Observational Limits on Machos in the Galactic Halo

We present final results from the first phase of the EROS search for gravitational microlensing of stars in the Magellanic Clouds by unseen deflectors (machos: MAssive Compact Halo Objects). The search is sensitive to events with time scales between 15 minutes and 200 days corresponding to deflector masses in the range 1.e-7 to a few solar masses. Two events were observed that are compatible with microlensing by objects of mass of about 0.1 Mo. By comparing the results with the expected number of events for various models of the Galaxy, we conclude that machos in the mass range [1.e-7, 0.02] Mo make up less than 20% (95% C.L.) of the Halo dark matter.Comment: 4 pages, 3 Postscript figures, to be published in Astronomy & Astrophysic

AGAPEROS: Searches for microlensing in the LMC with the Pixel Method; 2, Selection of possible microlensing events

We apply the pixel method of analysis (sometimes called ``pixel lensing'') to a small subset of the EROS-1 microlensing observations of the bar of the Large Magellanic Cloud (LMC). The pixel method is designed to find microlensing events of unresolved source stars and had heretofore been applied only to M31 where essentially all sources are unresolved. With our analysis optimised for the detection of long-duration microlensing events due to 0.01-1 Mo Machos, we detect no microlensing events and compute the corresponding detection efficiencies. We show that the pixel method should detect 10 to 20 times more microlensing events for M>0.05 Mo Machos compared to a classical analysis of the same data which latter monitors only resolved stars. In particular, we show that for a full halo of Machos in the mass range 0.1 -- 0.5 Mo, a pixel analysis of the three-year EROS-1 data set covering 0.39 deg^2 would yield 4 events.We apply the pixel method of analysis (sometimes called ''pixel lensing'') to a small subset of the EROS-1 microlensing observations of the bar of the Large Magellanic Cloud (LMC). The pixel method is designed to find microlensing events of unresolved source stars and had heretofore been applied only to M31 where essentially all sources are unresolved. With our analysis optimised for the detection of long-duration microlensing events due to 0.01-1 Mo Machos, we detect no microlensing events and compute the corresponding detection efficiencies. We show that the pixel method, applied to crowded fields, should detect 10 to 20 times more microlensing events for M>0.05 Mo Machos compared to a classical analysis of the same data which latter monitors only resolved stars. In particular, we show that for a full halo of Machos in the mass range 0.1-0.5 M $\bigodot$, a pixel analysis of the three-year EROS-1 data set covering $0.39deg^{2}$ would yield $\simeq 4$ events

AGAPEROS: Searches for microlensing in the LMC with the Pixel Method; 1, Data treatment and pixel light curves production

The presence and abundance of MAssive Compact Halo Objects (MACHOs) towards the Large Magellanic Cloud (LMC) can be studied with microlensing searches. The 10 events detected by the EROS and MACHO groups suggest that objects with 0.5 Mo could fill 50% of the dark halo. This preferred mass is quite surprising, and increasing the presently small statistics is a crucial issue. Additional microlensing of stars too dim to be resolved in crowded fields should be detectable using the Pixel Method. We present here an application of this method to the EROS 91-92 data (one tenth of the whole existing data set). We emphasize the data treatment required for monitoring pixel fluxes. Geometric and photometric alignments are performed on each image. Seeing correction and error estimates are discussed. 3.6" x 3.6" super-pixel light curves, thus produced, are very stable over the 120 days time-span. Fluctuations at a level of 1.8% of the flux in blue and 1.3% in red are measured on the pixel light curves. This level of stability is comparable with previous estimates. The data analysis dedicated to the search of possible microlensing events together with refined simulations will be presented in a companion paper.Recent surveys monitoring millions of light curves of resolved stars in the LMC have discovered several microlensing events. Unresolved stars could however significantly contribute to the microlensing rate towards the LMC. Monitoring pixels, as opposed to individual stars, should be able to detect stellar variability as a variation of the pixel flux. We present a first application of this new type of analysis (Pixel Method) to the LMC Bar. We describe the complete procedure applied to the EROS 91-92 data (one tenth of the existing CCD data set) in order to monitor pixel fluxes. First, geometric and photometric alignments are applied to each images. Averaging the images of each night reduces significantly the noise level. Second, one light curve for each of the 2.1 10^6 pixels is built and pixels are lumped into 3.6"x3.6" super-pixels, one for each elementary pixel. An empirical correction is then applied to account for seeing variations. We find that the final super-pixel light curves fluctuate at a level of 1.8% of the flux in blue and 1.3% in red. We show that this noise level corresponds to about twice the expected photon noise and confirms previous assumptions used for the estimation of the contribution of unresolved stars. We also demonstrate our ability to correct very efficiently for seeing variations affecting each pixel flux. The technical results emphasised here show the efficacy of the Pixel Method and allow us to study luminosity variations due to possible microlensing events and variable stars in two companion papers

