Outstanding Challenges in the Transferability of Ecological Models

Katherine L. Yates; Phil J. Bouchet; M. Julian Caley; Kerrie Mengersen; Christophe F. Randin; Stephen Parnell; Alan H. Fielding; Andrew J. Bamford; Stephen Ban; A. Márcia Barbosa; Carsten F. Dormann; Jane Elith; Clare B. Embling; Gary N. Ervin; Rebecca Fisher; Susan Gould; Roland F. Graf; Edward J. Gregr; Patrick N. Halpin; Risto K. Heikkinen; Stefan Heinänen; Alice R. Jones; Periyadan K. Krishnakumar; Valentina Lauria; Hector Lozano-Montes; Laura Mannocci; Camille Mellin; Mohsen B. Mesgaran; Elena Moreno-Amat; Sophie Mormede; Emilie Novaczek; Steffen Oppel; Crespo G Ortuño; A. Townsend Peterson; Giovanni Rapacciuolo; Jason J. Roberts; Rebecca E. Ross; Kylie L. Scales; David Schoeman; Paul Snelgrove; Göran Sundblad; Wilfried Thuiller; Leigh G. Torres; Heroen Verbruggen; Lifei Wang; Seth Wenger; Mark J. Whittingham; Yuri Zharikov; Damaris Zurell; Ana M.M. Sequeira

doi:10.1016/j.tree.2018.08.001

Outstanding Challenges in the Transferability of Ecological Models

Katherine L. Yates^*
, Phil J. Bouchet
, M. Julian Caley
, Kerrie Mengersen
, Christophe F. Randin
, Stephen Parnell
, Alan H. Fielding
, Andrew J. Bamford
, Stephen Ban
, A. Márcia Barbosa
, Carsten F. Dormann
, Jane Elith
, Clare B. Embling
, Gary N. Ervin
, Rebecca Fisher
, Susan Gould
, Roland F. Graf
, Edward J. Gregr
, Patrick N. Halpin
, Risto K. Heikkinen

Stefan Heinänen, Alice R. Jones, Periyadan K. Krishnakumar, Valentina Lauria, Hector Lozano-Montes, Laura Mannocci, Camille Mellin, Mohsen B. Mesgaran, Elena Moreno-Amat, Sophie Mormede, Emilie Novaczek, Steffen Oppel, Crespo G Ortuño, A. Townsend Peterson, Giovanni Rapacciuolo, Jason J. Roberts, Rebecca E. Ross, Kylie L. Scales, David Schoeman, Paul Snelgrove, Göran Sundblad, Wilfried Thuiller, Leigh G. Torres, Heroen Verbruggen, Lifei Wang, Seth Wenger, Mark J. Whittingham, Yuri Zharikov, Damaris Zurell, Ana M.M. Sequeira

^*Corresponding author for this work

School of Biological and Marine Sciences

Joint first authors
University of Queensland
University of Salford
University of Western Australia
Queensland University of Technology
University of Lausanne
Haworth Conservation Ltd
Wildfowl and Wetlands Trust
Canadian Parks and Wilderness Society
University of Évora
University of Freiburg
University of Melbourne
Mississippi State University
Griffith University Queensland
Zurich University of Applied Sciences
SciTech Environmental Consulting
University of British Columbia
Duke University
Finnish Environment Institute
DHI Water - Environment - Health
University of Adelaide
King Fahd University of Petroleum and Minerals
National Research Council of Italy
Université de Montpellier
Australian Institute of Marine Science
University of California at Davis
Technical University of Madrid
NIWA
Memorial University of Newfoundland
Royal Society for the Protection of Birds
University of Kansas
University of California Merced
University of the Sunshine Coast
Nelson Mandela University
Swedish University of Agricultural Sciences
Université Grenoble Alpes
Oregon State University
Gulf Maine Research Institute
University of Toronto
University of Georgia
Newcastle University
Parks Canada
Humboldt University of Berlin
Swiss Federal Institute for Forest, Snow and Landscape Research

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Abstract

Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions

Original language	English
Pages (from-to)	790-802
Number of pages	0
Journal	Trends in Ecology and Evolution
Volume	33
Issue number	10
Early online date	1 Oct 2018
DOIs	https://doi.org/10.1016/j.tree.2018.08.001
Publication status	Published - 1 Oct 2018

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10.1016/j.tree.2018.08.001

PIIS0169534718301812

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Yates, K. L., Bouchet, P. J., Caley, M. J., Mengersen, K., Randin, C. F., Parnell, S., Fielding, A. H., Bamford, A. J., Ban, S., Barbosa, A. M., Dormann, C. F., Elith, J., Embling, C. B., Ervin, G. N., Fisher, R., Gould, S., Graf, R. F., Gregr, E. J., Halpin, P. N., ... Sequeira, A. M. M. (2018). Outstanding Challenges in the Transferability of Ecological Models. Trends in Ecology and Evolution, 33(10), 790-802. https://doi.org/10.1016/j.tree.2018.08.001

