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Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach
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TLDR
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).Abstract:
Introduction * Information and Likelihood Theory: A Basis for Model Selection and Inference * Basic Use of the Information-Theoretic Approach * Formal Inference From More Than One Model: Multi-Model Inference (MMI) * Monte Carlo Insights and Extended Examples * Statistical Theory and Numerical Results * Summaryread more
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Journal ArticleDOI
Confronting collinearity: comparing methods for disentangling the effects of habitat loss and fragmentation
TL;DR: In this article, the authors used simulations to determine whether, under identical conditions, the following 7 methods generate different estimates of relative importance for realistically correlated landscape predictors: residual regression, model or variable selection, averaged coefficients from all supported models, summed Akaike weights, classical variance partitioning, hierarchical variance partitions, and a multiple regression model with no adjustments for collinearity.
Journal ArticleDOI
Driving factors of a vegetation shift from Scots pine to pubescent oak in dry Alpine forests
Andreas Rigling,Christof Bigler,Britta Eilmann,Elisabeth Feldmeyer-Christe,Urs Gimmi,Christian Ginzler,Ulrich Graf,Philipp Mayer,Giorgio Vacchiano,Pascale Weber,Thomas Wohlgemuth,Roman Zweifel,Matthias Dobbertin +12 more
TL;DR: The results suggest that an extended shift in species composition is actually occurring in the pine forests in the Valais, with the main driving factors found to be climatic variability, particularly drought, and variability in stand structure and topography.
Journal ArticleDOI
Patterns and causes of species richness: a general simulation model for macroecology
Nicholas J. Gotelli,Marti J. Anderson,Héctor T. Arita,Anne Chao,Robert K. Colwell,Sean R. Connolly,David J. Currie,Robert R. Dunn,Gary R. Graves,Jessica L. Green,John-Arvid Grytnes,Yi-Huei Jiang,Walter Jetz,S. Kathleen Lyons,Christy M. McCain,Anne E. Magurran,Carsten Rahbek,Thiago F. Rangel,Jorge Soberón,Campbell O. Webb,Michael R. Willig +20 more
TL;DR: Computer simulation models of the stochastic origin, spread, and extinction of species' geographical ranges in an environmentally heterogeneous, gridded domain and three of the 'control knobs' for a general simulation model that specify simple rules for dispersal, evolutionary origins and environmental gradients are described.
Journal ArticleDOI
Gaia Early Data Release 3: Parallax bias versus magnitude, colour, and position
Lennart Lindegren,Ulrich Bastian,M. Biermann,Alex Bombrun,A. de Torres,E. Gerlach,R. Geyer,Jose M Hernandez,Thomas Hilger,David Hobbs,Sergei A. Klioner,U. Lammers,Paul J. McMillan,M. Ramos-Lerate,H. Steidelmüller,C.A. Stephenson,F. van Leeuwen +16 more
TL;DR: In this article, the main dependencies of the parallax bias in EDR3 were investigated and the functional forms of the dependencies were explored by mapping the systematic differences between EDR-3 and DR-2, and the results showed that the bias depends in a non-trivial way on the magnitude, colour, and ecliptic latitude of the source.
Book ChapterDOI
How to use MIGRATE or why are Markov chain Monte Carlo programs difficult to use
TL;DR: Computer-intensive programs that can estimate parameters using genetic data under various coalescent models have been developed; for example, programs that estimate gene flow and the goal of these applications is to calculate the probability of the parameters of the chosen model given the data.