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E-grāmata: Joint Species Distribution Modelling: With Applications in R

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(University of Helsinki), (University of Helsinki)
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Joint Species Distribution Modelling is for graduate students and researchers interested in analysing data on community ecology and placing their results in the context of modern ecological theory. With the help of example R-scripts, readers will learn how to conduct and interpret statistical analyses.

Joint species distribution modelling (JSDM) is a fast-developing field and promises to revolutionise how data on ecological communities are analysed and interpreted. Written for both readers with a limited statistical background, and those with statistical expertise, this book provides a comprehensive account of JSDM. It enables readers to integrate data on species abundances, environmental covariates, species traits, phylogenetic relationships, and the spatio-temporal context in which the data have been acquired. Step-by-step coverage of the full technical detail of statistical methods is provided, as well as advice on interpreting results of statistical analyses in the broader context of modern community ecology theory. With the advantage of numerous example R-scripts, this is an ideal guide to help graduate students and researchers learn how to conduct and interpret statistical analyses in practice with the R-package Hmsc, providing a fast starting point for applying joint species distribution modelling to their own data.

Recenzijas

'The authors provide a perfect blend of ecological and statistical insight into joint species distribution modeling. This book is an excellent resource for any quantitative ecologist or ecological statistician.' Erin M. Schliep, The Quarterly Review of Biology 'This book is an excellent resource for any quantitative ecologist or ecological statistician interested in conducting science-driven analyses with statistical rigor to address pressing questions in community ecology.' Erin M. Schliep, The Quarterly Review of Biology

