The management and conservation of natural populations relies heavily on concepts and results generated from models of population dynamics. Yet this is the first book to present a unified and coherent explanation of the underlying theory. This novel text begins with a consideration of what makes a good state variable, progressing from the simplest models (those with a single variable such as abundance or biomass) to more complex models with other key variables of population structure (including age, size, life history stage, and space). Throughout the book, attention is paid to concepts such as population variability, population stability, population viability/persistence, and harvest yield. Later chapters address specific applications to conservation such as recovery planning for species at risk, fishery management, and the spatial management of marine resources. Population Dynamics for Conservation is suitable for graduate-level students. It will also be valuable to academic and applied researchers in population biology. This overview of population dynamic theory can serve to further their population research, as well as to improve their understanding of population management.
(source: Nielsen Book Data)

Republic of Apples, Democracy of Oranges presents nearly 100 poets and translators from China and the U.S.-the two countries most responsible for global carbon dioxide emissions and the primary contributors to extreme climate change. These poetic voices express the altered relationship that now exists between the human and non-human worlds, a situation in which we witness everyday the ways environmental destruction is harming our emotions and imaginations. "What can poetry say about our place in the natural world today?" ecologically minded poets ask. "How do we express this new reality in art or sing about it in poetry?" And, as poet Forrest Gander wonders, "how might syntax, line break, or the shape of the poem on the page express an ecological ethics?" Eco-poetry freely searches for possible answers. Sichuan poet Sun Wenbo writes: ... I feel so liberated I start writing about the republic of apples and democracy of oranges. When I see apples have not become tanks, oranges not bombs, I know I've not become a slave of words after all. The Chinese poets are from throughout the PRC and Taiwan, both minority and majority writers, from big cities and rural provinces, such as Liangshan Yi Autonomous Prefecture and Xinjiang Uyghur, Tibet, and Inner Mongolia Autonomous Regions. The American poets are both emerging and established, from towns and cities across the U.S. Included are images by celebrated photographer Linda Butler documenting the Three Gorges Dam, on the Yangtze River, and the aftermath of Hurricane Katrina, on the Mississippi River Basin.
(source: Nielsen Book Data)

• AcknowledgmentsIntroduction1. The WatersSusquehanna RiverGunpowder RiverCorsica RiverPatuxent RiverPotomac RiverJames RiverSouthern Bay2. The PeopleHarry HughesParris GlendeningJohn GriffinBonnie BickMichael BeerCarole MorisonOoker Eskridge3. The WildlifeOystersDermo and MSXBlue CrabsStriped BassAmerican EelsSturgeon4. The PoliciesEnforcementPennsylvaniaAir Pollution versus Water PollutionAgricultureClimate ChangeAdvocacy and Pollution TradingAccountabilityConclusionNotesIndex.
• (source: Nielsen Book Data)

When Captain John Smith arrived in Virginia in 1607, he discovered a paradise in the Chesapeake Bay. In the centuries that followed, the Bay changed vastly-and not for the better. European landowners and enslaved Africans slashed, burned, and cleared the surrounding forests to grow tobacco. Watermen overfished oysters, shad, and sturgeon, decimating these crucial species. Baltimore, Washington, and Richmond used its rivers as urban sewers. By the 1960s, the Chesapeake was dying. A crossroads of life and culture, the Chesapeake straddles the North and the South, mixes salt water with fresh, and is home to about 18 million people and 3,600 species of animals and plants. Although recent cleanup efforts have improved its overall health, they have not been enough to save this national treasure. In The Chesapeake in Focus, award-winning writer Tom Pelton examines which environmental policies have worked and which have failed. Based on Pelton's extensive experience as a journalist and as the host of the public radio program The Environment in Focus, this sweeping book takes readers on a tour of the histories of the Chesapeake, as well as the ecological challenges faced by its major tributaries. It details the management of blue crabs, striped bass, and other delicious wildlife, profiles leaders and little-known characters involved in the restoration campaign, and warns of the dangers of anti-regulatory politics that threaten to reverse what has been accomplished. Looking to the future, Pelton offers a provocative vision of the hard steps that must be taken if we truly want to save the Bay.
(source: Nielsen Book Data)

• Shifting baselines in Chesapeake Bay, the immense protein factory
• Why the Chesapeake Bay was so productive and what's changed
• The spring fishery for shad and river herring : a hectic scramble
• The world's greatest oyster fishery : an expansion, then a crash
• Diamond-backed terrapins : from pig food to gourmet delight to protected species
• Uncontrolled market hunting of waterfowl : a mass slaughter
• Sturgeon : a prehistoric high jumper fell from memory
• Blue crabs hung on
• Have diminished animal abundances remodeled the bay's food webs?

