What we use is not what we know: environmental predictors in plant distribution models
Aims: The choice of environmental predictor variables in correlative models of plant species distributions (hereafter SDMs) is crucial to ensure predictive accuracy and model realism, as highlighted in multiple earlier studies. Because variable selection is directly related to a model's capacit...
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| Published in | Journal of vegetation science Vol. 27; no. 6; pp. 1308 - 1322 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Blackwell Publishing Ltd
01.11.2016
John Wiley & Sons Ltd |
| Subjects | |
| Online Access | Get full text |
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| Abstract | Aims: The choice of environmental predictor variables in correlative models of plant species distributions (hereafter SDMs) is crucial to ensure predictive accuracy and model realism, as highlighted in multiple earlier studies. Because variable selection is directly related to a model's capacity to capture important species' environmental requirements, one would expect an explicit prior consideration of all ecophysiologically meaningful variables. For plants, these include temperature, water, soil nutrients, light, and in some cases, disturbances and biotic interactions. However, the set of predictors used in published correlative plant SDM studies varies considerably. No comprehensive review exists of what environmental predictors are meaningful, available (or missing) and used in practice to predict plant distributions. Contributing to answer these questions is the aim of this review. Methods: We carried out an extensive, systematic review of recently published plant SDM studies (years 2010-2015; n = 200) to determine the predictors used (and not used) in the models. We additionally conducted an in-depth review of SDM studies in selected journals to identify temporal trends in the use of predictors (years 2000-2015; n = 40). Results: A large majority of plant SDM studies neglected several ecophysiologically meaningful environmental variables, and the number of relevant predictors used in models has stagnated or even declined over the last 15 yr. Conclusions: Neglecting ecophysiologically meaningful predictors can result in incomplete niche quantification and can thus limit the predictive power of plant SDMs. Some of these missing predictors are already available spatially or may soon become available (e.g. soil moisture). However, others are not yet easily obtainable across whole study extents (e.g. soil pH and nutrients), and their development should receive increased attention. We conclude that more effort should be made to build ecologically more sound plant SDMs. This requires a more thorough rationale for the choice of environmental predictors needed to meet the study goal, and the development of missing ones. The latter calls for increased collaborative effort between ecological and geo-environmental sciences. |
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| AbstractList | AIMS: The choice of environmental predictor variables in correlative models of plant species distributions (hereafter SDMs) is crucial to ensure predictive accuracy and model realism, as highlighted in multiple earlier studies. Because variable selection is directly related to a model's capacity to capture important species' environmental requirements, one would expect an explicit prior consideration of all ecophysiologically meaningful variables. For plants, these include temperature, water, soil nutrients, light, and in some cases, disturbances and biotic interactions. However, the set of predictors used in published correlative plant SDM studies varies considerably. No comprehensive review exists of what environmental predictors are meaningful, available (or missing) and used in practice to predict plant distributions. Contributing to answer these questions is the aim of this review. METHODS: We carried out an extensive, systematic review of recently published plant SDM studies (years 2010–2015; n = 200) to determine the predictors used (and not used) in the models. We additionally conducted an in‐depth review of SDM studies in selected journals to identify temporal trends in the use of predictors (years 2000–2015; n = 40). RESULTS: A large majority of plant SDM studies neglected several ecophysiologically meaningful environmental variables, and the number of relevant predictors used in models has stagnated or even declined over the last 15 yr. CONCLUSIONS: Neglecting ecophysiologically meaningful predictors can result in incomplete niche quantification and can thus limit the predictive power of plant SDMs. Some of these missing predictors are already available spatially or may soon become available (e.g. soil moisture). However, others are not yet easily obtainable across whole study extents (e.g. soil pH and nutrients), and their development should receive increased attention. We conclude that more effort should be made to build ecologically more sound plant SDMs. This requires a more thorough rationale for the choice of environmental