ALBERT

Note: Contents: 1 Introduction. - 2 Basic Physical Properties of Soil. - 2.1 Geometry of the Soil Matrix. - 2.2 Soil Structure. - 2.3 Fractal Geometry. - 2.4 Geometry of the Pore Space. - 2.5 Specific Surface Area. - 2.6 Averaging. - 2.7 Bulk Density, Water Content and Porosity. - 2.8 Relationships between Variables. - 2.9 Typical Values of Physical Properties. - 2.10 Volumes and Volumetric Fractions for a Soil Prism. - 2.11 Soil Solid Phase. - 2.12 Soil Texture. - 2.13 Sedimentation Law. - 2.14 Exercises. - 3 Soil Gas Phase and Gas Diffusion. - 3.1 Transport Equations. - 3.2 The Diffiisivity of Gases in Soil. - 3.3 Computing Gas Concentrations. - 3.4 Simulating One-Dimensional Steady-State Oxygen Diffusion in a Soil Profile. - 3.5 Numerical Implementation. - 3.6 Exercises. - 4 Soil Temperature and Heat Flow. - 4.1 Differential Equations for Heat Conduction. - 4.2 Soil Temperature Data. - 4.3 Numerical Solution of the Heat Flow Equation. - 4.4 Soil Thermal Properties. - 4.5 Numerical Implementation. - 4.6 Exercises. - 5 Soil Liquid Phase and Soil-Water Interactions. - 5.1 Properties of Water. - 5.2 Soil Water Potential. - 5.3 Water Potential-Water Content Relations. - 5.4 Liquid- and Vapour-Phase Equilibrium. - 5.5 Exercises. - 6 Steady-State Water Flow and Hydraulic Conductivity. - 6.1 Forces on Water in Porous Media. - 6.2 Water Flow in Saturated Soils. - 6.3 Saturated Hydraulic Conductivity. - 6.4 Unsaturated Hydraulic Conductivity. - 6.5 Exercises. - 7 Variation in Soil Properties. - 7.1 Frequency Distributions. - 7.2 Probability Density Functions. - 7.3 Transformations. - 7.4 Spatial Correlation. - 7.5 Approaches to Stochastic Modelling. - 7.6 Numerical Implementation. - 7.7 Exercises. - 8 Transient Water Flow. - 8.1 Mass Conservation Equation. - 8.2 Water Flow. - 8.3 Infiltration. - 8.4 Numerical Simulation of Infiltration. - 8.5 Numerical Implementation. - 8.6 Exercises. - 9 Triangulated Irregular Network. - 9.1 Digital Terrain Model. - 9.2 Triangulated Irregular Network. - 9.3 Numerical Implementation. - 9.4 Main. - 9.5 Triangulation. - 9.6 GIS Functions. - 9.7 Boundary. - 9.8 Geometrical Properties of Triangles. - 9.9 Delaunay Triangulation. - 9.10 Refinement. - 9.11 Utilities. - 9.12 Visualization. - 9.13 Exercise. - 10 Water Flow in Three Dimensions. - 10.1 Governing Equations. - 10.2 Numerical Formulation. - 10.3 Coupling Surface and Subsurface Flow. - 10.4 Numerical Implementation. - 10.5 Simulation. - 10.6 Visualization and Results. - 10.7 Exercises. - 11 Evaporation. - 11.1 General Concepts. - 11.2 Simultaneous Transport of Liquid and Vapour in Isothermal Soil. - 11.3 Modelling evaporation. - 11.4 Numerical Implementation. - 11.5 Exercises. - 12 Modelling Coupled Transport. - 12.1 Transport Equations. - 12.2 Partial Differential Equations. - 12.3 Surface Boundary Conditions. - 12.4 Numerical Implementation. - 12.5 Exercises. - 13 Solute Transport in Soils. - 13.1 Mass Flow. - 13.2 Diffusion. - 13.3 Hydrodynamic Dispersion. - 13.4 Advection-Dispersion Equation. - 13.5 Solute-Soil Interaction. - 13.6 Sources and Sinks of Solutes. - 13.7 Analytical Solutions. - 13.8 Numerical Solution. - 13.9 Numerical Implementation. - 13.10 Exercises. - 14 Transpiration and Plant-Water Relations. - 14.1 Soil Water Content and Soil Water Potential under a Vegetated Surface. - 14.2 General Features of Water Flow in the SPAC. - 14.3 Resistances to Water Flow within the Plant. - 14.4 Effect of Environment on Plant Resistance. - 14.5 Detailed Consideration of Soil and Root Resistances. - 14.6 Numerical Implementation. - 14.7 Exercises. - 15 Atmospheric Boundary Conditions. - 15.1 Radiation Balance at the Exchange Surface. - 15.2 Boundary-Layer Conductance for Heat and Water Vapour. - 15.3 Evapotranspiration