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Incorporating uncertainty in predictive species distribution modelling

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Incorporating uncertainty in predictive species distribution modelling. / Beale, Colin M.; Lennon, Jack J.

In: Philosophical Transactions Of The Royal Society Of London Series B - Biological Sciences, Vol. 367, No. 1586, 19.01.2012, p. 247-258.

Research output: Contribution to journalLiterature reviewpeer-review

Harvard

Beale, CM & Lennon, JJ 2012, 'Incorporating uncertainty in predictive species distribution modelling', Philosophical Transactions Of The Royal Society Of London Series B - Biological Sciences, vol. 367, no. 1586, pp. 247-258. https://doi.org/10.1098/rstb.2011.0178

APA

Beale, C. M., & Lennon, J. J. (2012). Incorporating uncertainty in predictive species distribution modelling. Philosophical Transactions Of The Royal Society Of London Series B - Biological Sciences, 367(1586), 247-258. https://doi.org/10.1098/rstb.2011.0178

Vancouver

Beale CM, Lennon JJ. Incorporating uncertainty in predictive species distribution modelling. Philosophical Transactions Of The Royal Society Of London Series B - Biological Sciences. 2012 Jan 19;367(1586):247-258. https://doi.org/10.1098/rstb.2011.0178

Author

Beale, Colin M. ; Lennon, Jack J. / Incorporating uncertainty in predictive species distribution modelling. In: Philosophical Transactions Of The Royal Society Of London Series B - Biological Sciences. 2012 ; Vol. 367, No. 1586. pp. 247-258.

Bibtex - Download

@article{87e8599076094334ae260ac1c2bf042c,
title = "Incorporating uncertainty in predictive species distribution modelling",
abstract = "Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.",
author = "Beale, {Colin M.} and Lennon, {Jack J.}",
year = "2012",
month = jan,
day = "19",
doi = "10.1098/rstb.2011.0178",
language = "English",
volume = "367",
pages = "247--258",
journal = "Philosophical Transactions of the Royal Society B: Biological Sciences",
issn = "1471-2970",
publisher = "Royal Society of London",
number = "1586",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Incorporating uncertainty in predictive species distribution modelling

AU - Beale, Colin M.

AU - Lennon, Jack J.

PY - 2012/1/19

Y1 - 2012/1/19

N2 - Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

AB - Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.

UR - http://www.scopus.com/inward/record.url?scp=82755174178&partnerID=8YFLogxK

U2 - 10.1098/rstb.2011.0178

DO - 10.1098/rstb.2011.0178

M3 - Literature review

VL - 367

SP - 247

EP - 258

JO - Philosophical Transactions of the Royal Society B: Biological Sciences

JF - Philosophical Transactions of the Royal Society B: Biological Sciences

SN - 1471-2970

IS - 1586

ER -