Biological diversity : frontiers in measurement and assessment / edited by Anne E. Magurran and Brian J. McGill.

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Bibliographic Details
Published: Oxford ; New York : Oxford University Press, 2011.
Other Authors:
Magurran, Anne E., 1955-
McGill, Brian J.
Subjects:
Biodiversity.
Biodiversity > Monitoring.
Biodiversity conservation.
Format: Book
Biological diversity : frontiers in measurement and assessment /
Table of Contents:
  • Machine generated contents note: 1. Challenges and opportunities in the measurement and assessment of biological diversity / Brian J. McGill
  • 1.1. Introduction
  • 1.2. State of the field
  • 1.3. What is in this book
  • Acknowledgements
  • pt. I Basic Measurement Issues
  • 2. An overview of sampling issues in species diversity and abundance surveys / Norman Mercado-Silva
  • 2.1. Introduction
  • 2.2. State of the field
  • 2.2.1. Setting objectives
  • 2.2.2. An important partner: the statistician
  • 2.2.3. What species to sample
  • 2.2.4. Where to sample
  • 2.2.5. Bias, sampling error, and precision
  • 2.2.6. How to sample
  • 2.2.7. Quantifying the sample
  • 2.2.8. When to sample
  • 2.2.9. How many samples to collect
  • 2.2.10. Comparing information from different surveys
  • 2.2.11. Preparing for the field
  • 2.3. Prospectus
  • 2.4. Key points
  • 3. Biodiversity monitoring: the relevance of detectability / Stuart E. Newson
  • 3.1. Introduction
  • 3.2. State of the field: which biodiversity measure?
  • 3.3. Detectability: are species counts relevant for monitoring biodiversity?
  • 3.3.1. Individual detectability
  • 3.3.2. Estimating individual detectability
  • 3.3.3. Species detectability
  • 3.4. Case study: the UK Breeding Bird Survey
  • 3.5. Discussion
  • 3.6. Prospectus
  • 3.7. Key points
  • Acknowledgements
  • pt. II Diversity
  • 4. Estimating species richness / Robert K. Colwell
  • 4.1. Introduction
  • 4.2. State of the field
  • 4.2.1. Sampling models for biodiversity data
  • 4.2.2. The species accumulation curve
  • 4.2.3. Climbing the species accumulation curve
  • 4.2.4. Species richness versus species density
  • 4.2.5. Individual-based rarefaction
  • 4.2.6. Sample-based rarefaction
  • 4.2.7. Assumptions of rarefaction
  • 4.2.8. Estimating asymptotic species richness
  • 4.2.9. Comparing estimators of asymptotic species richness
  • 4.2.10. Software for estimating species richness from sample data
  • 4.3. Prospectus
  • 4.4. Key points
  • Acknowledgements
  • 5. Measurement of species diversity / Brian J. McGill
  • 5.1. Introduction
  • 5.2. State of the art
  • 5.2.1. Species diversity as variance
  • 5.2.2. Species diversity as information
  • 5.2.3. Traditional measures of various types of diversity
  • 5.2.4. Addressing the difference between the empirical and ecological samples: estimating species diversity components using empirical samples
  • 5.2.5. Testing for heterogeneity among ecological samples
  • 5.3. Prospectus
  • 5.4. Key points
  • 6. Compositional similarity and β (beta) diversity / Robin L. Chazdon
  • 6.1. Introduction
  • 6.2. State of the field
  • 6.2.1. Measures of relative compositional similarity and differentiation
  • 6.2.2. Diversity and compositional similarity
  • 6.2.3. Statistical estimation of assemblage differentiation and similarity
  • 6.3. Prospectus
  • 6.4. Key points
  • 7. Measuring biological diversity in time (and space) / Anne E. Magurran
  • 7.1. Introduction
  • 7.2. State of the field: timescales of change and community boundaries
  • 7.3. What is being measured?
