Introduction to distance sampling : estimating abundance of biological populations /

Introduction to distance sampling : estimating abundance of biological populations /

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Bibliographic Details
Imprint:Oxford ; New York : Oxford University Press, 2001.
Description:xv, 432 p. : ill., map ; 24 cm.
Language:English
Subject:
Animal populations -- Statistical methods.
Sampling (Statistics)
Animal populations -- Estimates.
Sampling (Statistics)
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/4543579
Hidden Bibliographic Details
Other authors / contributors:Buckland, S. T. (Stephen T.)
ISBN:019850649X
0198509278 (pbk.)
Notes:Includes bibliographical references (p. [381]-420) and index.
Table of Contents:
  • 1. Introductory concepts
  • 1.1. Introduction
  • 1.2. Distance sampling methods
  • 1.2.1. Quadrat sampling
  • 1.2.2. Strip transect sampling
  • 1.2.3. Line transect sampling
  • 1.2.4. Point counts
  • 1.2.5. Point transect sampling
  • 1.2.6. Trapping webs
  • 1.2.7. Cue counting
  • 1.2.8. Dung counts
  • 1.2.9. Related techniques
  • 1.3. The detection function
  • 1.4. Range of applications
  • 1.4.1. Objects of interest
  • 1.4.2. Method of transect coverage
  • 1.4.3. Clustered populations
  • 1.5. Types of data
  • 1.5.1. Ungrouped data
  • 1.5.2. Grouped data
  • 1.5.3. Data truncation
  • 1.5.4. Units of measurement
  • 1.5.5. Ancillary data
  • 1.6. Known constants and parameters
  • 1.6.1. Known constants
  • 1.6.2. Parameters
  • 1.7. Assumptions
  • 1.8. Fundamental concept
  • 1.9. Detection of objects
  • 1.9.1. Cue production
  • 1.9.2. Observer effectiveness
  • 1.9.3. Environment
  • 1.10. History of methods
  • 1.10.1. Line transects
  • 1.10.2. Point transects
  • 1.11. Program Distance
  • 2. Assumptions and modelling philosophy
  • 2.1. Assumptions
  • 2.1.1. Assumption 1: objects on the line or point are detected with certainty
  • 2.1.2. Assumption 2: objects are detected at their initial location
  • 2.1.3. Assumption 3: measurements are exact
  • 2.1.4. Other assumptions
  • 2.2. Fundamental models
  • 2.2.1. Line transects
  • 2.2.2. Point transects
  • 2.2.3. Summary
  • 2.3. Philosophy and strategy
  • 2.3.1. Model robustness
  • 2.3.2. Shape criterion
  • 2.3.3. Efficiency
  • 2.3.4. Model fit
  • 2.3.5. Test power
  • 2.4. Robust models
  • 2.5. Some analysis guidelines
  • 2.5.1. Exploratory phase
  • 2.5.2. Model selection
  • 2.5.3. Final analysis and inference
  • 3. Statistical theory
  • 3.1. General formula
  • 3.1.1. Standard distance sampling
  • 3.1.2. Line transect sampling
  • 3.1.3. Point transect sampling
  • 3.1.4. Distance sampling with multipliers
  • 3.2. The key function formulation for distance data
  • 3.3. Maximum likelihood methods
  • 3.3.1. Ungrouped data
  • 3.3.2. Grouped data
  • 3.3.3. Special cases
  • 3.3.4. The half-normal detection function
  • 3.3.5. Constrained maximum likelihood estimation
  • 3.4. Choice of model
  • 3.4.1. Criteria for robust estimation
  • 3.4.2. Akaike's Information Criterion
  • 3.4.3. The likelihood ratio test
  • 3.4.4. Goodness of fit
  • 3.5. Estimation for clustered populations
  • 3.5.1. Truncation
  • 3.5.2. Stratification by cluster size
  • 3.5.3. Weighted average of cluster sizes
  • 3.5.4. Regression estimators
  • 3.5.5. Use of covariates
  • 3.5.6. Replacing clusters by individual objects
  • 3.6. Density, variance and interval estimation
  • 3.6.1. Basic formulae
  • 3.6.2. Replicate lines or points
  • 3.6.3. The jackknife
  • 3.6.4. The bootstrap
  • 3.6.5. Estimating change in density
  • 3.6.6. A finite population correction factor
  • 3.7. Stratification and covariates
  • 3.7.1. Stratification
  • 3.7.2. Covariates
  • 3.8. Efficient simulation of distance data
  • 3.8.1. The general approach
  • 3.8.2. The simulated line transect data of Chapter 4
  • 3.8.3. The simulated size-biased point transect data of Chapter 5
  • 3.8.4. Discussion
  • 3.9. Exercises
  • 4. Line transects
  • 4.1. Introduction
  • 4.2. Example data
  • 4.3. Truncation
  • 4.3.1. Right-truncation
  • 4.3.2. Left-truncation
  • 4.4. Estimating the variance in sample size
  • 4.5. Analysis of grouped or ungrouped data
  • 4.6. Model selection
  • 4.6.1. The models
  • 4.6.2. Akaike's Information Criterion
  • 4.6.3. Likelihood ratio tests
  • 4.6.4. Goodness of fit
  • 4.7. Estimation of density and measures of precision
  • 4.7.1. The standard analysis
  • 4.7.2. Ignoring information from replicate lines
  • 4.7.3. Bootstrap variances and confidence intervals
  • 4.7.4. Satterthwaite degrees of freedom for confidence intervals
  • 4.8. Estimation when the objects are in clusters
  • 4.8.1. Observed cluster size independent of distance
  • 4.8.2. Observed cluster size dependent on distance
  • 4.9. Assumptions
  • 4.9.1. Independence
