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., Le Gall, L., Meslin, L., Morand, S., Morin, X., Morlon, H., Pinay, G., Pradel, R., Schurr, F.M., Thuiller, W. and Loreau, M. (2015).
Journal of Applied Ecology, 52, 1293-1310, doi:10.1111/1365-2664.12482
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.
(A) The need for surprise in ecology. The tentative reintroduction of rock lobsters in the South African Marcus Island failed because the released lobsters were immediately attacked and consumed by the overabundant whelks, which used to be their prey (Barkai & McQuaid 1988). Illustration Laurence Meslin. (B) The need for deontology to gain in credibility. In 1986, James Lighthill, president of the International Union of Theoretical and Applied Mechanics, made this statement: Here I have to pause, and to speak once again on behalf of the broad global fraternity of practitioners of mechanics. We are all deeply conscious today that the enthusiasm of our forebears for the marvellous achievements of Newtonian mechanics led them to make generalizations in this area of predictability which, indeed, we may have generally tended to believe before 1960, but which we now recognize were false. We collectively wish to apologize for having misled the general educated public by spreading ideas about the determinism of systems satisfying Newton laws of motion that, after 1960, were to be proved incorrect. This example of scientific integrity should motivate ecologists to build predictive ecology upon a strong deontological background to avoid having to make a similar statement in 20 years. Illustration (c) Laurence Meslin. (C) Data in ecology are organized along two constraints of control and scale of observation. These two axes trade off and allow addressing either ecological processes or patterns. This compromise limits our ability to address ecological complexity at particular spatial and temporal scales. The scales of projections needed to forecast the future of biodiversity and ecosystem functioning (mostly in zone b) concern scales that are not often reachable. (D) Data information content as a function of time. After being published by researchers, information content in dark data is naturally declining with time (lower curve). Inversely, information content in open data is continuously increasing with time (upper curve). Figure modified from Michener et al. (1997).
OTHER TOPICS: Aesthetics of Biodiversity, Biodiversity & Ecosystem Functioning, Biogeography, Macroecology & Ecophylogenetics, Experimental Evolution,
Functional Biogeography, Functional Rarity, Metacommunities, Metaecosystems, Reviews and Synthesis, Trophic Biogeography & Metaweb