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Population Ecology in Practice

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Population Ecology in Practice

Dennis L. Murray (Editor), Brett K. Sandercock

ISBN: 978-0-470-67414-7 November 2019 Wiley-Blackwell 472 Pages

Paperback
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€50.90

Description

A synthesis of contemporary analytical and modeling approaches in population ecology

The book provides an overview of the key analytical approaches that are currently used in demographic, genetic, and spatial analyses in population ecology. The chapters present current problems, introduce advances in analytical methods and models, and demonstrate the applications of quantitative methods to ecological data. The book covers new tools for designing robust field studies; estimation of abundance and demographic rates; matrix population models and analyses of population dynamics; and current approaches for genetic and spatial analysis. Each chapter is illustrated by empirical examples based on real datasets, with a companion website that offers online exercises and examples of computer code in the R statistical software platform. 

  • Fills a niche for a book that emphasizes applied aspects of population analysis
  • Covers many of the current methods being used to analyse population dynamics and structure
  • Illustrates the application of specific analytical methods through worked examples based on real datasets
  • Offers readers the opportunity to work through examples or adapt the routines to their own datasets using computer code in the R statistical platform

Population Ecology in Practice is an excellent book for upper-level undergraduate and graduate students taking courses in population ecology or ecological statistics, as well as established researchers needing a desktop reference for contemporary methods used to develop robust population assessments.

Dedication

Preface

Part 1: Tools for Population Biology

Chapter 1 - How to Ask Meaningful Ecological Questions
Charles J. Krebs

1.0 Summary 1

1.1 What problems do population ecologists try to solve? 2

1.2 What approaches do population ecologists use? 8

1.2.1 Generating and testing hypotheses in population ecology 14

1.3 Generality in population ecology 16

1.4 Final Thoughts 19

1.5 References 21

Chapter 2 - From Research Hypothesis to Model Selection: A Strategy for Robust Inference in Population Ecology
Dennis L. Murray, Guillaume Bastille-Rousseau, Lynne E. Beaty, Megan L. Hornseth, Jeffrey R. Row, and Daniel H. Thornton

2.0 Summary 1

2.1 Introduction 2

2.1.1 Inductive methods 3

2.1.2 Hypothetico-deductive methods 5

2.1.2 Multimodel inference 6

2.1.3 Bayesian methods 8

2.2 What constitutes a good research hypothesis? 9

2.3 Multiple hypotheses and information theoretics 13

2.3.1 How many is too many hypotheses? 14

2.4 From research hypothesis to statistical model 16

2.4.1 Functional relationships between variables 17

2.4.2 Interactions between predictor variables 18

2.4.3 Number and structure of predictor variables 20

2.5 Exploratory analysis and helpful remedies 22

2.5.1 Exploratory analysis and diagnostic tests 22

2.5.2 Missing data 24

2.5.3 Inter-relationships between predictors 27

2.5.4 Interpretability of model output 29

2.6 Model ranking and evaluation 32

2.6.1 Model selection 32

2.6.2 Multimodel inference 37

2.7 Model validation 39

2.8 Software tools 42

2.9 Online Exercises 43

2.10 Future directions 43

2.11 References 45

Part 2: Population Demography

Chapter 3 - Estimating Abundance or Occupancy from Unmarked Populations
Brett T. McClintock and Len Thomas