• Chapter 1: Introduction to spatial ecology and its relevance for conservation1.1 What is spatial ecology?1.2 The importance of space in ecology1.3 The importance of space in conservation 1.4 The growth of frameworks for spatial modeling1.5 The path aheadReferences Part I: Quantifying spatial pattern in ecological data
• Chapter 2: Scale2.1 Introduction2.2 Key concepts and approaches2.2.1 Scale defined and clarified2.2.2 Why is spatial scale important?2.2.3 Multi-scale and multi-level quantitative problems2.2.4 Spatial scale and study design2.3 Examples in R2.3.1 Packages in R2.3.2 The data2.3.3 A simple simulated example2.3.4 Multi-scale species response to land cover2.4 Next steps and advanced issues2.4.1 Identifying characteristic scales beyond species-environment relationships2.4.2 Sampling and scale2.5 ConclusionsReferences
• Chapter 3: Land-cover pattern and change3.1 Introduction3.2 Key concepts3.2.1 Land use versus land cover3.2.2 Conceptual models for land-cover and habitat change3.2.3 Habitat loss and fragmentation3.2.4 Quantifying land-cover pattern3.3 Examples in R3.3.1 Packages in R3.3.2 The data3.3.3 Quantifying land-cover variation at different scales3.3.4 Simulating land-cover: neutral landscapes3.4 Next steps and advanced issues3.4.1 Testing for pattern differences between landscapes3.4.2 Land-cover quantification via image processing3.4.3 Categorical versus continuous metrics3.5 ConclusionsReferences
• Chapter 4: Spatial dispersion and point data4.1 Introduction4.2 Key concepts and approaches4.2.1 Characteristics of point patterns4.2.2 Summary statistics for point patterns4.2.3 Common statistical models for point patterns4.3 Examples in R 4.3.1 Packages in R4.3.2 The data4.3.3 Creating point pattern data and visualizing it4.3.4 Univariate point patterns4.3.5 Marked point patterns4.3.6 Inhomogeneous point processes and point process models4.3.7 Alternative null models4.3.8 Simulating point processes4.4 Next steps and advanced issues4.4.1 Space-time analysis4.4.2 Replicated point patterns4.5 ConclusionsReferences
• Chapter 5: Spatial dependence and autocorrelation5.1 Introduction5.2 Key concepts and approaches5.2.1 The causes of spatial dependence5.2.2 Why spatial dependence matters5.2.3 Quantifying spatial dependence5.3 Examples in R5.3.1 Packages in R5.3.2 The data5.2.3 Correlograms5.3.3 Variograms5.3.4 Kriging5.3.5 Simulating spatially autocorrelated data5.3.6 Multiscale analysis5.4 Next steps and advanced issues5.4.1 Local spatial dependence5.4.2 Multivariate spatial dependence5.5 ConclusionsReferences
• Chapter 6: Accounting for spatial dependence in ecological data6.1 Introduction6.2 Key concepts and approaches6.2.1 The problem of spatial dependence in ecology and conservation6.2.2 The generalized linear model and its extensions6.2.3 General types of spatial models6.2.4 Common models that account for spatial dependence6.2.5 Inference versus prediction6. 3 Examples in R6.3.1 Packages in R6.3.2 The data6.3.3 Models that ignore spatial dependence6.3.4 Models that account for spatial dependence6.4 Next steps and advanced issues6.4.1 General Bayesian models for spatial dependence6.4.2 Detection errors and spatial dependence6.5 ConclusionsReferences Part II: Ecological responses to spatial pattern and conservation
• Chapter 7: Species distributions7.1 Introduction7.2 Key Concepts and approaches7.2.1 The niche concept7.2.2 Predicting distributions or niches?7.2.3 Mechanistic versus correlative distribution models7.2.4 Data for correlative distribution models7.2.5 Common types of distribution modeling techniques7.2.6 Combining models: ensembles7.2.7 Model evaluation7.3 Examples in R7.3.1 Packages in R7.3.2 The data7.3.3 Prepping the data for modeling7.3.4 Contrasting models7.3.5 Interpreting environmental relationships7.3.6 Model evaluation7.3.7 Combining models: ensembles7.4 Next steps and advanced issues7.4.1 Incorporating dispersal7.4.2 Integrating multiple data sources7.4.3 Dynamic models7.4.4 Multi-species models7.4.5 Sampling error and distribution models7.5 ConclusionsReferences
• Chapter 8: Space use and resource selection8.1 Introduction8.2 Key concepts and approaches8.2.1 Distinguishing among the diversity of habitat-related concepts and terms8.2.2 Habitat selection theory8.2.3 General types of habitat use and selection data8.2.4 Home range and space use approaches8.2.5 Resource selection functions at different scales8.3 Examples in R8.3.1 Packages in R8.3.1 The data8.3.2 Prepping the data for modeling8.3.3 Home range analysis8.3.4 Resource selection functions8.4 Next steps and advanced issues8.4.1 Mechanistic models and the identification of hidden states8.4.2 Biotic interactions8.4.3 Sampling error and resource selection models8.5 ConclusionsReferences
• Chapter 9: Connectivity9.1 Introduction9.2 Key concepts and approaches9.2.1 The multiple meanings of connectivity9.2.2 The connectivity concept9.2.3 Factors limiting connectivity9.2.4 Three common perspectives on quantifying connectivity9.3 Examples in R9.3.1 Packages in R9.3.2 The data9.3.3 Functional connectivity among protected areas for Florida panthers9.3.4 Patch-based networks and graph theory9.3.5 Combining connectivity mapping with graph theory9.4 Next steps and advanced issues9.4.1 Connectivity in space and time9.4.2 Individual-based models9.4.3 Diffusion models9.4.4 Spatial capture-recapture9.5 ConclusionsReferences
• Chapter 10: Population dynamics in space10.1 Introduction10.2 Key concepts and approaches10.2.1 Foundational population concepts10.2.2 Spatial population concepts10.2.3 Population viability analysis10.2.4 Common types of spatial population models10.3 Examples in R10.3.1 Packages in R10.3.2 The data10.3.3 Spatial correlation and synchrony10.3.4 Metapopulation metrics10.3.5 Estimating colonization-extinction dynamics10.3.6 Projecting dynamics10.3.7 Metapopulation viability and environmental change10.4 Next steps and advanced issues10.4.1 Spatial population matrix models10.4.2 Diffusion and spatial dynamics10.4.3 Agent-based models10.4.4 Integrated population models10.5 ConclusionsReferences
• Chapter 11: Spatially structured communities11.1 Introduction11.2 Key concepts and approaches11.2.1 Spatial community concepts11.2.2 Common approaches to understanding community-environment relationships11.2.3 Spatial models for communities11.3 Examples in R11.3.1 Packages in R11.3.2 The data11.3.3 Modeling communities and extrapolating in space11.3.4 Spatial dependence in communities11.3.5 Community models with explicit accounting for space11.4 Next steps and advanced issues11.4.1 Decomposition of space-environment effects11.4.2 Accounting for dependence among species11.4.3 Spatial networks11.5 ConclusionsReferences
• Chapter 12: What have we learned? Looking back and pressing forward12.1 The impact of spatial ecology and conservation12.2 Looking forward: frontiers for spatial ecology and conservation12.3 Where to go from here for advanced spatial modeling?12.4 Beyond R12.5 ConclusionsReferences Appendix: An introduction to RA.1 IntroductionA.2 R beginnings: before any analysisA.2.1 R packagesA.2.2 Editors for RA.2.3 The R prompt, console, and editorA.2.4 Getting help in RA.2.5 R classesA.2.6 Getting data into and out of RA.2.7 Functions in RA.3 Data access, management and manipulation in RA.3.1 Accessing dataA.3.2 Merging, appending, and removingA.3.3 Data subsetting and summariesA.3.4 Reformatting dataA.4 Graphics in RA.5 Spatial data in RA.5.1 Using spatial classesA.5.2 Projections and transformationsA.6 Next steps: where to for further R mastery?References.
• (source: Nielsen Book Data)

This book provides a foundation for modern applied ecology. Much of current ecology research and conservation addresses problems across landscapes and regions, focusing on spatial patterns and processes. This book is aimed at teaching fundamental concepts and focuses on learning-by-doing through the use of examples with the software R. It is intended to provide an entry-level, easily accessible foundation for students and practitioners interested in spatial ecology and conservation.
(source: Nielsen Book Data)

