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Conservation Biology for All$
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Navjot S. Sodhi and Paul R. Ehrlich

Print publication date: 2010

Print ISBN-13: 9780199554232

Published to Oxford Scholarship Online: February 2010

DOI: 10.1093/acprof:oso/9780199554232.001.0001

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The conservation biologist's toolbox – principles for the design and analysis of conservation studies

The conservation biologist's toolbox – principles for the design and analysis of conservation studies

(p.313) Chapter 16 The conservation biologist's toolbox – principles for the design and analysis of conservation studies
Conservation Biology for All

Corey J. A. Bradshaw

Barry W. Brook

Oxford University Press

In this chapter, Corey J. A. Bradshaw and Barry W. Brook, discuss measures of biodiversity patterns followed by an overview of experimental design and associated statistical paradigms. Conservation biology is a highly multidisciplinary science employing methods from ecology, Earth systems science, genetics, physiology, veterinary science, medicine, mathematics, climatology, anthropology, psychology, sociology, environmental policy, geography, political science, and resource management. Here we focus primarily on ecological methods and experimental design. It is impossible to census all species in an ecosystem, so many different measures exist to compare biodiversity: these include indices such as species richness, Simpson's diversity, Shannon's index and Brouillin's index. Many variants of these indices exist. The scale of biodiversity patterns is important to consider for biodiversity comparisons: α (local), β (between‐site), and γ (regional or continental) diversity. Often surrogate species ‐ the number, distribution or pattern of species in a particular taxon in a particular area thought to indicate a much wider array of taxa ‐ are required to simplify biodiversity assessments. Many similarity, dissimilarity, clustering, and multivariate techniques are available to compare biodiversity indices among sites. Conservation biology rarely uses completely manipulative experimental designs (although there are exceptions), with mensurative (based on existing environmental gradients) and observational studies dominating. Two main statistical paradigms exist for comparing biodiversity: null hypothesis testing and multiple working hypotheses – the latter paradigm is more consistent with the constraints typical of conservation data and so should be invoked when possible. Bayesian inferential methods generally provide more certainty when prior data exist. Large sample sizes, appropriate replication and randomization are cornerstone concepts in all conservation experiments. Simple relative abundance time series (sequential counts of individuals) can be used to infer more complex ecological mechanisms that permit the estimation of extinction risk, population trends, and intrinsic feedbacks. The risk of a species going extinct or becoming invasive can be predicted using cross‐taxonomic comparisons of life history traits. Population viability analyses are essential tools to estimate extinction risk over defined periods and under particular management interventions. Many methods exist to implement these, including count‐based, demographic, metapopulation, and genetic. Many tools exist to examine how genetics affects extinction risk, of which perhaps the measurement of inbreeding depression, gene flow among populations, and the loss of genetic diversity with habitat degradation are the most important.

Keywords:   Bayesian inferential methods, biodiversity patterns, ecological methods, experimental design, extinction risk, inbreeding depression, population viability analysis, species richness

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