J
Jin Li
Researcher at Geoscience Australia
Publications - 40
Citations - 11860
Jin Li is an academic researcher from Geoscience Australia. The author has contributed to research in topics: Biodiversity & Environmental niche modelling. The author has an hindex of 21, co-authored 39 publications receiving 10181 citations. Previous affiliations of Jin Li include Commonwealth Scientific and Industrial Research Organisation & Chinese Academy of Sciences.
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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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Effects of sample size on the performance of species distribution models
TL;DR: In this article, a broad suite of algorithms with independent presence-absence data from multiple species and regions were evaluated for 46 species (from six different regions of the world) at three sample sizes (100, 30 and 10 records).
Journal ArticleDOI
A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors
Jin Li,Andrew D. Heap +1 more
TL;DR: Comparison studies in environmental sciences are used to assess the performance and to quantify the impacts of data properties on the performance of spatial interpolation methods, finding data variation is a dominant impact factor and has significant effects on theperformance of the methods.
Journal ArticleDOI
Review: Spatial interpolation methods applied in the environmental sciences: A review
Jin Li,Andrew D. Heap +1 more
TL;DR: In this paper, the authors provide guidelines and suggestions regarding application of spatial interpolation methods to environmental data by comparing the features of the commonly applied methods which fall into three categories, namely: non-geostatistical interpolation, geostatistic interpolation and combined methods.
Journal ArticleDOI
Application of machine learning methods to spatial interpolation of environmental variables
TL;DR: This study confirmed the effectiveness of RF, in particular its combination with OK or IDS, and also confirmed the sensitivity of RF and its combined methods to the input variables, and opened an alternative source of methods for spatial interpolation of environmental properties.