3.0 Summary 1

3.1 Introduction 2

3.1.1 Why collect data from unmarked populations? 3

3.1.2 Relative indices and detection probability 4

3.1.2.1 Population abundance 4

3.1.2.2 Species occurrence 6

3.1.3 Hierarchy of sampling methods for unmarked individuals 7

3.2 Estimating abundance (or density) from unmarked individuals 11

3.2.1 Distance sampling 11

3.2.1.1 Classical distance sampling 13

3.2.1.2 Model-based distance sampling 19

3.2.2. Replicated counts of unmarked individuals 24

3.2.2.1 Spatially replicated counts 25

3.2.2.2 Removal sampling 29

3.3 Estimating species occurrence under imperfect detection 32

3.3.1 Single-season occupancy models 33

3.3.2 Multiple-season occupancy models 36

3.3.3 Other developments in occupancy estimation 38

3.3.3.1 Site heterogeneity in detection probability 39

3.3.3.2 Occupancy and abundance relationships 39

3.3.3.3 Multi-state and multi-scale occupancy models 41

3.3.3.4 Metapopulation occupancy models 43

3.3.3.5 False positive occupancy models 44

3.5 Software tools 46

3.6 Online Exercises 48

3.7 Future Directions 49

3.8 References 54

Chapter 4 - Analyzing Time Series Data: Single Species Abundance Modelling
Steven Delean, Thomas A.A. Prowse, Joshua V. Ross, and Jonathan Tuke

4.0 Summary 1

4.1 Introduction 2

4.1.1 Principal approaches to time-series analysis in ecology 5

4.1.2 Challenges to time-series analysis in ecology 8

4.2 Time series (ARMA) modelling 9

4.2.1 Time series models 9

4.2.2 Autoregressive moving average models (ARMA) 12

4.3 Regression models with correlated errors 13

4.4 Phenomenological models of population dynamics 16

4.4.1 Deterministic models 16

4.4.1.1 Exponential growth 16

4.4.1.2 Classic ODE single-species population models that incorporate density dependence 17

4.4.2 Discrete-time population growth models with stochasticity 20

4.5 State space modelling 24

4.5.1 Gompertz state space population model 26

4.5.2 Non-linear and non-Gaussian state space population models 28

4.6 Software Tools 29

4.7 Online Exercises 30

4.8 Future directions 30

4.9 References 33

Chapter 5 - Estimating Abundance from Capture-Recapture Data
J. Andrew Royle and Sarah J. Converse

5.0 Summary 1

5.1 Introduction 2

5.2 Genesis of Capture-Recapture Data 4

5.3 The Basic Closed Population Models: M0, Mt, Mb 5

5.4 Inference strategies 8

5.4.1 Likelihood inference 8

5.4.2 Bayesian Analysis 12

5.4.3 Other Inference Strategies 15

5.5 Models with Individual Heterogeneity in Detection 15

5.5.1 Model Mh 16

5.5.2 Individual Covariate Models 19

5.5.2.1 The full likelihood: 20

5.5.2.2 Horvitz-Thompson Estimation: 21

5.5.3 Distance Sampling 21

5.5.4 Spatial capture-recapture (SCR) models 22

5.5.4.1 The state-space 27

5.5.4.2 Inference in SCR models 27

5.6 Stratified Populations or Multi-session Models 28

5.6.1 Nonparametric Estimation 29

5.6.2 Hierarchical Capture-Recapture Models 29

5.7 Model selection and model fit 31

5.7.1 Model Selection 31

5.7.2 Goodness-of-Fit 34

5.7.3 What to do when your model does not fit 36

5.8 Open population models 37

5.9 Software Tools 40

5.10 Online Exercises 42

5.11 Future Directions 43

5.12 References 45

Chapter 6 - Estimating Survival and Cause-Specific Mortality from Continuous Time Observations
Dennis L. Murray and Guillaume Bastille-Rousseau

