Umweltwissenschaften ; Statistik ; Lehrbuch; Ecology / Statistical methods; Ecology / Statistical methods / Data processing; Biomathematik, Biokybernetik Relating ecological questions to statistics ; Aconceptual foundation: the statisticallinear model ; What we need readers to know ; How to get the most out of this book ; Approaches to statistical inference Michael A. McCarthy ; Introduction to statistical inference ; Ashort overview of some probability and sampling theory ; Approaches to statistical inference ; Sampie statistics and confidence intervals ; Null hypothesis significance testing ; Likelihood ; ]nformation-theoretic methods ; Bayesian methods ; Non-parametric methods ; Appropriate use of statistical methods 3; Having the right stuff: theeffects o,f data constraints on ecological daita anallysis Earl D. McCoy ; Introduction to data constraints ; Ecological data constraints ; Values and biases ; Biased behaviors in ecological research ; Potential effects of ecological data constraints ; Methodological underdetermination and cognitive biases ; Cognitive biases in ecological research? ; Ecological complexity, data constraints, flawed conclusions ; Patterns and processes at different scales ; Discrete and continuous patterns and processes ; Patterns and processes at different hierarchicallevels ; Conclusions and suggestions ; Likelihood and model selection Shane A. Richards ; Introduction to likelihood and model selection ; Likelihood functions ; Incorporating mechanism into models; Random effects; Multiple hypotheses; Approaches to model selection; Nu!] hypothesis testing; An information-theoretic approach; Using AIC to select models; Extending the Ale approach; Missing data: mechanisms, methods, and messages; Shinichi Nakagawa ; Introduction to dealing with missing data ; Mechanisms of missing data ; Missing data theory, mechanisms, and patterns ; Informal definitions of missing data mechanisms ; Formal definitions of missing data mechanisms ; Consequences of missing data mechanisms: an example ; Diagnostics and prevention ; Diagnosing missing data mechanisms ; How to prevent MNAR missingness ; Methods for missing data ; Data deletion, imputation, and augmentation ; Data deletion ; Single imputation ; Multiple imputation techniques ; Multiple imputation steps ; Multiple imputation with multilevel data ; Data augmentation ; Non-ignorable missing data and sensitivity analysis ; Discussion ; Practical issues ; Reporting guidelines ; Missing data in other contexts ; Final messages ; What you don't know can hurt you: censo,red and truncated data in eco!logical research Gordon A. Fox ; Censored data ; Basic concepts ; Some common methods you should not use ; Types of censored data ; Censoring in study designs ; Format of data ; Estimating means with censored data ; Regression for censored data ; Truncated data ; Introduction to truncated data ; Sweeping the issue under the rug ; Estimation ; Regression for truncated data ; Discussion ; Generalized linear models Yvonne M. Buckley ; Introduction to generalized linear models ; Structure of a GLM ; The linear predictor ; The errar structure ; The link function ; Whicherror distribution and link function are suitable for my data? ; Binomial distribution ; Poisson distribution ; Overdispersion ; Model fit and inference ; Computational methods and convergence ; A statistica,l symphony: instrumental variables reveal causality and control measurement error Bruce E. Kendoll ; Introduction to instrumental variables ; Sources of endogeneity ; Effects of endogeneity propagate to other variables ; The solution: instrumental variable regression ; imultaneous equation models ;Life-history trade-offs in Florida scrub-jays ; Other issues with instrumental variable regression ; Deciding to use instrumental variable regression ; Choosing instrumental variables ; Structural equation modeling: building and evaluating causal models James B. Grace, Samuel M. Scheiner, and Donald R. Schoo/master, Jr. ; Introduction to causal hypotheses ; The need for SEM ; An ecological example ; Astructural equation modeling perspective ; Background to structural equation modeling ; Causa] modeling and causa] hypotheses ; Mediatars, indirect effects, and conditional independence ; A key causal assumption: lack of confounding ; Statistical specifications ; Estimation options: global and local approaches ; Model evaluation, comparison, and selection ; Illustration of structural equation modeling ; Overview of the modeling process ; Conceptual models and causal diagrams ; Classic global-estimation modeling ; Agraph-theoretic approach using local-estimation methods ; Making informed choices about model form and estimation method ; Computing queries and making interpretations ; Reporting results ; Research synthesis methods in ecology Jessica Gurevitch and ShinichlNakagawa; Introduction to research synthesis; Generalizing from results; What is research synthesis?; What have ecologists investigated using research syntheses?; Introduction to worked examples; Systematic reviews: maldng reviewing a scientific process; Defining a research question; ldentifying and selecting papers ; Initial steps for meta-analysis in ecology; What not to do; Data: What do you need, and how do you get it?; Software for meta-analysis; Exploratory data analysis; Conceptual and computational tools for meta-analysis ; Effect size metrics; Fixed, random and mixed models; Heterogeneity; Meta-regression; Statistical inference; Applying our tools: statistical analysis of data; Plant responses to elevated Coz; Plant growth responses to ectomycorrhizal (ECM) interactions; Is there publication bias, and how much does it affect the results?; Other sensitivity analyses; Reporting results of a meta-analysis ; Objections to meta-analysis; Limitations to current practicein ecological meta-analysis; Moie advanced issues and approaches; Spatial variation and Unear modeUng of ecol'ogical data Simoneta Negrete-Yankefevieh and Gordon A. Fox; Introduction to spatial variation in ecoIogical data; Background; Spatially explicit data; Spatial structure; Scales of ecological processes and scales of studies; Case study: spatial structure of soil properties in a milpa plot; Spatial exploratory data analysis; Measures and models of spatial autocorrelation; Moran's land correlograms; Semi-variance and the variogram; Adding spatial structures to linear models; Generalized least squares models; Spatial autoregressive models ; Statistical approache.s to the problem 01 phyllogeneticailly correlatedl data Mare J. Lajeunesse and Gordon A. Fox ; Introduction to phylogenetically correlated data ; Statistical assumptions and the comparative phylogenetic method ; The assumptions of conventionallinear regression ; The assumption of independence and phylogenetic correlations ; What are phylogenetic correlations and how do they affect data? ; Why are phylogenetic correlations important for regression? ; The assumption of homoscedasticity and evolutionary models ; What happens when the incorrect model of evolution is assumed? ; Establishing confidence with the comparative phylogenetic method ; Mixture modelsfor overdispersed data Jonathan R. Rhodes ; Introduction to mixture models for overdispersed data ; Overdispersion ; What is overdispersion and what causes it? ; Detecting overdispersion ; Mixture models ; What is a mixture model? ; Mixture models used in ecology ; Empirical examples ; Using binomial mixtures to model dung decay ; Using Poisson mixtures to model lemur abundance ; Linear afld,generillized linear mixed models Benjamin M. Bolker ; Introduction to generalized linear mixed models ; Runningexamples ; Concepts; Model definition; Conditional, marginal, and restricted likelihood; Setting up a GLMM: practical considerations; Response distribution; Link function; Number and type of random effects; Estimation; Avoid!ing mixed models; Method of moments; Deterministic/frequentist algorithms; Stochastic/Bayesian algorithms; Model diagnostics and troubleshooting; Approximations for inference; Methods of inference; Reporting the GLMM results