• 1 OVERVIEW OF THIS BOOK 1 1.1 VOLUMES I AND II 1 1.1.1 Volume I 1 1.1.2 Volume II 1 1.2 WHAT TYPE OF SPATIAL DATA DO WE ANALYSE IN THIS BOOK? 1 1.2.1 Areal and lattice data 1 1.2.2 Geostatistical data 2 1.2.3 Spatial point pattern data 3 1.3 OUTLINE OF THIS BOOK 3 1.4 PREREQUISITES 4 1.5 AVAILABILITY OF R CODE AND DATA 4
• 2 RECOGNISING STATISTICAL DEPENDENCY 5 2.1 PSEUDOREPLICATION 5 2.2 LINEAR REGRESSION APPLIED TO SPATIAL DATA 7 2.2.1 Irish pH data 7 2.2.2 Protocol from Zuur et al. (2016) 8 2.2.3 Visualisation of the experimental design 9 2.2.4 Data exploration 9 2.2.5 Dependency 12 2.2.6 Statistical model 15 2.2.7 Fit the model 16 2.2.8 Model validation 17 2.3 GAM APPLIED TO TEMPORAL DATA 21 2.3.1 Subnivium temperature data 21 2.3.2 Sources of dependency 22 2.3.3 The model 23 2.3.4 Model validation 24 2.4 GLMM APPLIED ON HIERARCHICAL AND SPATIAL DATA 26 2.5 TECHNICALITIES 28 2.5.1 Matrix notation 28 2.5.2 How is dependency causing problems? 31 2.6 DISCUSSION 32
• 3 TIME SERIES AND GLS 33 3.1 OSPREYS 33 3.2 COVARIANCE AND CORRELATION COEFFICIENTS 33 3.3 LINEAR REGRESSION MODEL 35 3.4 FOCUSSING ON THE RESIDUAL COVARIANCE MATRIX 35 3.5 DEPENDENCY AND THE COVARIANCE MATRIX 36 3.6 GLS: DEALING WITH TEMPORAL DEPENDENCY 39 3.6.1 Adelie penguins 39 3.6.2 Do we have dependency? 40 3.6.3 Formulation of the linear regression model 40 3.6.4 Application of the linear regression model 41 3.6.5 R code for acf and variogram 45 3.6.6 Formulation of the GLS model 46 3.6.7 Implementation using the gls function 50 3.7 MULTIPLE TIME SERIES 51 3.8 DISCUSSION 53
• 4 SPATIAL DATA AND GLS 55 4.1 VARIOGRAM MODELS FOR SPATIAL DEPENDENCY 55 4.2 APPLICATION ON THE IRISH PH DATA 57 4.3 MATERN CORRELATION FUNCTION 59
• 5 LINEAR MIXED EFFECTS MODELS AND DEPENDENCY 61 5.1 WHITE STORKS 61 5.2 CONSIDERING THE DATA (WRONGLY) AS ONE-WAY NESTED 62 5.3 FITTING THE ONE-WAY NESTED MODEL USING LMER 65 5.4 MODEL VALIDATION 67 5.5 SKETCHING THE FITTED VALUES 68 5.6 CONSIDERING THE DATA (CORRECTLY) AS TWO-WAY NESTED 69 5.7 APPLICATIONS TO SPATIAL AND TEMPORAL DATA 72 5.8 DIFFERENCE WITH THE AR1 PROCESS APPROACH 72
• 6 MODELLING SPACE EXPLICITLY 73 6.1 MODEL FORMULATION 73 6.2 COVARIANCE MATRIX OF THE SPATIAL RANDOM EFFECT 75 6.3 SPATIAL-TEMPORAL CORRELATION* 79
• 7 INTRODUCTION TO BAYESIAN STATISTICS 83 7.1 WHY GO BAYESIAN? 83 7.2 GENERAL PROBABILITY RULES 84 7.3 THE MEAN OF A DISTRIBUTION* 85 7.4 BAYES' THEOREM AGAIN 87 7.5 CONJUGATE PRIORS 88 7.6 MARKOV CHAIN MONTE CARLO SIMULATION 93 7.6.1 Underlying idea 93 7.6.2 Installing JAGS and R2jags 94 7.6.3 Flowchart for running a model in JAGS 94 7.6.4 Preparing the data for JAGS 95 7.6.5 JAGS code 96 7.6.6 Initial values and parameters to save 98 7.6.7 Running JAGS 99 7.6.8 Accessing numerical output from JAGS 100 7.6.9 Assess mixing 100 7.6.10 Posterior information 101 7.7 INTEGRATED NESTED LAPLACE APPROXIMATION* 103 7.7.1 Joint posterior distribution 103 7.7.2 Marginal distributions 105 7.7.3 Back to high school 107 7.7.4 INLA 109 7.8 EXAMPLE USING R-INLA 110 7.9 DISCUSSION 114
• 8 MULTIPLE LINEAR REGRESSION IN R-INLA 115 8.1 INTRODUCTION 115 8.2 DATA EXPLORATION 116 8.3 MODEL FORMULATION 117 8.4 LINEAR REGRESSION RESULTS 117 8.4.1 Executing the model in R-INLA 117 8.4.2 Output for the betas 117 8.4.3 Output for the hyper-parameters 119 8.4.4 Fitted model 123 8.5 MODEL VALIDATION 123 8.6 MODEL SELECTION 126 8.6.1 Should we do it? 126 8.6.2 Using the DIC 126 8.6.3 Out of sample prediction 131 8.6.4 Posterior predictive check 133 8.7 VISUALISING THE MODEL 135
• 9 MIXED EFFECTS MODELLING IN R-INLA TO ANALYSE OTOLITH DATA 139 9.1 OTOLITHS IN PLAICE 139 9.2 MODEL FORMULATION 140 9.3 DEPENDENCY 140 9.4 DATA EXPLORATION 141 9.5 RUNNING THE MODEL IN R-INLA 143 9.6 MODEL VALIDATION 146 9.7 MODEL SELECTION 149 9.8 MODEL INTERPRETATION 149 9.8.1 Option 1 for prediction: Adding extra data 150 9.8.2 Option 2 for prediction: Using the inla.make.lincombs 153 9.8.3 Adding extra data or inla.make.lincombs? 155 9.9 MULTIPLE RANDOM EFFECTS 155 9.10 CHANGING PRIORS OF FIXED PARAMETERS 156 9.11 CHANGING PRIORS OF HYPERPARAMETERS 158 9.12 SHOULD WE CHANGE PRIORS? 164
• 10 POISSON, NEGATIVE BINOMIAL, BINOMIAL AND GAMMA GLMS IN R-INLA 165 10.1 POISSON AND NEGATIVE BINOMIAL GLMS IN R-INLA 165 10.1.1 Introduction 165 10.1.2 Poisson GLM in R-INLA 166 10.1.3 Negative binomial GLM in R-INLA 172 10.1.4 Model selection for the NB GLM 175 10.1.5 Visualisation of the NB GLM 177 10.2 BERNOULLI AND BINOMIAL GLM 180 10.2.1 Bernoulli GLM 181 10.2.2 Model selection with the marginal likelihood 184 10.2.3 Binomial GLM 185 10.3 GAMMA GLM 187
• 11 MATERN CORRELATION AND SPDE 191 11.1 CONTINUOUS GAUSSIAN FIELD 191 11.2 MODELS THAT WE HAVE IN MIND 191 11.3 MATERN CORRELATION 192 11.4 SPDE APPROACH 197
• 12 LINEAR REGRESSION MODEL WITH SPATIAL DEPENDENCY FOR THE IRISH PH DATA 205 12.1 INTRODUCTION 205 12.2 MODEL FORMULATION 205 12.3 LINEAR REGRESSION RESULTS 206 12.4 MODEL VALIDATION 207 12.5 ADDING SPATIAL CORRELATION TO THE MODEL 208 12.6 DEFINING THE MESH FOR THE IRISH PH DATA 212 12.7 DEFINE THE WEIGHT FACTORS AIK 216 12.8 DEFINE THE SPDE 218 12.9 DEFINE THE SPATIAL FIELD 218 12.10 DEFINE THE STACK 218 12.11 DEFINE THE FORMULA FOR THE SPATIAL MODEL 221 12.12 EXECUTE THE SPATIAL MODEL IN R 221 12.13 RESULTS 222 12.14 MODEL SELECTION 227 12.15 MODEL VALIDATION 228 12.16 MODEL INTERPRETATION 228 12.17 DETAILED INFORMATION ABOUT THE STACK* 232 12.17.1 Stack for the fitted model again 232 12.17.2 Stack for the new covariate values 234 12.17.3 Combine the two stacks 236 12.17.4 Run the model 236
• 13 SPATIAL POISSON MODELS APPLIED TO PLANT DIVERSITY 239 13.1 INTRODUCTION 239 13.2 DATA EXPLORATION 239 13.2.1 Sampling locations 239 13.2.2 Outliers 241 13.2.3 Collinearity 242 13.2.4 Relationships 243 13.2.5 Numbers of zeros 244 13.2.6 Conclusions data exploration 244 13.3 MODEL FORMULATION 244 13.4 GLM RESULTS 245 13.5 ADDING SPATIAL CORRELATION TO THE MODEL 248 13.5.1 Model formulation 248 13.5.2 Mesh 248 13.5.3 Projector matrix 253 13.5.4 SPDE 254 13.5.5 Spatial field 254 13.5.6 Stack 254 13.5.7 Formula 255 13.5.8 Run R-INLA 255 13.5.9 Inspect results 256 13.6 SIMULATING FROM THE MODEL 262 13.7 WHAT TO WRITE IN A PAPER 265
• 14 TIME-SERIES ANALYSIS IN R-INLA 267 14.1 SIMULATION STUDY 267 14.2 TRENDS IN MIGRATION DATES OF SOCKEYE SALMON 269 14.2.1 Applying a random walk trend model 269 14.2.2 Posterior distribution of the sigmas 272 14.2.3 Covariates and trends 273 14.2.4 Making the trend smoother 274 14.3 TRENDS IN POLAR BEAR MOVEMENTS 280 14.4 TRENDS IN WHALE STRANDINGS 283 14.5 MULTIVARIATE TIME SERIES FOR HAWAIIAN BIRDS 285 14.5.1 Importing and preparing the data 285 14.5.2 Data exploration 286 14.5.3 Model formulation 287 14.5.4 Executing the models 288 14.5.5 Mixing Poisson and negative binomial distributions 295 14.6 AR1 TRENDS 297 14.6.1 AR1 trend for regularly spaced time-series data 297 14.6.2 AR1 trend for irregularly spaced time-series data 299
• 15 SPATIAL-TEMPORAL MODELS FOR ORANGE-CROWNED WARBLERS COUNT DATA 307 15.1 INTRODUCTION 307 15.2 POISSON GLM 308 15.3 MODEL WITH SPATIAL CORRELATION 312 15.4 SPATIAL-TEMPORAL CORRELATION: AR1 318 15.4.1 Why do it? 318 15.4.2 Explanation of the model 318 15.4.4 Simulating a spatial-temporal AR random field 320 15.4.5 Implementation of AR1 model in R-INLA 323 15.4.6 More detailed information on the code 326 15.5 SPATIAL-TEMPORAL CORRELATION: EXCHANGEABLE 328 15.6 SPATIAL-TEMPORAL CORRELATION: REPLICATED 329 15.7 SIMULATION STUDY 330 15.8 DISCUSSION 333
• 16 SPATIAL-TEMPORAL BERNOULLI MODELS FOR CORAL DISEASE DATA 335 16.1 INTRODUCTION 335 16.2 BERNOULLI MODEL IN R-INLA 336 16.3 SPATIAL CORRELATED BERNOULLI MODEL 338 16.4 SPATIAL-TEMPORAL CORRELATED BERNOULLI MODEL 342 REFERENCES 347 INDEX 353 OTHER BOOKS 357.
• (source: Nielsen Book Data)