  • 7.4. Assessing change through time
  • 7.4.1. Temporal turnover: species time curves
  • 7.4.2. Temporal turnover: turnover indexes
  • 7.4.3. Using species abundance distributions to evaluate change
  • 7.4.4. Assessing change using biodiversity indexes
  • 7.5. Measuring change in the rate of change
  • 7.6. Using temporal change to shed light on community structure
  • 7.7. Partitioning diversity in space and time
  • 7.8. Prospectus
  • 7.9. Key points
  • pt. III Distribution
  • 8. Commonness and rarity / Peter A. Henderson
  • 8.1. Introduction
  • 8.2. State of the field
  • 8.3. Commonness and rarity: ecological context
  • 8.4. Assessing commonness and rarity
  • 8.5. Prospectus
  • 8.6. Key points
  • 9. Species abundance distributions / Brian J. McGill
  • 9.1. Introduction
  • 9.2. State of the field
  • 9.2.1. Visual approaches to SADs
  • 9.2.2. Parametric approaches to SADs
  • 9.2.3. Non-parametric approaches to SADs
  • 9.2.4. Multivariate approaches to SADs
  • 9.3. Identifying a useful, parsimonious subset of SAD metrics
  • 9.3.1. Efficiency and bias
  • 9.3.2. Independence of measures
  • 9.3.3. Overall assessment of useful, parsimonious metrics of SADs
  • 9.4. Prospectus
  • 9.5. Key points
  • Acknowledgements
  • 10. Fitting and empirical evaluation of models for species abundance distributions / Maria Dornelas
  • 10.1. Introduction
  • 10.2. State of the field
  • 10.2.1. Species abundance models
  • 10.2.2. Obtaining predicted abundances
  • 10.2.3. Choosing parameters
  • 10.2.4. Goodness-of-fit testing
  • 10.2.5. Model selection
  • 10.3. Prospectus
  • 10.3.1. Sampling theory for species abundance models
  • 10.3.2. Parameter estimation
  • 10.3.3. Goodness-of-fit testing
  • 10.3.4. Model selection
  • 10.3.5. Conclusions
  • 10.4. Key points
  • 11. Species occurrence and occupancy / Fangliang He
  • 11.1. Introduction
  • 11.2. State of the field
  • 11.2.1. Occupancy-area relationships
  • 11.2.2. Occupancy-abundance relationships
  • 11.2.3. Species occupancy distributions
  • 11.3. Prospectus
  • 11.4. Key points
  • Acknowledgements
  • 12. Measuring the spatial structure of biodiversity / Brian J. McGill
  • 12.1. Introduction
  • 12.1.1. What spatial structure is of interest?
  • 12.1.2. Number of variables recorded
  • pattern or association?
  • 12.1.3. Types of data
  • 12.2. State of the art
  • 12.2.1. Estimating intensity (first-order effects)
  • 12.2.2. Studying effects at a distance (second-order effects)
  • 12.2.3. Associations between two variables
  • 12.2.4. Software available
  • 12.3. Prospectus
  • 12.4. Key points
  • Acknowledgements
  • pt. IV Alternative measures of diversity
  • 13. A primer of trait and functional diversity / Evan Weiher
  • 13.1. Introduction
  • 13.1.1. General definitions
  • 13.1.2. General importance
  • 13.1.3. A brief history of trait and functional diversity
  • 13.2. State of the field
  • 13.2.1. Overview
  • 13.2.2. Indices of trait and functional diversity
  • 13.2.3. Partitioning the components of trait diversity
  • 13.2.4. Methodological issues
  • 13.2.5. Conceptual issues
  • 13.3. Prospectus
  • 13.3.1. Recommendations
  • 13.3.2. Future directions
  • 13.4. Key points
  • Acknowledgements
  • 14. Measuring phylogenetic biodiversity / Arne Ø. Mooers
  • 14.1. Introduction
  • 14.1.1. Overview
  • 14.1.2. Approaching the study of phylogenetic diversity
  • 14.2. State of the field
  • 14.2.1. Null models
  • 14.2.2. Simulation analyses
  • 14.2.3. Simulation results
  • 14.3. Prospectus
  • 14.3.1. Phylogenetic diversity in conservation
  • 14.3.2. Phylogenetic diversity in community ecology
  • 14.3.3. Abundance vs presence-absence data
