Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
ABSTRACT: BACKGROUND: Climate change poses unprecedented challenges, ranging from global and local policy challenges to personal and social action. Heat-related deaths are largely preventable, but interventions for the most vulnerable populations need improvement. Therefore, the prior identification of high risk areas at the community level is required to better inform planning and prevention. We aimed to demonstrate a simple and flexible conceptual framework relying upon satellite thermal data and other digital data with the goal of easily reproducing this framework in a variety of urban configurations. RESULTS: The study area encompasses Rennes, a medium-sized French city. A Landsat ETM + image (60 m resolution) acquired during a localized heatwave (June 2001) was used to estimate land surface temperature (LST) and derive a hazard index. A land-use regression model was performed to predict the LST. Vulnerability was assessed through census data describing four dimensions (socio-economic status, extreme age, population density and building obsolescence). Then, hazard and vulnerability indices were combined to deliver a heatwave health risk index. The LST patterns were quite heterogeneous, reflecting the land cover mosaic inside the city boundary, with hotspots of elevated temperature mainly observed in the city center. A spatial error regression model was highly predictive of the spatial variation in the LST (R2 = 0.87) and was parsimonious. Three land cover descriptors (NDVI, vegetation and water fractions) were negatively linked with the LST. A sensitivity analysis (based on an image acquired on July 2000) yielded similar results. Southern areas exhibited the most vulnerability, although some pockets of higher vulnerability were observed northeast and west of the city. The heatwave health risk map showed evidence of infra-city spatial clustering, with the highest risks observed in a north--south central band. Another sensitivity analysis gave a very high correlation between 2000 and 2001 risk indices (r = 0.98, p < 10-12). CONCLUSIONS: Building on previous work, we developed a reproducible method that can provide guidance for local planners in developing more efficient climate impact adaptations. We recommend, however, using the health risk index together with hazard and vulnerability indices to implement tailored programs because exposure to heat and vulnerability do not require the same prevention strategies.
ISSN: 1476-072X

inserm-00762303
http://www.hal.inserm.fr/inserm-00762303
http://www.hal.inserm.fr/inserm-00762303/document
http://www.hal.inserm.fr/inserm-00762303/file/1476-072X-11-38.pdf
DOI : 10.1186/1476-072X-11-38

Éditeur(s) : HAL CCSD Ecological Society of America
Résumé : Herbivory is viewed as a major driver of plant evolution and the mostimportant energy pathway from plants to higher trophic levels. Therefore, understandingpatterns of herbivory on plants remains a key focus in evolution and ecology. Theevolutionary impacts of leaf herbivory include altering plant fitness, local adaptation, theevolution of defenses, and the diversification of plants as well as natural enemies. Leafherbivory also impacts ecological processes such as plant productivity, communitycomposition, and ecosystem nutrient cycling. Understanding the impact of herbivory onthese ecological and evolutionary processes requires species-specific, as opposed tocommunity-level, measures of herbivory. In addition, species-specific data enables the use ofmodern comparative methods to account for phylogenetic non-independence. Althoughhundreds of studies have measured natural rates of leaf consumption, we are unaware of anyaccessible compilation of these data. We created such a data set to provide the raw dataneeded to test general hypotheses relating to plant–herbivore interactions and to test theinfluence of biotic and abiotic factors on herbivory rates across large spatial scales. A largerepository will make this endeavor more efficient and robust. In total, we compiled 2641population-level measures for either annual or daily rates of leaf herbivory across 1145 speciesof vascular plants collected from 189 studies. All damage measures represent naturaloccurrences of herbivory that span numerous angiosperm, gymnosperm, and fern species. Toenable researchers to explore the causes of variation in herbivory and how these mightinteract, we added information about the study sites including: geolocation, climateclassification, habitat descriptions (e.g., seashore, grassland, forest, agricultural fields), andplant trait information concerning growth form and duration (e.g., annual vs. perennial). Wealso included extensive details of the methodology used to measure leaf damage, includingseasons and months of sampling, age of leaves, and the method used to estimate percentagearea missing. We anticipate that these data will make it possible to test important hypothesesin the plant–herbivore literature, including the plant apparency hypothesis, the latitudinalherbivorydefense hypothesis, the resource availability hypothesis, and the macroevolutionaryescalation of defense hypothesis.
ISSN: 0012-9658

hal-01601627
https://hal.archives-ouvertes.fr/hal-01601627
DOI : 10.1890/13-1741.1
PRODINRA : 397136
PUBMED : 24870043

