Quantitative Analyses in Wildlife Science

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Quantitative Analyses in Wildlife Science

Name: Quantitative Analyses in Wildlife Science
Brand: JOHNS HOPKINS UNIVERSITY PRESS
SKU: 9781421431079
Price: 167.0 AUD
Availability: InStock

JOHNS HOPKINS UNIVERSITY PRESSISBN: 9781421431079

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$167.00

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In stock, 1 unit

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Edited by Leonard A. Brennan, Andrew N. Tri, Bruce G. Marcot
Imprint: JOHNS HOPKINS UNIVERSITY PRESS
Release Date: April 1, 2021
Format: HARDBACK
Pages: 344

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Over the past fifty years, wildlife science has become increasingly quantitative. But to wildlife scientists, many of whom have not been formally trained as biometricians, computer modelers, or mathematicians, the wide array of available techniques for analyzing wildlife populations and habitats can be overwhelming. This practical book aims to help students and professionals alike understand how to use quantitative methods to inform their work in the field.

Covering the most widely used contemporary approaches to the analysis of wildlife populations and habitats, Quantitative Analyses in Wildlife Science is divided into five broad areas:

general statistical methods

demographic estimation

dynamic process modeling

analysis of spatially-based data on animals and resources

numerical methods

Addressing a variety of topics, from population estimation and growth trend predictions to the study of migration patterns, this book presents fresh data on such pressing issues as sustainable take, control of invasives, and species reintroduction. Authored by leading researchers in wildlife science, each chapter considers the structure of data in relation to a particular analytical technique, as well as the structure of variation in those data. Providing conceptual and quantitative overviews of modern analytical methods, the techniques covered in this book also apply to conservation research and wildlife policy. Whether a quick refresher or a comprehensive introduction is called for, Quantitative Analyses in Wildlife Science is an indispensable addition to every wildlife professional's bookshelf.

Contributors: William M. Block, Leonard A. Brennan, Stephen T. Buckland, Christopher C. Chizinski, Evan C. Cooch, Raymond J. Davis, Stephen J. DeMaso, Randy W. DeYoung, Jane Elith, Joseph J. Fontane, Julie A. Heinrichs, Mevin B. Hooten, Julianna M. A. Jenkins, Zachary S. Laden, Damon B. Lesmeister, Daniel Linden, Jeffrey J. Lusk, Bruce G. Marcot, David L. Miller, Michael L. Morrison, Eric Rexstad, Jamie S. Sanderlin, Joseph P. Sands, Erica F. Stuber, Chris Sutherland, Andrew N. Tri, David B. Wester, Gary C. White, Christopher K. Williams, Damon L. Williford

List of Contributors

Foreword, by R. J. Gutierrez

Preface

Acknowledgments

1. Introduction, by Leonard A. Brennan, Andrew N. Tri, and Bruce G. Marcot

Part I: General Statistical Methods

2. Regression: Linear and Nonlinear, Parametric and Nonparametric, by David B. Wester

3. Multivariate Models and Analyses, by Erica F. Stuber, Christopher C. Chizinski, Jeffrey J. Lusk, and Joseph J. Fontaine

4. Comparing Ecological Models, by Mevin B. Hooten and Evan G. Cooch

Part II: Estimation of Abundance and Demographic Parameters

5. Estimation of Population Parameters Using Marked Animals, by Gary C. White

6. Distance Sampling, by Stephen T. Buckland, David L. Miller, and Eric Rexstad

7. Occupancy Modeling Application, by Chris Sutherland and Daniel Linden

Part III: Dynamic Modeling of Processes

8. Analysis of Population Monitoring Data, by Jamie S. Sanderlin, Michael L. Morrison, and William Block

9. Systems Analysis and Simulation, by Stephen J. DeMaso and Joseph P. Sands

10. Applications of Individual-based Models, by Bruce G. Marcot and Julie A. Heinrichs

11. Detecting and Analyzing Density Dependence, by Zachary S. Ladin and Christopher K. Williams

Part IV: Analysis of Spatially-based Data on Animals and Resources

12. Resource Selection Analysis, by Julianna M.A. Jenkins, Damon B. Lesmeister, and Raymond J. Davis

13. Spatial Statistics in Wildlife Research, by Andrew N. Tri

Part V: Numerical Methods

14. Bayesian Analysis of Molecular Genetics Data, by Randy W. DeYoung and Damon L. Williford

15. Machine Learning, Random Forests and Boosted Regression Trees, by Jane Elith

16. Causal Modeling and the Role of Expert Knowledge, by Bruce G. Marcot

17. Summary and Synthesis: Looking to the Future, by Andrew N. Tri, Bruce G. Marcot, and Leonard A. Brennan

Index

Quantitative Analyses in Wildlife Science

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