Exploring subtle land use and land cover changes: a framework for future landscape studies

UMR AMAP, équipe 3International audienceLand cover and land use changes can have a wide variety of ecological effects, including significant impacts on soils and water quality. In rural areas, even subtle changes in farming practices can affect landscape features and functions, and consequently the environment. Fine-scale analyses have to be performed to better understand the land cover change processes. At the same time, models of land cover change have to be developed in order to anticipate where changes are more likely to occur next. Such predictive information is essential to propose and implement sustainable and efficient environmental policies. Future landscape studies can provide a framework to forecast how land use and land cover changes is likely to react differently to subtle changes. This paper proposes a four step framework to forecast landscape futures at fine scales by coupling scenarios and landscape modelling approaches. This methodology has been tested on two contrasting agricultural landscapes located in the United States and France, to identify possible landscape changes based on forecasting and backcasting agriculture intensification scenarios. Both examples demonstrate that relatively subtle land cover and land use changes can have a large impact on future landscapes. Results highlight how such subtle changes have to be considered in term of quantity, location, and frequency of land use and land cover to appropriately assess environmental impacts on water pollution (France) and soil erosion (US). The results highlight opportunities for improvements in landscape modelling

Evaluating the spatial uncertainty of future land abandonment in a mountain valley (Vicdessos, Pyrenees-France) : insights form model parameterization and experiments

International audienceEuropean mountains are particularly sensitive to climatic disruptions and land use changes. The latter leads to high rates of natural reforestation over the last 50 years. Faced with the challenge of predicting possible impacts on ecosystem services, LUCC models offer new opportunities for land managers to adapt or mitigate their strategies. Assessing the spatial uncertainty of future LUCC is crucial for the defintion of sustainable land use strategies. However, the sources of uncertainty may differ, including the input parameters, the model itself, and the wide range of possible futures. The aim of this paper is to propose a method to assess the probability of occurrence of future LUCC that combines the inherent uncertainty of model parameterization and the ensemble uncertainty of the future based scenarios. For this purpose, we used the Land Change Modeler tool to simulate future LUCC on a study site located in the Pyrenees Mountains (France) and 2 scenarios illustratins 2 land use strategies. The model was parameterized with the same driving factors used for its calibration. The defintion of static vs. dynamic and quantitative vs. qualitative (discretized) driving factors, and their combination resulted in 4 parameterizations. The combination of model outcomes produced maps of spatial uncertainty of future LUCC. This work involves literature to future-based LUCC studies. It goes beyond the uncertainty of simulation models by integrating the unceertainty of the future to provide maps to help decision makers and land managers

Combining farmers' decision rules and landscape stochastic regularities for landscape modelling

