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Preface
Quantitative methods are needed in conservation biology more than ever as an
increasing number of threatened species find their way onto international and
national “red lists.” Objective evaluation of population decline and extinction
probability are required for sound decision making. Yet, as our colleague Selina
Heppell points out, population viability analysis and other forms of formal risk
assessment are underused in policy formation because of data uncertainty and a
lack of standardized methodologies and unambiguous criteria (i.e., “rules of
thumb”). Models used in conservation biology range from those that are purely
heuristic to some that are highly predictive. Model selection should be dependent
on the questions being asked and the data that are available. We need to develop a
toolbox of quantitative methods that can help scientists and managers with a wide
range of systems and that are subject to varying levels of data uncertainty and
environmental variability.
The methods outlined in the following chapters represent many of the tools
needed to fill that toolbox. When used in conjunction with adaptive management,
they should provide information for improved monitoring, risk assessment, and
evaluation of management alternatives.
The first two chapters describe the application of methods for detecting trends
and extinctions from sighting data. Presence/absence data are used in general
linear and additive models in Chapters 3 and 4 to predict the extinction proneness
of birds and to build habitat models for plants. Chapters 5 and 6 explore the
application of probabilistic models for decision support in wildlife management.
Chapter 7 examines the use of population abundance estimates in making conservation decisions. Chapter 8 describes some approaches to model development to
regulate harvesting, with a particular focus on whale population management.
Chapter 9 provides a new way of synthesizing qualitative and quantitative information in a form that is designed to support wildlife manager decisions. Chapter
10 describes matrix model approaches to population modeling. Chapters 11 to 13
outline different approaches to building stochastic population models, including
frequency-based models, individual-based models, and branching processes.
Chapters 14 to 16 provide a review of genetic techniques that have applications in
conservation biology and suggestions for the development of laboratory-based
Preface
Quantitative methods are needed in conservation biology more than ever as an
increasing number of threatened species find their way onto international and
national “red lists.” Objective evaluation of population decline and extinction
probability are required for sound decision making. Yet, as our colleague Selina
Heppell points out, population viability analysis and other forms of formal risk
assessment are underused in policy formation because of data uncertainty and a
lack of standardized methodologies and unambiguous criteria (i.e., “rules of
thumb”). Models used in conservation biology range from those that are purely
heuristic to some that are highly predictive. Model selection should be dependent
on the questions being asked and the data that are available. We need to develop a
toolbox of quantitative methods that can help scientists and managers with a wide
range of systems and that are subject to varying levels of data uncertainty and
environmental variability.
The methods outlined in the following chapters represent many of the tools
needed to fill that toolbox. When used in conjunction with adaptive management,
they should provide information for improved monitoring, risk assessment, and
evaluation of management alternatives.
The first two chapters describe the application of methods for detecting trends
and extinctions from sighting data. Presence/absence data are used in general
linear and additive models in Chapters 3 and 4 to predict the extinction proneness
of birds and to build habitat models for plants. Chapters 5 and 6 explore the
application of probabilistic models for decision support in wildlife management.
Chapter 7 examines the use of population abundance estimates in making conservation decisions. Chapter 8 describes some approaches to model development to
regulate harvesting, with a particular focus on whale population management.
Chapter 9 provides a new way of synthesizing qualitative and quantitative information in a form that is designed to support wildlife manager decisions. Chapter
10 describes matrix model approaches to population modeling. Chapters 11 to 13
outline different approaches to building stochastic population models, including
frequency-based models, individual-based models, and branching processes.
Chapters 14 to 16 provide a review of genetic techniques that have applications in
conservation biology and suggestions for the development of laboratory-based