10921oam 22005293 450 991096981780332120251117113019.01-315-15250-91-351-64854-3(CKB)4100000007506915(MiAaPQ)EBC5630530(EXLCZ)99410000000750691520240223d2019 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierHandbook of Environmental and Ecological Statistics1st ed.Milton :CRC Press LLC,2019.©2019.1 online resource (882 pages)1-4987-5202-0 Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- 1: Introduction -- I: Methodology for Statistical Analysis of Environmental Processes -- 2: Modeling for environmental and ecological processes -- 2.1 Introduction -- 2.2 Stochastic modeling -- 2.3 Basics of Bayesian inference -- 2.3.1 Priors -- 2.3.2 Posterior inference -- 2.3.3 Bayesian computation -- 2.4 Hierarchical modeling -- 2.4.1 Introducing uncertainty -- 2.4.2 Random effects and missing data -- 2.5 Latent variables -- 2.6 Mixture models -- 2.7 Random effects -- 2.8 Dynamic models -- 2.9 Model adequacy -- 2.10 Model comparison -- 2.10.1 Bayesian model comparison -- 2.10.2 Model comparison in predictive space -- 2.11 Summary -- 3: Time series methodology -- 3.1 Introduction -- 3.2 Time series processes -- 3.3 Stationary processes -- 3.3.1 Filtering preserves stationarity -- 3.3.2 Classes of stationary processes -- 3.3.2.1 IID noise and white noise -- 3.3.2.2 Linear processes -- 3.3.2.3 Autoregressive moving average processes -- 3.4 Statistical inference for stationary series -- 3.4.1 Estimating the process mean -- 3.4.2 Estimating the ACVF and ACF -- 3.4.3 Prediction and forecasting -- 3.4.4 Using measures of correlation for ARMA model identification -- 3.4.5 Parameter estimation -- 3.4.6 Model assessment and comparison -- 3.4.7 Statistical inference for the Canadian lynx series -- 3.5 Nonstationary time series -- 3.5.1 A classical decomposition for nonstationary processes -- 3.5.2 Stochastic representations of nonstationarity -- 3.6 Long memory processes -- 3.7 Changepoint methods -- 3.8 Discussion and conclusions -- 4: Dynamic models -- 4.1 Introduction -- 4.2 Univariate Normal Dynamic Linear Models (NDLM) -- 4.2.1 Forward learning: the Kalman filter -- 4.2.2 Backward learning: the Kalman smoother -- 4.2.3 Integrated likelihood.4.2.4 Some properties of NDLMs -- 4.2.5 Dynamic generalized linear models (DGLM) -- 4.3 Multivariate Dynamic Linear Models -- 4.3.1 Multivariate NDLMs -- 4.3.2 Multivariate common-component NDLMs -- 4.3.3 Matrix-variate NDLMs -- 4.3.4 Hierarchical dynamic linear models (HDLM) -- 4.3.5 Spatio-temporal models -- 4.4 Further aspects of spatio-temporal modeling -- 4.4.1 Process convolution based approaches -- 4.4.2 Models based on stochastic partial differential equations -- 4.4.3 Models based on integro-difference equations -- 5: Geostatistical Modeling for Environmental Processes -- 5.1 Introduction -- 5.2 Elements of point-referenced modeling -- 5.2.1 Spatial processes, covariance functions, stationarity and isotropy -- 5.2.2 Anisotropy and nonstationarity -- 5.2.3 Variograms -- 5.3 Spatial interpolation and kriging -- 5.4 Summary -- 6: Spatial and spatio-temporal point processes in ecological applications -- 6.1 Introduction - relevance of spatial point processes to ecology -- 6.2 Point processes as mathematical objects -- 6.3 Basic definitions -- 6.4 Exploratory analysis - summary characteristics -- 6.4.1 The Poisson process-a null model -- 6.4.2 Descriptive methods -- 6.4.3 Usage in