Évaluation des ressources en eau de la Martinique : calcul spatialisé de la pluie efficace et validation à l’échelle du bassin versant

L’évaluation des différents termes du bilan hydrologique à l’échelle d’un bassin versant constitue l’un des points clés de la gestion des ressources en eau, et ce, tout particulièrement dans les régions montagneuses présentant de fortes variations spatiales de la pluviométrie et de l’évapotranspiration. Une méthodologie, basée sur le modèle classique de Thornthwaite, est proposée. Elle prend en compte les différents types de sols, l’occupation des sols ainsi que les effets topographiques et calcule les différents termes du bilan hydrologique (pluie, évapotranspiration, pluie efficace, etc.). L’approche a été mise en oeuvre à l’échelle du kilomètre carré, pour l’ensemble de l’île de la Martinique (1 080 km2), puis validée à l’échelle du bassin versant, en comparant les pluies efficaces calculées avec les débits mesurés aux stations de jaugeage. Malgré l’absence de calage des différents paramètres du modèle, les résultats sont très satisfaisants. Une surestimation de la pluie efficace est néanmoins observée pour la plupart des bassins versants utilisés pour la validation du modèle. Cet écart est attribué à une sous-estimation de l’évapotranspiration potentielle, la plupart des bassins versants comportant une composante forestière significative, non prise en compte dans le modèle.The assessment of the various components of the hydrologic budget at catchment scale represents a key challenge for water resources management. This is especially true for regions characterized by important spatial variability in rainfall or evapotranspiration due, for example, to topographical effects. A methodology, based on the classical Thornthwaite model, is proposed to account for soil types, land cover changes and topographical effects on the main components of the water cycle (rainfall, evapotranspiration and efficient rainfall). The approach is developed for the whole Martinique Island (French West Indies, 1080 km2) using a 1-km2 resolution and validated at catchment scale comparing computed efficient rainfall with measured discharge at several gauging stations. Despite the absence of any calibration of the model parameters, the results are satisfying. A slight overestimation of the efficient rainfall is generally observed for the validation watersheds. This discrepancy is interpreted as an underestimation of potential evapotranspiration as the classical Penman-Montheit formula for grass is used despite the presence of forested areas in most of the watersheds

Modelling spatial distributions of alpine vegetation : a graph theory approach to delineate ecologically-consistent species assemblages

This work was partly funded by the French Ministry of Ecology, Sustainable Development and Energy in support of the development of the CARHab project (2011-2015) on mapping the terrestrial habitats of France. In addition, this work benefited from a synergy with the Divgrass project (Plant Functional Diversity of Permanent Grasslands) (CESAB/FRB funded, France).Safeguarding biodiversity has been one of the most important issues on the environmental and forest policies agenda since the 1990's. The problem remains in terms of decisions and knowledge on where to set appropriate conservation targets. Hence, we need detailed and reliable information about habitat structure and composition and methods for estimating this information over the whole spatial domain. In answer to this target, in France, the Ministry of Ecology launched an ambitious project to map the terrestrial vegetation at a scale of 1:25 000 known as CarHab. This project initiated in 2011, will be used as a strategic spatial planning tool in answer to key issues in relation to biodiversity, conservation, green infrastructures and to report on the conservation status of habitats and species of community interest. We use species-distribution models (SDMs) to identify areas that are ecologically suitable for the presence of species based on specific habitat characteristics. Available techniques using graph theory enable identification of groups of species (assemblages) based on ecological affinities. Species co-occurrences (present within the same assessment plot), revealing a shared ecological niche, are analysed using algorithms derived from graph theory in order to define different nodes of species affinities. Thus, the resulting assemblages are based on ecological similarities. Hence, these assemblages are used to develop models of the potential distribution of alpine vegetation communities. The BIOMOD platform is used to facilitate the simultaneous implementation of different modelling approaches that can be compared in order to choose the most suitable and accurate for each species assemblage obtained from graph theory. Using the different relevant spatially explicit results provides a more comprehensive vision of the communities' spatial distributions.PostprintPeer reviewe