Éditeur(s) : HAL CCSD Public Library of Science
Résumé : Tree mortality in tropical forests is a complex ecological process for which modelling approaches need to be improved to better understand, and then predict, the evolution of tree mortality in response to global change. The mortality model introduced here computes an individual probability of dying for each tree in a community. The mortality model uses the ontogenetic stage of the tree because youngest and oldest trees are more likely to die. Functional traits are integrated as proxies of the ecological strategies of the trees to permit generalization among all species in the community. Data used to parametrize the model were collected at Paracou study site, a tropical rain forest in French Guiana, where 20,408 trees have been censused for 18 years. A Bayesian framework was used to select useful covariates and to estimate the model parameters. This framework was developed to deal with sources of uncertainty, including the complexity of the mortality process itself and the field data, especially historical data for which taxonomic determinations were uncertain. Uncertainty about the functional traits was also considered, to maximize the information they contain. Four functional traits were strong predictors of tree mortality: wood density, maximum height, laminar toughness and stem and branch orientation, which together distinguished the light-demanding, fast-growing trees from slow-growing trees with lower mortality rates. Our modelling approach formalizes a complex ecological problem and offers a relevant mathematical framework for tropical ecologists to process similar uncertain data at the community level.
ISSN: 1932-6203

hal-01190561
https://hal.archives-ouvertes.fr/hal-01190561
PRODINRA : 208582
PUBMED : 23675500
DOI : 10.1371/journal.pone.0063678

Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
INTRODUCTION: In order to study social health inequalities, contextual (or ecologic) data may constitute an appropriate alternative to individual socioeconomic characteristics. Indices can be used to summarize the multiple dimensions of the neighborhood socioeconomic status. This work proposes a statistical procedure to create a neighborhood socioeconomic index. METHODS: The study setting is composed of three French urban areas. Socioeconomic data at the census block scale come from the 1999 census. Successive principal components analyses are used to select variables and create the index. Both metropolitan area-specific and global indices are tested and compared. Socioeconomic categories are drawn with hierarchical clustering as a reference to determine "optimal" thresholds able to create categories along a one-dimensional index. RESULTS: Among the twenty variables finally selected in the index, 15 are common to the three metropolitan areas. The index explains at least 57% of the variance of these variables in each metropolitan area, with a contribution of more than 80% of the 15 common variables. CONCLUSIONS: The proposed procedure is statistically justified and robust. It can be applied to multiple geographical areas or socioeconomic variables and provides meaningful information to public health bodies. We highlight the importance of the classification method. We propose an R package in order to use this procedure.
ISSN: 1475-9276

inserm-00809310
http://www.hal.inserm.fr/inserm-00809310
http://www.hal.inserm.fr/inserm-00809310/document
http://www.hal.inserm.fr/inserm-00809310/file/1475-9276-12-21.pdf
DOI : 10.1186/1475-9276-12-21

Éditeur(s) : HAL CCSD Nature Publishing Group
Résumé : International audience
Everyone is subject to environmental exposures from various sources, with negative health impacts (air, water and soil contamination, noise, etc.or with positive effects (e.g. green space). Studies considering such complex environmental settings in a global manner are rare. We propose to use statistical factor and cluster analyses to create a composite exposure index with a data-driven approach, in view to assess the environmental burden experienced by populations. We illustrate this approach in a large French metropolitan area. The study was carried out in the Great Lyon area (France, 1.2 M inhabitants) at the census Block Group (BG) scale. We used as environmental indicators ambient air NO2 annual concentrations, noise levels and proximity to green spaces, to industrial plants, to polluted sites and to road traffic. They were synthesized using Multiple Factor Analysis (MFA), a data-driven technique without a priori modeling, followed by a Hierarchical Clustering to create BG classes. The first components of the MFA explained, respectively, 30, 14, 11 and 9% of the total variance. Clustering in five classes group: (1) a particular type of large BGs without population; (2) BGs of green residential areas, with less negative exposures than average; (3) BGs of residential areas near midtown; (4) BGs close to industries; and (5) midtown urban BGs, with higher negative exposures than average and less green spaces. Other numbers of classes were tested in order to assess a variety of clustering. We present an approach using statistical factor and cluster analyses techniques, which seem overlooked to assess cumulative exposure in complex environmental settings. Although it cannot be applied directly for risk or health effect assessment, the resulting index can help to identify hot spots of cumulative exposure, to prioritize urban policies or to compare the environmental burden across study areas in an epidemiological framework.Journal of Exposure Science and Environmental Epidemiology advance online publication, 24 September 2014; doi:10.1038/jes.2014.66.
ISSN: 1559-0631

hal-01069587
https://hal.archives-ouvertes.fr/hal-01069587
https://hal.archives-ouvertes.fr/hal-01069587/document
https://hal.archives-ouvertes.fr/hal-01069587/file/Article_multiexpo_20140708_c.pdf
DOI : 10.1038/jes.2014.66

