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Analysis and Management of Animal Populations [Kietas viršelis]

4.50/5 (12 ratings by Goodreads)
(U.S. Geological Survey, Patuxent Wildlife Research Center, Laurel, MD, USA), (Georgia Cooperative Fish and Wildlife Research Unit, University of Georgia, A), (U.S. Geological Survey, Cooperative Research Units, Reston, Virginia, U.S.A.)
  • Formatas: Hardback, 817 pages, aukštis x plotis: 279x216 mm, weight: 2540 g, Illustrations
  • Išleidimo metai: 16-May-2002
  • Leidėjas: Academic Press Inc
  • ISBN-10: 0127544062
  • ISBN-13: 9780127544069
Kitos knygos pagal šią temą:
Analysis and Management of Animal PopulationsDidesnis paveikslėlis
  • Formatas: Hardback, 817 pages, aukštis x plotis: 279x216 mm, weight: 2540 g, Illustrations
  • Išleidimo metai: 16-May-2002
  • Leidėjas: Academic Press Inc
  • ISBN-10: 0127544062
  • ISBN-13: 9780127544069
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9780127544069.html
Raktažodžiai:
Analysis and Management of Animal Populations deals with the processes involved in making informed decisions about the management of animal populations. It covers the modeling of population responses to management actions, the estimation of quantities needed in the modeling effort, and the application of these estimates and models to the development of sound management decisions. The book synthesizes and integrates in a single volume the methods associated with these themes, as they apply to ecological assessment and conservation of animal populations.

Key Features
*Integrates population modeling, parameter estimation and decision-theoretic approaches to management in a single, cohesive framework
* Provides authoritative, state-of-the-art descriptions of quantitative approaches to modeling, estimation and decision-making
* Emphasizes the role of mathematical modeling in the conduct of science and management
* Utilizes a unifying biological context, consistent mathematical notation, and numerous biological examples

Recenzijos

"...certainly a 'must-have' for any Institute library in the relevant disciplines, and might usefully adorn the shelves of the more statistically literate researcher." --IBIS, 2004

"...the book ecologists have long sought to help them find their way around in the huge and rather technical literature on population ecology. Students will find it a gold mine. ...Professional ecologists will find a solid reference book within which to look up things. And managers and conservation biologists will find a book in which they can learn what the theoretical platform for management of wildlife populations ought to be." --Nils Chr. Stenseth for SCIENCE, October 2002

"This book is as important for its conceptual framework as for its comprehensive review of modern methods of population analysis. Modelling and data analysis are too often viewed as separate from, even at odds with, each other. The authors demonstrate convincingly that this is not the case, by an integrated treatment of population models, the statistics that link them to data, and the decision analyses that make them useful in management." --Hal Caswell, Woods Hole Oceanographic Institution, January 2002

"This well-organized book thoroughly covers an excellent variety of important topics. It will be useful as a textbook in multiple undergraduate and graduate-level courses and it will be a key reference book for working wildlife professionals. The authors have successfully accomplished a challenging task: it integrates reviews of population dynamics theory, modern estimation methods, and how to make optimal management decisions in the real world." --Jay Rotella, Ecology Dept, Montana State University, January 2002

"This is a major synthesis of literature covering nearly all aspects of population analysis and management, including sampling, estimation, model choice, analysis, and optimal decision-making." --David R. Anderson, Colorado Cooperative Fish & Wildlife Research Unit, December 2001

"The three authors of this book are preeminent population analysts because of their ability to link innovative quantitative approaches to fundamental understanding of population ecology. They have written a book that will be 'one-stop-shopping' for teachers and students of population dynamics, modeling, and estimation." --William R. Clark, Dept of Animal Ecology, Iowa State University, December 2001

"This is an important book, and should be on the desk - as opposed to sitting on the shelf - of anyone claiming to be involved in research on the dynamics and management of wild populations. Buy it. Study it." --Evan Cooch, Dept of Natural Resources, Cornell University, December 2001

"This books provides an essential synthesis of material relevant to the analysis and management of animal populations. ...The authors effectively capture and marry concepts that are essential for sound analysis and management of animal populations." --Mark Lindberg, Dept of Biology and Wildlife, University of Alaska, Fairbanks, December 2001

"This book will be an essential addition to any population biologist's library. The integration of modelling, statistical estimation, and decision analysis to solve applied problems is very compelling." --Kenneth H. Pollock, North Carolina State University, December 2001

