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• W2022962697 abstract "Ferson, S. & M. Burgman Quantitative Methods for Conservation Biology. , editors. 2000 . Springer-Verlag , New York . 322 pp. $69.95. ISBN 0-387-94322-6 . The use of models is rapidly increasing in the conservation biology literature (Groom & Pascual 1997; Beissinger & McCullough 2001). As might be expected, a variety of quantitative methods are used in conservation biology to assess population trends, develop and evaluate management actions, design reserve systems, and set acceptable levels of effect. Despite the recognized importance of quantitative methods for providing objective and standardized evaluations, their use by managers and applied conservation biologists is still limited, although growing. For example, a recent review of recovery plans for endangered species found that only 20% of plans since 1991 specified some type of population viability analysis (Morris et al. 2002). One of the reasons for this is simply a lack awareness of the many available methods, ranging from trend models based on simple presence/absence records to detailed individual and community spatial models. Quantitative Methods for Conservation Biology helps address this deficiency by presenting a collection of 17 independent chapters on different quantitative tools used by conservation biologists. The chapters can be roughly divided among four topics: current methods for single-species risk assessment, new quantitative methods, habitat analyses, and conservation genetics. Current methods are presented in eight chapters. Chapters 1 and 2 provide examples of assessing population status from different types of presence/absence data. Chapter 5 discusses a population simulation used to evaluate a proposed salmon introduction. Chapter 6 presents probabilistic models that have been used to estimate the risk of outbreaks of introduced exotic species. Chapter 8 reviews a model used to set whaling catch limits by the International Whaling Committee, especially useful because it explains how the model was selected and tested. Chapter 10 discusses the use of matrix models for investigating the effectiveness of different management actions and includes three case studies. Two chapters discuss issues relating to the effect of measurement error on model prescriptions. Chapter 7 discusses setting sustainable harvest levels in the face of uncertain abundance estimates. Chapter 11 uses a population viability model for spotted owls to illustrate how uncertainty in parameter estimates causes significant uncertainty in extinction-risk estimates. Three chapters introduce new quantitative methods that may prove useful for future risk assessments. Chapter 9 discusses Bayesian belief networks, which provide a framework for integrating beliefs and data about how habitat conditions, population attributes, and management effects interact to affect population trends and risks. Chapter 12 discusses the use of individual-based models for assessing population risk. Chapter 13 provides a nice introduction to branching processes as models for extinction risk and contrasts these models with other stochastic extinction models that are used more widely. An additional three chapters present methods relating to habitat associations or species spatial distributions. Chapter 3 discusses generalized linear models for quantifying the level of correlation between suites of ecological and morphological traits and high extinction risk. Chapter 4 compares a variety of methods for estimating species distribution from census and environmental data. Chapter 17 discusses the use of simulated annealing algorithms to select a system of reserves for protecting a large set of species under a variety of constraints. Finally, three chapters pertain to conservation genetics, only one of which actually discusses quantitative models. Chapter 14 reviews the evidence for inbreeding depression and the molecular tools for quantifying genetic diversity. Chapter 15 provides a nice review of experimental work attempting to validate various predictions from theoretical models used in conservation genetics. Chapter 16 uses a genetic model to investigate the theoretical conditions under which inbreeding depression causes an extinction vortex. Many of the chapters are case studies and present an approach used for a real-world conservation problem, such as a risk assessment or evaluation of a management action for a specific species. This gives the book a strong applied component. Unfortunately, the coverage of models is idiosyncratic. The 17 chapters highlight a few of the methods used in conservation biology, but other commonly used quantitative tools are not mentioned, such as deterministic population dynamic models, stochastic matrix models (and their corresponding diffusion approximations), metapopulation models, community- or ecosystem-level models (such as the widely used ECOPATH models), and a variety of genetic models. Certainly, a collection of case studies cannot be expected to cover fully the array of quantitative models used in conservation biology. Recognizing this constraint, I believe the book would have been stronger if each section began with a brief review of the range of quantitative approaches used in the field. This book will be especially useful for a graduate-student reading group, with the understanding that the chapters provide only a sample of the approaches used in assessment of population risk and evaluation of management actions. Also, readers should be aware that although most of the case studies do not discuss parameter estimation, parameter uncertainty, and model evaluation, such issues are central to viability analyses. Conservation biologists will find the book useful for broadening their knowledge of the medley of quantitative methods that have been used in conservation biology. The chapters are meant to introduce rather than teach the different approaches, and with few exceptions, one would have to refer to the cited literature in order to apply the methods." @default.
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• W2022962697 date "2002-01-18" @default.
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• W2022962697 title "Compute and Conserve" @default.
• W2022962697 doi "https://doi.org/10.1046/j.1523-1739.2002.00113.x" @default.
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