@article{72de76f009454c51b73cb12ede475e7e,

title = "Outstanding Challenges in the Transferability of Ecological Models",

abstract = "Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions",

author = "Yates, \{Katherine L.\} and Bouchet, \{Phil J.\} and Caley, \{M. Julian\} and Kerrie Mengersen and Randin, \{Christophe F.\} and Stephen Parnell and Fielding, \{Alan H.\} and Bamford, \{Andrew J.\} and Stephen Ban and Barbosa, \{A. M{\'a}rcia\} and Dormann, \{Carsten F.\} and Jane Elith and Embling, \{Clare B.\} and Ervin, \{Gary N.\} and Rebecca Fisher and Susan Gould and Graf, \{Roland F.\} and Gregr, \{Edward J.\} and Halpin, \{Patrick N.\} and Heikkinen, \{Risto K.\} and Stefan Hein{\"a}nen and Jones, \{Alice R.\} and Krishnakumar, \{Periyadan K.\} and Valentina Lauria and Hector Lozano-Montes and Laura Mannocci and Camille Mellin and Mesgaran, \{Mohsen B.\} and Elena Moreno-Amat and Sophie Mormede and Emilie Novaczek and Steffen Oppel and Ortu{\~n}o, \{Crespo G\} and Peterson, \{A. Townsend\} and Giovanni Rapacciuolo and Roberts, \{Jason J.\} and Ross, \{Rebecca E.\} and Scales, \{Kylie L.\} and David Schoeman and Paul Snelgrove and G{\"o}ran Sundblad and Wilfried Thuiller and Torres, \{Leigh G.\} and Heroen Verbruggen and Lifei Wang and Seth Wenger and Whittingham, \{Mark J.\} and Yuri Zharikov and Damaris Zurell and Sequeira, \{Ana M.M.\}",

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doi = "10.1016/j.tree.2018.08.001",

language = "English",

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journal = "Trends in Ecology and Evolution",

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Yates, KL, Bouchet, PJ, Caley, MJ, Mengersen, K, Randin, CF, Parnell, S, Fielding, AH, Bamford, AJ, Ban, S, Barbosa, AM, Dormann, CF, Elith, J, Embling, CB, Ervin, GN, Fisher, R, Gould, S, Graf, RF, Gregr, EJ, Halpin, PN, Heikkinen, RK, Heinänen, S, Jones, AR, Krishnakumar, PK, Lauria, V, Lozano-Montes, H, Mannocci, L, Mellin, C, Mesgaran, MB, Moreno-Amat, E, Mormede, S, Novaczek, E, Oppel, S, Ortuño, CG, Peterson, AT, Rapacciuolo, G, Roberts, JJ, Ross, RE, Scales, KL, Schoeman, D, Snelgrove, P, Sundblad, G, Thuiller, W, Torres, LG, Verbruggen, H, Wang, L, Wenger, S, Whittingham, MJ, Zharikov, Y, Zurell, D & Sequeira, AMM 2018, 'Outstanding Challenges in the Transferability of Ecological Models', Trends in Ecology and Evolution, vol. 33, no. 10, pp. 790-802. https://doi.org/10.1016/j.tree.2018.08.001

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T1 - Outstanding Challenges in the Transferability of Ecological Models

AU - Yates, Katherine L.

AU - Bouchet, Phil J.

AU - Caley, M. Julian

AU - Mengersen, Kerrie

AU - Randin, Christophe F.

AU - Parnell, Stephen

AU - Fielding, Alan H.

AU - Bamford, Andrew J.

AU - Ban, Stephen

AU - Barbosa, A. Márcia

AU - Dormann, Carsten F.

AU - Elith, Jane

AU - Embling, Clare B.

AU - Ervin, Gary N.

AU - Fisher, Rebecca

AU - Gould, Susan

AU - Graf, Roland F.

AU - Gregr, Edward J.

AU - Halpin, Patrick N.

AU - Heikkinen, Risto K.

AU - Heinänen, Stefan

AU - Jones, Alice R.

AU - Krishnakumar, Periyadan K.

AU - Lauria, Valentina

AU - Lozano-Montes, Hector

AU - Mannocci, Laura

AU - Mellin, Camille

AU - Mesgaran, Mohsen B.

AU - Moreno-Amat, Elena

AU - Mormede, Sophie

AU - Novaczek, Emilie

AU - Oppel, Steffen

AU - Ortuño, Crespo G

AU - Peterson, A. Townsend

AU - Rapacciuolo, Giovanni

AU - Roberts, Jason J.

AU - Ross, Rebecca E.

AU - Scales, Kylie L.

AU - Schoeman, David

AU - Snelgrove, Paul

AU - Sundblad, Göran

AU - Thuiller, Wilfried

AU - Torres, Leigh G.

AU - Verbruggen, Heroen

AU - Wang, Lifei

AU - Wenger, Seth

AU - Whittingham, Mark J.

AU - Zharikov, Yuri

AU - Zurell, Damaris

AU - Sequeira, Ana M.M.

PY - 2018/10/1

Y1 - 2018/10/1

N2 - Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions

AB - Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions

UR - https://pearl.plymouth.ac.uk/bms-research/142/

U2 - 10.1016/j.tree.2018.08.001

DO - 10.1016/j.tree.2018.08.001

M3 - Article

SN - 0169-5347

VL - 33

SP - 790

EP - 802

JO - Trends in Ecology and Evolution

JF - Trends in Ecology and Evolution

IS - 10

ER -

Outstanding Challenges in the Transferability of Ecological Models

Abstract

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