Near-term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has been no cross-ecosystem analysis of near-term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near-term (≤10-yr forecast horizon) ecological forecasting papers to understand the development and current state of near-term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated that near-term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, some best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near-term ecological forecasting has the potential to make significant contributions to our understanding of forecastability across scales and variables. In this study, we found that forecastability (defined here as realized forecast accuracy) decreased in predictable patterns over 1–7 d forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in forecastability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables.

中文翻译：

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在生态预测中更多地采用最佳实践可以比较可预测性

近期迭代预测是生态决策支持的有力工具，并有可能改变我们对生态可预测性的理解。然而，到目前为止，还没有对近期生态预测进行跨生态系统分析，因此很难综合各种研究工作并优先考虑这一新兴领域的未来发展。在这项研究中，我们分析了 178 篇近期（≤10 年预测期）生态预测论文，以了解近期生态预测文献的发展和现状，并比较跨尺度和变量的预测准确性。我们的研究结果表明，近期生态预测很普遍并且不断增长：已经对所有七大洲的站点进行了预测，并且预测发布的速度随着时间的推移而增加。随着预测生产的加速，一些最佳实践被提出，这些最佳实践的应用正在增加。特别是，随着时间的推移，数据发布、预测存档和工作流自动化都显着增加。然而，总体而言，建议的最佳实践的采用率仍然很低：例如，尽管不确定性经常被认为是生态预测的重要组成部分，但只有 45% 的论文在其预测输出中包含了不确定性。随着这些建议的最佳实践的使用增加，近期生态预测有可能对我们对跨尺度和变量的可预测性的理解做出重大贡献。在这项研究中，我们发现可预测性（此处定义为实现的预测准确性）在 1-7 天的预测范围内以可预测的模式下降。密切相关的变量（即叶绿素和浮游植物）在可预测性方面表现出非常相似的趋势，而更远相关的变量（即花粉和蒸散量）则表现出显着不同的模式。在生态预测中越来越多地使用建议的最佳实践将使我们能够检查未来其他变量和时间尺度的可预测性，为生态变量的基本可预测性提供稳健的分析。花粉和蒸散）表现出明显不同的模式。在生态预测中越来越多地使用建议的最佳实践将使我们能够检查未来其他变量和时间尺度的可预测性，为生态变量的基本可预测性提供稳健的分析。花粉和蒸散）表现出明显不同的模式。在生态预测中越来越多地使用建议的最佳实践将使我们能够检查未来其他变量和时间尺度的可预测性，为生态变量的基本可预测性提供稳健的分析。