UMR Espace-Dev

Abstract. The Surface Ocean CO2 Atlas (SOCAT) is a synthesis of quality-controlled fCO2 (fugacity of carbon dioxide) values for the global surface oceans and coastal seas with regular updates. Version 3 of SOCAT has 14.7 million fCO2 values from 3646 data sets covering the years 1957 to 2014. This latest version has an additional 4.6 million fCO2 values relative to version 2 and extends the record from 2011 to 2014. Version 3 also significantly increases the data availability for 2005 to 2013. SOCAT has an average of approximately 1.2 million surface water fCO2 values per year for the years 2006 to 2012. Quality and documentation of the data has improved. A new feature is the data set quality control (QC) flag of E for data from alternative sensors and platforms. The accuracy of surface water fCO2 has been defined for all data set QC flags. Automated range checking has been carried out for all data sets during their upload into SOCAT. The upgrade of the interactive Data Set Viewer (previously known as the Cruise Data Viewer) allows better interrogation of the SOCAT data collection and rapid creation of high-quality figures for scientific presentations. Automated data upload has been launched for version 4 and will enable more frequent SOCAT releases in the future. High-profile scientific applications of SOCAT include quantification of the ocean sink for atmospheric carbon dioxide and its long-term variation, detection of ocean acidification, as well as evaluation of coupled-climate and ocean-only biogeochemical models. Users of SOCAT data products are urged to acknowledge the contribution of data providers, as stated in the SOCAT Fair Data Use Statement. This ESSD (Earth System Science Data) "living data" publication documents the methods and data sets used for the assembly of this new version of the SOCAT data collection and compares these with those used for earlier versions of the data collection (Pfeil et al., 2013; Sabine et al., 2013; Bakker et al., 2014). Individual data set files, included in the synthesis product, can be downloaded here: doi:10.1594/PANGAEA.849770. The gridded products are available here: doi:10.3334/CDIAC/OTG.SOCAT_V3_GRID.

Local and indigenous knowledge is being transformed globally, particularly being eroded when pertaining to ecology. In many parts of the world, rural and indigenous communities are facing tremendous cultural, economic and environmental changes, which contribute to weaken their local knowledge base. In the face of profound and ongoing environmental changes, both cultural and biological diversity are likely to be severely impacted as well as local resilience capacities from this loss. In this global literature review, we analyse the drivers of various types of local and indigenous ecological knowledge transformation and assess the directionality of the reported change. Results of this analysis show a global impoverishment of local and indigenous knowledge with 77% of papers reporting the loss of knowledge driven by globalization, modernization, and market integration. The recording of this loss, however, is not symmetrical, with losses being recorded more strongly in medicinal and ethnobotanical knowledge. Persistence of knowledge (15% of the studies) occurred in studies where traditional practices were being maintained consiously and where hybrid knowledge was being produced as a resut of certain types of incentives created by economic development. This review provides some insights into local and indigenous ecological knowledge change, its causes and implications, and recommends venues for the development of replicable and comparative research. The larger implication of these results is that because of the interconnection between cultural and biological diversity, the loss of local and indigenous knowledge is likely to critically threaten effective conservation of biodiversity, particularly in community-based conservation local efforts.

BACKGROUND: Leptospirosis is the most common bacterial zoonoses and has been identified as an important emerging global public health problem in Southeast Asia. Rodents are important reservoirs for human leptospirosis, but epidemiological data is lacking. METHODOLOGY/PRINCIPAL FINDINGS: We sampled rodents living in different habitats from seven localities distributed across Southeast Asia (Thailand, Lao PDR and Cambodia), between 2009 to 2010. Human isolates were also obtained from localities close to where rodents were sampled. The prevalence of Leptospira infection was assessed by real-time PCR using DNA extracted from rodent kidneys, targeting the lipL32 gene. Sequencing rrs and secY genes, and Multi Locus Variable-number Tandem Repeat (VNTR) analyses were performed on DNA extracted from rat kidneys for Leptospira isolates molecular typing. Four species were detected in rodents, L. borgpetersenii (56% of positive samples), L. interrogans (36%), L. kirschneri (3%) and L. weilli (2%), which were identical to human isolates. Mean prevalence in rodents was approximately 7%, and largely varied across localities and habitats, but not between rodent species. The two most abundant Leptospira species displayed different habitat requirements: L. interrogans was linked to humid habitats (rice fields and forests) while L. borgpetersenii was abundant in both humid and dry habitats (non-floodable lands). CONCLUSION/SIGNIFICANCE: L. interrogans and L. borgpetersenii species are widely distributed amongst rodent populations, and strain typing confirmed rodents as reservoirs for human leptospirosis. Differences in habitat requirements for L. interrogans and L. borgpetersenii supported differential transmission modes. In Southeast Asia, human infection risk is not only restricted to activities taking place in wetlands and rice fields as is commonly accepted, but should also include tasks such as forestry work, as well as the hunting and preparation of rodents for consumption, which deserve more attention in future epidemiological studies.

