Home | Archives | About | Login | Submissions | Notify | Contact | Search
 ES Home > Vol. 7, No. 2 > Art. 2

Quantitative conservation biology: theory and practice of population viability analysis. Sinauer Associates, Sunderland, Massachusetts, USA Copyright © 2003 by the author(s). Published here under license by The Resilience Alliance.

The following is the established format for referencing this article:
Sabo, J. 2003. Morris, W. F., and D. F. Doak. 2003. Quantitative conservation biology: theory and practice of population viability analysis. Sinauer Associates, Sunderland, Massachusetts, USA. Conservation Ecology 7(2): 2. [online] URL: http://www.consecol.org/vol7/iss2/art2/

Book Review

Morris, W. F., and D. F. Doak. 2003. Quantitative Conservation Biology: Theory and Practice of Population Viability Analysis. Sinauer Associates, Sunderland, Massachusetts, USA

John Sabo

Arizona State University

Published: July 16, 2003

Although one of the quintessential tools of the conservation biologist is population viability analysis (PVA), the field of conservation science has until recently lacked a comprehensive guide to the mechanics of the many varieties of PVA available to the practitioner. Quantitative Conservation Biology: Theory and Practice of Population Viability Analysis, by William F. Morris and Daniel F. Doak, fills this gap by providing a much-needed "instructions manual" for PVA that does not bury the reader in unnecessary theoretical detail.

The authors make a valiant effort to simplify the mathematics involved in conducting a PVA; for example, they provide very lucid explanations of the mechanics of the maximum likelihood and information criteria methods as applied to conservation. Nonetheless, this book is too advanced for use in the undergraduate classroom. The target audience is graduate students and empirically oriented conservation biologists looking for a more in-depth explanation of the techniques involved in a particular PVA approach. Morris and Doak (2003) set out to accomplish two goals: (1) to explain concepts and methods of population modeling to " ... a majority of field biologists and not a minority of theoreticians ..." and (2) to teach PVA by working carefully through each step of a particular PVA and " ... linking the models to real data." The book accomplishes these goals in two ways.

First, the authors provide a thorough and comprehensive overview of the full gamut of PVA methods, from time series to matrix models and basic to more realistic models that include complex population dynamics. After a brief introduction to the philosophy and goals of PVA in Chapter 1 and the definitions of viability in Chapter 2, Morris and Doak devote Chapters 3–5 to simple PVAs based on time series of abundance data. These chapters start with the most basic PVA based on a diffusion approximation of population growth through time (e.g., Dennis et al. 2001). They then teach the reader how to add complexities such as density dependence and observation error into this simple PVA model. In Chapters 6–9, the authors expand the discourse to include demographic PVAs and matrix population modeling. In Chapter 6, they lay out the logistics for designing various sampling protocols for gathering data for use in a demographic PVA. In the following chapter, these protocols are linked to mathematical techniques for estimating vital rates, including both deterministic and stochastic projection matrices, which are the most simple demographic PVAs. Chapter 8 presents a set of complicated demographic PVAs that include demographic stochasticity, density dependence, and temporal autocorrelation, including a very patient description and illustration, again using real data, of cross correlations among vital rates and between adjacent census years. Chapter 9 provides a primer on demographic sensitivity and elasticity analysis suitable for the theoretically oriented first-year graduate student gearing up to read Caswell's (2001) text on these topics. The next two chapters offer an overview of methods for conducting PVAs on populations with spatial structure using both time series and demographic approaches. Finally, in Chapter 12, the authors address the topic of the assumptions and limitations of PVA. On the whole, the book treats most of the important PVA methods with just enough detail to demystify them to the casual critic.

Second, the authors succeed in linking models to real data by patiently and carefully working through examples of each of the viability methods listed above. In each case, examples are illustrated using real data from textbook conservation case studies involving species such as Red-cockaded Woodpeckers, grizzly bears, desert tortoises, and mountain golden heather. Morris and Doak begin each chapter with an overview of a PVA technique, its assumptions, and instructions for testing the data to see if they meet the assumptions of the model. Using a real data set, the authors then step through the process of constructing the model and executing the analysis using programs written in Matlab and SAS. In all cases, the code is provided in handy text boxes, is well documented, and can be downloaded from Sinauer Associates' Web site. In this way, the book provides a hands-on tool for learning programming in two very useful higher-level languages.

For critics of PVA, the authors provide discussions scattered throughout the text and a summary in Chapter 12 of the caveats and limitations of PVA. Specifically, they outline four common criticisms of PVA near the end of their book: (1) there are insufficient data to justify with confidence the predictions of most PVA approaches; (2) observation error severely corrupts risk estimates from PVA even when data are abundant; (3) PVAs are too simple, because most of them ignore basic and real processes that significantly affect viability; and (4) PVAs are rarely validated. In response to these criticisms, the authors urge caution when conducting a particular PVA and provide a longer laundry list of recommendations for practitioners in need of a tool for assessing extinction risk, however imperfect that tool.

Some of these recommendations are new and potentially field-defining. For example, Morris and Doak advocate very specific thresholds for the amount of data needed to perform certain PVAs, e.g., 4 yr of vital rates for demographic PVAs and 10 yr of count data for time series PVAs. These criteria seem realistic and fair given recent validations of various PVA techniques using both real and simulated data (e.g., Meir and Fagan 1999, Brook et al. 2000, Holmes 2001) and will no doubt exert a strong influence on the criteria used to deem a PVA acceptable in future conservation efforts. Many of the other recommendations provided in this chapter are either common sense or common practice on the ground. Nevertheless, for those simply reading the book to understand the basics of PVA, it is important to have all of these recommendations, new or otherwise, summarized in one place.

Overall, Quantitative Conservation Biology provides the first complete guide to PVA. This book should be required reading for any Ph.D. student in population or conservation biology and makes a useful reference for field-oriented conservation biologists seeking a detailed overview of the concepts of population viability.


Morris, W. F., and D. F. Doak. 2003. Quantitative Conservation Biology: Theory and Practice of Population Viability Analysis. Sinauer Associates, Sunderland, Masschusetts, USA. 480 pp., paperback, U.S.$38.95, ISBN 0-878-93546-0.


Responses to this article are invited. If accepted for publication, your response will be hyperlinked to the article. To submit a comment, follow this link. To read comments already accepted, follow this link.


Brook, B. W., J. J. O'Grady, A. P. Chapman, M. A. Burgman, R. Akcakaya, and R. Frankham. 2000. Predictive accuracy of population viability analysis in conservation biology. Nature 404: 385-387.

Caswell, H. 2001. Matrix population models: construction, analysis, and interpretation. Second edition. Sinauer Associates, Sunderland, Massachusetts, USA.

Dennis, B., P. L. Munholland, and J. M. Scott. 1991. Estimation of growth and extinction parameters for endangered species. Ecological Monographs 61:115-143.

Holmes, E. E. 2001. Estimating risks in declining populations with poor data. Proceedings of the National Academy of Sciences (USA) 98:5072-5077.

Meir, E., and W. F. Fagan. 1999. Will observation error and biases ruin the use of simple extinction models? Conservation Biology 14: 148-154.

Morris, W. F., and D. F. Doak. 2003. Quantitative conservation biology: theory and practice of population viability analysis. Sinauer Associates, Sunderland, Massachusetts, USA.

Address of Correspondent:
John Sabo
P.O. Box 871501
Department of Biology
Arizona State University
Tempe, Arizona 85287-1501 USA
Phone: (480) 965-5905

Home | Archives | About | Login | Submissions | Notify | Contact | Search