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Hal
Doctoral thesis . 2023
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Using predictive mapping to better understand the species' ecology and adapt the forests to climate change

Authors: Piedallu, Christian;

Using predictive mapping to better understand the species' ecology and adapt the forests to climate change

Abstract

Face au réchauffement climatique en cours, qui entraine un déclin des forêts dans différents biomes de la planète, un besoin important de connaissances et d’outils se fait sentir pour adapter les peuplements aux nouvelles conditions environnementales et à leurs évolutions à venir. La cartographie prédictive peut permettre la production d’une grande quantité d’informations, à fine résolution spatiale, pour différentes périodes de temps, et sur de vastes emprises géographiques. Nous détaillerons l’intérêt des cartes prédictives des facteurs du milieu décrivant les propriétés du sol et du climat, principalement réalisées à l’échelle nationale ou régionale, qui peuvent être combinées avec des observations de terrain et/ou des données issues d’images satellitales pour progresser dans la connaissance de l’écologie des espèces d’arbres. Nous montrerons également comment ces données peuvent être utilisées pour évaluer et cartographier la vulnérabilité des essences au regard des changements en cours, permettant d’identifier le niveau de risque en fonction du peuplement en place et du contexte environnemental. La dynamique spatiale et temporelle de la quantité d’eau disponible pour les plantes est un facteur essentiel pour évaluer ces risques, dont les effets sont fortement modulés en fonction de la composition et de la structure des peuplements en place. L’identification des marges de manœuvre avant que les limites écologiques des espèces ne soient atteintes est aujourd’hui un enjeu crucial pour aider les acteurs de la gestion forestière à adapter les différents types de peuplement de nos forêts au changement de climat.

To cope with ongoing global warming, which is leading to a decline in forests in different biomes on the planet, there is a significant need for knowledge and tools to adapt stands to new environmental conditions and their future evolutions. Predictive mapping can enable the production of a large amount of information at fine spatial resolution, for different periods of time, over broad geographic areas. We’ ll detail the interest of predictive maps of environmental factors concerning soil and climate properties, mainly elaborated at national or regional scale, which can be combined with field observations and/or data from satellite images to improve our knowledge about the ecology of tree species. We’ll also show how they can be used to assess and map the vulnerability of the tree species to ongoing changes, identifting the level of risk based on information about the stands characteristics and the environmental context. The spatial and temporal dynamics of the water available to plants is a crucial factor for assessing these risks, with variations according to the stands composition and structure. Identifying available margins before the ecological limits of species are reached is today a crucial issue to help forest management stakeholders to adapt the different stands of our forests to climate change.

Keywords

[SDE] Environmental Sciences, changement climatique, biogéographie, modeling, adaptation, écologie, forest, climate change, forêt, cartographie, ecology, mapping, biogeography, modélisation

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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