Climate change is already affecting agro-ecosystems and threatening food security by reducing crop productivity and increasing harvest uncertainty. Mobilizing crop diversity could be an efficient way to mitigate its impact. We test this hypothesis in pearl millet, a nutritious staple cereal cultivated in arid and low-fertility soils in sub-Saharan Africa. We analyze the genomic diversity of 173 landraces collected in West Africa together with an extensive climate dataset composed of metrics of agronomic importance. Mapping the pearl millet genomic vulnerability at the 2050 horizon based on the current genomic-climate relationships, we identify the northern edge of the current areas of cultivation of both early and late flowering varieties as being the most vulnerable to climate change. We predict that the most vulnerable areas will benefit from using landraces that already grow in equivalent climate conditions today. However, such seed-exchange scenarios will require long distance and trans-frontier assisted migrations. Leveraging genetic diversity as a climate mitigation strategy in West Africa will thus require regional collaboration.

Radar altimetry is now commonly used for the monitoring of water levels in large river basins. In this study, an altimetry-based network of virtual stations was defined in the quasi ungauged Ogooué river basin, located in Gabon, Central Africa, using data from seven altimetry missions (Jason-2 and 3, ERS-2, ENVISAT, Cryosat-2, SARAL, Sentinel-3A) from 1995 to 2017. The performance of the five latter altimetry missions to retrieve water stages and discharges was assessed through comparisons against gauge station records. All missions exhibited a good agreement with gauge records, but the most recent missions showed an increase of data availability (only 6 virtual stations (VS) with ERS-2 compared to 16 VS for ENVISAT and SARAL) and accuracy (RMSE lower than 1.05, 0.48 and 0.33 and R² higher than 0.55, 0.83 and 0.91 for ERS-2, ENVISAT and SARAL respectively). The concept of VS is extended to the case of drifting orbits using the data from Cryosat-2 in several close locations. Good agreement was also found with the gauge station in Lambaréné (RMSE = 0.25 m and R2 = 0.96). Very good results were obtained using only one year and a half of Sentinel-3 data (RMSE < 0.41 m and R2 > 0.89). The combination of data from all the radar altimetry missions near Lamabréné resulted in a long-term (May 1995 to August 2017) and significantly improved water-level time series (R² = 0.96 and RMSE = 0.38 m). The increase in data sampling in the river basin leads to a better water level peak to peak characterization and hence to a more accurate annual discharge over the common observation period with only a 1.4 m3·s−1 difference (i.e., 0.03%) between the altimetry-based and the in situ mean annual discharge.

Satellite precipitation products (SPPs) provide alternative precipitation data for regions with sparse rain gauge measurements. However, SPPs are subject to different types of error that need correction. Most SPP bias correction methods use the statistical properties of the rain gauge data to adjust the corresponding SPP data. The statistical adjustment does not make it possible to correct the pixels of SPP data for which there is no rain gauge data. The solution proposed in this article is to correct the daily SPP data for the Guiana Shield using a novel two set approach, without taking into account the daily gauge data of the pixel to be corrected, but the daily gauge data from surrounding pixels. In this case, a spatial analysis must be involved. The first step defines hydroclimatic areas using a spatial classification that considers precipitation data with the same temporal distributions. The second step uses the Quantile Mapping bias correction method to correct the daily SPP data contained within each hydroclimatic area. We validate the results by comparing the corrected SPP data and daily rain gauge measurements using relative RMSE and relative bias statistical errors. The results show that analysis scale variation reduces rBIAS and rRMSE significantly. The spatial classification avoids mixing rainfall data with different temporal characteristics in each hydroclimatic area, and the defined bias correction parameters are more realistic and appropriate. This study demonstrates that hydroclimatic classification is relevant for implementing bias correction methods at the local scale.

