Journal ArticleDOI
Spatial bias in the GBIF database and its effect on modeling species' geographic distributions
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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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Invasive alien insects represent a clear but variable threat to biodiversity
TL;DR: Invasive alien insects as a driver of biodiversity change are an important yet understudied component of the general threat of biological invasions, with evidence to suggest that an insect species global maximum impact is likely to increase in severity as it increases its non-native distribution as mentioned in this paper .
On the distribution of the rare solitary bee Coelioxys lanceolata Nylander, 1852 (Hymenoptera, Megachilidae) in Norway
Markus A. K. Sydenham,Frode Ødegaard,Kaj-Andreas Hanevik,Daniel Ingvar Jeuderan Skoog,Helene Totland Müller,Mari Steinert +5 more
TL;DR: The relative uncommonness of C. lanceolata compared to other bee species may be due to sampling biases, and a sampling scheme that would allow assessing if such bias is influencing knowledge of the status of Norwegian bees is suggested.
Journal ArticleDOI
Ecological Niche models using MaxEnt in Google Earth engine: Evaluation, guidelines and recommendations
TL;DR: In this article , the authors present the first MaxEnt models in GEE, as well as its first statistical and spatial evaluation, and they also identify the limitations of the approach, providing guidelines and recommendations for its easier applicability in Google Earth Engine (GEE).
Potential Climate-Driven Impacts on the Distribution of Generalist Treefrogs in
TL;DR: In this article, the authors evaluated the potential spatial effects that climate change might exert on four wide-ranging generalist anurans from South America and found that these treefrog species are predicted to experience a contraction in their geographical ranges at magnitudes varying from 13.90% to 52.06% due to the loss of suitable areas.
Extensive protected area coverage and an updated global population estimate for the Endangered Madagascar Serpent-eagle Eutriorchis astur
Luke J. Sutton,Armand Benjara,Lily-Arison Rene de Roland,Russell Thorstrom,Christopher J. W. McClure +4 more
TL;DR: In this paper , the authors used SDMs correlating occurrences with remote-sensing covariates to calculate a first estimate of AOH for the Endangered Madagascar Serpent-eagle Eutriorchis astur, and then updated additional IUCN range metrics and the current global population estimate.
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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