In Chapter 2 we discuss an important topic: dependency. Ignoring this means that we have pseudoreplication. We present a series of examples and discuss how dependency can manifest itself. We briefly discuss frequentist tools that are available for the analysis of temporal and spatial data in Chapters 3 and 4, and we will conclude that their application is rather limited, especially if non-Gaussian distributions are required. We will therefore consider alternative models, but these require Bayesian techniques. In Chapter 5 we discuss linear mixed-effects models to analyse hierarchical (i.e. clustered or nested) data, and in Chapter 6 we outline how we add spatial and spatial-temporal dependency to regression models via spatial (and/or temporal) correlated random effects. In Chapter 7 we introduce Bayesian analysis, Markov chain Monte Carlo techniques (MCMC), and Integrated Nested Laplace Approximation (INLA). INLA allows us to apply models to spatial, temporal, or spatial-temporal data. In Chapters 8 through 16 we present a series of INLA examples. We start by applying linear regression and mixed-effects models in INLA (Chapters 8 and 9), followed by GLM examples in Chapter 10. In Chapters 11 through 13 we show how to apply GLM models on spatial data. In Chapter 14 we discuss time-series techniques and how to implement them in INLA. Finally, in Chapters 15 and 16 we analyse spatial-temporal models in INLA.
(source: Nielsen Book Data)

• I Basic Concepts Introduction A Crash Course on R Statistical Assumptions Statistical Inference II Statistical Modeling Linear Models Nonlinear Models Classi cation and Regression Tree Generalized Linear Model III Advanced Statistical Modeling Simulation for Model Checking and Statistical Inference Multilevel Regression Using Simulation for Evaluating Models Based on Statistical Signicance Testing Bibliography.
• (source: Nielsen Book Data)

Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R, Second Edition, connects applied statistics to the environmental and ecological fields. Using examples from published works in the ecological and environmental literature, the book explains the approach to solving a statistical problem, covering model specification, parameter estimation, and model evaluation. It includes many examples to illustrate the statistical methods and presents R code for their implementation. The emphasis is on model interpretation and assessment, and using several core examples throughout the book, the author illustrates the iterative nature of statistical inference. The book starts with a description of commonly used statistical assumptions and exploratory data analysis tools for the verification of these assumptions. It then focuses on the process of building suitable statistical models, including linear and nonlinear models, classification and regression trees, generalized linear models, and multilevel models. It also discusses the use of simulation for model checking, and provides tools for a critical assessment of the developed models. The second edition also includes a complete critique of a threshold model. Environmental and Ecological Statistics with R, Second Edition focuses on statistical modeling and data analysis for environmental and ecological problems. By guiding readers through the process of scientific problem solving and statistical model development, it eases the transition from scientific hypothesis to statistical model.
(source: Nielsen Book Data)

A follow up to Over and Under the Snow and Up in the Garden and Down in the Dirt, this time focusing on the rich, interconnected ecosystem of a mountain pond. As parent and child launch a canoe from the muddy shore and paddle through water lilies, they see frogs jump and painted turtles slide off logs, disappearing beneath the murky water. What's happening down there? Under the pond, leeches lurk, crayfish scuttle under rocks, nymphs build intricate shells, and microscopic animals break down fallen leaves to recharge the water with nutrients. Over the pond, fuzzy cattails sway in the breeze, and a pair of loons swim by, laughing in the dappled sunlight. An author's note discusses not only the different organisms featured, but also the truly remarkable balance of this wetland ecosystem, the way plants and animals create a chemical balance that sustains the lives of the pond.
(source: Nielsen Book Data)

