TY - JOUR
T1 - The practice of prediction
T2 - What can ecologists learn from applied, ecology-related fields?
AU - Pennekamp, Frank
AU - Adamson, Matthew W.
AU - Petchey, Owen L.
AU - Poggiale, Jean Christophe
AU - Aguiar, Maíra
AU - Kooi, Bob W.
AU - Botkin, Daniel B.
AU - DeAngelis, Donald L.
N1 - sem pdf conforme despacho.
Swiss National Science Foundation Grant 31003A_159498. DLD was supported by the USGS’s Greater Everglades Priority Ecosystem Research program. MA was funded by DENFREE (grant 282378) and supported by Fundação para a Ciência e a Tecnologia (grant UID/MAT/04561/2013).
PY - 2017/12/1
Y1 - 2017/12/1
N2 - 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.
AB - 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.
KW - Forecast
KW - Forecast horizon
KW - Hindcast
KW - Predictive proficiency
UR - http://www.scopus.com/inward/record.url?scp=85007574387&partnerID=8YFLogxK
U2 - 10.1016/j.ecocom.2016.12.005
DO - 10.1016/j.ecocom.2016.12.005
M3 - Article
AN - SCOPUS:85007574387
SN - 1476-945X
VL - 32
SP - 156
EP - 167
JO - Ecological Complexity
JF - Ecological Complexity
ER -