Advanced Distance Sampling

Name: Advanced Distance Sampling | Estimating abundance of biological populations
Brand: OUP Oxford
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Estimating abundance of biological populations

S. T. Buckland D. R. Anderson K. P. Burnham J. L. Laake D. L. Borchers L. Thomas(Editor)

OUP Oxford (Publisher)

1st Edition

Published on 19. August 2004

435 pages

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978-0-19-154519-1 (ISBN)

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Intro
Contents
1 Introduction to advanced distance sampling
2 General formulation for distance sampling
2.1 Introduction
2.2 CDS revisited
2.2.1 Conventional line transect estimator
2.2.2 Conventional point transect estimator
2.3 Horvitz-Thompson: a versatile estimator
2.3.1 Animals that occur as individuals
2.3.2 Animals that occur in clusters
2.3.3 CDS estimators
2.4 Maximum likelihood estimation
2.4.1 'Covered' animals
2.4.2 Random detection with known probability
2.4.3 CDS likelihoods
2.5 Summary so far and preview of advances
2.5.1 Summary
2.5.2 Preview of advances
2.6 Advanced methods for detection function estimation
2.6.1 Multiple covariate distance sampling
2.6.2 Mark-recapture distance sampling
2.6.3 Estimation when p(y) is unknown
2.7 Estimating animal density surfaces
2.7.1 The count method
2.7.2 The waiting distance method
2.7.3 Cluster size surface estimation
2.8 Survey design
2.8.1 Likelihood-based inference
2.8.2 Design-based inference
2.8.3 Adaptive distance sampling
2.9 Model selection
2.10 Summary
3 Covariate models for the detection function
3.1 Introduction
3.2 A conditional likelihood framework for distance sampling
3.3 Line transect sampling
3.3.1 The conditional likelihood
3.3.2 Incorporating covariates into semiparametric models for the detection function
3.3.3 Abundance estimation
3.4 Point transect sampling
3.5 Example
3.6 Discussion
4 Spatial distance sampling models
4.1 Introduction
4.2 Spatial line transect models
4.2.1 Deriving a likelihood
4.2.2 A likelihood based on inter-detection distances
4.2.3 Clustered populations
4.3 Practical implementations of spatial line transect models
4.3.1 A waiting distance model
4.3.2 A count model
4.4 Spatial distribution of Antarctic minke whales
4.5 Spatial point transect models
4.5.1 Deriving a likelihood
4.5.2 A point transect count model
4.6 Discussion
5 Temporal inferences from distance sampling surveys
5.1 Introduction
5.2 Concepts
5.2.1 Sampling and population variation
5.2.2 Sampling covariance
5.2.3 Empirical and process models
5.2.4 Trend
5.2.5 Abundance as a fixed or random quantity
5.3 Trend estimation from global abundance estimates
5.3.1 Graphical exploration
5.3.2 Linear trend models
5.3.3 Smoothing
5.3.4 Trend estimation when samples covary
5.4 Spatio-temporal analysis
5.4.1 Transect-level models of trend
5.4.2 Spatio-temporal modelling
5.5 Process models
5.5.1 State-space models
5.5.2 Generalizing state-space models
5.6 Other analysis methods
5.6.1 Time series methods
5.6.2 Quality control methods
5.7 Survey design
5.7.1 Repeating transects
5.7.2 Sample size
5.7.3 Planning long-term studies
6 Methods for incomplete detection at distance zero
6.1 Introduction
6.2 Likelihood and Horvitz-Thompson
6.2.1 Constant detection probability
6.2.2 Detection probability changing with distance
6.2.3 Independence issues
6.2.4 Multiple covariates
6.2.5 Unobserved heterogeneity
6.3 State and observation models
6.3.1 State models
6.3.2 Observation models
6.3.3 Observation configurations
6.4 Example data
6.5 Estimation for independent configuration
6.5.1 Distance only
6.5.2 Distance and covariates
6.6 Estimation for trial configuration
6.6.1 Distance and covariates
6.6.2 Distance, covariates, and responsive movement
6.7 Estimation for removal configuration
6.8 Dealing with availability bias
6.8.1 Static availability
6.8.2 Hazard-rate models for dynamic availability
6.8.3 Discrete availability: animal-based
6.8.4 Discrete availability: cue-based
6.8.5 Intermittent availability
6.8.6 Design-based availability estimation
6.9 Special topics
6.9.1 Uncertain duplicate identification
6.9.2 When should double-observer methods be used?
6.10 Field methods
