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Copyright © 2003 by the author(s). Published here under license by The Resilience Alliance.

The following is the established format for referencing this article:
McCarthy, M. 2003. Hof, J. G., and M. Bevers. 2002. Spatial optimization in ecological applications. Columbia University Press, New York, New York, USA. Conservation Ecology 7(1): 1. [online] URL: http://www.consecol.org/vol7/iss1/art1/

Book Review

Hof, J. G., and M. Bevers. 2002. Spatial Optimization in Ecological Applications. Columbia University Press, New York, New York, USA

Michael McCarthy

Australian Research Centre for Urban Ecology

Published: January 15, 2003

As noted enthusiastically by Hugh Possingham (2001), ecology has many theories about the dynamics of ecosystems, species, and populations, but often has little to tell managers about how to decide among competing solutions. Where should habitat restoration occur to maximize biodiversity benefits? Where should pest control be conducted? These are the sorts of questions that applied ecology should help us answer, but rarely are the relevant theories and models couched in terms that allow competing solutions to be assessed. The book “Spatial Optimization in Ecological Applications” by John G. Hof and Michael Bevers aims to provide tools to help answer these sorts of questions. In scope, it is an attempt to answer a question that all conservation ecologists should consider, "How can we best manage natural resources and develop hypotheses about ecosystems?"

This book may be considered a sequel to their previous work "Spatial Optimization for Managed Ecosystems." It is not a second edition; rather it addresses the shortcomings of the original by moving from models at a relatively small scale to landscape-scale management problems.

It contains an introduction, 13 main chapters, and a short postscript. The main chapters cover the following topics: simple proximity relationships (e.g., how the spatial arrangement of timber harvesting can be optimized to manage stormflow); reaction-diffusion models (e.g., how to manage habitat and release of black-footed ferrets); control models (e.g., how to control wildfire); and using optimization to develop hypotheses about ecosystems (e.g., how trees should most efficiently produce carbohydrates).

In many ways, I like this book—particularly its attempt to convert ecological models into tools that can be used to answer management questions. The case studies are described in considerable detail, and they are topics that are of personal interest (e.g., forest management, endangered species management, population ecology, streamflow management). People familiar with mathematics shouldn't have too many problems with the equations, but the authors don't attempt to cater to non-mathematical readers. They suggest that such people "can skip over the math and still get the general idea," although there seems little of substance for those not interested in quantitative methods.

All the main chapters are slight modifications of previously published journal articles by the authors. I believe that this is the main weakness of the book; the case studies and topics have a relatively narrow focus and relevant topics by other authors get very little attention. Readers who want a comprehensive introduction to spatial optimization may be disappointed. Two possible topics that weren't included are the design of conservation reserves, and inclusion of models with stochastic components. Although the authors mention some of these topics in the postscript and suggest they are interesting areas for further research, what has been done in some of these other areas is largely ignored. Additionally, many of the problems have a very similar basis, e.g., many of the population models are based on discrete reaction-diffusion models. Similarly, the authors present a relatively narrow range of the possible methods for solving the problems. By far the most frequently used is linear programming, with integer and non-linear programming also being mentioned. Linear programming is useful for the size of the problems considered by the authors, but other methods for optimization (e.g., simulated annealing, stochastic dynamic programming, genetic algorithms) are ignored.

The book would be improved if the authors made the models available online. This would allow readers to conduct their own analyses, let them explore the methods in more detail, and build confidence in their use.

Although "Spatial Optimization in Ecological Applications" is not a comprehensive review of methods for spatial optimization, I'm pleased to have this book in my personal library. It will be of interest to those resource managers who are required to cope with complex spatial planning, and for researchers who hope to develop models and quantitative tools that will be useful for natural resource planning.


Hof, J. G., and M. Bevers. 2002. Spatial optimization in ecological applications. Columbia University Press, New York, New York, USA. 320 p., Hardcover, U.S. $74.50, ISBN 023110636X


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Possingham, H. 2001. The business of biodiversity: applying decision theory principles to conservation. Tela Paper No. 9. Australian Conservation Foundation. Melbourne, Australia. [Online] URL: http://www.acfonline.org.au/docs/publications/tp010.pdf.

Address of Correspondent:
Michael McCarthy
Australian Research Centre for Urban Ecology,
Royal Botanic Gardens Melbourne
c/o School of Botany,
University of Melbourne,
Parkville, Victoria, 3010
Phone: +61 3 8344 6856

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