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

Frank Pennekamp, Matthew W Adamson, Owen L. Petchey, Jean-Christophe Poggiale, Maíra Aguiar, B.W. Kooi, Daniel B. Botkin, Donald L. DeAngelis

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)156-167
JournalEcological Complexity
Volume32, Part B
Issue numberDecember
DOIs
Publication statusPublished - 5 Dec 2017

Fingerprint

ecologists
ecology
prediction
global change
epidemiology
biosphere
environmental change
mechanistic models
decision making
biodiversity
timescale
applied ecology
uncertainty
case studies
modeling

Keywords

  • Forecast
  • Hindcast
  • Forecast horizon

Cite this

Pennekamp, F., Adamson, M. W., Petchey, O. L., Poggiale, J-C., Aguiar, M., Kooi, B. W., ... DeAngelis, D. L. (2017). The practice of prediction: What can ecologists learn from applied, ecology-related fields? Ecological Complexity, 32, Part B(December), 156-167. https://doi.org/10.1016/j.ecocom.2016.12.005
Pennekamp, Frank ; Adamson, Matthew W ; Petchey, Owen L. ; Poggiale, Jean-Christophe ; Aguiar, Maíra ; Kooi, B.W. ; Botkin, Daniel B. ; DeAngelis, Donald L. . / The practice of prediction: What can ecologists learn from applied, ecology-related fields?. In: Ecological Complexity. 2017 ; Vol. 32, Part B, No. December. pp. 156-167.
@article{d7407ce4a0f1464d8a743a4d6b780771,
title = "The practice of prediction: What can ecologists learn from applied, ecology-related fields?",
abstract = "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 theexpected 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 collectedinformation 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 isevaluated 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.",
keywords = "Forecast , Hindcast, Forecast horizon",
author = "Frank Pennekamp and Adamson, {Matthew W} and Petchey, {Owen L.} and Jean-Christophe Poggiale and Ma{\'i}ra Aguiar and B.W. Kooi and Botkin, {Daniel B.} and DeAngelis, {Donald L.}",
year = "2017",
month = "12",
day = "5",
doi = "10.1016/j.ecocom.2016.12.005",
language = "English",
volume = "32, Part B",
pages = "156--167",
journal = "Ecological Complexity",
issn = "1476-945X",
publisher = "Elsevier",
number = "December",

}

Pennekamp, F, Adamson, MW, Petchey, OL, Poggiale, J-C, Aguiar, M, Kooi, BW, Botkin, DB & DeAngelis, DL 2017, 'The practice of prediction: What can ecologists learn from applied, ecology-related fields?' Ecological Complexity, vol. 32, Part B, no. December, pp. 156-167. https://doi.org/10.1016/j.ecocom.2016.12.005

The practice of prediction: What can ecologists learn from applied, ecology-related fields? / Pennekamp, Frank; Adamson, Matthew W; Petchey, Owen L.; Poggiale, Jean-Christophe; Aguiar, Maíra; Kooi, B.W.; Botkin, Daniel B. ; DeAngelis, Donald L. .

In: Ecological Complexity, Vol. 32, Part B, No. December, 05.12.2017, p. 156-167.

Research output: Contribution to JournalArticleAcademicpeer-review

TY - JOUR

T1 - The practice of prediction: 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, B.W.

AU - Botkin, Daniel B.

AU - DeAngelis, Donald L.

PY - 2017/12/5

Y1 - 2017/12/5

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 theexpected 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 collectedinformation 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 isevaluated 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 theexpected 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 collectedinformation 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 isevaluated 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 - Hindcast

KW - Forecast horizon

U2 - 10.1016/j.ecocom.2016.12.005

DO - 10.1016/j.ecocom.2016.12.005

M3 - Article

VL - 32, Part B

SP - 156

EP - 167

JO - Ecological Complexity

JF - Ecological Complexity

SN - 1476-945X

IS - December

ER -

Pennekamp F, Adamson MW, Petchey OL, Poggiale J-C, Aguiar M, Kooi BW et al. The practice of prediction: What can ecologists learn from applied, ecology-related fields? Ecological Complexity. 2017 Dec 5;32, Part B(December):156-167. https://doi.org/10.1016/j.ecocom.2016.12.005