6.0 Summary 1

6.1 Introduction 2

6.1.1 Assumption of no handling, marking or monitoring effects 4

6.1.2 Cause of death assessment 6

6.1.3 Historical origins of survival estimation 7

6.2 Survival and hazard functions in theory 9

6.3 Developing continuous time survival datasets 13

6.3.1 Dataset structure 14

6.3.2 Right-censoring 15

6.3.3 Delayed entry and other time considerations 17

6.3.4 Sampling heterogeneity 19

6.3.5 Time-dependent covariates 20

6.4 Survival and hazard functions in practice 22

6.4.1 Mayfield and Heisey-Fuller survival estimation 22

6.4.2 Kaplan-Meier estimator 24

6.4.3 Nelson-Aalen estimator 27

6.5 Statistical analysis of survival 28

6.5.1 Simple hypothesis tests 28

6.5.2 Cox proportional hazards 28

6.5.3 Proportionality of hazards 31

6.5.4 Extended CPH 33

6.5.5 Further extensions 34

6.5.6 Parametric models 35

6.6 Cause-specific survival analysis 37

6.6.1 The case for cause-specific mortality data 37

6.6.2 Cause-specific hazards and mortality rates 40

6.6.3 Competing risks analysis 42

6.6.4 Additive versus compensatory mortality 45

6.7 Software tools 48

6.8 Online exercises 49

6.9 Future directions 49

6.10 References 53

Chapter 7 - Mark-Recapture Models for Estimation of Demographic Parameters
Brett K. Sandercock

7.0 Summary 1

7.1 Introduction 2

7.2 Live Encounter Data 5

7.3 Encounter Histories and Model Selection 8

7.4 Return Rates 13

7.5 Cormack-Jolly-Seber Models 14

7.6 The Challenge of Emigration 16

7.7 Extending the CJS Model 20

7.8 Time-Since-Marking and Transient Models 22

7.9 Temporal Symmetry Models 24

7.10 Jolly-Seber Model 26

7.11 Multilevel Models 27

7.12 Spatially-Explicit Models 29

7.13 Robust Design Models 31

7.14 Mark-Resight Models 34

7.15 Young Survival Model 37

7.16 Multistate Models 38

7.17 Multistate Models with Unobservable States 44

7.18 Multievent Models with Uncertain States 47

7.19 Joint Models 49

7.20 Integrated Population Models 51

7.21 Frequentist vs. Bayesian Methods 52

7.22 Software Tools 55

7.23 Online Exercises 56

7.24 Future Directions 57

7.25 References 58

Part 3: Population Models

Chapter 8 - Projecting Populations
Stéphane Legendre

8.0 Summary 1

8.1 Introduction 1

8.2. The life cycle graph 2

8.2.1. Description 2

8.2.2. Construction. 4

8.3. Matrix models 5

8.3.1. The projection equation 5

8.3.2. Demographic descriptors 8

8.3.3. Sensitivities 9

8.4. Accounting for the environment 11

8.5 Density dependence 12

8.5.1 Density dependent scalar models 16

8.5.2 Density dependent matrix models 17

8.5.3 Parameterizing density dependence 18

8.8.4 Density dependent sensitivities 18

8.6 Environmental stochasticity 14

8.6.1. Models of the environment 15

8.6.2 Stochastic dynamics 16

8.6.3. Parameterizing environmental stochasticity 19

8.7. Spatial structure 19

8.8. Demographic stochasticity 20

8.8.1. Branching processes 20

8.8.2. Two-sex models 22

8.9. Demographic heterogeneity 24

8.9.1 Integral projection models 25

8.10 Software Tools 26

8.11 Online Exercises 27

8.12 Future directions

8.13 References

Chapter 9 - Combining Counts of Unmarked Individuals and Demographic Data Using Integrated Population Models
Michael Schaub

9.0 Summary 1

9.1 Introduction 2

9.2 Construction of integrated population models 5

9.2.1 Development of a population model 5

9.2.2 Construction of the likelihood for different data sets 7

9.2.3 The joint likelihood 10

9.2.4 Fitting an integrated population model 13

9.3 Model extensions 14

9.3.1 Environmental stochasticity 15

9.3.2 Direct density-dependence 17

9.3.3 Open population models and other extensions 22

9.3.4 Alternative observation models 23

9.4 Inference about population dynamics 26

9.4.1 Retrospective population analyses 26

9.4.2 Population viability analyses 27

9.5 Missing data 30

9.6 Goodness-of-fit and model selection 31

9.7 Software tools 33

9.8 Online exercises 34

9.9 Future directions 34

9.10 References 37

Chapter 10 - Individual and Agent-Based Models in Population Ecology and Conservation Biology
Eloy Revilla