• Preface xi
• 1. Planning
• 2. Data recording
• 3. Beginning data exploration - using software tools
• 4. Exploring data - looking at numbers
• 5. Exploring data - which test is right?
• 6. Exploring data - using graphs
• 7. Tests for differences
• 8. Tests for linking data - correlations
• 9. Tests for linking data - associations
• 10. Differences between more than two samples
• 11. Tests for linking several factors
• 12. Community ecology
• 13. Reporting results
• 14. Summary Glossary Appendices Index.
• (source: Nielsen Book Data)

This is a book about the scientific process and how you apply it to data in ecology. You will learn how to plan for data collection, how to assemble data, how to analyze data and finally how to present the results. The book uses Microsoft Excel and the powerful Open Source R program to carry out data handling as well as producing graphs. Statistical approaches covered include: data exploration; tests for difference - t-test and U-test; correlation - Spearman's rank test and Pearson product-moment; association including Chi-squared tests and goodness of fit; multivariate testing using analysis of variance (ANOVA) and Kruskal-Wallis test; and multiple regression. Key skills taught in this book include: how to plan ecological projects; how to record and assemble your data; how to use R and Excel for data analysis and graphs; how to carry out a wide range of statistical analyses including analysis of variance and regression; how to create professional looking graphs; and how to present your results. New in this edition: a completely revised chapter on graphics including graph types and their uses, Excel Chart Tools, R graphics commands and producing different chart types in Excel and in R; an expanded range of support material online, including; example data, exercises and additional notes & explanations; a new chapter on basic community statistics, biodiversity and similarity; chapter summaries and end-of-chapter exercises. Praise for the first edition: This book is a superb way in for all those looking at how to design investigations and collect data to support their findings. - Sue Townsend, Biodiversity Learning Manager, Field Studies Council [M]akes it easy for the reader to synthesise R and Excel and there is extra help and sample data available on the free companion webpage if needed. I recommended this text to the university library as well as to colleagues at my student workshops on R. Although I initially bought this book when I wanted to discover R I actually also learned new techniques for data manipulation and management in Excel - Mark Edwards, EcoBlogging A must for anyone getting to grips with data analysis using R and excel. - Amazon 5-star review It has been very easy to follow and will be perfect for anyone. - Amazon 5-star review A solid introduction to working with Excel and R. The writing is clear and informative, the book provides plenty of examples and figures so that each string of code in R or step in Excel is understood by the reader. - Goodreads, 4-star review.
(source: Nielsen Book Data)

• Introduction (Stephen J. Hawkins)
• Preface and acknowledgements
• Translational ecology
• Air pollution
• With every breath you take
• What happens when the lights go out
• Raindrops keep fallin' on my head
• The ozone hole
• Biodiversity
• Size matters
• Sprawl
• Another turn of the screw
• Extinction : gone with the wind
• Do species matter?
• Dispersal
• Wildlife Is where you find It
• Songbird : It's what's for dinner
• On the ethics of driving species extinct
• Climate change : causes and impacts
• A climate change primer
• Volcanic CO2
• Melting at the bottom of the earth
• The Ice that floats in our global cocktail
• On Moonshine and earthshine
• The artistic record of nature
• Riding contrails to our future
• Geoengineering
• The Hockey game
• Not the time to get bogged down
• Energy sources
• One day, the Oil hunt will be fruitless
• Coming from a coal ash pile near you
• Let's take the emotion out of fracking
• Oil sands redux
• Beyond keystone XL
• Off-shore oil drilling
• Whatever happened to peak oil?
• Natural gas versus coal, It's all in the leak rate
• Carbon trading
• Biomass, biofuels
• The Nuclear option
• Solar for Us!
• Wind power : but not in my backyard
• Energy Sprawl
• Freshwater
• What makes a healthy stream?
• Don't be a salty dog
• Your water on drugs
• Color me blue
• Groundwater dynamics
• Evaporation from the golden state
• Coping with a drought-prone future
• Greenhouse gases
• The Missing sink
• Human carbon
• Biochar reality check
• Methane : the other Greenhouse gas
• Clathrates
• Nitrous oxide : no laughing matter
• Exporting our Emissions
• Habitat
• Save the wet spot
• Designer ecosystems
• Hello darkness, my old friend
• Desertification
• Marine ecosystems and fisheries
• Ocean acidity
• I just want to say one word to you. just one word ... plastics
• Hypoxia
• Scraping bottom
• Is sea-level rise Illegal?
• Warming the gulf of maine
• Population growth and sustainability
• A modest proposal
• Sustainability ... not
• Crowd-sourcing environmental problems
• Divestment
• If I Had a hammer
• Forecasting disasters
• Recycling
• What goes around comes around
• Phosphorus
• The old copper kettle
• Lithium : It's not just for bad moods anymore
• E-Waste
• Toxins
• Plumbing the origins of lead in nature
• Reading the mad hatter's diary
• Chromium and the myth of tolerable exposure
• Neonicotinoids : to be or not to bee
• Better living through chemistry
• Your lifestyle
• Your car
• Old car, new car?
• Your food
• Food miles
• Organic foods : who couldn't like them?
• Beef : It's what's for dinner
• How green is your booze?
• Crops and climate
• Spring sugarbush
• Your health
• Can't beat the deet
• Fluorine
• "You have cancer"
• Blood-sucking insects and ticks, oh my!
• Antibiotic resistance
• Your home
• Kitchen disposals
• Nanomaterials
• Oh tannenbaum
• Your kids
• We need more free-range kids
• Your yard
• Lawn gazing
• On watching the autumn leaves fall
• Success stories
• Not all the news is bad
• Oxygen
• Whatever happened to acid rain?
• Ask not for whom the bell tolls
• In quest of the steady-state
• When science informs policy
• An open letter to donald trump
• William H. Schlesinger : a laudatio (Harold A. Mooney)
• The international ecology institute
• Index.

• What is group selection?
• Group selection in the 1970s
• Career beginnings and science after the thesis
• Experimental studies of population heritability
• Population ecology and population heritability
• The evolution of sociality
• Calibrating the laboratory to nature
• Experimental studies of Wright's shifting balance theory
• Beyond the shifting balancing theory.