Daugiau informacijos

Key Features *Integrates population modeling, parameter estimation and decision-theoretic approaches to management in a single, cohesive framework * Provides authoritative, state-of-the-art descriptions of quantitative approaches to modeling, estimation and decision-making * Emphasizes the role of mathematical modeling in the conduct of science and management * Utilizes a unifying biological context, consistent mathematical notation, and numerous biological examples
Preface xiii
Acknowledgments xvii
PART I FRAMEWORK FOR MODELING, ESTIMATION, AND MANAGEMENT OF ANIMAL POPULATIONS
Introduction to Population Ecology
Some Definitions
3(1)
Population Dynamics
4(1)
Factors Affecting Populations
4(2)
Management of Animal Populations
6(1)
Individuals, Fitness, and Life History Characteristics
7(2)
Community Dynamics
9(1)
Discussion
9(2)
Scientific Process in Animal Ecology
Causation in Animal Ecology
11(1)
Approaches to the Investigation of Causes
12(1)
Scientific Methods
13(3)
Hypothesis Confirmation
16(1)
Inductive Logic in Scientific Method
17(1)
Statistical Inference
18(1)
Investigating Complementary Hypotheses
18(1)
Discussion
19(3)
Models and the Investigation of Populations
Types of Biological Models
22(1)
Keys to Successful Model Use
22(1)
Uses of Models in Population Biology
23(5)
Determinants of Model Utility
28(2)
Hypotheses, Models, and Science
30(1)
Discussion
31(3)
Estimation and Hypothesis Testing in Animal Ecology
Statistical Distributions
34(8)
Parameter Estimation
42(8)
Hypothesis Testing
50(5)
Information-Theoretic Approaches
55(2)
Bayesian Extension of Likelihood Theory
57(1)
Discussion
58(2)
Survey Sampling and the Estimation of Population Parameters
Sampling Issues
60(1)
Features of a Sampling Design
61(1)
Simple Random and Stratified Random Sampling
62(5)
Other Sampling Approaches
67(7)
Common Problems in Sampling Designs
74(2)
Discussion
76(4)
Design of Experiments in Animal Ecology
Principles of Experimental Design
80(3)
Completely Randomized Designs
83(6)
Randomized Block Designs
89(2)
Covariation and Analysis of Covariance
91(1)
Hierarchical Designs
92(5)
Random Effects and Nested Designs
97(3)
Statistical Power and Experimental Design
100(2)
Constrained Experimental Designs and Quasi-Experiments
102(4)
Discussion
106(7)
PART II DYNAMIC MODELING OF ANIMAL POPULATIONS
Principles of Model Development and Assessment
Modeling Goals
113(1)
Attributes of Population Models
114(3)
Describing Population Models
117(5)
Constructing a Population Model
122(4)
Model Assessment
126(5)
A Systematic Approach to the Modeling of Animal Populations
131(3)
Discussion
134(2)
Traditional Models of Population Dynamics
Density-Independent Growth-The Exponential Model
136(3)
Density-Dependent Growth-The Logistic Model
139(2)
Cohort Models
141(2)
Models with Age Structure
143(14)
Models with Size Structure
157(2)
Models with Geographic Structure
159(2)
Lotka-Volterra Predator-Prey Models
161(3)
Models of Competing Populations
164(6)
A General Model for Interacting Species
170(1)
Discussion
171(3)
Model Identification with Time Series Data
Model Identification Based on Ordinary Least Squares
174(2)
Other Measures of Model Fit
176(2)
Correlated Estimates of Population Size
178(1)
Optimal Identification
178(1)
Identifying Models with Population Size as a Function of Time
179(2)
Identifying Models Using Lagrangian Multipliers
181(1)
Stability of Parameter Estimates
181(1)
Identifying System Properties in the Absence of a Specified Model
182(2)
Discussion
184(5)
Stochastic Processes in Population Models
Bernoulli Counting Processes
189(3)
Poisson Counting Processes
192(5)
Discrete Markov Processes
197(5)
Continuous Markov Processes
202(3)
Semi-Markov Processes
205(2)
Markov Decision Processes
207(3)
Brownian Motion
210(3)
Other Stochastic Processes
213(7)
Discussion
220(3)
The Use of Models in Conservation and Management
Dynamics of Harvested Populations
223(8)
Conservation and Extinction of Populations
231(6)
Discussion
237(5)
PART III ESTIMATION METHODS FOR ANIMAL POPULATIONS
Estimating Abundance Based on Counts
Overview of Abundance Estimation
242(1)
A Canonical Population Estimator
243(2)
Population Censuses
245(1)
Complete Detectability of Individuals on Sample Units of Equal Area
245(2)
Complete Detectability of Individuals on Sample Units of Unequal Area
247(3)
Partial Detectability of Individuals on Sample Units
250(7)
Indices to Population Abundance or Density
257(4)
Discussion
261(2)
Estimating Abundance with Distance-Based Methods
Point-to-Object Methods
263(2)
Line Transect Sampling
265(13)
Point Sampling
278(3)
Design of Line Transect and Point Sampling Studies
281(5)
Other Issues
286(1)
Discussion
287(3)
Estimating Abundance for Closed Populations with Mark-Recapture Methods
Two-Sample Lincoln-Petersen Estimator
290(6)
K-Sample Capture-Recapture Models
296(18)
Density Estimation with Capture-Recapture
314(6)
Removal Methods
320(5)
Change-in-Ratio Methods
325(6)
Discussion
331(3)