and the Penman-Monteith Equation. - 15.4 Partitioning of Evapotranspiration. - 15.5 Exercise. - Appendix A: Basic Concepts and Examples of Python Programming. - A.1 Basic Python. - A.2 Basic Concepts of Computer Programming. - A.3 Data Representation: Variables. - A.4 Comments Rules and Indendation. - A.5 Arithmetic Expression. - A.6 Functions. - A.7 Flow Control. - A.8 File Input and Output. - A.9 Arrays. - A.10 Reading Date Time. - A.11 Object-Oriented Programming in Python. - A.12 Output and Visualization. - A.13 Exercises. - Appendix B: Computational Tools. - B.1 Numerical Differentiation. - B.2 Numerical Integration. - B.3 Linear Algebra. - B.4 Exercises. - List of Symbols. - List of Python Variables. - List of Python Projects. - References. - Index.

Note: Contents Acknowledgments 1 Introduction to environmental DNA (eDNA) 1.1 Definitions 1.2 A brief history of eDNA analysis 1.3 Constraints when working with eDNA 1.4 Workflow in eDNA studies and main methods used 1.5 Environmental DNA as a monitoring tool 2 DNA metabarcode choice and design 2.1 Which DNA metabarcode? 2.2 Properties of the ideal DNA metabarcode 2.3 In silica primer design and testing 2.3.1 Prerequisites 2.3.2 Reference sequences: description, filtering, and formatting for ecoPrimers 2.3.3 In silica primer design with ecoPrimers 2.3.3.1 'Ihe ecoPrimers output 2.3.4 In silica primer testing with ecoPCR 2.3.4.1 The ecoPCR output 2.3.4.2 Filtering of the ecoPCR output 2.3.4.3 Evaluation of primer conservation 2.3.4.4 Taxonomic resolution and Bs index 2.4 Examples of primer pairs available for DNA metabarcoding 3 Reference databases 3.1 Extracting reference databases from EMBL/GenBank/DDBJ 3.1.1 Downloading a local copy of EMBL 3.1.2 Identifying sequences corresponding to the relevant metabarcode 3.2 Marker-specific reference databases 3.2.1 Nuclear rRNA gene reference databases 3.2.2 Eukaryote-specific databases 3.3 Building a local reference database 3.3.1 PCR-based local reference database 3.3.2 Shotgun-based local reference database 3.4 Current challenges and future directions 4 Sampling 4.1 The cycle of eDNA in the environment 4.1.1 State and origin 4.1.2 Fate 4.1.3 Transport 4.2 Sampling design 4.2.1 Focusing on the appropriate DNA population 4.2.2 Defining the sampling strategy 4.3 Sample preservation 5 DNA extraction 5.1 From soil samples 5.2 From sediment 5.3 From litter 5.4 From fecal samples 5.5 From water samples 6 DNA amplification and multiplexing 6.1 Principle of the PCR 6.2 Which polymerase to choose? 6.3 The standard PCR reaction 6.4 The importance of including appropriate controls 6.4.1 Extraction negative controls 6.4.2 PCR negative controls 6.4.3 PCR positive controls 6.4.4 Tagging system controls 6.4.5 Internal controls 6.5 PCR optimization 6.6 How to limit the risk of contamination? 6.7 Blocking oligonucleotides for reducing the amplification of undesirable sequences 6.8 How many PCR replicates? 6.9 Multiplexing several metabarcodes within the same PCR 6.10 Multiplexing many samples on the same sequencing lane 6.10.1 Overview of the problem 6.10.2 Strategy 1: single-step PCR with Illumina adapters 6.10.3 Strategy 2: two-step PCR with Illumina adapters 6.10.4 Strategy 3: single-step PCR with tagged primers 7 DNA sequencing 7.1 Overview of the first, second, and third generations of sequencing technologies 7.2 The Illumina technology 7.2.1 Library preparation 7.2.2 Flow cell, bridge PCR, and clusters 7.2.3 Sequencing by synthesis 7.2.4 Quality scores of the sequence reads 8 DNA metabarcoding data analysis 8.1 Basic sequence handling and curation 8.1.1 Sequencing quality 8.1.1.1 The pros and cons of read quality-based filtering 8.1.1.2 Quality trimming software 8.1.2 Paired-end read pairing 8.1.3 Sequence demultiplexing 