Assessing the impact/adaptation of human activities on/to climate change is a key issue, especially in the tropics that concentrate major anthropogenic dynamics such as deforestation and nearly two-thirds of the planetary rainfall. However, this task is often made tough because human activities such as agricultural dynamics are usually analysed at local or regional scale whereas climate related studies are led at large to global scales due to a lack of reliable data, especially in the tropics. In this article we argue that the increased spatial resolution of remote sensing-based rainfall estimates enables assessing the spatiotemporal variability of rainfall regimes at regional and local scales, thus allowing fine analysis of the interactions with human activities. We processed Tropical Rainfall Measuring Mission (TRMM) 3B42 daily rainfall estimates over the state of Mato Grosso (southern Brazilian Amazon) for the 1998–2012 study period in order to compute rainfall metrics such as annual rainfall and duration, onset and end dates of the rainy season based on the Anomalous Accumulation methodology (at a 0.25° spatial resolution). We then crossed these metrics with agricultural maps (produced at a 250 m spatial resolution) and proved that the adoption of intensive agricultural practices such as double cropping systems is partly the result of a strategy to adapt practices to local climatic conditions. Finally, we discuss how such results raise important issues regarding the sustainability of the agricultural development model in the Southern Amazon.

Abstract Tropical cyclones ( TC s) are large‐scale disturbances that regularly impact tropical forests. Although long‐term impacts of TC s on forest structure have been proposed, a global test of the relationship between forest structure and TC frequency and intensity is lacking. We test on a pantropical scale whether TC s shape the structure of tropical and subtropical forests in the long term. We compiled forest structural features (stem density, basal area, mean canopy height and maximum tree size) for plants ≥10 cm in diameter at breast height from published forest inventory data (438 plots ≥0.1 ha, pooled into 250 1 × 1‐degree grid cells) located in dry and humid forests. We computed maps of cyclone frequency and energy released by cyclones per unit area (power dissipation index, PDI ) using a high‐resolution historical database of TC s trajectories and intensities. We then tested the relationship between PDI and forest structural features using multivariate linear models, controlling for climate (mean annual temperature and water availability) and human disturbance (human foot print). Forests subject to frequent cyclones (at least one TC s per decade) and high PDI exhibited higher stem density and basal area, and lower canopy heights. However, the relationships between PDI and basal area or canopy height were partially masked by lower water availability and higher human foot print in tropical dry forests. Synthesis . Our results provide the first evidence that tropical cyclones have a long‐term impact on the structure of tropical and subtropical forests in a globally consistent way. The strong relationship between power dissipation index and stem density suggests that frequent and intense tropical cyclones reduce canopy cover through defoliation and tree mortality, encouraging higher regeneration and turnover of biomass. The projected increase in intensity and poleward extension of tropical cyclones due to anthropogenic climate change may therefore have important and lasting impacts on the structure and dynamics of forests in the future.

"Protocols for field and laboratory rodent studies" present the best practices for the studies of rodents and rodent-borne pathogens and parasites from the field to the laboratory. It aims at covering the different steps of rodent studies: 1) Trapping, 2) Dissection and preparation of tissue samples for pathogens screening, 3) Identification of species, 4) Isolation of helminths, 5) Preparing rodent specimens for collections. This book gathers accurate recommendations and techniques, based on those genérally accepted in the literature and adapted from the experience of the different authors during rodent surveys and laboratory work. Its presentation is designed to work in the fields with a clear and colorful organization of each chapter with: inserts providing definitions and recommendations, protocols detailed step by step, and an emphasis on illustrations with several photographs.

Assessing how much management of agricultural landscapes, in addition to protected areas, can offset biodiversity erosion in the tropics is a central issue for conservation that still requires cross-taxonomic and landscape-scale studies. We measured the effects of Amazonia deforestation and subsequent land-use intensification in 6 agricultural areas (landscape scale), where we sampled plants and 4 animal groups (birds, earthworms, fruit flies, and moths). We assessed land-use intensification with a synthetic index based on landscape metrics (total area and relative percentages of land uses, edge density, mean patch density and diversity, and fractal structures at 5 dates from 1990 to 2007). Species richness decreased consistently as agricultural intensification increased despite slight differences in the responses of sampled groups. Globally, in moderately deforested landscapes species richness was relatively stable, and there was a clear threshold in biodiversity loss midway along the intensification gradient, mainly linked to a drop in forest cover and quality. Our results suggest anthropogenic landscapes with high-quality forest covering >40 % of the surface area may prevent biodiversity loss in Amazonia.

