Journal ArticleDOI
Selecting from correlated climate variables: a major source of uncertainty for predicting species distributions under climate change
TLDR
This article used four highly correlated climate variables together with a constant set of landscape variables in order to predict current (2010) and future (2050) distributions of four mountain bird species in central Europe.Citations
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Standards for distribution models in biodiversity assessments
Miguel B. Araújo,Miguel B. Araújo,Miguel B. Araújo,Robert P. Anderson,Robert P. Anderson,Robert P. Anderson,A. Márcia Barbosa,Colin M. Beale,Carsten F. Dormann,Regan Early,Raquel A. Garcia,Antoine Guisan,Luigi Maiorano,Luigi Maiorano,Babak Naimi,Robert B. O'Hara,Niklaus E. Zimmermann,Niklaus E. Zimmermann,Carsten Rahbek,Carsten Rahbek +19 more
TL;DR: It is argued that implementation of agreed-upon standards for models in biodiversity assessments would promote transparency and repeatability, eventually leading to higher quality of the models and the inferences used in assessments.
Journal ArticleDOI
ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling
TL;DR: The ENVIREM dataset as discussed by the authors is a set of 16 climatic and two topographic variables in the literature, which are likely to have direct relevance to ecological or physiological processes determining species distributions.
Journal ArticleDOI
Paintings predict the distribution of species, or the challenge of selecting environmental predictors and evaluation statistics
TL;DR: The findings confirm the crucial importance of variable selection and the inability of current evaluation metrics to assess the biological significance of distribution models and recommend that researchers carefully select variables according to the species’ ecology and evaluate models only according to their capacity to be transfered in distant areas.
Journal ArticleDOI
Improving the Use of Species Distribution Models in Conservation Planning and Management under Climate Change
Luciana L. Porfirio,Rebecca M. B. Harris,Edward C. Lefroy,Sonia Hugh,Susan F. Gould,G Lee,Nathaniel L. Bindoff,Brendan Mackey +7 more
TL;DR: The effects of choice of variables, climate models and emissions scenarios can have on future species distribution models using two endangered species: one a short-lived invertebrate species (Ptunarra Brown Butterfly) and the other a long-lived paleo-endemic tree species (King Billy Pine).
Journal ArticleDOI
A quantitative synthesis of the importance of variables used in MaxEnt species distribution models
TL;DR: In this article, the authors synthesize the MaxEnt SDM literature to inform which variables have been used in MaxEnt models for different taxa and quantify how frequently they have been important for species distributions.
References
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Journal ArticleDOI
The measurement of observer agreement for categorical data
J. R. Landis,Gary G. Koch +1 more
TL;DR: A general statistical methodology for the analysis of multivariate categorical data arising from observer reliability studies is presented and tests for interobserver bias are presented in terms of first-order marginal homogeneity and measures of interob server agreement are developed as generalized kappa-type statistics.
Journal ArticleDOI
A Coefficient of agreement for nominal Scales
TL;DR: In this article, the authors present a procedure for having two or more judges independently categorize a sample of units and determine the degree, significance, and significance of the units. But they do not discuss the extent to which these judgments are reproducible, i.e., reliable.
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
Greedy function approximation: A gradient boosting machine.
TL;DR: A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.
Journal ArticleDOI
Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.
TL;DR: A nonparametric approach to the analysis of areas under correlated ROC curves is presented, by using the theory on generalized U-statistics to generate an estimated covariance matrix.
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