Open AccessBook
Habitat Suitability and Distribution Models
TLDR
In this article, the authors introduce the key stages of niche-based habitat suitability model building, evaluation and prediction required for understanding and predicting future patterns of species and biodiversity, including the main theory behind ecological niches and species distributions.Abstract:
This book introduces the key stages of niche- based habitat suitability model building, evaluation and prediction required for understanding and predicting future patterns of species and biodiversity. Beginning with the main theory behind ecological niches and species distributions, the book proceeds through all major steps of model building, from conceptualization and model training to model evaluation and spatio- temporal predictions. Extensive examples using R support graduate students and researchers in quantifying ecological niches and predicting species distributions with their own data, and help to address key environmental and conservation problems. Reflecting this highly active field of research, the book incorporates the latest developments from informatics and statistics, as well as using data from remote sources such as satellite imagery. A website at www.unil.ch/ hsdm contains the codes and supporting material required to run the examples and teach courses. All three authors are recognized specialists of and have contributed substantially to the development of spatial prediction methods for species’ habitat suitability and distribution modeling. They published a large number of papers, overall cumulating tens of thousands of citations, and are ISI Highly Cited Researchers.read more
Citations
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Journal ArticleDOI
A standard protocol for reporting species distribution models
Damaris Zurell,Janet Franklin,Christian König,Phil J. Bouchet,Carsten F. Dormann,Jane Elith,Guillermo Fandos,Xiao Feng,Gurutzeta Guillera-Arroita,Antoine Guisan,José J. Lahoz-Monfort,Pedro J. Leitão,Daniel S. Park,A. Townsend Peterson,Giovanni Rapacciuolo,Dirk R. Schmatz,Boris Schröder,Josep M. Serra-Diaz,Wilfried Thuiller,Katherine L. Yates,Niklaus E. Zimmermann,Cory Merow +21 more
TL;DR: This work proposes a standard protocol for reporting SDMs, and introduces a structured format for documenting and communicating the models, ensuring transparency and reproducibility, facilitating peer review and expert evaluation of model quality, as well as meta-analyses.
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blockCV: An r package for generating spatially or environmentally separated folds for k‐fold cross‐validation of species distribution models
TL;DR: The r package blockCV as mentioned in this paper is a toolbox for cross-validation of species distribution modeling, which can be used for any spatial modelling. But it is not suitable for the analysis of structured data, as it may lead to underestimation of prediction error and may result in inappropriate model selection.
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A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD
TL;DR: Despite proliferation of ensemble models, there is no oversight of how and where they are used for modelling distributions, and how well they perform, according to how they perform.
Journal ArticleDOI
Climate change vulnerability assessment of species
Wendy Foden,Wendy Foden,Bruce E. Young,Bruce E. Young,H. Resit Akçakaya,H. Resit Akçakaya,Raquel A. Garcia,Raquel A. Garcia,Ary A. Hoffmann,Bruce A. Stein,Bruce A. Stein,Chris D. Thomas,Christopher J. Wheatley,Christopher J. Wheatley,David Bickford,Jamie A. Carr,Jamie A. Carr,David G. Hole,Tara G. Martin,Tara G. Martin,Michela Pacifici,Michela Pacifici,James W. Pearce-Higgins,James W. Pearce-Higgins,Philip J. Platts,Philip J. Platts,Piero Visconti,James E. M. Watson,James E. M. Watson,Brian Huntley,Brian Huntley +30 more
TL;DR: The authors provide an overview of the rapidly developing field of climate change vulnerability assessment (CCVA) and describe key concepts, terms, steps and considerations, and stress the importance of identifying the full range of pressures, impacts and their associated mechanisms that species face and using this as a basis for selecting the appropriate assessment approaches for quantifying vulnerability.
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Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping.
TL;DR: To reduce uncertainty, creating more generalizable, more stable, and less sensitive models, ensemble forecasting approaches and in particular the EMmedian is recommended for flood susceptibility assessment.
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.
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A new look at the statistical model identification
TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
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Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Book
Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach
TL;DR: The second edition of this book is unique in that it focuses on methods for making formal statistical inference from all the models in an a priori set (Multi-Model Inference).
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.