Black rats are major invasive vertebrate pests with severe ecological, economic and health impacts. Remarkably, their evolutionary history has received little attention, and there is no firm agreement on how many species should be recognized within the black rat complex. This species complex is native to India and Southeast Asia. According to current taxonomic classification, there are three taxa living in sympatry in several parts of Thailand, Cambodia and Lao People's Democratic Republic, where this study was conducted: two accepted species (Rattus tanezumi, Rattus sakeratensis) and an additional mitochondrial lineage of unclear taxonomic status referred to here as 'Rattus R3'. We used extensive sampling, morphological data and diverse genetic markers differing in rates of evolution and parental inheritance (two mitochondrial DNA genes, one nuclear gene and eight microsatellite loci) to assess the reproductive isolation of these three taxa. Two close Asian relatives, Rattus argentiventer and Rattus exulans, were also included in the genetic analyses. Genetic analyses revealed discordance between the mitochondrial and nuclear data. Mitochondrial phylogeny studies identified three reciprocally monophyletic clades in the black rat complex. However, studies of the phylogeny of the nuclear exon interphotoreceptor retinoid-binding protein gene and clustering and assignation analyses with eight microsatellites failed to separate R. tanezumi and R3. Morphometric analyses were consistent with nuclear data. The incongruence between mitochondrial and nuclear (and morphological) data rendered R. tanezumi/R3 paraphyletic for mitochondrial lineages with respect to R. sakeratensis. Various evolutionary processes, such as shared ancestral polymorphism and incomplete lineage sorting or hybridization with massive mitochondrial introgression between species, may account for this unusual genetic pattern in mammals.

An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a
\ngrowing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation
\nof yield simulations from process-based eco-physiological models for climate change impact assessment is still
\nmissing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In
\nthis first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under
\nvarying temperature and atmospheric CO2 concentration [CO2] to quantify the uncertainty in soybean yield
\nsimulations in response to these factors. Models were first parametrized with high quality measured data from
\nfive contrasting environments. We found considerable variability among models in simulated yield responses to
\nincreasing temperature and [CO2]. For example, under a + 3 ◦C temperature rise in our coolest location in
\nArgentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38%
\ndecrease under + 3 ◦C temperature rise to no effect on yield. Similarly, when increasing [CO2] from 360 to 540
\nppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce
\nvariability across models but had an unexpected effect on modifying yield responses to temperature for some of he models. The high uncertainty in model responses indicates the limited applicability of individual models for
\nclimate change food projections. However, the ensemble mean of simulations across models was an effective tool
\nto reduce the high uncertainty in soybean yield simulations associated with individual models and their
\nparametrization. Ensemble mean yield responses to temperature and [CO2] were similar to those reported from
\nthe literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain
\nlegume models for climate change food projections, and highlights that further soybean model development with
\nexperiments under elevated [CO2] and temperature is needed to reduce the uncertainty from the individual
\nmodels.

To date, there is no effective treatment to cure dengue fever, a mosquito-borne disease which has a major impact on human populations in tropical and sub-tropical regions. Although the characteristics of dengue infection are well known, factors associated with landscape are highly scale dependent in time and space, and therefore difficult to monitor. We propose here a mapping review based on 78 articles that study the relationships between landscape factors and urban dengue cases considering household, neighborhood and administrative levels. Landscape factors were retrieved from survey questionnaires, Geographic Information Systems (GIS), and remote sensing (RS) techniques. We structured these into groups composed of land cover, land use, and housing type and characteristics, as well as subgroups referring to construction material, urban typology, and infrastructure level. We mapped the co-occurrence networks associated with these factors, and analyzed their relevance according to a three-valued interpretation (positive, negative, non significant). From a methodological perspective, coupling RS and GIS techniques with field surveys including entomological observations should be systematically considered, as none digital land use or land cover variables appears to be an univocal determinant of dengue occurrences. Remote sensing urban mapping is however of interest to provide a geographical frame to distribute human population and movement in relation to their activities in the city, and as spatialized input variables for epidemiological and entomological models.

