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
Citations
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The iterative process of plant species inventorying for obtaining reliable biodiversity patterns
Silvia C. Aranda,Silvia C. Aranda,Helena Hespanhol,Nídia Homem,Paulo A. V. Borges,Jorge M. Lobo,Jorge M. Lobo,Rosalina Gabriel +7 more
TL;DR: This study illustrates the difficulties of planning field surveys to obtain reliable biodiversity patterns, even when prior information and standardized sampling protocols are explicitly considered.
Journal ArticleDOI
Species Distribution 2.0: An Accurate Time- and Cost-Effective Method of Prospection Using Street View Imagery
TL;DR: An alternative method of prospection using geo-located street view imagery (SVI) provides the means to accurately locate highly visible taxa, reinforce absence data, and predict species distribution without long and expensive in situ prospection.
Dissertation
A General Framework for Predicting the Optimal Computing Configurations for Climate-Driven Ecological Forecasting Models
TL;DR: A general conceptual framework for approaching tradeoffs between model accuracy, computing cost, and model execution time and a model for determining the optimal data-hardware configuration for a species distribution modeling (SDM) workflow are introduced.
Journal ArticleDOI
Enhancing VGI application semantics by accounting for spatial bias
TL;DR: It is demonstrated that VGI application semantics can be enhanced by accounting for the spatial bias in VGI observations, as gauged by SDM model performance and accounting for bias enhances application semantics.
Journal ArticleDOI
Distribution models combined with standardized surveys reveal widespread habitat loss in a threatened turtle species
TL;DR: In this paper , a two-phase modeling approach along with occurrence records and field surveys was used to estimate the potential and current distributions of the wood turtle (Glyptemys insculpta), a semi-terrestrial fluvial specialist of rangewide conservation concern; assess how climate, geomorphology, and land-use relate to its distribution; and estimate habitat loss.
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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