Éditeur(s) : HAL CCSD Public Library of Science
Résumé : Climate models for the coming century predict rainfall reduction in the Amazonian region, including change in water availability for tropical rainforests. Here, we test the extent to which climate variables related to water regime, temperature and irradiance shape the growth trajectories of neotropical trees. . We developed a diameter growth model explicitly designed to work with asynchronous climate and growth data. Growth trajectories of 205 individual trees from 54 neotropical species censused every 2 months over a 4-year period were used to rank 9 climate variables and find the best predictive model. . About 9% of the individual variation in tree growth was imputable to the seasonal variation of climate. Relative extractable water was the main predictor and alone explained more than 60% of the climate effect on tree growth, i.e. 5.4% of the individual variation in tree growth. Furthermore, the global annual tree growth was more dependent on the diameter increment at the onset of the rain season than on the duration of dry season. . The best predictive model included 3 climate variables: relative extractable water, minimum temperature and irradiance. The root mean squared error of prediction (0.035 mm.d(-1)) was slightly above the mean value of the growth (0.026 mm.d(-1)). . Amongst climate variables, we highlight the predominant role of water availability in determining seasonal variation in tree growth of neotropical forest trees and the need to include these relationships in forest simulators to test, in silico, the impact of different climate scenarios on the future dynamics of the rainforest.
ISSN: 1932-6203

hal-01268350
https://hal.archives-ouvertes.fr/hal-01268350
DOI : 10.1371/journal.pone.0034074
PRODINRA : 215438
PUBMED : 22506012

Éditeur(s) : HAL CCSD
Résumé : International audience
Background: Everyone is subject to environmental exposures from various sources, with negative health impacts (air, water and soil contamination, noise...) or with positive effects (e.g. green space). Studies considering such complex environmental settings in a global manner are rare.Aims: We propose to use data mining techniques to create a composite exposure index with a data-driven approach, in view to assess the environmental burden experienced by populations. We illustrate this approach on a large French metropolitan area.Methods: The study was carried out in the Great Lyon area (France, 1,260,348 inhabitants in 2007, 527 km²) at the census block group (BG) scale. Indicators on NO2 annual concentrations, noise levels, proximity to green spaces and to industrial plants, and road traffic were synthetized using Multiple Factor Analysis (MFA), which allows to explore the relations between exposures and BGs without a priori knowledge, and to synthetize indicators of different types. Hierarchical clustering was then used to create BG classes. Results: The four first components of the MFA explained respectively 23, 14, 13 and 12% of the total variance. Clustering in 3 classes group: 1) BGs far from industries, greener and with less noise and air pollution than the average; 2) BGs close to industries with less green spaces and air pollution than the average; and 3)BGs far from industries but with higher levels of noise, air pollution and traffic. Greater numbers of classes were tested in order to assess a variety of clustering.Conclusions: We present a data driven approach using data mining techniques, which seem overlooked for cumulative exposure assessment in complex environmental settings. Although it cannot be applied directly for risk or health effect assessment, the resulting index can help to identify hot spots of cumulative exposure, to prioritize urban policies or to compare the environmental burden across study areas in an epidemiological framework.
Environment and Health – Bridging South, North, East and West. Conference of ISEE, ISES and ISIAQ.
Basel, Switzerland

hal-01432884
https://hal.archives-ouvertes.fr/hal-01432884
https://hal.archives-ouvertes.fr/hal-01432884/document
https://hal.archives-ouvertes.fr/hal-01432884/file/Poster_2013_ISEE_20130711.pdf

Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
Introduction In order to study social health inequalities, contextual (or ecologic) data may constitute an appropriate alternative to individual socioeconomic characteristics. Indices can be used to summarize the multiple dimensions of the neighborhood socioeconomic status. This work proposes a statistical procedure to create a neighborhood socioeconomic index. Methods The study setting is composed of three French urban areas. Socioeconomic data at the census block scale come from the 1999 census. Successive principal components analyses are used to select variables and create the index. Both metropolitan area-specific and global indices are tested and compared. Socioeconomic categories are drawn with hierarchical clustering as a reference to determine "optimal" thresholds able to create categories along a one-dimensional index. Results Among the twenty variables finally selected in the index, 15 are common to the three metropolitan areas. The index explains at least 57% of the variance of these variables in each metropolitan area, with a contribution of more than 80% of the 15 common variables. Conclusions The proposed procedure is statistically justified and robust. It can be applied to multiple geographical areas or socioeconomic variables and provides meaningful information to public health bodies. We highlight the importance of the classification method. We propose an R package in order to use this procedure.
International Journal for Equity in Health

hal-00854028
https://hal.archives-ouvertes.fr/hal-00854028
https://hal.archives-ouvertes.fr/hal-00854028/document
DOI : 10.1186/1475-9276-12-21