All organisms live in clusters, but such fractured local populations, or demes, nonetheless maintain connectivity with one another by some amount of gene flow between them. Most such metapopulations occur naturally, like clusters of amphibians in vernal ponds or baboon troops spread across the African veldt. Others have been created as human activities fragment natural landscapes, as in stands of trees separated by roads. As landscape change has accelerated, understanding how these metapopulations function and specifically how they adapt has become crucial to ecology and to our very understanding of evolution itself. With "Adaptation in Metapopulations, " Michael J. Wade explores a key component of this new understanding of evolution: interaction. Synthesizing decades of work in the lab and in the field in a book both empirically grounded and underpinned by a strong conceptual framework, Wade looks at the role of interaction across scales from gene selection to selection at the level of individuals, kin, and groups. In so doing, he integrates molecular and organismal biology to reveal the true complexities of evolutionary dynamics from genes to metapopulations.".
(source: Nielsen Book Data)

• 1. Introduction
• 2. Essential distributions for zero-inflated models
• 3. Introducing zero-inflated Poisson models
• 4. Zero-inflated models applied to orange-crowned warblers
• 5. Zero-inflated models applied to shark abundance data
• 6. Hurdle models for riparian spider counts
• 7. Models for zero-inflated continuous data applied to Chinese tallow trees
• 8. Linear mixed effects models
• 9. Zero-altered models with two-way nested and crossed random effects
• 10. Introduction to Bayesian statistics
• 11. Bayesian analysis for Poisson, NB, ZIP and Bernoulli models
• 12. Bayesian analysis for linear mixed effects models
• Beaver and Lilies
• 13. The zero trick to fit any distribution in a Bayesian analysis
• 14. Bayesian model selection techniques
• 15. Bayesian model selection applied to zero-inflated butterfly data
• 16. Zero-inflated seagrass coverage data
• 17. Other distributions
• 18. Multivariate GLMM.

This book provides the statistical tools to aid analysis of datasets. It deals with two main difficulties faced with large datasets, lots of zeros and dependency.

• Contributors Preface and Acknowledgments Marine and Terrestrial Maps of California
• 1. Introduction (Erika Zavaleta and Harold Mooney) DRIVERS
• 2. Climate (Sam F. Iacobellis, Daniel R. Cayan, John T. Abatzoglou, and Harold Mooney)
• 3. Fire as an Ecosystem Process (Jon E. Keeley and Hugh D. Safford)
• 4. Geomorphology and Soils (Robert C. Graham and Toby A. O'Geen)
• 5. Population and Land Use (Peter S. Alagona, Tim Paulson, Andrew B. Esch, and Jessica Marter-Kenyon)
• 6. Oceanography (Steven J. Bograd, Andrew Leising, and Elliott L. Hazen)
• 7. Atmospheric Chemistry (Andrzej Bytnerowicz, Mark Fenn, Edith B. Allen, and Ricardo Cisneros) HISTORY
• 8. Ecosystems Past: Vegetation Prehistory (Constance I. Millar and Wallace B. Woolfenden)
• 9. Paleovertebrate Communities (Elizabeth A. Hadly and Robert S. Feranec)
• 10. Indigenous California (Terry L. Jones and Kacey Hadick) BIOTA
• 11. Biodiversity (Bernie Tershy, Susan Harrison, Abraham Borker, Barry Sinervo, Tara Cornelisse, Cheng Li, Dena Spatz, Donald Croll, and Erika Zavaleta)
• 12. Vegetation (Christopher R. Dolanc, Todd Keeler-Wolf, and Michael G. Barbour)
• 13. Biological Invasions (Erika Zavaleta, Elissa Olimpi, Amelia A. Wolf, Bronwen Stanford, Jae Pasari, Sarah Skikne, Paulo Quadri, Katherine Ennis, and Flavia Oliveira)
• 14. Climate Change Impacts (Christopher B. Field, Nona R. Chiariello, and Noah S. Diffenbaugh)
• 15. Introduction to Concepts of Biodiversity, Ecosystem Functioning, Ecosystem Services, and Natural Capital (Rebecca Chaplin-Kramer, Lisa Mandle, Elizabeth Rauer, and Suzanne Langridge) ECOSYSTEMS
• 16. The Offshore Ecosystem (Steven J. Bograd, Elliott L. Hazen, Sara M. Maxwell, Andrew W. Leising, Helen Bailey, and Richard D. Brodeur)
• 17. Shallow Rocky Reefs and Kelp Forests (Mark H. Carr and Daniel C. Reed)
• 18. Intertidal (Carol A. Blanchette, Mark W. Denny, John M. Engle, Brian Helmuth, Luke P. Miller, Karina J. Nielsen, and Jayson Smith)
• 19. Estuaries: Life on the Edge (James E. Cloern, Patrick Barnard, Erin Beller, John Callaway, J. Letitia Grenier, Edwin D. Grosholz, Robin Grossinger, Kathy Hieb , James T. Holligaugh, Noah Knowles, Martha Sutula, Samuel Veloz, Kerstin Wasson, and Alison Whipple)
• 20. Sandy Beaches (Jenifer E. Dugan and David M. Hubbard)
• 21. Coastal Dunes (Peter Alpert)
• 22. Coastal Sage Scrub (Elsa E. Cleland, Jennifer Funk, and Edith B. Allen)
• 23. Grasslands (Valerie T. Eviner)
• 24. Chaparral (V. Thomas Parker, R. Brandon Pratt, and Jon E. Keeley)
• 25. Oak Woodlands (Frank W. Davis, Dennis D. Baldocchi, and Claudia M. Tyler)
• 26. Coast Redwood Forests (Harold Mooney and Todd E. Dawson)
• 27. Montane Forests (Malcolm North, Brandon Collins, Hugh Safford, and Nathan L. Stephenson)
• 28. Subalpine Forests (Constance I. Millar and Philip W. Rundel)
• 29. Alpine Ecosystems (Philip W. Rundel and Constance I. Millar)
• 30. Deserts (Jayne Belnap, Robert H. Webb , Todd C. Esque, Matthew L. Brooks, Lesley A. DeFalco, and James A. MacMahon)
• 31. Wetlands (Walter G. Duffy, Philip Garone, Brenda J. Grewell, Sharon Kahara, Joseph Fleskes, Brent Helm, Peter Moyle, Rosemary Records, and Joseph Silveira)
• 32. Lakes (John Melack and S. Geoffrey Schladow)
• 33. Rivers (Mary E. Power, Sarah J. Kupferberg, Scott D. Cooper, and Michael L. Deas) Managed Systems
• 34. Managed Island Ecosystems (Kathryn McEachern, Tanya Atwater, Paul W. Collins, Kate Faulkner, and Daniel V. Richards)
• 35. Marine Fisheries (Eric P. Bjorkstedt, John C. Field, Milton Love, Laura Rogers-Bennett, and Rick Starr)
• 36. Forestry (William Stewart, Benktesh Sharma, Rob York, Lowell Diller, Nadia Hamey, Roger Powell, and Robert Swiers)
• 37. Range Ecosystems (Sheri Spiegal, Lynn Huntsinger, Peter Hopkinson, and James Bartolome)
• 38. Agriculture (Alex McCalla and Richard Howitt)
• 39. Urban Ecosystems (Diane E. Pataki, G. Darrel Jenerette, Stephanie Pincetl, Tara L. E. Trammell, and La'Shaye Ervin) POLICY AND STEWARDSHIP
• 40. Land Use Regulation for Resource Conservation (Stephanie Pincetl, Terry Watt, and Maria Santos)
• 41. Stewardship, Conservation, and Restoration in the Context of Environmental Change (Adina M. Merenlender, David D. Ackerly, Katherine Suding, M. Rebecca Shaw, and Erika Zavaleta) INDEX.
• (source: Nielsen Book Data)

