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Sieler R.,Institute Francais Of Pondichery
Medical anthropology quarterly

It is often argued that biomedicine alienates patients from doctors, from ailments and from understanding treatment processes, while indigenous and alternative healing systems are portrayed as respectful of patients and their experience. Specifically, South Indian siddha medicine has been seen as diverging from biomedicine in empowering its patients. This approach not only assumes biomedicine to be a homogeneous practice, but also lumps together diverse therapeutic techniques under the labels of "traditional" or "alternative." Analysis of a manual subdiscipline of siddha medicine cautions against such analytic imprecision and active/passive binaries in physician-patient encounters. Practitioners of vital spot medicine claim to "heal the hidden." They rarely communicate diagnostic insights verbally and object to auxiliary devices. However, their physical engagement with patients' ailing bodies highlights the corporeal nature of manual medicine in particular and processual, situational, and reciprocal characteristics of curing in general. © 2014 by the American Anthropological Association. Source

Hamrouni A.,Institute Francais Of Pondichery | Vega C.,IGN | Renaud J.-P.,ONF | Durrleu S.,IRSTEA | Bouvier M.,IRSTEA
Revue Francaise de Photogrammetrie et de Teledetection

We developed an-object based framework to assess individual tree volume from airborne LiDAR data in a pine-dominated forest. Individual tree crowns were extracted using a point-based segmentation algorithm and total tree volume was estimated using height and either tree or crown bounding volume information using nonlinear models. Tree-level models provided root mean squared errors (RMSE) around 30%. Scaling volume at the plot level allows to reduce RMSE by a factor 2, i.e. around 15%. This scale change may benefits from error compensation associated to segmentation involving false tree detections or tree omissions leading to crown fusions. Along with height, crown volume was found to be a good predictor of tree volume, but suffers from computational issues that may further induce variability in the models. Future work should integrate an analysis of tree neighborhood in order to improve tree- models by the use of indices reflecting competition and growth conditions. Source

Vega C.,Institute Francais Of Pondichery | Vega C.,IRSTEA | Durrieu S.,IRSTEA | Morel J.,Institute Francais Of Pondichery | Allouis T.,IRSTEA
Computers and Geosciences

This paper introduces a sequential iterative dual-filter method for filtering Lidar point clouds acquired over rough and forested terrain and computing a digital terrain model (DTM). The method belongs to the family of virtual deforestation algorithms that iteratively detect and filter objects above-the ground surface. The method uses both points and raster models to do so. The algorithm performance was first tested over a complex badlands environment and compared to a reference model obtained using a traditional TIN-Iterative approach. It was further tested on a benchmark site of the ISPRS (site 5) representing mainly forests and slopes. Over badlands, the resulting DTM elevation RMSE was 0.14. m over flat areas, and increased to 0.28. m under forested and rough terrain. The later value was 12.5% lower than the one obtained with a TIN-Iterative approach. Over the ISPRS site, the TIN-Iterative model provided better results for 3 out of the 4 sample sites. But the proposed algorithm, still worked fairly well provided a total classification error of 5.52%, and is well ranked compared with other algorithms. While the TIN-iterative approach might work better with low density, the proposed one is a good alternative to process high density point cloud and compute DTMs suitable for modeling either hydrodynamic or morphological processes under forest cover at a local scale. © 2012 Elsevier Ltd. Source

Dray S.,University of Lyon | Dray S.,University Claude Bernard Lyon 1 | Pelissier R.,Montpellier University | Pelissier R.,Institute Francais Of Pondichery | And 18 more authors.
Ecological Monographs

Species spatial distributions are the result of population demography, behavioral traits, and species interactions in spatially heterogeneous environmental conditions. Hence the composition of species assemblages is an integrative response variable, and its variability can be explained by the complex interplay among several structuring factors. The thorough analysis of spatial variation in species assemblages may help infer processes shaping ecological communities. We suggest that ecological studies would benefit from the combined use of the classical statistical models of community composition data, such as constrained or unconstrained multivariate analyses of site-by-species abundance tables, with rapidly emerging and diversifying methods of spatial pattern analysis. Doing so allows one to deal with spatially explicit ecological models of beta diversity in a biogeographic context through the multiscale analysis of spatial patterns in original species data tables, including spatial characterization of fitted or residual variation from environmental models. We summarize here the recent progress for specifying spatial features through spatial weighting matrices and spatial eigenfunctions in order to define spatially constrained or scale-explicit multivariate analyses. Through a worked example on tropical tree communities, we also show the potential of the overall approach to identify significant residual spatial patterns that could arise from the omission of important unmeasured explanatory variables or processes. © 2012 by the Ecological Society of America. Source

Osuri A.M.,Tata Institute of Fundamental Research | Ratnam J.,Tata Institute of Fundamental Research | Varma V.,Tata Institute of Fundamental Research | Alvarez-Loayza P.,Duke University | And 13 more authors.
Nature Communications

Defaunation is causing declines of large-seeded animal-dispersed trees in tropical forests worldwide, but whether and how these declines will affect carbon storage across this biome is unclear. Here we show, using a pan-tropical data set, that simulated declines of large-seeded animal-dispersed trees have contrasting effects on aboveground carbon stocks across Earth's tropical forests. In our simulations, African, American and South Asian forests, which have high proportions of animal-dispersed species, consistently show carbon losses (2-12%), but Southeast Asian and Australian forests, where there are more abiotically dispersed species, show little to no carbon losses or marginal gains (±1%). These patterns result primarily from changes in wood volume, and are underlain by consistent relationships in our empirical data (B2,100 species), wherein, large-seeded animal-dispersed species are larger as adults than small-seeded animal-dispersed species, but are smaller than abiotically dispersed species. Thus, floristic differences and distinct dispersal mode-seed size-adult size combinations can drive contrasting regional responses to defaunation. Source

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