REVIEW: Predictive ecology in a changing world

Abstract

1. In a rapidly changing world, ecology has the potential to move from empirical and conceptual stages to application and management issues. It is now possible to make large-scale predictions up to continental or global scales, ranging from the future distribution of biological diversity to changes in ecosystem functioning and services. With these recent developments, ecology has a historical opportunity to become a major actor in the development of a sustainable human society. With this opportunity, however, also comes an important responsibility in developing appropriate predictive models, correctly interpreting their outcomes and communicating their limitations. There is also a danger that predictions grow faster than our understanding of ecological systems, resulting in a gap between the scientists generating the predictions and stakeholders using them (conservation biologists, environmental managers, journalists, policymakers). 2. Here, we use the context provided by the current surge of ecological predictions on the future of biodiversity to clarify what prediction means, and to pinpoint the challenges that should be addressed in order to improve predictive ecological models and the way they are understood and used.3. Synthesis and applications. Ecologists face several challenges to ensure the healthy development of an operational predictive ecological science: (i) clarity on the distinction between explanatory and anticipatory predictions; (ii) developing new theories at the interface between explanatory and anticipatory predictions; (iii) open data to test and validate predictions; (iv) making predictions operational; and (v) developing a genuine ethics of prediction. Supporting InformationAdditional Supporting Information may be found in the online version of this article.Appendix S1. Characteristics of mechanistic and phenomenological models in ecology.Appendix S2. Non-exhaustive list, of international initiatives of the scientific community aiming for sharing ecological data.

Outstanding Challenges in the Transferability of Ecological Models

“…This leads to a catch-22, where the absence of knowledge encourages the search for transferable models but also impedes their evaluation. Concerted efforts to increase both data quality and data availability are therefore crucial to enhancing the practice of model transfers in ecology [2,76]. Ideally, data should be: unbiased, with explicit coverage of important gradients, high-frequency, long-term, and real-time, so as to maximize opportunities for anticipatory predictions that can be validated with minimal delay [76].…”

“…Predictive models have thus become ubiquitous in numerous scientific disciplines, including ecology [2], where they provide means for mapping species distributions, explaining population trends, or quantifying the risks of biological invasions and disease outbreaks (e.g., [3,4]). The practical value of predictive models in supporting policy and decision making has therefore grown rapidly (Box 1) [5].…”

“…The practical value of predictive models in supporting policy and decision making has therefore grown rapidly (Box 1) [5]. With that has come an increasing desire to predict (see Glossary) the state of ecological features (e.g., species, habitats) and our likely impacts upon them [5], prompting a shift from explanatory models to anticipatory predictions [2]. However, in many situations, severe data deficiencies preclude the development of specific models, and the collection of new data can be prohibitively costly or simply impossible [6].…”

“…Correlative and mechanistic modeling are two contrasting modeling philosophies that respectively emphasize patterns versus processes [2,87]. Correlative models draw statistical linkages between response variables (e.g., species occurrence) and features of the environment (i.e., biotic and abiotic predictors), but have been criticized for failing to explicitly capture the underlying processes (e.g., dispersal ability) that affect said response variable [41].…”

Predicting stochastic community dynamics in grasslands under the assumption of competitive symmetry

“…The 58 next step is to quantitatively relate empirical observations to the underlying dynamical processes (Jabot & Chave 2011;de Mazancourt et al 2013). In this vein, a growing number 60 of studies aim at building dynamical models of community dynamics based on explicit ecological processes, and at calibrating these models with field data (Lande et al 2003;62 Beaumont 2010), thereby belonging to the more general trend towards a more predictive ecology (Mouquet et al 2015). 64…”