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Anthony Lehmann
Researcher at University of Geneva
Publications - 119
Citations - 15975
Anthony Lehmann is an academic researcher from University of Geneva. The author has contributed to research in topics: Climate change & Global Earth Observation System of Systems. The author has an hindex of 35, co-authored 110 publications receiving 14042 citations. Previous affiliations of Anthony Lehmann include United Nations Environment Programme & Landcare Research.
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Journal ArticleDOI
Novel methods improve prediction of species' distributions from occurrence data
Jane Elith,Catherine H. Graham,Robert P. Anderson,Miroslav Dudík,Simon Ferrier,Antoine Guisan,Robert J. Hijmans,Falk Huettmann,John R. Leathwick,Anthony Lehmann,Jin Li,Lúcia G. Lohmann,Bette A. Loiselle,Glenn Manion,Craig Moritz,Miguel Nakamura,Yoshinori Nakazawa,Jacob C. M. Mc Overton,A. Townsend Peterson,Steven J. Phillips,Karen Richardson,Ricardo Scachetti-Pereira,Robert E. Schapire,Jorge Soberón,Stephen E. Williams,Mary S. Wisz,Niklaus E. Zimmermann +26 more
TL;DR: This work compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date and found that presence-only data were effective for modelling species' distributions for many species and regions.
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Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data
Steven J. Phillips,Miroslav Dudík,Jane Elith,Catherine H. Graham,Anthony Lehmann,John R. Leathwick,Simon Ferrier +6 more
TL;DR: It is argued that increased awareness of the implications of spatial bias in surveys, and possible modeling remedies, will substantially improve predictions of species distributions and as large an effect on predictive performance as the choice of modeling method.
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Predicting species spatial distributions using presence-only data: a case study of native New Zealand ferns
TL;DR: In this paper, the authors compared generalized additive models (GAM) and ecological niche factor analysis (ENFA) models fitted with identical presence data and computer generated "pseudo" absences.
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Making better biogeographical predictions of species’ distributions
Antoine Guisan,Anthony Lehmann,Simon Ferrier,Mike P. Austin,Jacob McC. Overton,Richard Aspinall,Trevor Hastie +6 more
TL;DR: Six issues are discussed in a methodological framework for generalized regression: links with ecological theory, optimal use of existing data and artificially generated data, incorporating spatial context, integrating ecological and environmental interactions, and assessing prediction errors and uncertainties.
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Using Niche-Based Models to Improve the Sampling of Rare Species
Antoine Guisan,Olivier Broennimann,Robin Engler,Mathias Vust,Nigel G. Yoccoz,Anthony Lehmann,Niklaus E. Zimmermann +6 more
TL;DR: The model-based approach to sampling for rare species helps in the discovery of new populations of the target species in remote areas where the predicted habitat suitability is high and may save up to 70% of the time spent in the field.