Matching the forecast horizon with the relevant ecological processes

Abstract Most 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. Here we consider how the forecast horizon determines whether the most accurate predictions come from the time-series approach, the space-for-time approach, or a combination of the two. We use two simulation case studies to show that forecasts for short and long-time scales need to focus on different ecological processes, which are reflected in different kinds of data. In the short-term, dynamics reflect initial conditions and fast processes such as birth and death, and the phenomenological time-series approach makes the best predictions. In the long-term, dynamics reflect the additional influence of slower processes such as evolutionary and ecological selection, colonization and extinction, which the space-for-time approach can effectively capture. At intermediate time-scales, a weighted average of the two approaches shows promise. However, making this weighted model operational will require new research to predict the rate at which slow processes begin to influence dynamics.

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