10.0 Summary 1

10.1 Individual and agent-based models 2

10.1.1 What an individual-based model is and what it is not 4

10.1.2 When to use an individual-based model 5

10.1.3 Criticisms on the use of IBMs: Advantages or disadvantages 6

10.2 Building the core model 8

10.2.1 Design phase: The question and the conceptual model 8

10.2.2 Implementation of the core model 10

10.2.3 Individuals and their traits 11

10.2.4 Functional relationships 11

10.2.5 The environment and its relevant properties 13

10.2.6 Time and space: domains, resolutions, boundary conditions and scheduling 14

10.2.7 Single model run, data input, model output 16

10.3 Protocols for model documentation 17

10.3.1. The Overview, Design concepts and Details (ODD) protocol 19

10.4 Model analysis and inference 21

10.4.1 Model debugging and checking the consistency of model behavior 21

10.4.2 Model structural uncertainty and sensitivity analyses 23

10.4.3 Model selection, validation and calibration 28

10.4.4 Answering your questions 34

10.5 Software tools 36

10.6 Online exercises 37

10.7 Future directions 37

10.8 References 38

Part 4: Population Genetics and Spatial Ecology

Chapter 11 - Genetic Insights into Population Ecology
Jeffery R. Row and Stephen C. Lougheed

11.0 Summary 1

11.1 Introduction 2

11.2 Types of Genetic Markers 4

11.2.1 Mitochondrial DNA 5

11.2.2 Nuclear introns 6

11.2.3 Microsatellites 6

11.2.4 Single nucleotide polymorphisms 7

11.2.5 Next Generation Sequencing (NGS) 8

11.3 Quantifying Population Structure with individual-based analyses 9

11.3.1 Bayesian clustering 11

11.3.2 Multivariate analysis of genetic data through ordinations 14

11.3.3 Spatial autocorrelation analysis 17

11.3.4 Population-level considerations 20

11.4 Estimating population size and trends 21

11.4.1 Estimating census population size 23

11.4.2 Estimating contemporary effective population size with one sample methods 24

11.4.3 Estimating contemporary effective population size with temporal sampling 26

11.4.4 Diagnosing recent population bottlenecks 28

11.5 Estimating dispersal and gene flow 30

11.5.1 Estimating dispersal and recent gene flow 32

11.5.2 Estimating sustained levels of gene flow 34

11.5.3 Network analysis of genetic connectivity 37

11.6 Software tools 39

11.6.1 Individual based analysis 40

11.6.2 Population-based population size 41

11.6.3 Dispersal and gene flow 42

11.7 Online Exercises 43

11.8 Future Directions 43

11.9 References 46

Chapter 12 - Spatial Structure in Population Data
Marie-Josée Fortin

12.0 Summary 1

12.1 Introduction 2

12.2 Data acquisition and spatial scales 6

12.3 Point data analysis 7

12.4 Abundance data analysis 10

12.5 Spatial interpolation 15

12.6 Spatial density mapping 17

12.7 Multiple scale analysis 18

12.8 Spatial regression 20

12.9 Software tools 23

12.10 Online exercises 24

12.11 Future directions 25

12.12 References 26

Chapter 13 - Animal Home Ranges: Concepts, Uses and Estimation
Jon S. Horne, John Fieberg, Luca Börger, Janet L. Rachlow, Justin M. Calabrese, and Chris H. Fleming

13.0 Summary 1

13.1 What is a Home Range? 2

13.1.1 Quantifying animal home ranges as a probability density function 5

13.1.2 Why estimate animal home ranges? 7

13.2 Estimating Home Ranges: Preliminary Considerations 8

13.3 Estimating Home Ranges: the Occurrence Distribution 12

13.3.1 Minimum Convex Polygon 13

13.3.2 Kernel smoothing 13

13.3.3 Models based on animal movements 15

13.3.4 Estimation from a 1-dimensional path 18

13.4 Estimating Home Ranges: the Range Distribution 19

13.4.1 Bivariate Normal Models 19

13.4.2 The Synoptic Model 20

13.4.3 Mechanistic models 23

13.4.4 Kernel Smoothing 25

13.5 Software tools 26

13.6 Online exercises 26

13.7 Future directions 27

13.7.1 Choosing a Home Range Model 27

13.7.2 The Future of Home Range Modeling 29

13.8 References 31

Chapter 14 - Analysis of Resource Selection by Animals
Joshua J. Millspaugh, Christopher T. Rota, Robert A. Gitzen, Robert A. Montgomery, Thomas W. Bonnot, Jerrold L. Belant, Christopher R. Ayers, Dylan C. Kesler, David A. Eads, and Catherine M. Bodinof Jachowski