predictors needed to meet the study goal, and the development of missing ones. The latter calls for increased collaborative effort between ecological and geo‐environmental sciences. Aims The choice of environmental predictor variables in correlative models of plant species distributions (hereafter SDMs) is crucial to ensure predictive accuracy and model realism, as highlighted in multiple earlier studies. Because variable selection is directly related to a model's capacity to capture important species' environmental requirements, one would expect an explicit prior consideration of all ecophysiologically meaningful variables. For plants, these include temperature, water, soil nutrients, light, and in some cases, disturbances and biotic interactions. However, the set of predictors used in published correlative plant SDM studies varies considerably. No comprehensive review exists of what environmental predictors are meaningful, available (or missing) and used in practice to predict plant distributions. Contributing to answer these questions is the aim of this review. Methods We carried out an extensive, systematic review of recently published plant SDM studies (years 2010–2015; n = 200) to determine the predictors used (and not used) in the models. We additionally conducted an in‐depth review of SDM studies in selected journals to identify temporal trends in the use of predictors (years 2000–2015; n = 40). Results A large majority of plant SDM studies neglected several ecophysiologically meaningful environmental variables, and the number of relevant predictors used in models has stagnated or even declined over the last 15 yr. Conclusions Neglecting ecophysiologically meaningful predictors can result in incomplete niche quantification and can thus limit the predictive power of plant SDMs. Some of these missing predictors are already available spatially or may soon become available (e.g. soil moisture). However, others are not yet easily obtainable across whole study extents (e.g. soil pH and nutrients), and their development should receive increased attention. We conclude that more effort should be made to build ecologically more sound plant SDMs. This requires a more thorough rationale for the choice of environmental predictors needed to meet the study goal, and the development of missing ones. The latter calls for increased collaborative effort between ecological and geo‐environmental sciences. Predictors included in species distribution models (SDMs) vary greatly between studies. This review identifies the predictors omitted in plant SDMs and reasons for their omission. We conclude that effort is needed to develop more ecologically sound predictors and related SDMs. This requires increased collaboration between ecological and geo‐environmental sciences and a more theoretically solid basis for the selection of predictors. Aims The choice of environmental predictor variables in correlative models of plant species distributions (hereafter SDMs) is crucial to ensure predictive accuracy and model realism, as highlighted in multiple earlier studies. Because variable selection is directly related to a model's capacity to capture important species' environmental requirements, one would expect an explicit prior consideration of all ecophysiologically meaningful variables. For plants, these include temperature, water, soil nutrients, light, and in some cases, disturbances and biotic interactions. However, the set of predictors used in published correlative plant SDM studies varies considerably. No comprehensive review exists of what environmental predictors are meaningful, available (or missing) and used in practice to predict plant distributions. Contributing to answer these questions is the aim of this review. Methods We carried out an extensive, systematic review of recently published plant SDM studies (years 2010-2015; n = 200) to determine the predictors used (and not used) in the models. We additionally conducted an in-depth review of SDM studies in selected journals to identify temporal trends in the use of predictors (years 2000-2015; n = 40). Results A large majority of plant SDM studies neglected several ecophysiologically meaningful environmental variables, and the number of relevant predictors used in models has stagnated or even declined over the last 15 yr. Conclusions Neglecting ecophysiologically meaningful predictors can result in incomplete niche quantification and can thus limit the predictive power of plant SDMs. Some of these missing predictors are already available spatially or may soon become available (e.g. soil moisture). However, others are not yet easily obtainable across whole study extents (e.g. soil pH and nutrients), and their development should receive increased attention. We conclude that more effort should be made to build ecologically more sound plant SDMs. This requires a more thorough rationale for the choice of environmental predictors needed to meet the study goal, and the development of missing ones. The latter calls for increased collaborative effort between ecological and geo-environmental sciences. Predictors included in species distribution models (SDMs) vary greatly between studies. This review identifies the predictors omitted in plant SDMs and reasons for their omission. We conclude that effort is needed to develop more ecologically sound predictors and related SDMs. This requires increased collaboration between ecological and geo-environmental sciences and a more theoretically solid basis for the selection of predictors. |
