Journal of Applied Ecology · 2015

Predictive ecology in a changing world

Mouquet N., Lagadeuc Y., Devictor V., Doyen L., Duputie A., Eveillard D., Faure D., Garnier E., Gimenez O., Huneman P., Jabot F., Jarne P., Joly D., Julliard R., Kefi S., Kergoat G.J., Lavorel S., Gall L.L., Meslin L., Morand S., Morin X., Morlon H., Pinay G., Pradel R., Schurr F.M., Thuiller W., Loreau M.

doi.org/10.1111/1365-2664.12482
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Key Message

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. Scientific 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. 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. 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.

Figure from Mouquet et al. 2015
(A) The need for surprise in ecology. The reintroduction of rock lobsters on Marcus Island (South Africa) failed because they were immediately consumed by overabundant whelks, formerly their prey (Barkai & McQuaid 1988). Illustration Laurence Meslin. (B) The need for deontology to maintain credibility. In 1986, James Lighthill, president of the International Union of Theoretical and Applied Mechanics, acknowledged that confidence in the predictability of Newtonian systems had been overstated and formally apologized for misleading the public. This example of scientific integrity highlights the need for ecology to adopt strong deontological principles when developing predictive frameworks. Illustration Laurence Meslin. (C) Ecological data are structured along two constraints, control and scale of observation, which trade off and limit our ability to address complexity across spatial and temporal scales. The scales required to forecast biodiversity and ecosystem functioning often remain difficult to reach. (D) Data information content changes over time: it declines for unpublished dark data but increases for open data. Figure modified from Michener et al. (1997).
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