or actual animal conflicts in social foraging (among other topics), and a verbal consid- eration of the applicability of game theory to important aspects of human behaviour such as social norms. The common framework for the selec- tion of papers is classic evolutionary game theory – perhaps one of the most influential paradigms of the century – initiated by Von Neumann and Morgenstern 2 , Nash 3 and Maynard Smith 4 . This equilibrium- oriented approach proves to be as useful as expected from past and recent develop- ments in the discipline. Realistic, ‘field- motivated’ situations of the many different types of animal conflict covered in the book fit nicely into elegant, mathematically sim- ple models. Most of the authors compare model predictions with sound empirical evidence – although the amount of evi- dence and taxonomic diversity discussed in each chapter are quite different. One has the impression that empirical work keeps pace with game-theoretical modelling in the study of animal behaviour, which is an envi- able state of affairs compared with some other disciplines of theoretical biology. The general applicability of classic evo- lutionary game theory to problems far from those of animal conflicts is a recurrent claim of publications on the subject. The usual list of disciplines taking advantage of such secondary applications includes economics, social science and even the humanities. Interestingly, it is rarely stated that evolutionary game theory has much to say to other fields of population biology. For example, within-community multi- species coevolution of plants calls for a game-theoretic approach, with growth forms and/or dispersal techniques consid- ered as strategies, and possible stable equilibria representing evolutionarily sta- ble coalitions. There are good reasons to expect that game theory will help us explain plant ‘behaviour’ and, on that basis, certain aspects of community diversity. This would be yet another link in the promising relationship between game theory and evolutionary ecology. Tamás Czárán Ecological Modelling Research Group, Hungarian Academy of Science and Eötvös University, Budapest, Hungary (czaran@ludens.elte.hu) References 1 Durrett, R. and Levin, S.A. (1994) Theor. Popul. Biol. 46, 363–394 2 Von Neumann, J. and Morgenstern, O. (1944) Theory of Games and Economic Behaviour, Princeton University Press 3 Nash, J.F. (1951) Ann. Math. 54, 286–295 4 Maynard Smith, J. (1982) Evolution and the Theory of Games, Cambridge University Press Rivers of life Restoring Life in Running Waters: Better Biological Monitoring by J.R. Karr and E.W. Chu Island Press, 1998. $29.95 pbk (xiv + 206 pages) ISBN 1 55963 674 2 T he concept of ecological services has been warmly embraced as a pragmatic justification for the conservation of biologi- cal diversity 1,2 . Experiments reveal that more diverse terrestrial communities are better at fixing carbon dioxide (CO 2 ) or show increased resilience in the face of drought or other perturbations 3,4 . Unfortu- nately, the generality of these findings is unclear because the experimental commu- nities involved are, of necessity, small or artificially contrived, and the challenges of extrapolating the results, to rain forests for example, are considerable. In freshwater systems, by contrast, the logic for conser- vation is incontrovertible. Clean water is fundamental to life. Healthy river systems deliver not just a water supply, but also fish, power generation, transport, recre- ation and cultural identity. The demand for these services will intensify as populations grow and countries develop, and it is widely predicted that the battles of the 21st century will be waged over water resources rather than oil or territory. Biologists are faced with the challenge of monitoring the wellbeing of freshwater habitats. Happily, the task is relatively straightforward because the status of the living organisms found there is the best indicator of condition. Even more propi- tiously, the preservation or restoration of natural systems, and the associated biologi- cal diversity, emerges as the most effective way of guarding freshwater resources. With this impetus, it is not surprising that con- siderable effort has been devoted to creat- ing measures that encapsulate biological quality in a single figure. Karr and Chu review this approach and make a strong case for the adoption of multimetric biologi- cal indices in river assessment and manage- ment. Although their book is focused on the United States, much of the discussion is of wider relevance. Multimetric measures are used to gen- erate an index of biological integrity (IBI). This is a composite of around eight to 12 metrics, or variables, each chosen to reflect some aspect of the system’s biological health. The metrics span a range of attrib- utes, including the richness of specified taxa, trophic structure, such as the relative abundance of predators, and rates of dis- ease or malformation in target species. They need not be independent. Indeed, a typical IBI, such as one used in the midwestern USA, includes the total number of fish species as metrics, as well as the richness of sep- arate taxonomic groupings, such as darters and sunfish. Each metric is ranked from one to five, with five being the value expected for a pristine habitat. The metrics are summed to produce the final index, which is then used to compare and assess sites. Ecologists are often suspicious of meas- ures that pool disparate variables. Experi- ence with diversity statistics, such as the Shannon index, has for instance revealed how an assumed virtue – in this case, the integration of richness and evenness scores – can thwart interpretation. Nonethe- less, the authors discuss 37 premises (e.g. ‘statistical decision rules are no substitute for biological judgement’) on which the case for multimetric measures is based, and criticize seven ‘myths’ (e.g. ‘the sensi- tivity of multimetric measures is unknown’) that have been advanced against them. Karr and Chu also note that multimetric indices are statistically versatile and can be analysed with familiar techniques, such as ANOVA or regression, and that the preci- sion of sampling protocols can be esti- mated in conventional ways. Moreover, IBIs are not just confined to rivers. The book lists a variety of environments, including lakes and coastal and marine systems, in which they have been successfully applied. Karr and Chu argue that multimetric measures are easily comprehensible and draw an analogy with economic indicators such as the Dow Jones industrial average. But whereas the Dow Jones generates a single measure of the economic status of one coun- try, IBIs are faced with a broader task. Pris- tine freshwater habitats can vary markedly from place to place and estimates of species richness (a central component of many IBIs) are not an infallible guide to ecological quality. Headwater streams, for example, typi- cally support fewer species than large rivers. The authors address this concern, maintain that multimetric measures can be wide- ranging and cite, as evidence, a fish IBI that successfully classified sites in six US regions ranging from Chicago to Arkansas. However, they also recognize that the proper classification of sites, so that like is com- pared with like, is the key to a successful IBI. There is no doubt that IBIs have been a valuable tool in the monitoring and conser- vation of American rivers and Karr and Chu provide a compelling case for their univer- sal adoption. Given the importance of benchmark data in the selection of metrics and appropriate deployment of IBIs, it remains to be seen if they can fulfil their promise in diverse, and increasingly threat- ened, tropical rivers for which we have depressingly little biological information. BOOK REVIEWS TREE vol. 14, no. 6 June 1999 0169-5347/99/$ – see front matter © 1999 Elsevier Science. All rights reserved. 247