8.1.4 Sequence dereplication 8.1.5 Rough sequence curation 8.2 Sequence classification 8.2.1 Taxonomic classification 8.2.2 Unsupervised classification 8.2.3 Chimera identification 8.3 Taking advantages of experimental controls 8.3.1 Filtering out potential contaminants 8.3.2 Removing dysfunctional PCRs 8.4 General considerations on ecological analyses 8.4.1 Sampling effort and representativeness 8.4.1.1 Evaluating representativeness of the sequencing per PCR 8.4.1.2 Evaluating representativeness at the sampling unit or site level 8.4.2 Handling samples with varying sequencing depth 8.4.3 Going further and adapting the ecological models to metabarcoding 9 Single-species detection 9.1 Principle of the quantitative PCR (qPCR) 9.1.1 Recording amplicon accumulation in real time via fluorescence measurement 9.1.2 The typical amplification curve 9.1.3 Quantification of target sequences with the Ct method 9.2 Design and testing of qPCR barcodes targeting a single species 9.2.1 1he problem of specificity 9.2.2 qPCR primers and probe 9.2.3 Candidate qPCR barcodes 9.3 Additional experimental considerations 9.3.1 General issues associated with sampling, extraction, and PCR amplification 9.3.2 The particular concerns of contamination and inhibition 10 Environmental DNA for functional diversity 10.1 Functional diversity from DNA metabarcoding 10.1.1 Functional inferences 10.1.2 Targeting active populations 10.2 Metagenomics and metatranscriptomics: sequencing more than a barcode 10.2.1 General sampling constraints 10.2.1.1 Optimization of the number of samples 10.2.1.2 Enrichment in target organisms 10.2.1.3 Enrichment in functional information 10.2.2 General molecular constraints 10.2.3 From sequences to functions 10.2.3.1 Assembling (or not) a metagenome 10.2.3.2 Sorting contigs or reads in broad categories 10.2.3.3 Extracting functional information via taxonomic inferences 10.2.3.4 Functional annotation of metagenomes 11 Some early landmark studies 11.1 Emergence of the concept of eDNA and first results on microorganisms 11.2 Examining metagenomes to explore the functional information carried by eDNA 11.3 Extension to macroorganisms 12 Freshwater ecosystems 12.1 Production, persistence, transport, and delectability of eDNA in freshwater ecosystems 12.1.1 Production 12.1.2 Persistence 12.1.3 Transport/ diffusion distance 12.1.4 Detectability 12.2 Macroinvertebrates 12.3 Diatoms and microeukaryotes 12.4 Aquatic plants 12.5 Fish, amphibians, and other vertebrates 12.5.1 Species detection 12.5.2 Biomass estimates 12.6 Are rivers conveyer belts of biodiversity information? 13 Marine environments 13.1 Environmental DNA cycle and transport in marine ecosystems 13.2 Marine microbial diversity 13.3 Environmental DNA for marine macroorganisms 14 Terrestrial ecosystems 14.1 Delectability, persistence, and mobility of eDNA in soil 14.2 Plant community characterization 14.3 Earthworm community characterization 14.4 Bacterial community or metagenome characterization 14.5 Multitaxa diversity surveys 1 5 Paleoenvironments 15.1 Lake sediments 15.1.1 Pollen, macrofossils, and DNA metabarcoding 15.1.2 Plants and mammals from Lake Anteme 15.1.3 Viability in the ice-free corridor in North America 15.2 Permafrost 15.2.1 Overview of the emergence of permafrost as a source of eDNA 15.2.2 Large-scale analysis of permafrost samples for reconstructing past plant communities 15.3 Archaeological midden material 15.3.1 Bulk archaeological fish bones from Madagascar 15.3.2 Midden from Greenland to assess past human diet 16 Host-associated microbiota 16.1 DNA dynamics 16.2 Early molecular-based works 16.3 Post-holobiont works 17 Diet analysis 17.1 Some seminal diet studies 17.1.1 Proof of concept-analyzing herbivore diet using next-generation