ecology -- 6.5 Point process models -- 6.5.1 Modelling environmental heterogeneity - inhomogeneous Poisson processes and Cox processes -- 6.5.2 Modelling clustering - Neyman Scott processes -- 6.5.3 Modelling inter-individual interaction - Gibbs processes -- 6.5.4 Model fitting - approaches and software -- 6.5.4.1 Approaches -- 6.5.4.2 Relevant software packages -- 6.6 Point processes in ecological applications -- 6.7 Marked point processes - complex data structures -- 6.7.1 Different roles of marks in point patterns -- 6.7.2 Complex models - dependence between marks and patterns -- 6.7.3 Marked point pattern models reflecting the sampling process.6.8 Modelling partially observed point patterns -- 6.8.1 Point patterns observed in small subareas -- 6.8.2 Distance sampling -- 6.9 Discussion -- 6.9.1 Spatial point processes and geo-referenced data -- 6.9.2 Spatial point process modeling and statistical ecology -- 6.9.3 Other data structures -- 6.9.3.1 Telemetry data -- 6.9.3.2 Spatio-temporal patterns -- 6.9.4 Conclusion -- 6.10 Acknowledgments -- 7: Data assimilation -- 7.1 Introduction -- 7.2 Algorithms for data assimilation -- 7.2.1 Optimal interpolation -- 7.2.2 Variational approaches -- 7.2.3 Sequential approaches: the Kalman filter -- 7.3 Statistical approaches to data assimilation -- 7.3.1 Joint modeling approaches -- 7.3.2 Regression-based approaches -- 8: Univariate and Multivariate Extremes for the Environmental Sciences -- 8.1 Extremes and Environmental Studies -- 8.2 Univariate Extremes -- 8.2.1 Theoretical underpinnings -- 8.2.2 Modeling Block Maxima -- 8.2.3 Threshold exceedances -- 8.2.4 Regression models for extremes -- 8.2.5 Application: Fitting a time-varying GEV model to climate model output -- 8.2.5.1 Analysis of individual ensembles and all data -- 8.2.5.2 Borrowing strength across locations -- 8.3 Multivariate Extremes -- 8.3.1 Multivariate EVDs and componentwise block maxima -- 8.3.2 Multivariate threshold exceedances -- 8.3.3 Application: Santa Ana winds and dryness -- 8.3.3.1 Assessing tail dependence -- 8.3.3.2 Risk region occurrence probability estimation -- 8.4 Conclusions -- 9: Environmental Sampling Design -- 9.1 Introduction -- 9.2 Sampling Design for Environmental Monitoring -- 9.2.1 Design framework -- 9.2.2 Model-based design -- 9.2.2.1 Covariance estimation-based criteria -- 9.2.2.2 Prediction-based criteria -- 9.2.2.3 Mean estimation-based criteria -- 9.2.2.4 Multi-objective and entropy-based criteria -- 9.2.3 Probability-based spatial design.9.2.3.1 Simple random sampling -- 9.2.3.2 Systematic random sampling -- 9.2.3.3 Stratified random sampling -- 9.2.3.4 Variable probability sampling -- 9.2.4 Space-filling designs -- 9.2.5 Design for multivariate data and stream networks -- 9.2.6 Space-time designs -- 9.2.7 Discussion -- 9.3 Sampling for Estimation of Abundance -- 9.3.1 Distance sampling -- 9.3.1.1 Standard probability-based designs -- 9.3.1.2 Adaptive distance sampling designs -- 9.3.1.3 Designed distance sampling experiments -- 9.3.2 Capture-recapture -- 9.3.2.1 Standard capture-recapture -- 9.3.2.2 Spatial capture-recapture -- 9.3.3 Discussion -- 10: Accommodating so many zeros: univariate and multivariate data -- 10.1 Introduction -- 10.2 Basic univariate modeling ideas -- 10.2.1 Zeros and ones -- 10.2.2 Zero-inflated count data -- 10.2.2.1 The k-ZIG -- 10.2.2.2 Properties of