14.0 Summary 2

14.1 Introduction 3

14.2 Definitions 7

14.2.1 Terminology and currencies of use and availability 7

14.2.2 Use-availability, paired use-availability, use and non-use (case-control), and use-only designs 9

14.2.3 Differences between RSF, RSPF, and RUF 10

14.3 Considerations in Studies of Resource Selection 11

14.3.1 Two Important Sampling Considerations: Selecting Sample Units and Time of Day 12

14.3.2 Estimating the number of animals and locations needed 13

14.3.3 Location error and fix rate bias resource selection studies 15

14.3.4 Consideration of animal behavior in resource selection studies 16

14.3.5 Biological seasons in resource selection studies 17

14.3.6 Scaling in resource selection studies 18

14.3.7 Linking resource selection to fitness 20

14.4 Methods of Analysis and Examples 20

14.4.1 Compositional analysis 21

14.4.2 Logistic regression 22

14.4.3 Sampling Designs for Logistic Regression Modeling 24

14.4.3.1 Random sampling of units within the study area 24

14.4.3.2 Random sampling of used and unused units 25

14.4.3.3 Random sample of used and available sampling units 28

14.4.4 Discrete choice models 31

14.4.5 Poisson regression 33

14.4.6 Resource Utilization Function 35

14.4.7 Ecological Niche Factor Analysis 37

14.4.8 Mixed models 39

14.5 Software Tools 40

14.6 Online Exercises 41

14.7 Future Directions 42

14.8 References 45

Chapter 15 - Species Distribution Modeling
Daniel H. Thornton and Michael J.L. Peers

15.0 Summary 1

15.1 Introduction 2

15.1.1 Relationship of distribution to other population parameters 3

15.1.2 Species distribution models and the niche concept 6

15.2 Building a species distribution model 9

15.2.1 Species data 9

15.2.2 Environmental data 11

15.2.3 Model fitting 14

15.2.4 Interpretation of model output 18

15.2.5 Model accuracy 20

15.3 Common problems when fitting species distribution models 26

15.3.1 Overfitting 26

15.3.2 Sample selection bias 27

15.3.3 Background selection 30

15.3.4 Extrapolation 32

15.3.5 Violation of assumptions 33

15.4 Recent advances 34

15.4.1 Incorporating dispersal 35

15.4.2 Incorporating population dynamics 37

15.4.3 Incorporating biotic interactions 39

15.5 Software tools 42

15.5.1 Fitting and evaluation of models 42

15.5.2 Incorporating dispersal or population dynamics 43

15.6 Online exercises 44

15.7 Future directions 44

15.8 References 48

Part 5: Software Tools

Chapter 16 - The R Software for Data Analysis and Modeling
Clément Calenge

16.0 Summary 1

16.1 An introduction to R 2

16.1.1 The nature of the R language 2

16.1.2 Qualities and Limits: 4

16.1.3 R for ecologists 5

16.1.4 R is an environment 6

16.2 Basics of R 8

16.2.1 Several basic modes of data 9

16.2.2 Several basic types of objects 11

16.2.3 Finding help and installing new packages 18

16.2.4 How to write a function 23

16.2.5 The for loop 26

16.2.6 The concept of attributes and S3 data classes 27

16.2.7 Two important classes: the class factor and the class data.frame 34

16.2.8 Drawing graphics 37

16.2.9 S4 classes: why it is useful to understand them 40

16.3 Online exercises 45

16.4 Final directions 45

16.5 References 47