Can we predict forest composition across space and time in Central Africa
Background. Predicting the current and future natural distributions of species is challenging, especially in the tropics where large remote areas remain poorly known. Such challenge can only be met with an in-depth understanding of the drivers of species distribution, a well-designed and extensive survey and appropriate statistical models. Method. In this study, we use a large dataset of forest inventories from logging companies, which provides information on the abundance of 123 tree genera, in 140,000 plots spread over four Central African countries. In order to predict the current and future distribution of these tree genera, we use a set of bioclimatic, geological and anthropogenic variables. We rely on a recently published methodology, called Supervised Component Generalized Linear Regression (SCGLR), which identifies the most predictive dimensions among a large set of predictors. Result. Using a calibration and validation scheme, we show that the distribution of most tree genera can be well predicted over the whole study area at the present time. At the community level, the floristic and functional composition of tree genera is also inferred with a good accuracy. Finally, using spatially explicit null models, we show that species-climate association are in most cases not better than chance, thus challenging our ability to predict how forest composition will be affected by climatic changes. Conclusion. Overall, our study shows that tropical tree distributions can be predicted with good accuracy at the present time, offering new perspectives to manage tropical forests at large spatial scales, but that predicting shifts in species distribution under climate change scenarios is challenging. (Texte intégral)
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Subjects: | K01 - Foresterie - Considérations générales, F70 - Taxonomie végétale et phytogéographie, U10 - Informatique, mathématiques et statistiques, P40 - Météorologie et climatologie, F40 - Écologie végétale, |
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dig-cirad-fr-5812532021-06-04T15:22:33Z http://agritrop.cirad.fr/581253/ http://agritrop.cirad.fr/581253/ Can we predict forest composition across space and time in Central Africa. Rejou-Mechain Maxime, Mortier Frédéric, Barbier Nicolas, Bastin Jean-François, Benedetti Christine, Bry Xavier, Chave Jérôme, Cornu Guillaume, Dauby Gilles, Doucet Jean-Louis, Fayolle Adeline, Gourlet-Fleury Sylvie. 2016. In : Tropical ecology and society reconciliating conservation and sustainable use of biodiversity. Program and abstracts. Plinio Sist (ed.), Stéphanie Carrière (ed.), Pia Parolin (ed.), Pierre-Michel Forget (ed.). ATBC. Storrs : ATBC, Résumé, p. 349. Annual Meeting of the Association for Tropical Biology and Conservation (ATBC 2016), Montpellier, France, 19 Juin 2016/23 Juin 2016. Researchers Can we predict forest composition across space and time in Central Africa Rejou-Mechain, Maxime Mortier, Frédéric Barbier, Nicolas Bastin, Jean-François Benedetti, Christine Bry, Xavier Chave, Jérôme Cornu, Guillaume Dauby, Gilles Doucet, Jean-Louis Fayolle, Adeline Gourlet-Fleury, Sylvie eng 2016 ATBC Tropical ecology and society reconciliating conservation and sustainable use of biodiversity. Program and abstracts K01 - Foresterie - Considérations générales F70 - Taxonomie végétale et phytogéographie U10 - Informatique, mathématiques et statistiques P40 - Météorologie et climatologie F40 - Écologie végétale Background. Predicting the current and future natural distributions of species is challenging, especially in the tropics where large remote areas remain poorly known. Such challenge can only be met with an in-depth understanding of the drivers of species distribution, a well-designed and extensive survey and appropriate statistical models. Method. In this study, we use a large dataset of forest inventories from logging companies, which provides information on the abundance of 123 tree genera, in 140,000 plots spread over four Central African countries. In order to predict the current and future distribution of these tree genera, we use a set of bioclimatic, geological and anthropogenic variables. We rely on a recently published methodology, called Supervised Component Generalized Linear Regression (SCGLR), which identifies the most predictive dimensions among a large set of predictors. Result. Using a calibration and validation scheme, we show that the distribution of most tree genera can be well predicted over the whole study area at the present time. At the community level, the floristic and functional composition of tree genera is also inferred with a good accuracy. Finally, using spatially explicit null models, we show that species-climate association are in most cases not better than chance, thus challenging our ability to predict how forest composition will be affected by climatic changes. Conclusion. Overall, our study shows that tropical tree distributions can be predicted with good accuracy at the present time, offering new perspectives to manage tropical forests at large spatial scales, but that predicting shifts in species distribution under climate change scenarios is challenging. (Texte intégral) conference_item info:eu-repo/semantics/conferenceObject Conference info:eu-repo/semantics/publishedVersion http://agritrop.cirad.fr/581253/1/Page%20349%20de%20ATBC%202016-14.pdf text Cirad license info:eu-repo/semantics/openAccess https://agritrop.cirad.fr/mention_legale.html http://agritrop.cirad.fr/581138/ |