BACKGROUND: Given the scarcity of resources in developing countries, malaria treatment requires new strategies that target specific populations, time periods and geographical areas. While the spatial pattern of malaria transmission is known to vary depending on local conditions, its temporal evolution has yet to be evaluated. The aim of this study was to determine the spatio-temporal dynamic of malaria in the central region of Burkina Faso, taking into account meteorological factors. METHODS: Drawing on national databases, 101 health areas were studied from 2011 to 2015, together with weekly meteorological data (temperature, number of rain events, rainfall, humidity, wind speed). Meteorological factors were investigated using a principal component analysis (PCA) to reduce dimensions and avoid collinearities. The Box-Jenkins ARIMA model was used to test the stationarity of the time series. The impact of meteorological factors on malaria incidence was measured with a general additive model. A change-point analysis was performed to detect malaria transmission periods. For each transmission period, malaria incidence was mapped and hotspots were identified using spatial cluster detection. RESULTS: Malaria incidence never went below 13.7 cases/10,000 person-weeks. The first and second PCA components (constituted by rain/humidity and temperatures, respectively) were correlated with malaria incidence with a lag of 2 weeks. The impact of temperature was significantly non-linear: malaria incidence increased with temperature but declined sharply with high temperature. A significant positive linear trend was found for the entire time period. Three transmission periods were detected: low (16.8-29.9 cases/10,000 person-weeks), high (51.7-84.8 cases/10,000 person-weeks), and intermediate (26.7-32.2 cases/10,000 person-weeks). The location of clusters identified as high risk varied little across transmission periods. CONCLUSION: This study highlighted the spatial variability and relative temporal stability of malaria incidence around the capital Ouagadougou, in the central region of Burkina Faso. Despite increasing efforts in fighting the disease, malaria incidence remained high and increased over the period of study. Hotspots, particularly those detected for low transmission periods, should be investigated further to uncover the local environmental and behavioural factors of transmission, and hence to allow for the development of better targeted control strategies.

Mosquitoes are responsible for the transmission of major pathogens worldwide. Modelling their population dynamics and mapping their distribution can contribute effectively to disease surveillance and control systems. Two main approaches are classically used to understand and predict mosquito abundance in space and time, namely empirical (or statistical) and process-based models. In this work, we used both approaches to model the population dynamics in Reunion Island of the 'Tiger mosquito', Aedes albopictus, a vector of dengue and chikungunya viruses, using rainfall and temperature data. We aimed to i) evaluate and compare the two types of models, and ii) develop an operational tool that could be used by public health authorities and vector control services. Our results showed that Ae. albopictus dynamics in Reunion Island are driven by both rainfall and temperature with a non-linear relationship. The predictions of the two approaches were consistent with the observed abundances of Ae. albopictus aquatic stages. An operational tool with a user-friendly interface was developed, allowing the creation of maps of Ae. albopictus densities over the whole territory using meteorological data collected from a network of weather stations. It is now routinely used by the services in charge of vector control in Reunion Island.

International audience

Food security is a crucial issue in the Sahel and could be endangered by climate change and demographic pressure during the 21st century. Higher temperatures and changes in rainfall induced by global warming are threatening rainfed agriculture in this region while the population is expected to increase approximately three-fold until 2050. Our study quantifies the impact of climate change on food security by combining climate modelling (16 models from CMIP5), crop yield (simulated by agronomic model, SARRA-O) and demographic evolution (provided by UN projection) under two future climatic scenarios. We simulate yield for the main crops in five countries in West Africa and estimate the population pressure on crop production to assess the number of available cereal production per capita. We found that, although uncertain, the African monsoon evolution leads to an increase of rainfall in Eastern Sahel and a decrease in Western Sahel under the RCP8.5 (Representative Concentration Pathway) scenario from IPCC, leading to the higher temperature increase by the end of the 21st century. With regard to the abundance of food for the inhabitants, all the scenarios in each country show that in 2050, local agricultural production will be below 50 kg per capita. This situation can have impact on crop import and regional migration.

Combinatorial problems involving sets and relations are currently tackled by integer programming and expressed with vectors or matrices of 0-1 variables. This is efficient but not flexible and unnatural in problem formulation. Toward a natural programming of combinatorial problems based on sets, graphs or relations, we define a new CLP language with set constraints. This language Conjunto 1 aims at combining the declarative aspect of Prolog with the efficiency of constraint solving techniques. We propose to constrain a set variable to range over finite set domains specified by lower and upper bounds for set inclusion. Conjunto is based on the inclusion and disjointness constraints applied to set expressions which comprise the union, intersection and difference symbols. The main contribution herein is the constraint handler which performs constraint propagation by applying consistency techniques over set constraints. 1 Introduction Various systems of set constraints have been define...

International audience

Abstract Detection and attribution of human influence on sea level rise are important topics that have not yet been explored in depth. We question whether the sea level changes (SLC) over the past century were natural in origin. SLC exhibit power law long‐term correlations. By estimating Hurst exponent through Detrended Fluctuation Analysis and by applying statistics of Lennartz and Bunde [2009], we search the lower bounds of statistically significant external sea level trends in longest tidal records worldwide. We provide statistical evidences that the observed SLC, at global and regional scales, is beyond its natural internal variability. The minimum anthropogenic sea level trend (MASLT) contributes to the observed sea level rise more than 50% in New York, Baltimore, San Diego, Marseille, and Mumbai. A MASLT is about 1 mm/yr in global sea level reconstructions that is more than half of the total observed sea level trend during the XXth century.