6.10.1 Marked animals
6.10.2 Observation configuration
6.10.3 Data collection and recording
7 Design of distance sampling surveys and Geographic Information Systems
7.1 The potential role of GIS in survey design
7.2 Automated survey design
7.2.1 Point transect design
7.2.2 Line transect design using lines of fixed length
7.2.3 Line transect design using lines that span the full width of the survey region
7.2.4 Zigzag samplers
7.3 Estimation for uneven coverage probability designs
7.3.1 Objects that occur singly
7.3.2 Objects that occur in clusters
7.3.3 Variance estimation
7.4 Choosing between survey designs by simulation
8 Adaptive distance sampling surveys
8.1 Introduction
8.2 Design-unbiased adaptive point transect surveys
8.2.1 Survey design
8.2.2 Estimation
8.2.3 Simulated example
8.2.4 Discussion
8.3 Design-unbiased adaptive line transect surveys
8.3.1 Survey design
8.3.2 Estimation
8.3.3 Discussion
8.4 Fixed-effort adaptive line transect surveys
8.4.1 Survey design
8.4.2 Estimation
8.4.3 Simulation
8.4.4 Discussion
9 Passive approaches to detection in distance sampling
9.1 Introduction
9.2 Trapping webs
9.2.1 Density estimation
9.2.2 Including data from recaptures
9.2.3 Design of trapping webs
9.2.4 An example
9.2.5 A critique of the trapping web
9.3 Trapping line transects
9.3.1 Density estimation
9.3.2 Including data from recaptures
9.3.3 Design of trapping line transects
9.3.4 An example
9.3.5 A critique of the trapping line transect
9.4 Discussion and summary
10 Assessment of distance sampling estimators
10.1 Introduction
10.1.1 Notation
10.2 Estimation framework
10.3 Model and design
10.3.1 Model-based inference
10.3.2 Design-based inference
10.3.3 Distance sampling: a composite approach
10.4 Simulation framework
10.4.1 Testing the design
10.4.2 Testing the model
10.4.3 Testing the full line transect estimation procedure
10.5 Example: testing the design
10.5.1 Testing equal coverage designs for var( N)
10.5.2 A design without equal coverage probability
10.6 Example: non-uniformity within the strip
10.6.1 Estimation of N[sub(c)]
10.6.2 Asymptotic result when ? is estimated
10.7 Example: full estimation procedure
10.8 Trial by simulation: a completely model-based approach
10.9 Summary
11 Further topics in distance sampling
11.1 Distance sampling in three dimensions
11.1.1 Three-dimensional line transect sampling
11.1.2 Three-dimensional point transect sampling
11.2 Conventional distance sampling: full likelihood examples
11.2.1 Line transects: simple examples
11.2.2 Point transects: simple examples
11.2.3 Some numerical confidence interval comparisons
11.3 Line transect surveys with random line length
11.3.1 Introduction
11.3.2 Line transect sampling with fixed n and random L, under Poisson object distribution
11.3.3 Technical comments
11.3.4 Discussion
11.4 Models for the search process
11.4.1 Continuous hazard-rate models
11.4.2 Discrete hazard-rate models
11.4.3 Further modelling of the detection process
11.5 Combining mark-recapture and distance sampling surveys
11.6 Combining removal methods and distance sampling
11.6.1 Introduction
11.6.2 Combining removal methods with distance sampling
11.7 Point transect sampling of cues
11.7.1 Introduction
11.7.2 Estimation
11.8 Migration counts
11.8.1 Background
11.8.2 Modelling migration rate
11.8.3 Modelling detection probabilities
11.8.4 An example: gray whales
11.9 Estimation with distance measurement errors
11.9.1 Conventional distance sampling: g(0) = 1
11.9.2 Independent multiplicative measurement errors
11.9.3 Mark-recapture distance sampling: p(0) & 1
11.9.4 Maximum likelihood vs pdf correction approach
11.10 Relating object abundance to population abundance for indirect sampling
11.10.1 Introduction
11.10.2 Discrete-time modelling
11.10.3 Continuous-time modelling
11.10.4 Conclusions
11.11 Goodness of fit tests and q-q plots
11.11.1 Quantile-quantile plots
11.11.2 Kolmogorov-Smirnov test
11.11.3 Cramér-von Mises test
11.11.4 The Cramér-von Mises family of tests
11.12 Pooling robustness
References
Index
A
B
C
D
E
F
G
H
I
K
L
M
N
P
Q
R
S
T
V
Z

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Advanced Distance Sampling

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