The practice of prediction: What can ecologists learn from applied, ecology-related fields?

Ecological Complexity
By: , and 



The pervasive influence of human induced global environmental change affects biodiversity across the globe, and there is great uncertainty as to how the biosphere will react on short and longer time scales. To adapt to what the future holds and to manage the impacts of global change, scientists need to predict the expected effects with some confidence and communicate these predictions to policy makers. However, recent reviews found that we currently lack a clear understanding of how predictable ecology is, with views seeing it as mostly unpredictable to potentially predictable, at least over short time frames. However, in applied, ecology-related fields predictions are more commonly formulated and reported, as well as evaluated in hindsight, potentially allowing one to define baselines of predictive proficiency in these fields. We searched the literature for representative case studies in these fields and collected information about modeling approaches, target variables of prediction, predictive proficiency achieved, as well as the availability of data to parameterize predictive models. We find that some fields such as epidemiology achieve high predictive proficiency, but even in the more predictive fields proficiency is evaluated in different ways. Both phenomenological and mechanistic approaches are used in most fields, but differences are often small, with no clear superiority of one approach over the other. Data availability is limiting in most fields, with long-term studies being rare and detailed data for parameterizing mechanistic models being in short supply. We suggest that ecologists adopt a more rigorous approach to report and assess predictive proficiency, and embrace the challenges of real world decision making to strengthen the practice of prediction in ecology.

Publication type Article
Publication Subtype Journal Article
Title The practice of prediction: What can ecologists learn from applied, ecology-related fields?
Series title Ecological Complexity
DOI 10.1016/j.ecocom.2016.12.005
Volume 32
Issue B
Year Published 2017
Language English
Publisher Elsevier
Contributing office(s) Wetland and Aquatic Research Center
Description 12 p.
First page 156
Last page 167
Online Only (Y/N) Y
Google Analytic Metrics Metrics page
Additional publication details