Comparison of climate change impacts on the recharge of two karst systems computing different modelling approaches

International audienceKarst systems constitute aquifers in which infiltration and groundwater flows are generally complex processes and are characterized by limited knowledge in terms of geometry and structure. Nonetheless, they often represent interesting groundwater resources, some of them being subjected to intensive exploitation and others non exploited due to their poor understanding. In the future, it is likely that climate change impact on water resources will increase the interest for such a kind of aquifers due to their strong infiltration and storage capacity, in a broad context of higher water scarcity.The Lez and the Lison karst systems in Southern and Eastern France, respectively, provide 2 examples of such systems of several km² under two contrasted climate conditions, the first one being heavily exploited. This study presents a comparative climate change assessment onboth karst systems. Nine climate scenarios corresponding to the Fourth assessment report of the IPCC (SRES A1B scenario), downscaled using weather-type methods by the CERFACS, have been applied to various recharge modelling approaches, as standard analytical solutions of recharge estimation and soil-water balance models. Results are compared and discussed in order to assess the influence on climate change impacts of i) the climate conditions(geographic location), ii) the groundwater exploitation and iii) the modelling approach

Recommandations pour l'éducation et l'accueil des jeunes enfants (EAJE) dans les trois Communautés de Belgique

Ce document, produit par le groupe OMEP Belgique (Organisation Mondiale d'Education Préscolaire), composé d'acteurs et actrices de l'accueil de la petite enfance, de l'école maternelle, de la formation et de la recherche des trois communautés linguistiques de Belgique propose six recommandations pour développer la qualité des services d'éducation et d'accueil des jeunes enfants en accord avec les recommandations internationales et les orientations à privilégier localement.Organisation Mondiale pour l’Education Préscolaire (OMEP) - Belgiqu

Caribbean Corals in Crisis: Record Thermal Stress, Bleaching, and Mortality in 2005

BACKGROUND The rising temperature of the world's oceans has become a major threat to coral reefs globally as the severity and frequency of mass coral bleaching and mortality events increase. In 2005, high ocean temperatures in the tropical Atlantic and Caribbean resulted in the most severe bleaching event ever recorded in the basin. METHODOLOGY/PRINCIPAL FINDINGS Satellite-based tools provided warnings for coral reef managers and scientists, guiding both the timing and location of researchers' field observations as anomalously warm conditions developed and spread across the greater Caribbean region from June to October 2005. Field surveys of bleaching and mortality exceeded prior efforts in detail and extent, and provided a new standard for documenting the effects of bleaching and for testing nowcast and forecast products. Collaborators from 22 countries undertook the most comprehensive documentation of basin-scale bleaching to date and found that over 80% of corals bleached and over 40% died at many sites. The most severe bleaching coincided with waters nearest a western Atlantic warm pool that was centered off the northern end of the Lesser Antilles. CONCLUSIONS/SIGNIFICANCE Thermal stress during the 2005 event exceeded any observed from the Caribbean in the prior 20 years, and regionally-averaged temperatures were the warmest in over 150 years. Comparison of satellite data against field surveys demonstrated a significant predictive relationship between accumulated heat stress (measured using NOAA Coral Reef Watch's Degree Heating Weeks) and bleaching intensity. This severe, widespread bleaching and mortality will undoubtedly have long-term consequences for reef ecosystems and suggests a troubled future for tropical marine ecosystems under a warming climate.This work was partially supported by salaries from the NOAA Coral Reef Conservation Program to the NOAA Coral Reef Conservation Program authors. NOAA provided funding to Caribbean ReefCheck investigators to undertake surveys of bleaching and mortality. Otherwise, no funding from outside authors' institutions was necessary for the undertaking of this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Cooperation in wild Barbary macaques: factors affecting free partner choice