Papildus informācija

A comprehensive account of joint species distribution modelling, covering statistical analyses in light of modern community ecology theory.
Preface xi
Acknowledgements xiv
Part I Introduction to Community Ecology: Theory and Methods
1(50)
1 Historical Development of Community Ecology
3(16)
1.1 What Is Community Ecology?
3(1)
1.2 What Is an Ecological Community?
4(2)
1.3 Early Community Ecology: A Descriptive Science
6(3)
1.4 Emergence of the First Theories
9(2)
1.5 Current Community Ecology: Search for the Unifying Theory
11(8)
2 Typical Data Collected by Community Ecologists
19(11)
2.1 Community Data
20(3)
2.2 Environmental Data
23(1)
2.3 Spatio-temporal Context
24(2)
2.4 Trait Data
26(1)
2.5 Phylogenetic Data
27(1)
2.6 Some Remarks about How to Organise Data
28(2)
3 Typical Statistical Methods Applied by Community Ecologists
30(9)
3.1 Ordination Methods
30(3)
3.2 Co-occurrence Analysis
33(1)
3.3 Analyses of Diversity Metrics
34(1)
3.4 Species Distribution Modelling
35(4)
4 An Overview of the Structure and Use of HMSC
39(12)
4.1 HMSC Is a Multivariate Hierarchical Generalised Linear Mixed Model
39(2)
4.2 The Overall Structure of HMSC
41(4)
4.3 Linking HMSC to Community Ecology Theory
45(2)
4.4 The Overall Workflow for Applying HMSC
47(4)
Part II Building a Joint Species Distribution Model Step by Step
51(202)
5 Single-Species Distribution Modelling
53(51)
5.1 How Do Species Distribution Models Link to Species Niches?
53(2)
5.2 The Linear Model
55(3)
5.3 Generalised Linear Models
58(5)
5.4 Mixed Models
63(6)
5.5 Partitioning Explained Variation among Groups of Explanatory Variables
69(1)
5.6 Simulated Case Studies with HMSC
70(22)
5.7 Real Data Case Study with HMSC: The Distribution of Corvus Monedula in Finland
92(12)
6 Joint Species Distribution Modelling: Variation in Species Niches
104(38)
6.1 Stacked versus Joint Species Distribution Models
104(3)
6.2 Modelling Variation in Species Niches in a Community
107(3)
6.3 Explaining Variation in Species Niches by Their Traits
110(4)
6.4 Explaining Variation in Species Niches by Phylogenetic Relatedness
114(3)
6.5 Explaining Variation in Species Niches by Both Traits and Phylogeny
117(3)
6.6 Simulated Case Studies with HMSC
120(13)
6.7 Real Case Study with HMSC: How Do Plant Traits Influence Their Distribution?
133(9)
7 Joint Species Distribution Modelling: Biotic Interactions
142(42)
7.1 Strategies for Estimating Biotic Interactions in Species Distribution Models
143(1)
7.2 Occurrence and Co-occurrence Probabilities
144(3)
7.3 Using Latent Variables to Model Co-occurrence
147(5)
7.4 Accounting for the Spatio-temporal Context through Latent Variables
152(4)
7.5 Covariate-Dependent Species Associations
156(3)
7.6 A Cautionary Note about Interpreting Residual Associations as Biotic Interactions
159(1)
7.7 Using Residual Species Associations for Making Improved Predictions
160(5)
7.8 Simulated Case Studies with HMSC
165(7)
7.9 Real Case Study with HMSC: Sequencing Data on Dead Wood-Inhabiting Fungi
172(12)
8 Bayesian Inference in HMSC
184(33)
8.1 The Core HMSC Model
185(2)
8.2 Basics of Bayesian Inference: Prior and Posterior Distributions and Likelihood of Data
187(1)
8.3 The Prior Distribution of Species Niches
188(9)
8.4 The Prior Distribution of Species Associations
197(9)
8.5 The Prior Distribution of Data Models
206(1)
8.6 What HMSC Users Need and Do Not Need to Know about Posterior Sampling
207(3)
8.7 Sampling from the Prior with HMSC
210(5)
8.8 How Long Does It Take to Fit an HMSC Model?
215(2)
9 Evaluating Model Fit and Selecting among Multiple Models
217(36)
9.1 Preselection of Candidate Models
218(1)
9.2 The Many Ways of Measuring Model Fit
219(6)
9.3 The Widely Applicable Information Criterion (WAIC)
225(3)
9.4 Variable Selection by a Spike and Slab Prior
228(14)
9.5 Reduced Rank Regression (RRR)
242(11)
Part III Applications and Perspectives
253(94)
10 Linking HMSC Back to Community Assembly Processes
255(45)
10.1 Simulating an Agent-Based Model of a Competitive Metacommunity
256(10)
10.2 Statistical Analyses of the Spatial Data Collected by a Virtual Ecologist
266(22)
10.3 Statistical Analyses of the Time-Series Data Collected by a Virtual Ecologist
288(9)
10.4 What Did the Virtual Ecologists Learn from Their Data?
297(3)
11 Illustration of HMSC Analyses: Case Study of Finnish Birds
300(37)
11.1 Steps 1-5 of the HMSC Workflow
300(16)
11.2 Measuring the Level of Statistical Support and Propagating Uncertainty into Predictions
316(5)
11.3 Using HMSC for Conservation Prioritisation
321(3)
11.4 Using HMSC for Bioregionalisation: Regions of Common Profile
324(5)
11.5 Comparing HMSC to Other Statistical Methods in Community Ecology
329(8)
12 Conclusions and Future Directions
337(10)
12.1 The Ten Key Strengths of HMSC
337(4)
12.2 Future Development Needs
341(6)
Epilogue 347(3)
References 350(19)
Index 369
Otso Ovaskainen is Professor of Mathematical Ecology at the University of Helsinki, Finland. He has published 170 papers in mathematical, statistical and empirical ecology, with a particular focus on metapopulation ecology, movement ecology, population genetics, molecular species identification and community ecology. Nerea Abrego is a post-doctoral researcher at the University of Helsinki. After obtaining her Ph.D. in fungal ecology, she expanded her research to genera community ecology. She has published thirty papers, many of which relate to recent developments in joint species distribution modelling.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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