This long-anticipated reference and source book for California's remarkable ecological abundance provides an integrated assessment of each major ecosystem type - its distribution, structure, function, and management. With a comprehensive synthesis of our knowledge about this biologically diverse state, Ecosystems of California covers the state from oceans to mountaintops using multiple lenses: past and present, flora and fauna, aquatic and terrestrial, natural and managed. Each chapter evaluates natural processes for a specific ecosystem, describes drivers of change, and discusses how that ecosystem may be altered in the future. This book also explores the drivers of California's ecological patterns and the history of the state's various ecosystems, outlining how the challenges of climate change and invasive species and opportunities for regulation and stewardship could potentially affect the state's ecosystems. The text explicitly incorporates both human impacts and conservation and restoration efforts and shows how ecosystems support human well-being. Edited by two esteemed ecosystem ecologists and with overviews by leading experts on each ecosystem, this definitive work will be indispensable for natural resource management and conservation professionals as well as for undergraduate or graduate students of California's environment and curious naturalists.
(source: Nielsen Book Data)

The Effective Ecologist covers the stuff that no-one told you about at university - how to develop your office-related and business skills to succeed in your career as a professional ecologist. This book shows you how to be more effective in your role, providing you with the skills and effective behaviours within the workplace that will enable your development as an ecologist. It explains what it means to be effective in the workplace and describes positive behaviours and how they can be adopted. It contains the skills needed for effective communication, organising projects, advice on planning, reporting and meetings and provides you with everything you need for a brilliant and successful career. In a clearly written and honest account full of real life examples, the author leaves no stone unturned as he describes how making small changes in your behaviour can have a positive impact upon your performance and how you are perceived in your working environment. Essential reading for anyone commencing or already pursuing a career in ecology who wants to perform at the highest level. In addition this work will be of great interest to team managers, business leaders and those responsible for the development of staff as a point of reference and guidance for their team. .
(source: Nielsen Book Data)

The Long-Term Ecological Research (LTER) Program is, in a sense, an experiment to transform the nature of science, and represents one of the most effective mechanisms for catalyzing comprehensive site-based research that is collaborative, multidisciplinary, and long-term in nature. The scientific contributions of the Program are prodigious, but the broader impacts of participation have not been examined in a formal way. This book captures the consequences of participation in the Program on the perspectives, attitudes, and practices of environmental scientists. The edited volume comprises three sections. The first section includes two chapters that provide an overview of the history, goals, mission, and inner workings of the LTER network of sites. The second section comprises three dozen retrospective essays by scientists, data managers or educators who represent a broad spectrum of LTER sites from deserts to tropical forests and from arctic to marine ecosystems. Each essay addresses the same series of probing questions to uncover the extent to which participation has affected the ways that scientists conduct research, educate students, or provide outreach to the public. The final section encompasses 5 chapters, whose authors are biophysical scientists, historians, behavioral scientists, or social scientists. This section analyzes, integrates, or synthesizes the content of the previous chapters from multiple perspectives and uncovers emergent themes and future directions.
(source: Nielsen Book Data)

This novel, interdisciplinary text achieves an integration of empirical data and theory with the aid of mathematical models and statistical methods. The emphasis throughout is on spatial ecology and evolution, especially on the interplay between environmental heterogeneity and biological processes. The book provides a coherent theme by interlinking the modelling approaches used for different subfields of spatial ecology: movement ecology, population ecology, community ecology, and genetics and evolutionary ecology (each being represented by a separate chapter). Each chapter starts by describing the concept of each modelling approach in its biological context, goes on to present the relevant mathematical models and statistical methods, and ends with a discussion of the benefits and limitations of each approach. The concepts and techniques discussed throughout the book are illustrated throughout with the help of empirical examples. This is an advanced text suitable for any biologist interested in the integration of empirical data and theory in spatial ecology/evolution through the use of quantitative/statistical methods and mathematical models. The book will also be of relevance and use as a textbook for graduate-level courses in spatial ecology, ecological modelling, theoretical ecology, and statistical ecology.
(source: Nielsen Book Data)

• Preface ix I Fundamentals 1
• 1 PREVIEW 3 1.1 A Line of Inference for Ecology 4 1.2 An Example Hierarchical Model 11 1.3 What Lies Ahead? 15
• 2 DETERMINISTIC MODELS 17 2.1 Modeling Styles in Ecology 17 2.2 A Few Good Functions 21
• 3 PRINCIPLES OF PROBABILITY 29 3.1 Why Bother with First Principles? 29 3.2 Rules of Probability 31 3.3 Factoring Joint Probabilities 36 3.4 Probability Distributions 39
• 4 LIKELIHOOD 71 4.1 Likelihood Functions 71 4.2 Likelihood Profiles 74 4.3 Maximum Likelihood 76 4.4 The Use of Prior Information in Maximum Likelihood 77
• 5 SIMPLE BAYESIAN MODELS 79 5.1 Bayes' Theorem 81 5.2 The Relationship between Likelihood and Bayes' 85 5.3 Finding the Posterior Distribution in Closed Form 86 5.4 More about Prior Distributions 90
• 6 HIERARCHICAL BAYESIAN MODELS 107 6.1 What Is a Hierarchical Model? 108 6.2 Example Hierarchical Models 109 6.3 When Are Observation and Process Variance Identifiable? 141 II Implementation 143
• 7 MARKOV CHAIN MONTE CARLO 145 7.1 Overview 145 7.2 How Does MCMC Work? 146 7.3 Specifics of the MCMC Algorithm 150 7.4 MCMC in Practice 177
• 8 INFERENCE FROM A SINGLE MODEL 181 8.1 Model Checking 181 8.2 Marginal Posterior Distributions 190 8.3 Derived Quantities 194 8.4 Predictions of Unobserved Quantities 196 8.5 Return to the Wildebeest 201
• 9 INFERENCE FROM MULTIPLE MODELS 209 9.1 Model Selection 210 9.2 Model Probabilities and Model Averaging 222 9.3 Which Method to Use? 227 III Practice in Model Building 231
• 10 WRITING BAYESIAN MODELS 233 10.1 A General Approach 233 10.2 An Example of Model Building: Aboveground Net Primary Production in Sagebrush Steppe 237
• 11 PROBLEMS 243 11.1 Fisher's Ticks 244 11.2 Light Limitation of Trees 245 11.3 Landscape Occupancy of Swiss Breeding Birds 246 11.4 Allometry of Savanna Trees 247 11.5 Movement of Seals in the North Atlantic 248
• 12 SOLUTIONS 251 12.1 Fisher's Ticks 251 12.2 Light Limitation of Trees 256 12.3 Landscape Occupancy of Swiss Breeding Birds 259 12.4 Allometry of Savanna Trees 264 12.5 Movement of Seals in the North Atlantic 268 Afterword 273 Acknowledgments 277 A Probability Distributions and Conjugate Priors 279 Bibliography 283 Index 293.
• (source: Nielsen Book Data)