A key aspect of cooperation is partner choice: choosing the best available partner improves the chances of a successful cooperative interaction and decreases the likelihood of being exploited. However, in studies on cooperation subjects are rarely allowed to freely choose their partners. Group-living animals live in a complex social environment where they can choose among several social partners differing in, for example, sex, age, temperament, or dominance status. Our study investigated whether wild Barbary macaques succeed to cooperate using an experimental apparatus, and whether individual and social factors affect their choice of partners and the degree of cooperation. We used the string pulling task that requires two monkeys to manipulate simultaneously a rope in order to receive a food reward. The monkeys were free to interact with the apparatus or not and to choose their partner. The results showed that Barbary macaques are able to pair up with a partner to cooperate using the apparatus. High level of tolerance between monkeys was necessary for the initiation of successful cooperation, while strong social bond positively affected the maintenance of cooperative interactions. Dominance status, sex, age, and temperament of the subjects also affected their choice and performance. These factors thus need to be taken into account in cooperative experiment on animals. Tolerance between social partners is likely to be a prerequisite for the evolution of cooperation

Quality indicators for patients with traumatic brain injury in European intensive care units

Background: The aim of this study is to validate a previously published consensus-based quality indicator set for the management of patients with traumatic brain injury (TBI) at intensive care units (ICUs) in Europe and to study its potential for quality measur

Changing care pathways and between-center practice variations in intensive care for traumatic brain injury across Europe

Purpose: To describe ICU stay, selected management aspects, and outcome of Intensive Care Unit (ICU) patients with traumatic brain injury (TBI) in Europe, and to quantify variation across centers. Methods: This is a prospective observational multicenter study conducted across 18 countries in Europe and Israel. Admission characteristics, clinical data, and outcome were described at patient- and center levels. Between-center variation in the total ICU population was quantified with the median odds ratio (MOR), with correction for case-mix and random variation between centers. Results: A total of 2138 patients were admitted to the ICU, with median age of 49 years; 36% of which were mild TBI (Glasgow Coma Scale; GCS 13–15). Within, 72 h 636 (30%) were discharged and 128 (6%) died. Early deaths and long-stay patients (> 72 h) had more severe injuries based on the GCS and neuroimaging characteristics, compared with short-stay patients. Long-stay patients received more monitoring and were treated at higher intensity, and experienced worse 6-month outcome compared to short-stay patients. Between-center variations were prominent in the proportion of short-stay patients (MOR = 2.3, p < 0.001), use of intracranial pressure (ICP) monitoring (MOR = 2.5, p < 0.001) and aggressive treatme

Traumatic brain injury: integrated approaches to improve prevention, clinical care, and research

No abstract available

Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury

Objective: We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting: We performed logistic regression (LR), lasso regression, and ridge regression with key baseline predictors in the IMPACT-II database (15 studies, n = 11,022). ML algorithms included support vector machines, random forests, gradient boosting machines, and artificial neural networks and were trained using the same predictors. To assess generalizability of predictions, we performed internal, internal-external, and external validation on the recent CENTER-TBI study (patients with Glasgow Coma Scale <13, n = 1,554). Both calibration (calibration slope/intercept) and discrimination (area under the curve) was quantified. Results: In the IMPACT-II database, 3,332/11,022 (30%) died and 5,233(48%) had unfavorable outcome (Glasgow Outcome Scale less than 4). In the CENTER-TBI study, 348/1,554(29%) died and 651(54%) had unfavorable outcome. Discrimination and calibration varied widely between the studies and less so between the studied algorithms. The mean area under the curve was 0.82 for mortality and 0.77 for unfavorable outcomes in the CENTER-TBI study. Conclusion: ML algorithms may not outperform traditional regression approaches in a low-dimensional setting for outcome prediction after moderate or severe traumatic brain injury. Similar to regression-based prediction models, ML algorithms should be rigorously validated to ensure applicability to new populations