J
John R. Leathwick
Researcher at National Institute of Water and Atmospheric Research
Publications - 64
Citations - 25673
John R. Leathwick is an academic researcher from National Institute of Water and Atmospheric Research. The author has contributed to research in topics: Biodiversity & Species richness. The author has an hindex of 39, co-authored 61 publications receiving 22103 citations. Previous affiliations of John R. Leathwick include 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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Species Distribution Models: Ecological Explanation and Prediction Across Space and Time
Jane Elith,John R. Leathwick +1 more
TL;DR: Species distribution models (SDMs) as mentioned in this paper are numerical tools that combine observations of species occurrence or abundance with environmental estimates, and are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time.
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A working guide to boosted regression trees
TL;DR: This study provides a working guide to boosted regression trees (BRT), an ensemble method for fitting statistical models that differs fundamentally from conventional techniques that aim to fit a single parsimonious model.
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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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Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees
TL;DR: In this paper, the authors analysed relationships between demersal fish species richness, environment and trawl characteristics using an extensive collection of trawl data from the oceans around New Zealand.