Корично изображение Електронна книга

Occupancy estimation and modeling : inferring patterns and dynamics of species occurrence /

Други автори: MacKenzie, Darryl I.
Формат: Електронна книга
Език: English
Публикувано: Amsterdam ; Boston : Elsevier/Academic Press, ℗♭2006.
Предмети:
Animal populations > Estimates.
Animal populations > Mathematical models.
Animals.
Population Dynamics.
Models, Statistical.
SCIENCE > Life Sciences > Zoology > General.
Electronic books.
Онлайн достъп: http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=166224
Подобни документи: Print version:: Occupancy estimation and modeling.
Съдържание:
  • Cover
  • Table of Contents
  • Preface
  • Acknowledgments
  • CHAPTER 1: Introduction
  • 1.1. OPERATIONAL DEFINITIONS
  • 1.2. SAMPLING ANIMAL POPULATIONS AND COMMUNITIES: GENERAL PRINCIPLES
  • WHY?
  • WHAT?
  • HOW?
  • 1.3. INFERENCE ABOUT DYNAMICS AND CAUSATION
  • GENERATION OF SYSTEM DYNAMICS
  • STATICS AND PROCESS VS. PATTERN
  • 1.4. DISCUSSION
  • CHAPTER 2: Occupancy in Ecological Investigations
  • 2.1. GEOGRAPHIC RANGE
  • 2.2. HABITAT RELATIONSHIPS AND RESOURCE SELECTION
  • 2.3. METAPOPULATION DYNAMICS
  • INFERENCE BASED ON SINGLE-SEASON DATA
  • INFERENCE BASED ON MULTIPLE-SEASON DATA
  • 2.4. LARGE-SCALE MONITORING
  • 2.5. MULTISPECIES OCCUPANCY DATA
  • INFERENCE BASED ON STATIC OCCUPANCY PATTERNS
  • INFERENCE BASED ON OCCUPANCY DYNAMICS
  • 2.6. DISCUSSION
  • CHAPTER 3: Fundamental Principles of Statistical Inference
  • 3.1. DEFINITIONS AND KEY CONCEPTS
  • RANDOM VARIABLES, PROBABILITY DISTRIBUTIONS, AND THE LIKELIHOOD FUNCTION
  • EXPECTED VALUES
  • INTRODUCTION TO METHODS OF ESTIMATION
  • PROPERTIES OF POINT ESTIMATORS
  • COMPUTER-INTENSIVE METHODS
  • 3.2. MAXIMUM LIKELIHOOD ESTIMATION METHODS
  • MAXIMUM LIKELIHOOD ESTIMATORS
  • PROPERTIES OF MAXIMUM LIKELIHOOD ESTIMATORS
  • VARIANCES, COVARIANCE (AND STANDARD ERROR) ESTIMATION
  • CONFIDENCE INTERVAL ESTIMATORS
  • 3.3. BAYESIAN METHODS OF ESTIMATION
  • THEORY
  • COMPUTING METHODS
  • 3.4. MODELING AUXILIARY VARIABLES
  • THE LOGIT LINK FUNCTION
  • ESTIMATION
  • 3.5. HYPOTHESIS TESTING
  • BACKGROUND AND DEFINITIONS
  • LIKELIHOOD RATIO TESTS
  • GOODNESS OF FIT TESTS
  • 3.6. MODEL SELECTION
  • THE AKAIKE INFORMATION CRITERION (AIC)
  • GOODNESS OF FIT AND OVERDISPERSION
  • QUASI-AIC
  • MODEL AVERAGING AND MODEL SELECTION UNCERTAINTY
  • 3.7. DISCUSSION
  • CHAPTER 4: Single-species, Single-season Occupancy Models
  • 4.1. THE SAMPLING SITUATION
  • 4.2. ESTIMATION OF OCCUPANCY IF PROBABILITY OF DETECTION IS 1 OR KNOWN WITHOUT ERROR
  • 4.3. TWO-STEP AD HOC APPROACHES
  • GEISSLER-FULLER METHOD
  • AZUMA-BALDWIN-NOON METHOD
  • NICHOLS-KARANTH METHOD
  • 4.4. MODEL-BASED APPROACH
  • BUILDING A MODEL
  • ESTIMATION
  • EXAMPLE: BLUE-RIDGE TWO-LINED SALAMANDERS
  • MISSING OBSERVATIONS
  • COVARIATE MODELING
  • VIOLATIONS OF MODEL ASSUMPTIONS
  • ASSESSING MODEL FIT
  • EXAMPLES
  • 4.5. ESTIMATING OCCUPANCY FOR A FINITE POPULATION OR SMALL AREA
  • PREDICTION OF UNOBSERVED OCCUPANCY STATE
  • A BAYESIAN FORMULATION OF THE MODEL
  • BLUE-RIDGE TWO-LINED SALAMANDERS REVISITED
  • 4.6. DISCUSSION
  • CHAPTER 5: Single-species, Single-season Models with Heterogeneous Detection Probabilities
  • 5.1. SITE OCCUPANCY MODELS WITH HETEROGENEOUS DETECTION
  • GENERAL FORMULATION
  • FINITE MIXTURES
  • CONTINUOUS MIXTURES
  • ABUNDANCE MODELS
  • MODEL FIT
  • 5.2. EXAMPLE: BRE.

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