Growing interest in ecological forecasting presents a new opportunity to address fundamental questions about the predictability of nature. Are some kinds of ecological responses easier to predict than others? Does predictability vary with the level of ecological organization, spatial or temporal scale, phylogeny, abiotic conditions, or modeling approach? EFI's Theory working group is formulating hypotheses about the factors that determine the predictability of ecological dynamics. Some of these hypotheses can be tested with simulations. To demonstrate this approach, we considered the hypothesis that different forecast horizons could require different modeling approaches. We asked, at what forecast horizons will more accurate predictions about species’ responses to environmental change come from a time-series approach, a space-for-time approach, or a combination of the two? We addressed this question with two simulation case studies, one featuring a metacommunity model, and the second an eco-evolutionary model.
Results/Conclusions
We found that forecasts for short and long forecast horizons 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 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. Our approach could be applied to many of the hypotheses about predictability now being generated by the EFI Theory working group.