Most phenomenological, statistical models used to generate ecological forecasts take either a time‐series approach, based on long‐term data from one location, or a space‐for‐time approach, based on data describing spatial patterns across environmental gradients. However, the magnitude and even the sign of environment–response relationships detected using these two approaches often differs, leading to contrasting predictions about responses to future environmental change. Here we consider how the forecast horizon determines whether more accurate predictions come from the time‐series approach, the space‐for‐time approach or a combination of the two. As proof of concept, we use simulated case studies to show that forecasts for short and long forecast horizons need to focus on different ecological processes, which are reflected in different kinds of data. First, we simulated population or community dynamics under stationary temperature using two simple, mechanistic models. Second, we fit statistical models to the simulated data using a time‐series approach, a space‐for‐time approach or a weighted average. We then forecast the response to a temperature increase using the statistical models, and compared these forecasts to temperature effects simulated by the mechanistic models. We found that the time‐series approach made accurate short‐term predictions because it captured initial conditions and effects of fast processes such as birth and death. The space‐for‐time approach made more accurate long‐term predictions because it better captured the influence of slower processes such as evolutionary and ecological selection. The weighted average made accurate predictions at all time scales, including intermediate time‐scales where the other two approaches performed poorly. A weighted average of time‐series and space‐for‐time approaches shows promise, but making this weighted model operational will require new research to predict the rate at which slow processes begin to influence dynamics.

中文翻译：

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使预测范围与相关的时空过程和数据源相匹配

用于生成生态预测的大多数现象学，统计模型都采用基于一个位置的长期数据的时间序列方法，或基于描述跨环境梯度的空间格局的数据的时空方法。但是，使用这两种方法检测到的环境-响应关系的大小甚至符号经常不同，从而导致对未来环境变化的响应的预测相反。在这里，我们考虑预测范围是如何确定更准确的预测是来自时间序列方法，时间间隔方法还是两者的组合。作为概念证明，我们使用模拟案例研究来表明，短期和长期预测范围的预测需要关注不同的生态过程，反映在不同类型的数据中。首先，我们使用两个简单的机械模型模拟了固定温度下的种群或群落动态。其次，我们使用时间序列方法，时间间隔方法或加权平均值将统计模型拟合到模拟数据。然后，我们使用统计模型预测对温度升高的响应，并将这些预测与机械模型模拟的温度影响进行比较。我们发现时间序列方法可以做出准确的短期预测，因为它可以捕获初始条件和快速过程（如出生和死亡）的影响。时空方法可以做出更准确的长期预测，因为它可以更好地捕获诸如进化和生态选择等缓慢过程的影响。加权平均值可以在所有时间范围内做出准确的预测，包括其他两种方法效果不佳的中间时间范围。时间序列方法和时间间隔方法的加权平均值显示出希望，但要使此加权模型可操作，将需要进行新的研究来预测缓慢过程开始影响动力学的速率。