forecasting for making ecological forecasts more relevant to decision