Spatio-temporal functional regression on paleoecological data
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| Název: | Spatio-temporal functional regression on paleoecological data |
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| Autoři: | Bel, Liliane, L., Bar-Hen, Avner, A., Petit, Remy, R., Cheddadi, Rachid, R. |
| Přispěvatelé: | Mathématiques et Informatique Appliquées (MIA-Paris), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions - CNRS Mathématiques (INSMI-CNRS)-Centre National de la Recherche Scientifique (CNRS), Biodiversité, Gènes & Communautés (BioGeCo), Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB), Institut des Sciences de l'Evolution de Montpellier (UMR ISEM), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Institut de recherche pour le développement IRD : UR226-Centre National de la Recherche Scientifique (CNRS) |
| Zdroj: | ISSN: 0266-4763. |
| Informace o vydavateli: | HAL CCSD Taylor & Francis (Routledge) |
| Rok vydání: | 2011 |
| Témata: | SPATIO-TEMPORAL MODELING, CLIMATE CHANGE, BIODIVERSITY, SPATIAL REGRESSION, HETRE COMMUN, GENETIQUE DES POPULATIONS, RÉGRESSION SPATIALE, MODÉLISATION SPATIO-TEMPORELLE, FUNCTIONAL DATA ANALYSIS, [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, geo, envir |
| Popis: | There is much interest in predicting the impact of global warming on the genetic diversity of natural populations and the influence of climate on biodiversity is an important ecological question. Since Holocene, we face many climate perturbations and the geographical ranges of plant taxa have changed substantially. Actual genetic diversity of plant is a result of these processes and a first step to study the impact of future climate change is to understand the important features of reconstructed climate variables such as temperature or precipitation for the last 15,000 years on actual genetic diversity of forest. We model the relationship between genetic diversity in the European beech (Fagus sylvatica) forests and curves of temperature and precipitation reconstructed from pollen databases. Our model links the genetic measure to the climate curves. We adapt classical functional linear model to take into account interactions between climate variables as a bilinear form. Since the data are georeferenced, our extensions also account for the spatial dependence among the observations. The practical issues of these methodological extensions are discussed. |
| Druh dokumentu: | article in journal/newspaper |
| Jazyk: | English |
| Relation: | https://hal.science/hal-01000440 |
| Dostupnost: | https://hal.science/hal-01000440 |
| Rights: | undefined |
| Přístupové číslo: | edsbas.B58CBA51 |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | There is much interest in predicting the impact of global warming on the genetic diversity of natural populations and the influence of climate on biodiversity is an important ecological question. Since Holocene, we face many climate perturbations and the geographical ranges of plant taxa have changed substantially. Actual genetic diversity of plant is a result of these processes and a first step to study the impact of future climate change is to understand the important features of reconstructed climate variables such as temperature or precipitation for the last 15,000 years on actual genetic diversity of forest. We model the relationship between genetic diversity in the European beech (Fagus sylvatica) forests and curves of temperature and precipitation reconstructed from pollen databases. Our model links the genetic measure to the climate curves. We adapt classical functional linear model to take into account interactions between climate variables as a bilinear form. Since the data are georeferenced, our extensions also account for the spatial dependence among the observations. The practical issues of these methodological extensions are discussed. |
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