the k-ZIG model -- 10.2.2.3 Incorporating the covariates -- 10.2.2.4 Model fitting and inference -- 10.2.2.5 Hurdle models -- 10.2.3 Zeros with continuous density G(y) -- 10.3 Multinomial trials -- 10.3.1 Ordinal categorical data -- 10.3.2 Nominal categorical data -- 10.4 Spatial and spatio-temporal versions -- 10.5 Multivariate models with zeros -- 10.5.1 Multivariate Gaussian models -- 10.5.2 Joint species distribution models -- 10.5.3 A general framework for zero-dominated multivariate data -- 10.5.3.1 Model elements -- 10.5.3.2 Specific data types -- 10.6 Joint Attribute Modeling Application -- 10.6.1 Host state and its microbiome composition -- 10.6.2 Forest traits -- 10.7 Summary and Challenges -- 11: Gradient Analysis of Ecological Communities (Ordination) -- 11.1 Introduction -- 11.2 History of ordination methods -- 11.3 Theory and background -- 11.3.1 Properties of community data -- 11.3.2 Coenospace -- 11.3.3 Alpha, beta, gamma diversity -- 11.3.4 Ecological similarity and distance.11.4 Why ordination? -- 11.5 Exploratory analysis and hypothesis testing -- 11.6 Ordination vs. Factor Analysis -- 11.7 A classification of ordination -- 11.8 Informal techniques -- 11.9 Distance-based techniques -- 11.9.1 Polar ordination -- 11.9.1.1 Interpretation of ordination scatter plots -- 11.9.2 Principal coordinates analysis -- 11.9.3 Nonmetric Multidimensional Scaling -- 11.10 Eigenanalysis-based indirect gradient analysis -- 11.10.1 Principal Components Analysis -- 11.10.2 Correspondence Analysis -- 11.10.3 Detrended Correspondence Analysis -- 11.10.4 Contrast between DCA and NMDS -- 11.11 Direct gradient analysis -- 11.11.1 Canonical Correspondence Analysis -- 11.11.2 Environmental variables in CCA -- 11.11.3 Hypothesis testing -- 11.11.4 Redundancy Analysis -- 11.12 Extensions of direct ordination -- 11.13 Conclusions -- II: Topics in Ecological Processes -- 12: Species distribution models -- 12.1 Aims of species distribution modelling -- 12.2 Example data used in this chapter -- 12.3 Single species distribution models -- 12.4 Joint species distribution models -- 12.4.1 Shared responses to environmental covariates -- 12.4.2 Statistical co-occurrence -- 12.5 Prior distributions -- 12.6 Acknowledgments -- 13: Capture-Recapture and distance sampling to estimate population sizes -- 13.1 Basic ideas -- 13.2 Inference for closed populations -- 13.2.1 Censuses and finite population sampling -- 13.2.2 The problem of imperfect detection -- 13.2.3 Capture-recapture on closed populations -- 13.2.4 Distance sampling methods on closed populations -- 13.2.5 N-mixture models for closed populations -- 13.2.6 Count regression -- 13.3 Inference for open populations -- 13.3.1 Crosbie-Manly-Schwarz-Arnason model -- 13.3.2 Cormack-Jolly-Seber model and tag-recovery models -- 13.3.3 Pollock's robust design.13.3.4 Capture recapture models for population growth rate.This handbook focuses on the enormous literature applying statistical methodology and modelling to environmental and ecological processes.Environmental sciencesStatistical methodsEcologyStatistical methodsEnvironmental sciencesStatistical methods.EcologyStatistical methods.557.072/7Gelfand Alan E460540Fuentes Montserrat1853858Hoeting Jennifer A614464Smith Richard Lyttleton1853859MiAaPQMiAaPQMiAaPQBOOK9910969817803321Handbook of Environmental and Ecological Statistics4450623UNINA