Résumé : L'importance des forêts tropicales dans le cycle du carbone à l'échelle planétaire est majeure, tant en terme de stock qu’en terme de flux de CO2. Plusieurs études mettent en évidence des changements au sein des forêts tropicales au cours des 20 dernières années, notamment des changements de la dynamique forestière et une augmentation de la biomasse aérienne. Les déterminants de ces variations sont aujourd’hui discutés et nous proposons ici d’apporter une contribution à ce débat. Les données utilisées dans ce travail proviennent du dispositif de suivi forestier à long terme de Paracou, Guyane Française, mis en place en 1984 et qui recouvre plus de 120 ha de forêt tropicale humide. Les données météorologiques proviennent de la tour à flux du dispositif, Guyaflux. Les mesures de diamètre proviennent de la base Guyafor pour les données annuelles et bisannuelles, et des mesures de 260 arbres à proximité de la tour à flux pour les mesures diamétriques intra-annuelles.Cette thèse se divise en deux grandes parties. La première concerne l’analyse de la biomasse du dispositif de suivi forestier de Paracou en Guyane et l’implication des changements de structure de cette forêt sur le bilan de carbone. Cette partie est constituée de deux points. (i) Quelles échelles temporelles et spatiales sont pertinentes pour analyser les composantes de structure (biomasse, aire basale et nombre de tiges) et de dynamique (croissance, recrutement et mortalité) des forêts tropicales afin de minimiser les effets d’échantillonnage ? Nous avons établi une méthodologie permettant de relier les intervalles de temps et la surface de mesure aux coefficients de variation de chacune des variables de structure et de dynamique de la forêt. (ii) Quels processus démographiques sont prépondérants dans l’explication des variations de biomasse et comment se redistribue la biomasse accumulée dans le système ? L’augmentation de la biomasse observée sur le dispositif de Paracou serait liée à la rareté des évènements de mortalité des gros arbres qui portent une part très importante de la biomasse.La deuxième partie de la thèse concerne l’analyse de l’effet du climat à moyen terme, inter et intra-annuel, et les effets directs du climat dans les changements de dynamique de la forêt. Cette partie se divise en 3 points. (i) Comment quantifier le stress hydrique en forêt tropicale humide ? Nous avons réalisé un modèle journalier de réserve en eau du sol pour les arbres en forêt tropicale. (ii) Quelles variables sont explicatives de la croissance des arbres en forêt guyanaise ? Nous avons montré que l’eau dans le sol est le facteur le plus explicatif du déterminisme climatique parmi un panel de variables climatiques. (iii) Quels traits fonctionnels sont prédicteurs de la réponse des arbres aux variations climatique ? Nous avons déterminé que des traits spécifiques, densité du bois et la hauteur maximale, ainsi que le diamètre de l'arbre au moment de la mesure, modulent la croissance des arbres en réponse au climat.
At a global scale tropical forest play a major role in term of carbon stock as well as in term of CO2 fluxes. Several studies have highlighted changes in tropical forest functioning during the last 20 years including a faster turnover and an increase of above ground biomass. The drivers of these changes are discussed and throughout this thesis we propose to contribute to this debate. We use the data from the Paracou experimental site in French Guiana established in 1984 on 120 hectares of moist tropical forest. Meteorological data come from the flux tower of the site, Guyaflux. We use annual and bisannual diameter measurements from the Guyafor database, and intra-annual diameter increments from the measurements of 260 trees near the flux tower.This thesis has two main parts. In the first part we present the biomass analysis of the Paracou permanent plots and the impact of structural changes in this forest on the carbon budget. The first part is constituted by two points. (i) Which temporal and spatial scale used to analyze the structure (biomass, basal area and stem density) and dynamics (tree growth, recruitment and mortality)components of tropical forest in order to minimized sample bias ? We establish a simple method to rely measurement interval between census and surface of measurement to the coefficient of variation of forests structure and dynamic components (ii) Which demographic process are involved in the explanation of biomass variation and how the biomass is distributed in the system ? The observed increase of biomass at Paracou could be link to the rarity of big trees mortality events. These big trees represent the larger part of the biomass. In the second part, we present the analysis of intra and inter-annual climate variation effects on forest dynamic changes. This part is divided in two points. (i) How to model drought stress in moist tropical forest ? We built a daily water balance model for tropical trees. (ii) Which climate variables explain the tree growth in guianian forests? We shown that soil water availability is the determinant factor of tree growth among a panel of climate variables. (iii) Which functional traits are involved in the tropical tree growth responses to climate? In this analysis, we determined that wood specific gravity, maximum tree height and tree diameter modulate the tree growth response to climate variations.

http://www.theses.fr/2011AGUY0481/document