Journal ArticleDOI
Spatial bias in the GBIF database and its effect on modeling species' geographic distributions
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TLDR
A subsampling routine is used as an exemplar taxon to provide evidence that range model quality is decreasing due to the spatial clustering of distributional records in GBIF and shows that data with less spatial bias produce better predictive models even though they are based on less input data.About:
This article is published in Ecological Informatics.The article was published on 2014-01-01. It has received 424 citations till now.read more
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Distribution models calibrated with independent field data predict two million ancient and veteran trees in England
TL;DR: In this paper , the authors present a nationwide, independent field validation of distribution models of ancient and veteran trees, a group of organisms of high conservation importance, built using a large and internationally unique citizen science database: the Ancient Tree Inventory (ATI).
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Sample size and spatial configuration of volunteered geographic information affect effectiveness of spatial bias mitigation
Guiming Zhang,A-Xing Zhu +1 more
Assessing the Geographical Structure of Species Richness Data with Interactive Graphics
TL;DR: This work designs and implements two interactive visualisation approaches to help assess how species richness data varies over continuous geographical space and provides perspectives on their use for assessing geographical incompleteness in species richness.
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Predicting Distribution of the Asian Longhorned Beetle, Anoplophora glabripennis (Coleoptera: Cerambycidae) and Its Natural Enemies in China
TL;DR: In this paper , the authors used MaxEnt to simulate the distribution of Dastarcus helophoroides and Dendrocopos major in China, and their suitable areas were superimposed to pinpoint which regions are potentially appropriate to release or establish natural enemy populations under current and future conditions.
Journal ArticleDOI
Persistent spatial gaps in ornithological study in Australia, 1901-2011
TL;DR: The authors used a century-long bibliometric database of the journal Emu and Austral Ornithology to index the spatial patterns in the field of ecology research effort at the continental scale.
References
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Journal ArticleDOI
Maximum entropy modeling of species geographic distributions
TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.
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.
Journal ArticleDOI
Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
TL;DR: This paper presents a tuning method that uses presence-only data for parameter tuning, and introduces several concepts that improve the predictive accuracy and running time of Maxent and describes a new logistic output format that gives an estimate of probability of presence.
Journal ArticleDOI
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.
Journal ArticleDOI
AUC: a misleading measure of the performance of predictive distribution models
TL;DR: The area under the receiver operating characteristic (ROC) curve, known as the AUC, is currently considered to be the standard method to assess the accuracy of predictive distribution models as discussed by the authors.
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