LEADER 04812nam 22007695 450 001 9910586580903321 005 20250326144018.0 010 $a9783030884437$b(electronic bk.) 010 $z9783030884420 024 7 $a10.1007/978-3-030-88443-7 035 $a(MiAaPQ)EBC7072669 035 $a(Au-PeEL)EBL7072669 035 $a(CKB)24368777700041 035 $a(PPN)264193601 035 $a(DE-He213)978-3-030-88443-7 035 $a(EXLCZ)9924368777700041 100 $a20220810d2022 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEco-Stats: Data Analysis in Ecology $eFrom t-tests to Multivariate Abundances /$fby David I Warton 205 $a1st ed. 2022. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2022. 215 $a1 online resource (434 pages) 225 1 $aMethods in Statistical Ecology,$x2199-3203 311 08$aPrint version: Warton, David I. Eco-Stats: Data Analysis in Ecology Cham : Springer International Publishing AG,c2022 9783030884420 320 $aIncludes bibliographical references and index. 327 $a1. "Stats 101" Revision -- 2. An important equivalence result -- 3. Regression with multiple predictor variables -- 4. Linear models ? anything goes -- 5. Model selection -- 6. Mixed effects models -- 7. Correlated samples in time, space, phylogeny -- 8. Wiggly Models -- 9. Design-based inference -- 10. Analysing discrete data -- 11. Multivariate analysis -- 12. Visualising many responses -- 13. Allometric line-fitting -- 14. Multivariate abundances and environmental association -- 15. Predicting multivariate abundances -- 16. Explaining variation in response across taxa -- 17. Studying co-occurrence patterns -- 18. Closing advice. 330 $aThis book introduces ecologists to the wonderful world of modern tools for data analysis, especially multivariate analysis. For biologists with relatively little prior knowledge of statistics, it introduces a modern, advanced approach to data analysis in an intuitive and accessible way. The book begins by reviewing some core principles in statistics, and relates common methods to the linear model, a general framework for modeling data where the response is continuous. This is then extended to discrete data using generalized linear models, to designs with multiple sampling levels via mixed models, and to situations where there are multiple response variables via model-based approaches to multivariate analysis. Along the way there is an introduction to: important principles in model selection; adaptations of the model to handle non-linearity and cyclical variables; dependence due to structured correlation in time, space or phylogeny; and design-based techniques for inference that can relax some of the modelling assumptions. It concludes with a range of advanced topics in model-based multivariate analysis relevant to the modern ecologist, including fourth corner, latent variable and copula models. Examples span a variety of applications including environmental monitoring, species distribution modeling, global-scale surveys of plant traits, and small field experiments on biological controls. Math Boxes throughout the book explain some of the core ideas mathematically for readers who want to delve deeper, and R code is used throughout. Accompanying code, data, and solutions to exercises can be found in the ecostats R package on CRAN. 410 0$aMethods in Statistical Ecology,$x2199-3203 606 $aStatistics 606 $aBioinformatics 606 $aBiotic communities 606 $aPopulation biology 606 $aBiometry 606 $aEcology 606 $aStatistics 606 $aStatistical Theory and Methods 606 $aBioinformatics 606 $aCommunity and Population Ecology 606 $aBiostatistics 606 $aEcology 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 615 0$aStatistics. 615 0$aBioinformatics. 615 0$aBiotic communities. 615 0$aPopulation biology. 615 0$aBiometry. 615 0$aEcology. 615 0$aStatistics. 615 14$aStatistical Theory and Methods. 615 24$aBioinformatics. 615 24$aCommunity and Population Ecology. 615 24$aBiostatistics. 615 24$aEcology. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 676 $a577.0727 676 $a577.015195 700 $aWarton$b David$01369685 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910586580903321 996 $aEco-Stats: Data Analysis in Ecology$94349616 997 $aUNINA