Bayesian modeling has become an indispensable tool for ecological research because it is uniquely suited to deal with complexity in a statistically coherent way. This textbook provides a comprehensive and accessible introduction to the latest Bayesian methods--in language ecologists can understand. Unlike other books on the subject, this one emphasizes the principles behind the computations, giving ecologists a big-picture understanding of how to implement this powerful statistical approach. Bayesian Models is an essential primer for non-statisticians. It begins with a definition of probability and develops a step-by-step sequence of connected ideas, including basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and inference from single and multiple models. This unique book places less emphasis on computer coding, favoring instead a concise presentation of the mathematical statistics needed to understand how and why Bayesian analysis works. It also explains how to write out properly formulated hierarchical Bayesian models and use them in computing, research papers, and proposals. This primer enables ecologists to understand the statistical principles behind Bayesian modeling and apply them to research, teaching, policy, and management. * Presents the mathematical and statistical foundations of Bayesian modeling in language accessible to non-statisticians* Covers basic distribution theory, network diagrams, hierarchical models, Markov chain Monte Carlo, and more* Deemphasizes computer coding in favor of basic principles* Explains how to write out properly factored statistical expressions representing Bayesian models.
(source: Nielsen Book Data)

• Introduction
• 1. Approaches to Statistical Inference
• 2. Having the Right Stuff: the Effects of Data Constraints on Ecological Data Analysis
• 3. Likelihood and Model Selection
• 4. Missing Data: Mechanisms, Methods and Messages
• 5. What You Don't Know Can Hurt You: Censored and Truncated Data in Ecological Research
• 6. Generalized Linear Models
• 7. A Statistical Symphony: Instrumental Variables Reveal Causality and Control Measurement Error
• 8. Structural Equation Modeling: Building and Evaluating Causal Models
• 9. Research Synthesis Methods in Ecology
• 10. Spatial Variation and Linear Modeling of Ecological Data
• 11. Statistical Approaches to the Problem of Phylogenetically Correlated Data
• 12. Mixture Models for Overdispersed Data
• 13. Linear and Generalized Linear Mixed Models
• Appendix.
• (source: Nielsen Book Data)

The application and interpretation of statistics are central to ecological study and practice. Ecologists are now asking more sophisticated questions than in the past. These new questions, together with the continued growth of computing power and the availability of new software, have created a new generation of statistical techniques. These have resulted in major recent developments in both our understanding and practice of ecological statistics. This novel book synthesizes a number of these changes, addressing key approaches and issues that tend to be overlooked in other books such as missing/censored data, correlation structure of data, heterogeneous data, and complex causal relationships. These issues characterize a large proportion of ecological data, but most ecologists' training in traditional statistics simply does not provide them with adequate preparation to handle the associated challenges. Uniquely, Ecological Statistics highlights the underlying links among many statistical approaches that attempt to tackle these issues. In particular, it gives readers an introduction to approaches to inference, likelihoods, generalized linear (mixed) models, spatially or phylogenetically-structured data, and data synthesis, with a strong emphasis on conceptual understanding and subsequent application to data analysis. Written by a team of practicing ecologists, mathematical explanations have been kept to the minimum necessary. This user-friendly textbook will be suitable for graduate students, researchers, and practitioners in the fields of ecology, evolution, environmental studies, and computational biology who are interested in updating their statistical tool kits. A companion web site provides example data sets and commented code in the R language.
(source: Nielsen Book Data)

• Introduction Bryan Manly and Jorge Navarro Why a Book on Ecological Sampling and Analysis? The Scope and Contents of the Book
• Standard Sampling Methods and Analyses Bryan Manly Simple Random Sampling Estimation of Mean Values Estimation of Totals Sample Sizes for Estimation of Means Errors in Sample Surveys Estimation of Population Proportions Determining Sample Sizes for the Estimation of Proportions Stratified Random Sampling Systematic Sampling Some Other Design Strategies Unequal Probability Sampling
• Adaptive Sampling Methods Jennifer Brown Adaptive Cluster Sampling Other Adaptive Sampling Designs Discussion
• Line Transect Sampling Jorge Navarro and Raul Diaz-Gamboa Basic Procedures in Line Transect Sampling The Detection Function Estimation from Sighting Distances and Angles Estimation of Standard Errors in Line Transect Sampling Size-Biased Line Transect Surveys Probability of Detection on the Line of Less than One Point Transect Sampling Software for Line and Point Transect Sampling and Estimation
• Removal and Change-in-Ratio Methods Lyman McDonald and Bryan Manly Removal Method The Change-in-Ratio Method Relationship between Change-in-Ratio and Mark-Recapture Methods
• Plotless Sampling Jorge Navarro T-Square Sampling Performance of T-Square Sampling Applications The Wandering-Quarter Method Further Extensions and Recent Developments in Plotless Sampling Methods Computational Tools for Density Estimation in Plotless Sampling
• Introduction to Mark-Recapture Sampling and Closed-Population Models Jorge Navarro, Bryan Manly, and Roberto Barrientos-Medina Terminology and Assumptions Closed-Population Methods Recent Advances for Closed-Population Models
• Open-Population Mark-Recapture Models Bryan Manly, Jorge Navarro, and Trent McDonald The Jolly-Seber Model The Manly-Parr Method Recoveries of Dead Animals Estimation Using Radio-Tagged Individuals Flexible Modeling Procedures Tests of Goodness of Fit An Example of Mark-Recapture Modeling Recent Advances with Open-Population Models General Computer Programs for Capture-Recapture Analyses
• Occupancy Models Darryl MacKenzie General Overview Single-Season Models Multiseason Models Including Covariates Study Design Discussion
• Sampling Designs for Environmental Monitoring Trent McDonald Design Characteristics Monitoring versus Research Spatial Designs Summary
• Models for Trend Analysis Timothy J. Robinson and Jennifer Brown Basic Methods for Trend Analysis Unit Analyses of Trends Pooled Analysis of Trends Checking for Model Adequacy Summary
• References.
• (source: Nielsen Book Data)

An Easy-to-Understand Treatment of Ecological Sampling Methods and Data Analysis Including only the necessary mathematical derivations, Introduction to Ecological Sampling shows how to use sampling procedures for ecological and environmental studies. It incorporates both traditional sampling methods and recent developments in environmental and ecological sampling methods. After an introduction, the book presents standard sampling methods and analyses. Subsequent chapters delve into specialized topics written by well-known researchers. These chapters cover adaptive sampling methods, line transect sampling, removal and change-in-ratio methods, plotless sampling, mark-recapture sampling of closed and open populations, occupancy models, sampling designs for environmental modeling, and trend analysis. The book explains the methods as simply as possible, keeping equations and their derivations to a minimum. It provides references to important, more advanced sampling methods and analyses. It also directs readers to computer programs that can be used to perform the analyses. Accessible to biologists, the text only assumes a basic knowledge of statistical methods. It is suitable for an introductory course on methods for collecting and analyzing ecological and environmental data.
(source: Nielsen Book Data)