rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Quantitative Methods for Conservation Biology
  name:
    name part: Ferson, Scott.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Burgman, Mark.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2000
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XI, 322 p. 5 illus. online resource.
  abstract: 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.
  table of contents: Detecting Extinction in Sighting Data -- Inferring Threat from Scientific Collections: Power Tests and an Application to Western Australian Acacia Species -- Identifying the Ecological Correlates of Extinction-Prone Species: A Case Study of New Zealand Birds -- Quantitative Methods for Modeling Species Habitat: Comparative Performance and an Application to Australian Plants -- Risk Assessment of a Proposed Introduction of Pacific Salmon in the Delaware River Basin -- Likelihood of Introducing Nonindigenous Organisms with Agricultural Commodities: Probabilistic Estimation -- “Best” Abundance Estimates and Best Management: Why They Are Not the Same -- Whaling Models for Cetacean Conservation -- Assessing Land-Use Impacts on Bull Trout Using Bayesian Belief Networks -- Using Matrix Models to Focus Research and Management Efforts in Conservation -- Variability and Measurement Error in Extinction Risk Analysis: The Northern Spotted Owl on the Olympic Peninsula -- Can Individual-Based Models Yield a Better Assessment of Population Variability? -- Potential of Branching Processes as a Modeling Tool for Conservation Biology -- Role of Genetics in Conservation Biology -- Modeling Problems in Conservation Genetics Using Laboratory Animals Richard Frankham -- Theoretical Properties of Extinction by Inbreeding Depression Under Stochastic Environments -- Mathematical Methods for Identifying Representative Reserve Networks.
  note: by Scott Ferson, Mark Burgman.
  subject:
    topic: Life sciences
  subject:
    topic: Community ecology, Biotic
  subject:
    topic: Ecology
  subject:
    topic: Life Sciences
  subject:
    topic: Community & Population Ecology
  subject:
    topic: Theoretical Ecology/Statistics
  classification: QH540-549.5
  classification: 577.82
  related item:
    title info:
      title: Springer eBooks
  identifier: 9780387226484
  identifier: http://dx.doi.org/10.1007/b97704
  record info:
    record creation date: 100301
    record change date: 20151204145542.0
    record identifier: 978-0-387-22648-4