  • 14.4. Key points
  • 15. Genetic methods for biodiversity assessment / Hans-Werner Herrmann
  • 15.1. Introduction
  • 15.2. Genetic methods in biodiversity assessment
  • 15.2.1. Mitochondrial, chloroplast, and nuclear DNA
  • 15.2.2. Genome technologies
  • 15.3. Biodiversity assessments
  • 15.3.1. Phylogenies for biodiversity assessment using mtDNA and nuclear DNA
  • 15.3.2. Non-invasively monitoring for biodiversity
  • 15.3.3. DNA barcoding for biodiversity assessment
  • 15.3.4. Genome technologies for biodiversity assessment
  • 15.4. Prospectus
  • 15.5. Key points
  • pt. V Applications
  • 16. Microbial diversity and ecology / Thomas P. Curtis
  • 16.1. Introduction
  • 16.2. The diversity concept
  • 16.3. Phylogeny
  • 16.4. rRNA as an evolutionary chronometer
  • 16.5. Methods for assessing diversity
  • 16.5.1. PCR-based methods
  • 16.5.2. Pyrosequencing
  • 16.5.3. Metagenomics
  • 16.6. Sampling, scale, and thresholds
  • 16.7. Mathematical tools for estimating diversity
  • 16.7.1. Collectors curves
  • 16.7.2. Chao's non-parametric estimators
  • 16.7.3. Parametric estimators that assume a distribution
  • 16.7.4. Estimating diversity by inferring a distribution from the data
  • 16.8. Estimation of required sample size
  • 16.9. In-depth metagenome analyses
  • 16.10. Prospectus
  • 16.11. Key points
  • 17. Biodiversity and disturbance / Karl Inne Ugland
  • 17.1. Introduction
  • 17.2. What is a disturbance?
  • 17.2.1. Source of the disturbance
  • 17.2.2. Timescale
  • 17.2.3. Spatial scale
  • 17.2.4. Intensity
  • 17.2.5. Specificity
  • 17.2.6. Summary
  • 17.3. State of the field: measuring the effects of disturbance on biodiversity
  • 17.3.1. Univariate metrics
  • 17.3.2. Species abundance distribution based metrics
  • 17.3.3. Multivariate analysis
  • 17.4. Prospectus
  • 17.5. Key points
  • Acknowledgements
  • 18. Measuring biodiversity in managed landscapes / Melodie A. McGeoch
  • 18.1. Introduction
  • 18.2. State of the field
  • 18.2.1. Variation in biodiversity measurement goals
  • 18.2.2. Bioindicators and monitoring
  • 18.2.3. Measuring biodiversity for management
  • 18.2.4. Matrices for measurement
  • 18.3. Prospectus
  • 18.4. Key points
  • Acknowledgements
  • 19. Estimating extinction with the fossil record / S. Kathleen Lyons
  • 19.1. Introduction
  • 19.2. State of the field
  • 19.2.1. Basic metrics
  • 19.2.2. Survivorship curves
  • 19.2.3. The importance of sampling
  • 19.2.4. Relevant studies
  • 19.2.5. Occurrence-based diversity estimates
  • 19.2.6. Gap analyses
  • 19.3. Prospectus
  • 19.4. Key points
  • 20. Estimating species density / Chi Yuan
  • 20.1. Introduction
  • 20.1.1. The problem: what is the density of species?
  • 20.1.2. Defining the density of species
  • 20.1.3. Species density takes on new importance in an era of environmental concern
  • 20.2. Data set
  • 20.2.1. Data description
  • 20.2.2. Data manipulation
  • 20.2.3. NP: our surrogate for A
  • 20.3. Density estimates
  • 20.3.1. First density estimate
  • 20.3.2. Density estimates for subsets with a uniform plot size
  • 20.4. Curvature in SPARs
  • 20.5. Reducing the bias
  • 20.5.1. Extrapolation
  • 20.5.2. Estimators based on the frequency of scarce species
  • 20.6. Applying bias reduction
  • 20.7. Checking our results on the scale of all of Virginia
  • 20.8. Why species density?
  • 20.8.1. Species density as an environmental indicator
  • 20.8.2. Species density as a topic of study
  • 20.9. Key points
  • Acknowledgements
  • pt. VI Conclusions
  • 21. Conclusions / Anne E. Magurran.

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