Estimation of Demographic Parameters
Detectability and Demographic Rate Parameters
334(3)
Analysis of Age Frequencies
337(6)
Analysis of Discrete Survival and Nest Success Data
343(8)
Analysis of Failure Times
351(10)
Random Effects and Known-Fate Data
361(1)
Discussion
362(4)
Estimation of Survival Rates with Band Recoveries
Single-Age Models
366(17)
Multiple-Age Models
383(8)
Reward Studies for Estimating Reporting Rates
391(7)
Analysis of Band Recoveries for Nonharvested Species
398(4)
Poststratification of Recoveries and Analysis of Movements
402(4)
Design of Banding Studies
406(8)
Discussion
414(4)
Estimating Survival, Movement, and Other State Transitions with Mark-Recapture Methods
Single-Age Models
418(20)
Multiple-Age Models
438(16)
Multistate Models
454(14)
Reverse-Time Models
468(8)
Mark-Recapture with Auxiliary Data
476(13)
Study Design
489(3)
Discussion
492(4)
Estimating Abundance and Recruitment with Mark-Recapture Methods
Data Structure
496(1)
Jolly-Seber Approach
497(11)
Superpopulation Approach
508(3)
Pradel's Temporal Symmetry Approach
511(7)
Relationships among Approaches
518(2)
Study Design
520(2)
Discussion
522(2)
Combining Closed and Open Mark-Recapture Models: The Robust Design
Data Structure
524(5)
Ad Hoc Approach
529(6)
Likelihood-Based Approach
535(3)
Special Estimation Problems
538(14)
Study Design
552(1)
Discussion
553(3)
Estimation of Community Parameters
An Analogy between Populations and Communities
556(1)
Estimation of Species Richness
557(4)
Estimating Parameters of Community Dynamics
561(11)
Discussion
572(6)
PART IV DECISION ANALYSIS FOR ANIMAL POPULATIONS
Optimal Decision Making in Population Biology
Optimization and Population Dynamics
578(1)
Objective Functions
579(1)
Stationary Optimization under Equilibrium Conditions
579(1)
Stationary Optimization under Nonequilibrium Conditions
580(1)
Discussion
581(3)
Traditional Approaches to Optimal Decision Analysis
The Geometry of Optimization
584(1)
Unconstrained Optimization
585(8)
Classical Programming
593(4)
Nonlinear Programming
597(4)
Linear Programming
601(5)
Discussion
606(2)
Modern Approaches to Optimal Decision Analysis
Calculus of Variations
608(10)
Pontryagin's Maximum Principle
618(9)
Dynamic Programming
627(11)
Heuristic Approaches
638(1)
Discussion
639(5)
Uncertainty, Learning, and Decision Analysis
Decision Analysis in Natural Resource Conservation
644(5)
General Framework for Decision Analysis
649(1)
Uncertainty and the Control of Dynamic Resources
650(1)
Optimal Control with a Single Model
651(1)
Optimal Control with Multiple Models
652(1)
Adaptive Optimization and Learning
653(1)
Expected Value of Perfect Information
654(1)
Partial Observability
655(1)
Generalizations of Adaptive Optimization
656(2)
Accounting for All Sources of Uncertainty
658(1)
``Passive'' Adaptive Optimization
658(2)
Discussion
660(4)
Case Study: Management of the Sport Harvest of North American Waterfowl
Background and History
664(3)
Components of a Regulatory Process
667(4)
Adaptive Harvest Management
671(1)
Modeling Population Dynamics
672(4)
Harvest Objectives
676(1)
Regulatory Alternatives
677(2)
Identifying Optimal Regulations
679(1)
Some Ongoing Issues in Waterfowl Harvest Management
680(4)
Discussion
684(1)
Appendix A Conditional Probability and Bayes' Theorem 685(2)
Appendix B Matrix Algebra 687(6)
B.1. Definitions
B.2. Matrix Addition and Multiplication
B.3. Matrix Determinants
B.4. Inverse of a Matrix
B.5. Orthogonal and Orthonormal Matrices
B.6. Trace of a Matrix
B.7. Eigenvectors and Eigenvalues
B.8. Linear and Quadratic Forms
B.9. Positive-Definite and Semidefinite Matrices
B.10. Matrix Differentiation
Appendix C Differential Equations 693(16)
C.1. First-Order Linear Homogeneous Equations
C.2. Nonlinear Homogeneous Equations-Stability Analysis
C.3. Graphical Methods
Appendix D Difference Equations 709(12)
D.1. First-Order Linear Homogeneous Equations
D.2. Nonlinear Homogeneous Equations-Stability Analysis
Appendix E Some Probability Distributions and Their Properties 721(12)
E.1. Discrete Distributions
E.2. Continuous Distributions
Appendix F Methods for Estimating Statistical Variation 733(6)
F.1. Distribution-Based Variance Estimation
F.2. Empirical Variance Estimation
F.3. Estimating Variances and Covariances with the Information Matrix
F.4. Approximating Variance with the Delta Method
F.5. Jackknife Estimators of Mean and Variance
F.6. Bootstrap Estimation
Appendix G Computer Software for Population and Community Estimation 739(6)
G.1. Estimation of Abundance and Density for Closed Populations
G.2. Estimation of Abundance and Demographic Parameters for Open Populations
G.3. Estimation of Community Parameters
G.4. Software Availability
Appendix H The Mathematics of Optimization 745(22)
H.1. Unconstrained Optimization
H.2. Classical Programming
H.3. Nonlinear Programming
H.3. Linear Programming
H.5. Calculus of Variations
H.6. Pontryagin's Maximum Principle
H.7. Dynamic Programming
References 767(26)
Index 793