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K01 - Foresterie - Considérations générales F70 - Taxonomie végétale et phytogéographie U10 - Informatique, mathématiques et statistiques P40 - Météorologie et climatologie F40 - Écologie végétale K01 - Foresterie - Considérations générales F70 - Taxonomie végétale et phytogéographie U10 - Informatique, mathématiques et statistiques P40 - Météorologie et climatologie F40 - Écologie végétale |
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K01 - Foresterie - Considérations générales F70 - Taxonomie végétale et phytogéographie U10 - Informatique, mathématiques et statistiques P40 - Météorologie et climatologie F40 - Écologie végétale K01 - Foresterie - Considérations générales F70 - Taxonomie végétale et phytogéographie U10 - Informatique, mathématiques et statistiques P40 - Météorologie et climatologie F40 - Écologie végétale Rejou-Mechain, Maxime Mortier, Frédéric Barbier, Nicolas Bastin, Jean-François Benedetti, Christine Bry, Xavier Chave, Jérôme Cornu, Guillaume Dauby, Gilles Doucet, Jean-Louis Fayolle, Adeline Gourlet-Fleury, Sylvie Can we predict forest composition across space and time in Central Africa |
description |
Background. Predicting the current and future natural distributions of species is challenging, especially in the tropics where large remote areas remain poorly known. Such challenge can only be met with an in-depth understanding of the drivers of species distribution, a well-designed and extensive survey and appropriate statistical models. Method. In this study, we use a large dataset of forest inventories from logging companies, which provides information on the abundance of 123 tree genera, in 140,000 plots spread over four Central African countries. In order to predict the current and future distribution of these tree genera, we use a set of bioclimatic, geological and anthropogenic variables. We rely on a recently published methodology, called Supervised Component Generalized Linear Regression (SCGLR), which identifies the most predictive dimensions among a large set of predictors. Result. Using a calibration and validation scheme, we show that the distribution of most tree genera can be well predicted over the whole study area at the present time. At the community level, the floristic and functional composition of tree genera is also inferred with a good accuracy. Finally, using spatially explicit null models, we show that species-climate association are in most cases not better than chance, thus challenging our ability to predict how forest composition will be affected by climatic changes. Conclusion. Overall, our study shows that tropical tree distributions can be predicted with good accuracy at the present time, offering new perspectives to manage tropical forests at large spatial scales, but that predicting shifts in species distribution under climate change scenarios is challenging. (Texte intégral) |
format |
conference_item |
topic_facet |
K01 - Foresterie - Considérations générales F70 - Taxonomie végétale et phytogéographie U10 - Informatique, mathématiques et statistiques P40 - Météorologie et climatologie F40 - Écologie végétale |
author |
Rejou-Mechain, Maxime Mortier, Frédéric Barbier, Nicolas Bastin, Jean-François Benedetti, Christine Bry, Xavier Chave, Jérôme Cornu, Guillaume Dauby, Gilles Doucet, Jean-Louis Fayolle, Adeline Gourlet-Fleury, Sylvie |
author_facet |
Rejou-Mechain, Maxime Mortier, Frédéric Barbier, Nicolas Bastin, Jean-François Benedetti, Christine Bry, Xavier Chave, Jérôme Cornu, Guillaume Dauby, Gilles Doucet, Jean-Louis Fayolle, Adeline Gourlet-Fleury, Sylvie |
author_sort |
Rejou-Mechain, Maxime |
title |
Can we predict forest composition across space and time in Central Africa |
title_short |
Can we predict forest composition across space and time in Central Africa |
title_full |
Can we predict forest composition across space and time in Central Africa |
title_fullStr |
Can we predict forest composition across space and time in Central Africa |
title_full_unstemmed |
Can we predict forest composition across space and time in Central Africa |
title_sort |
can we predict forest composition across space and time in central africa |
publisher |
ATBC |
url |
http://agritrop.cirad.fr/581253/ http://agritrop.cirad.fr/581253/1/Page%20349%20de%20ATBC%202016-14.pdf |
work_keys_str_mv |
AT rejoumechainmaxime canwepredictforestcompositionacrossspaceandtimeincentralafrica AT mortierfrederic canwepredictforestcompositionacrossspaceandtimeincentralafrica AT barbiernicolas canwepredictforestcompositionacrossspaceandtimeincentralafrica AT bastinjeanfrancois canwepredictforestcompositionacrossspaceandtimeincentralafrica AT benedettichristine canwepredictforestcompositionacrossspaceandtimeincentralafrica AT bryxavier canwepredictforestcompositionacrossspaceandtimeincentralafrica AT chavejerome canwepredictforestcompositionacrossspaceandtimeincentralafrica AT cornuguillaume canwepredictforestcompositionacrossspaceandtimeincentralafrica AT daubygilles canwepredictforestcompositionacrossspaceandtimeincentralafrica AT doucetjeanlouis canwepredictforestcompositionacrossspaceandtimeincentralafrica AT fayolleadeline canwepredictforestcompositionacrossspaceandtimeincentralafrica AT gourletfleurysylvie canwepredictforestcompositionacrossspaceandtimeincentralafrica |
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