R
Raphaël Pélissier
Researcher at Centre national de la recherche scientifique
Publications - 106
Citations - 6618
Raphaël Pélissier is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: Rainforest & Basal area. The author has an hindex of 35, co-authored 101 publications receiving 5317 citations. Previous affiliations of Raphaël Pélissier include Institut national de la recherche agronomique & Natural Resources Canada.
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Journal ArticleDOI
Improved allometric models to estimate the aboveground biomass of tropical trees
Jérôme Chave,Maxime Réjou-Méchain,Alberto Búrquez,E. N. Chidumayo,Matthew S. Colgan,Welington Braz Carvalho Delitti,Alvaro Duque,Tron Eid,Philip M. Fearnside,Rosa C. Goodman,Matieu Henry,Angelina Martínez-Yrízar,Wilson A. Mugasha,Helene C. Muller-Landau,Maurizio Mencuccini,Bruce Walker Nelson,Alfred Ngomanda,Euler Melo Nogueira,Edgar Ortiz-Malavassi,Raphaël Pélissier,Pierre Ploton,Casey M. Ryan,Juan Saldarriaga,Ghislain Vieilledent +23 more
TL;DR: This work analyzed a global database of directly harvested trees at 58 sites, spanning a wide range of climatic conditions and vegetation types, and found a pantropical model incorporating wood density, trunk diameter, and the variable E outperformed previously published models without height.
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Community ecology in the age of multivariate multiscale spatial analysis
Stéphane Dray,Raphaël Pélissier,Raphaël Pélissier,Pierre Couteron,Marie-Josée Fortin,Pierre Legendre,Pedro R. Peres-Neto,Edwige Bellier,Roger Bivand,F. G. Blanchet,M. De Caceres,Anne-Béatrice Dufour,Einar Heegaard,Thibaut Jombart,Thibaut Jombart,François Munoz,Jari Oksanen,Jean Thioulouse,Helene H. Wagner +18 more
TL;DR: In this paper, the authors 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.
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Avoiding misinterpretation of biotic interactions with the intertype K12-function: population independence vs. random labelling hypotheses
TL;DR: It is demonstrated that the risk of misinterpretation is quite high, and that extreme misinterpretations, i.e. cases leading to opposite conclusions in terms of spatial interaction, can occur in a significant number of cases, are demonstrated.
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Spatial validation reveals poor predictive performance of large-scale ecological mapping models.
Pierre Ploton,Frédéric Mortier,Maxime Réjou-Méchain,Nicolas Barbier,Nicolas Picard,Vivien Rossi,Carsten F. Dormann,Guillaume Cornu,Gaëlle Viennois,Nicolas Bayol,Alexei Lyapustin,Sylvie Gourlet-Fleury,Raphaël Pélissier +12 more
TL;DR: This study underscores how a common practice in big data mapping studies shows an apparent high predictive power, even when predictors have poor relationships with the ecological variable of interest, thus possibly leading to erroneous maps and interpretations.
Journal ArticleDOI
On explicit formulas of edge effect correction for Ripley's K‐function
TL;DR: In this paper, the local correcting factor of edge effect for Ripley's K-function, which can also be used for other statistics of spatial analysis based on the counting of neighbours within a given distance.