Byron Kenneth Williams is Chief of the Cooperative Research Units, U.S. Geological Survey, where he oversees a national program of research units at 39 universities in 37 states. Prior to his current position he was Executive Director of North American Waterfowl and Wetlands Office, U.S. Fish and Wildlife Service, where he served as the Co-chair of the North American Waterfowl Management Plan Committee, Coordinator of the North American Wetlands Conservation Council, and Administrator of the North American Wetlands Conservation Fund. Dr. Williams established the Vermont Cooperative Fish and Wildlife Research Unit at the University of Vermont, where he served for 6 years as the Unit Leader with a collateral faculty appointment as Associate Professor. Previous positions also include the Assistant Chief and Acting Chief of the Office of Migratory Bird Management, U.S. Fish and Wildlife Service, and several positions at the Patuxent Wildlife Research Center in Laurel, Maryland as a scientist and science manager. Dr. Williams received BS and MA degrees in mathematics from Oklahoma University, an MS degree in statistics from Colorado State University, and a Ph.D. from Colorado State University in range ecology. He is a member of the American Association for the Advancement of Science, Biometric Society, Ecological Society of America, and The Wildlife Society. He is widely published in areas as diverse as adaptive harvest management, biological modeling, multivariate statistics, vertebrate mapping, waterfowl management, scientific methodology, endangered species conservation, habitat conservation, population monitoring, and dynamic optimization in natural resource management. James Nichols received a B.S. in Biology from Wake Forest Univ., M.S. in Wildlife Management from Louisiana State Univ., and Ph.D. in Wildlife Ecology from Michigan State Univ. He has spent his entire research career at Patuxent Wildlife Research Center working for the U.S. Fish and Wildlife Service, the National Biological Service, and now the U.S. Geological Survey. He is currently a Senior Scientist at Patuxent. His research interests focus on the dynamics and management of animal populations and on methods for estimating population parameters. Michael Conroy is with the U.S. Geological Service, where he holds a position as Assistant Unit Leader in the Georgia Cooperative Fish and Wildlife Research Unit at the University of Georgia. He received B.S. and M.S. degrees in wildlife ecology and management from Michigan State University, and Ph.D. in Forest Biometrics from Virginia Polytechnic Institute and State University. His research interests are (1) development of statistical methods for the estimation of population parameters and the testing of biological hypotheses about populations; (2) extension of decision theoretic methods to conservation decision making; and (3) development of adaptive decision support systems. Dr. Conroy has taught numerous courses in quantitative ecology and biometrical methods, and has published widely in such journals as Biometrics, Paleobiology, Ecological Applications, Journal of Wildlife Management, Ecological Modelling, and Auk.