International audienceLandscape spatial organization (LSO) strongly impacts many environmental issues. Modelling agricultural landscapes and describing meaningful landscape patterns are thus regarded as key-issues for designing sustainable landscapes. Agricultural landscapes are mostly designed by farmers. Their decisions dealing with crop choices and crop allocation to land can be generic and result in landscape regularities, which determine LSO. This paper comes within the emerging discipline called "landscape agronomy", aiming at studying the organization of farming practices at the landscape scale. We here aim at articulating the farm and the landscape scales for landscape modelling. To do so, we develop an original approach consisting in the combination of two methods used separately so far: the identification of explicit farmer decision rules through on-farm surveys methods and the identification of landscape stochastic regularities through data-mining. We applied this approach to the Niort plain landscape in France. Results show that generic farmer decision rules dealing with sunflower or maize area and location within landscapes are consistent with spatiotemporal regularities identified at the landscape scale. It results in a segmentation of the landscape, based on both its spatial and temporal organization and partly explained by generic farmer decision rules. This consistency between results points out that the two modelling methods aid one another for land-use modelling at landscape scale and for understanding the driving forces of its spatial organization. Despite some remaining challenges, our study in landscape agronomy accounts for both spatial and temporal dimensions of crop allocation: it allows the drawing of new spatial patterns coherent with land-use dynamics at the landscape scale, which improves the links to the scale of ecological processes and therefore contributes to landscape ecology.L'organisation du paysage influe sur les problèmes environnementaux. Modéliser les paysages pour les décrire à l'aide de formes significatives est une étage clé. Les paysages agricoles sont principalement construits par les agriculteurs dont les décision d'assolement peuvent être génériques et déterminer des régularités dans l'organisation du paysage. Cet article contribue à l'agronomie des paysage qui est une discipline émergente. Nous cherchons à articuler les échelles du paysage et de l'exploitation agricole en développant deux méthodes : l'une consiste à identifier les décisions des agriculteurs par le bais d'enquêtes, l'autre consiste à retrouver des régularités stochastiques dans le paysage par le bais de fouille de données. Nous avons appliqué cette approche au paysage de la plaine de Niort en France. Les résultats montrent que les décisions des agriculteurs en matière de tournesol et maïs sont génériques et ont des effets sur le paysages que des méthodes de fouille de données révèlent et quantifient

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The landscapes currently studied in ecology are either “discontinuous ” (category-based or patchbased), as in the case of mosaics of agricultural units, or of more “continuous ” type (raster lattices), as used for representing elevation or other ecological gradients. The main landscape models either involve explicit processes or are neutral, recreating spatial patterns in the absence of studied processes (using statistical rules). This article presents neutral models suitable for the creation and handling of patchy landscapes. These models (Patchy Landscape Neutral Models) adapt the Gibbs process already used successfully in forestry and biology to describe the local interactions between landscape units. These interactions can be either ecological, if justified for example by natural mechanisms of dispersal (plant species dynamics) or crop successions in anthropized landscapes, or statistical (geometrical). We define a global "cost function " representative of the landscape to be simulated by summing the "pair function " that expresses the interactions between units for the whole landscape. This generic approach makes it possible to reconstruct different kinds of patchy landscape compositions (land cover) and opens the way to studying changes in landscape configuration (unit arrangements) as well as an analytical description of landscapes. Key words: forestry; landscape ecology; Gibbs process; ecosystem modelling; categorical ma

Parameterization of a process-based tree growth model : comparison of optimization, MCMC and particle filtering algorithms

Finely tuned process-based tree-growth models are of considerable help in understanding the variations of biomass increments measured in the dendrochronological series. Using site and species parameters, as well as daily climate variables, the MAIDEN model computes the water balance at ecosystem level and the daily increment of carbon storage in the stem through photosynthesis processes to reproduce the structure of the tree-ring series. In this paper, we use three techniques to calibrate this model with Pinus halepensis data sampled in the Mediterranean part of France: a standard optimization (PEST), Monte Carlo Markov Chains (MCMC) and Particle Filtering (PF). Contrary to PEST, which tries to find an optimum fit (giving the lowest error between observations and simulations), the principle of MCMC and PF is to walk, from a priori distributions, in the parameter space according to particular statistical rules to compute each parameter distribution. The PEST and MCMC calibrations of our dendrochronological series lead to rather similar adjustments between simulations and observations. PF and MCMC calibrations give different parameter distributions, showing how complementary are these methods, with a better fit for MCMC. Yet, independent validations over 11 independent meteorological years show a higher efficiency of the recent PF method over the others