sequencing 17.1.2 Assessing the efficiency of conservation actions in Bialowieza forest 17.1.3 Characterizing carnivore diet, or how to disentangle predator and prey eDNA 17.1.4 Analyzing an omnivorous diet, or integrating several diets in a single one 17.2 Methodological and experimental specificities of eDNA diet analyses 17.2.1 eDNAsources 17.2.1.1 Feces 17.2.1.2 Gut content 17.2.1.3 Whole body 17.2.2 Quantitative aspects 17.2.2.1 Relationship between the amount of ingested food and DNA quantity in the sample 17.2.2.2 Quantifying DNA with PCR and next-generation sequencing 17.2.2.3 Empirical correction of abundances 17.2.3 Diet as a sample of the existing biodiversity 17.2.4 Problematic diets 18 Analysis of bulk samples 18.1 What is a bulk sample? 18.2 Case studies 18.2.1 Bulk insect samples for biodiversity monitoring 18.2.2 Nematode diversity in tropical rainforest 18.2.3 Marine metawan diversity in benthic ecosystems 18.3 Metabarcoding markers for bulk samples 18.4 Alternative strategies 19 The future of eDNA metabarcoding 19.1 PCR-based approaches 19.1.1 Singl

Note: Contents Preface Introduction to the second edition What this book is about How the book is organized Why R? Updates Acknowledgements Chapter 1: Getting and Getting Acquainted with R 1.1 Getting started 1.2 Getting R 1.3 Getting R Studio 1.4 Let's play 1.5 Usin g R as a giant calculator (the size of your computer) 1.6 Your first script 1.7 Intermezzo remarks 1.8 Important functionality: packages 1.9 Getting help 1.10 A mini-practical - some in-depth play 1.11 Some more top tips and hints for a successful first (and more) R experience Appendix 1a Mini-tutorial solutions Appendix 1b File extensions and operating systems Chapter 2: Getting Your Data into R 2.1 Getting data ready for R 2.2 Getting your data into R 2.3 Checking that your data are your data 2.4 Basic troubleshooting while importing data 2.5 Summing up Appendix Advanced activity: dealing with untidy data Chapter 3: Data Management, Manipulation, and Exploration with dplyr 3.1 Summary statistics for each variable 3.2 dplyr verbs 3.3 Subsetting 3.4 Transforming 3.5 Sorting 3.6 Mini-summary and two top tips 3.7 Calculating summary statistics about groups of your data 3.8 What have you learned ... lots Appendix 3a Comparing classic methods and dplyr Appendix 3b Advanced dplyr Chapter 4: Visualizing Your Data 4.1 The first step in every data analysis — making a picture 4.2 ggplot2: a grammar for graphics 4.3 Box-and-whisker plots 4.4 Distributions: making histograms of numeric variables 4.5 Saving your graphs for presentation, documents, etc. 4.6 Closing remarks Chapter 5: Introducing Statistics in R 5.1 Getting started doing statistics in R 5.2 x2 contingency table analysis 5.3 Two-sample t-test 5.4 Introducing ... linear models 5.5 Simple linear regression 5.6 Analysis of variance: the one-way ANOVA 5.7 Wrapping up Appendix Getting packages not on CRAN Chapter 6: Advancing Your Statistics in R 6.1 Getting started with more advanced statistics 6.2 The two-way ANOVA 6.3 Analysis of covariance (ANCOVA) 6.4 Overview: an analysis workflow Chapter 7: Getting Started with Generalized Linear Models 7.1 Introduction 7.2 Counts and rates — Poisson GLMs 7.3 Doing it wrong 7.4 Doing it right — the Poisson GLM 7.5 When a Poisson GLM isn’t good for counts 7.6 Summary, and beyond simple Poisson regression Chapter 8: Pimping Your Plots: Scales and Themes in ggplot2 8.1 What you already know about graphs 8.2 Preparation 8.3 What you may want to customize 8.4 Axis labels, axis limits, and annotation 8.5 Scales 8.6 The theme 8.7 Summing up Chapter 9: Closing Remarks: Final Comments and Encouragement General Appendices Appendix 1 Data Sources Appendix 2 Further Reading Appendix 3 R Markdown Index