Quantitative Analyses in Wildlife Science

Leonard A. Brennan; Andrew N. Tri; Bruce G. Marcot

doi:10.1353/book.67476

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

Publication Date

September 10, 2019

An authoritative guide to quantitative methods that will help wildlife scientists improve analysis and decision-making.

Short-listed for the Wildlife Society Outstanding Edited Book

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...

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

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Reviews

This is primarily a book for students and working wildlife management professionals; however that is not to say that it is, or should be, outside the realm of consideration by those with a sincere interest in how wildlife researchers collect and interpret data. As so many aspects of contemporary wildlife conservation revolve around what the number are, how they were collected, and how they are interpreted, most anyone with a serious interest in wildlife conservation would do well to at least understand how the metaphorical sausage is made – even if they're not making it themselves. To that end, this is most certainly the book from which to best acquire such an understanding.

— Johannes E. Riutta — The Well-Read Naturalist

A book of this nature is long overdue, but this one was well worth the wait. Quantitative aspects of wildlife science have advanced considerably in recent decades and researchers and managers often struggle to keep up with increasingly complex quantitative concepts being utilized in wildlife science. With chapters written by leading experts, this book ameliorates that struggle by providing broad, understandable overviews of major quantitative tools and concepts used in wildlife science. This book will definitely be on my shelf.

— Alan Franklin, USDA National Wildlife Research Center

Rapid advances in computing speed and increasing use of open-source software has provided wildlife biologists with unprecedented access to powerful analytical tools. This book will be an essential companion for all graduate students and researchers, becoming an indispensable guide to the most effective means of analyzing and interpreting their data.

— Alan York, University of Melbourne

Book Details

Publication Date

September 10, 2019

Status

Available

Trim Size

Pages

344

ISBN

9781421431079

DOI

https://doi.org/10.1353/book.67476

Illustration Description

105 b&w illus.

Series

Wildlife Management and Conservation

Subject

Science

Life Sciences

Table of Contents

List of Contributors
Foreword, by R. J. Gutiérrez
Preface
Acknowledgments

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

PART I GENERAL STATISTICAL METHODS
2 Regression: Linear and

List of Contributors
Foreword, by R. J. Gutiérrez
Preface
Acknowledgments

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

PART I GENERAL STATISTICAL METHODS
2 Regression: Linear and Nonlinear, Parametricband Nonparametric
David B. Wester
3 Multivariate Models and Analyses
Erica F. Stuber, Christopher C. Chizinski, Jeffrey J. Lusk, and Joseph J. Fontaine
4 Comparing Ecological Models
Mevin B. Hooten and Evan G. Cooch

PART II ESTIMATION OF ABUNDANCE AND DEMOGRAPHIC PARAMETERS
5 Estimation of Population Parameters Using Marked Animals
Gary C. White
6 Distance Sampling
Stephen T. Buckland, David L. Miller, and Eric Rexstad
7 Occupancy Modeling Applications
Chris Sutherland and Daniel W. Linden

PART III DYNAMIC MODELING OF PROCESSES
8 Analysis of Population Monitoring Data
Jamie S. Sanderlin, Michael L. Morrison, and William M. Block
9 Systems Analysis and Simulation
Stephen J. Demaso and Joseph P. Sands
10 Applications of Individual-Based Models
Julie A. Heinrichs and Bruce G. Marcot
11 Detecting and Analyzing Density Dependence
Zachary S. Ladin and Christopher K. Williams

PART IV ANALYSIS OF SPATIALLY BASED DATA ON ANIMALS AND RESOURCES
12 Resource Selection Analysis
Julianna M. A. Jenkins, Damon B. Lesmeister, and Raymond J. Davis
13 Spatial Statistics in Wildlife Research
Andrew N. Tri

PART V NUMERICAL METHODS
14 Bayesian Analysis of Molecular Genetics Data
Damon L. Williford and Randy W. Deyoung
15 Machine Learning, Random Forests, and Boosted Regression Trees
Jane Elith
16 Causal Modeling and the Role of Expert Knowledge
Bruce G. Marcot
17 Summary and Synthesis: Looking to the Future
Andrew N. Tri, Bruce G. Marcot, and Leonard A. Brennan

Index

See Less

Author Bios

Featured Contributor

Leonard A. Brennan

Leonard A. Brennan is a research scientist and is the C. C. Winn Endowed Chair for Quail Research Professor at the Caesar Kleberg Wildlife Research Institute, Texas A&M University–Kingsville. He is the coeditor of Quantitative Analyses in Wildlife Science.

Featured Contributor

Andrew N. Tri

Andrew N. Tri is a research wildlife biologist with the Forest Wildlife and Populations Research Group at the Minnesota Department of Natural Resources and an adjunct faculty member at the Caesar Kleberg Wildlife Research Institute, Texas A&M University–Kingsville.

Featured Contributor

Bruce G. Marcot

Bruce G. Marcot is a research wildlife biologist with the USDA Forest Service, Pacific Northwest Research Station, and the coauthor of Wildlife-Habitat Relationships: Concepts and Applications.

Quantitative Analyses in Wildlife Science

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Leonard A. Brennan

Andrew N. Tri

Bruce G. Marcot

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