  • 4.9.2. Detection on the line
  • 4.9.3. Movement prior to detection
  • 4.9.4. Inaccuracy in distance measurements
  • 4.10. Summary
  • 4.11. Exercises
  • 5. Point transects
  • 5.1. Introduction
  • 5.2. Example data
  • 5.3. Truncation
  • 5.3.1. Right-truncation
  • 5.3.2. Left-truncation
  • 5.4. Estimating the variance in sample size
  • 5.5. Analysis of grouped or ungrouped data
  • 5.6. Model selection
  • 5.6.1. The models
  • 5.6.2. Akaike's Information Criterion
  • 5.6.3. Likelihood ratio tests
  • 5.6.4. Goodness of fit
  • 5.7. Estimation of density and measures of precision
  • 5.7.1. The standard analysis
  • 5.7.2. Bootstrap variances and confidence intervals
  • 5.8. Estimation when the objects are in clusters
  • 5.8.1. Standard method with additional truncation
  • 5.8.2. Replacement of clusters by individuals
  • 5.8.3. Stratification
  • 5.8.4. Regression estimator
  • 5.9. Assumptions
  • 5.10. Summary
  • 5.11. Exercises
  • 6. Related methods
  • 6.1. Introduction
  • 6.2. Dung and nest surveys
  • 6.2.1. Background
  • 6.2.2. Field methods
  • 6.2.3. Analysis
  • 6.2.4. Assumptions
  • 6.3. Line transect surveys for objects that are not continuously available for detection
  • 6.3.1. Periods of detectability interspersed with periods of unavailability
  • 6.3.2. Objects that give discrete cues
  • 6.4. Cue counting
  • 6.4.1. Introduction
  • 6.4.2. Density estimation
  • 6.4.3. Assumptions
  • 6.4.4. Example
  • 6.5. Distance sampling surveys for fast-moving objects
  • 6.5.1. Line transect surveys
  • 6.5.2. Point transect surveys
  • 6.6. Other models
  • 6.6.1. Binomial models
  • 6.6.2. Estimators based on the empirical cdf
  • 6.6.3. Estimators based on shape restrictions
  • 6.6.4. Kernel estimators
  • 6.6.5. Hazard-rate models
  • 6.7. Distance sampling surveys when the observed area is incompletely covered
  • 6.8. Trapping webs
  • 6.8.1. Survey design and field methods
  • 6.8.2. Assumptions
  • 6.8.3. Estimation of density
  • 6.8.4. Monte Carlo simulations
  • 6.8.5. A simple example
  • 6.8.6. Darkling beetle surveys
  • 6.9. Point-to-object and nearest neighbour methods
  • 6.10. Exercises
  • 7. Study design and field methods
  • 7.1. Introduction
  • 7.2. Survey design
  • 7.2.1. Transect layout
  • 7.2.2. Sample size
  • 7.3. Survey protocol and searching behaviour
  • 7.3.1. Line transects
  • 7.3.2. Point transects
  • 7.4. Data measurement and recording
  • 7.4.1. Distance measurement
  • 7.4.2. Angle measurement
  • 7.4.3. Distance measurement error
  • 7.4.4. Cluster size
  • 7.4.5. Line length measurement
  • 7.4.6. Ancillary data
  • 7.4.7. Data recording
  • 7.5. Training observers
  • 7.6. Aerial surveys
  • 7.6.1. Aircraft and survey characteristics
  • 7.6.2. Search and survey protocol
  • 7.6.3. Distance measurement
  • 7.7. Marine shipboard surveys
  • 7.7.1. Vessel and survey characteristics
  • 7.7.2. Search and survey protocol
  • 7.7.3. Distance measurement
  • 7.8. Land-based surveys
  • 7.8.1. Surveys of small objects
  • 7.8.2. Stratification by habitat
  • 7.8.3. Permanent transects and repeat transects
  • 7.8.4. Cut transects
  • 7.8.5. Roads, tracks and paths as transects
  • 7.8.6. Spotlight and thermal imager surveys
  • 7.8.7. Objects detected away from the line
  • 7.8.8. Bird surveys
  • 7.8.9. Surveys in riparian habitats
  • 7.9. Special circumstances
  • 7.9.1. Multi-species surveys
  • 7.9.2. Surveys of animals that occur at high densities
  • 7.9.3. One-sided transects
  • 7.9.4. Uneven terrain and contour transects
  • 7.9.5. Uncertain detection on the trackline
  • 7.10. Field comparisons between line transects, point transects and mapping censuses
  • 7.10.1. Breeding birds in Californian coastal scrub
  • 7.10.2. Breeding birds in Sierran subalpine forest
  • 7.10.3. Bobolink surveys in New York state
  • 7.10.4. Breeding birds in Californian oak-pine woodlands
  • 7.10.5. Breeding birds along the Colorado River
  • 7.10.6. Birds of Miller Sands Island, Oregon
  • 7.10.7. Concluding remarks
  • 7.11. Exercises
  • 8. Illustrative examples
  • 8.1. Introduction
  • 8.2. Lake Huron brick data
  • 8.3. Wooden stake data
  • 8.4. Studies of nest density
  • 8.4.1. Spatial distribution of duck nests
  • 8.4.2. Estimation of density
  • 8.4.3. Nest detection in differing habitat types
  • 8.4.4. Models for the detection function g(x)
  • 8.4.5. Estimating trend in nest numbers
  • 8.5. Fin whale abundance in the North Atlantic
  • 8.6. House wren densities in South Platte River bottomland
  • 8.7. Songbird point transect surveys in Arapaho NWR
  • 8.8. Assessing the effects of habitat on density
  • Bibliography
  • Common and scientific names of plants and animals
  • Glossary of notation and abbreviations
  • Index
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