| Author | Scherrer, Daniel Mod, Heidi K. Luoto, Miska Guisan, Antoine |
| Author_xml | – sequence: 1 givenname: Heidi K. surname: Mod fullname: Mod, Heidi K. email: heidi.mod@helsinki.fi, heidi.mod@helsinki.fi organization: Department of Geosciences and Geography, University of Helsinki, PO Box 64 (Gustaf Hällstöminkatu 2a), FI-00014, Helsinki, Finland – sequence: 2 givenname: Daniel surname: Scherrer fullname: Scherrer, Daniel email: daniel.scherrer@unil.ch organization: Department of Ecology and Evolution, University of Lausanne, Biophore, CH-1015, Lausanne, Switzerland – sequence: 3 givenname: Miska surname: Luoto fullname: Luoto, Miska email: miska.luoto@helsinki.fi organization: Department of Geosciences and Geography, University of Helsinki, PO Box 64 (Gustaf Hällstöminkatu 2a), FI-00014, Helsinki, Finland – sequence: 4 givenname: Antoine surname: Guisan fullname: Guisan, Antoine email: antoine.guisan@unil.ch organization: Department of Ecology and Evolution, University of Lausanne, Biophore, CH-1015, Lausanne, Switzerland |
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| ContentType | Journal Article |
| Copyright | Copyright © 2017 International Association for Vegetation Science 2016 International Association for Vegetation Science |
| Copyright_xml | – notice: Copyright © 2017 International Association for Vegetation Science – notice: 2016 International Association for Vegetation Science |
| DBID | BSCLL AAYXX CITATION 7SN 7ST C1K SOI 7S9 L.6 |
| DOI | 10.1111/jvs.12444 |
| DatabaseName | Istex CrossRef Ecology Abstracts Environment Abstracts Environmental Sciences and Pollution Management Environment Abstracts AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef Ecology Abstracts Environment Abstracts Environmental Sciences and Pollution Management AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA Ecology Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Botany |
| EISSN | 1654-1103 |
| Editor | Scheiner, Sam |
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| EndPage | 1322 |
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| Issue | 6 |
| Language | English |
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| Notes | Kordelin Foundation istex:EEDED045A510C903A7A86A56D1BAE32FDBF91834 ark:/67375/WNG-310F8WX1-K Research Foundation of the University of Helsinki Swiss National Science Foundation - No. 31003A-152866/1 ArticleID:JVS12444 Appendix S1. Ecophysiological meaning of different categories of variables for plant species. Appendix S2. Journals and numbers of studies included in the paper. Appendix S3. Variables included in the different classes and categories. Academy of Finland - No. 286950 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
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| PublicationCentury | 2000 |
| PublicationDate | 2016-11 20161101 November 2016 2016-11-00 |
| PublicationDateYYYYMMDD | 2016-11-01 |
| PublicationDate_xml | – month: 11 year: 2016 text: 2016-11 |
| PublicationDecade | 2010 |
| PublicationTitle | Journal of vegetation science |
| PublicationTitleAlternate | J Veg Sci |
| PublicationYear | 2016 |
| Publisher | Blackwell Publishing Ltd John Wiley & Sons Ltd |
| Publisher_xml | – name: Blackwell Publishing Ltd – name: John Wiley & Sons Ltd |
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| Snippet | Aims: The choice of environmental predictor variables in correlative models of plant species distributions (hereafter SDMs) is crucial to ensure predictive... Aims The choice of environmental predictor variables in correlative models of plant species distributions (hereafter SDMs) is crucial to ensure predictive... Aims The choice of environmental predictor variables in correlative models of plant species distributions (hereafter SDMs) is crucial to ensure predictive... AIMS: The choice of environmental predictor variables in correlative models of plant species distributions (hereafter SDMs) is crucial to ensure predictive... |
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| SubjectTerms | biocenosis Covariate Environment environmental factors Habitat suitability Independent variable Niche nutrients Plant population distribution Predictor soil nutrients soil pH soil water Species distribution SYNTHESIS systematic review temperature |
| Title | What we use is not what we know: environmental predictors in plant distribution models |
| URI | https://api.istex.fr/ark:/67375/WNG-310F8WX1-K/fulltext.pdf https://www.jstor.org/stable/44132717 https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fjvs.12444 https://www.proquest.com/docview/1868337113 https://www.proquest.com/docview/2020898043 |
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