Journal ArticleDOI
Modeling a spatially restricted distribution in the Neotropics: How the size of calibration area affects the performance of five presence-only methods
João Gabriel Ribeiro Giovanelli,Marinez Ferreira de Siqueira,Célio F. B. Haddad,João Alexandrino +3 more
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TLDR
In this article, the authors examined species distribution models for a Neotropical anuran restricted to ombrophilous areas in the Brazilian Atlantic Forest hotspot, using GPS field surveys and selected bioclimatic and topographic variables to model the species distribution.About:
This article is published in Ecological Modelling.The article was published on 2010-01-24. It has received 147 citations till now.read more
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A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter
TL;DR: A detailed explanation of how MaxEnt works and a prospectus on modeling options are provided to enable users to make informed decisions when preparing data, choosing settings and interpreting output to highlight the need for making biologically motivated modeling decisions.
Journal ArticleDOI
Species distribution modelling of marine benthos: a North Sea case study
TL;DR: Of the environmental variables, bottom water temperature and depth had the greatest effect on the distribution of 14 benthic species, based on MAXENT results, which can most likely be attributed to the restricted spatial scale and the model evaluation procedure.
Journal ArticleDOI
Selecting predictors to maximize the transferability of species distribution models: lessons from cross-continental plant invasions
TL;DR: Transferring SDMs at the macroclimatic scale, and thus anticipating invasions, is possible for the large majority of invasive plants considered in this study, but the accuracy of the predictions relies strongly on the choice of predictors.
Journal ArticleDOI
The Predictive Performance and Stability of Six Species Distribution Models
TL;DR: According to the prediction performance and stability of SDMs, other SDMs (MAHAL, RF, MAXENT, and SVM) had higher prediction accuracy, smaller confidence intervals, and were more stable and less affected by the random variable (randomly selected pseudo-absence points).
Journal ArticleDOI
Can species distribution modelling provide estimates of population densities? A case study with jaguars in the Neotropics
Natália Mundim Tôrres,Natália Mundim Tôrres,Paulo De Marco Júnior,Thiago de Santana Santos,Leandro Silveira,Anah Tereza de Almeida Jácomo,José Alexandre Felizola Diniz-Filho +6 more
TL;DR: To test the prediction that environmental suitability derived from species distribution modelling (SDM) could be a surrogate for jaguar local population density estimates, SDM is used as a proxy for species distribution model estimates.
References
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The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Journal ArticleDOI
Biodiversity hotspots for conservation priorities
Norman Myers,Russell A. Mittermeier,Cristina G. Mittermeier,Gustavo A. B. da Fonseca,Jennifer Kent +4 more
TL;DR: A ‘silver bullet’ strategy on the part of conservation planners, focusing on ‘biodiversity hotspots’ where exceptional concentrations of endemic species are undergoing exceptional loss of habitat, is proposed.
Journal ArticleDOI
Very high resolution interpolated climate surfaces for global land areas.
Robert J. Hijmans,Susan E. Cameron,Susan E. Cameron,Juan L. Parra,Peter G. Jones,Andy Jarvis +5 more
TL;DR: In this paper, the authors developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution).
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.
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Novel methods improve prediction of species' distributions from occurrence data
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