Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201

The Theory of Island Biogeography

An essential textbook for any student or researcher in biology needing to design experiments, sample programs or analyse the resulting data The text begins with a revision of estimation and hypothesis testing methods, covering both classical and Bayesian philosophies, before advancing to the analysis of linear and generalized linear models Topics covered include linear and logistic regression, simple and complex ANOVA models (for factorial, nested, block, split-plot and repeated measures and covariance designs), and log-linear models Multivariate techniques, including classification and ordination, are then introduced Special emphasis is placed on checking assumptions, exploratory data analysis and presentation of results The main analyses are illustrated with many examples from published papers and there is an extensive reference list to both the statistical and biological literature The book is supported by a website that provides all data sets, questions for each chapter and links to software

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Experimental Design and Data Analysis for Biologists

This paper presents a new and simple method to find indicator species and species assemblages characterizing groups of sites The novelty of our approach lies in the way we combine a species relative abundance with its relative frequency of occurrence in the various groups of sites This index is maximum when all individuals of a species are found in a single group of sites and when the species occurs in all sites of that group; it is a symmetric indicator The statistical significance of the species indicator values is evaluated using a randomization procedure Contrary to TWINSPAN, our indicator index for a given species is independent of the other species relative abundances, and there is no need to use pseudospecies The new method identifies indicator species for typologies of species releves obtained by any hierarchical or nonhierarchical classification procedure; its use is independent of the classification method Because indicator species give ecological meaning to groups of sites, this method provides criteria to compare typologies, to identify where to stop dividing clusters into subsets, and to point out the main levels in a hierarchical classification of sites Species can be grouped on the basis of their indicator values for each clustering level, the heterogeneous nature of species assemblages observed in any one site being well preserved Such assemblages are usually a mixture of eurytopic (higher level) and stenotopic species (characteristic of lower level clusters) The species assemblage approach demonstrates the importance of the ''sampled patch size,'' ie, the diversity of sampled ecological combinations, when we compare the frequencies of core and satellite species A new way to present species-site tables, accounting for the hierarchical relationships among species, is proposed A large data set of carabid beetle distributions in open habitats of Belgium is used as a case study to illustrate the new method

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Species assemblages and indicator species:the need for a flexible asymmetrical approach

Principal component analysis PCA is a multivariate technique that analyzes a data table in which observations are described by several inter-correlated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and to display the pattern of similarity of the observations and of the variables as points in maps. The quality of the PCA model can be evaluated using cross-validation techniques such as the bootstrap and the jackknife. PCA can be generalized as correspondence analysis CA in order to handle qualitative variables and as multiple factor analysis MFA in order to handle heterogeneous sets of variables. Mathematically, PCA depends upon the eigen-decomposition of positive semi-definite matrices and upon the singular value decomposition SVD of rectangular matrices. Copyright © 2010 John Wiley & Sons, Inc.

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Principal component analysis

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Modern Applied Statistics With S

Multiple regression analysis continues to be a quantitative tool used extensively in the ecological literature. Consequently, methods for model selection and validation are important considerations...

Torturing data for the sake of generality: How valid are our regression models?

Peres-Neto, P. R., Olden, J. D. and Jackson, D. A. 2001. Environmentally con-strained null models: site suitability as occupancy criterion. – Oikos 93: 110–120.Null models have proven to be an important quantitative tool in the search forecological processes driving local diversity and species distribution. However, thereremains an important concern that different processes, such as environmental condi-tions and biotic interactions may produce similar patterns in species distributions. Inthis paper we present an analytical protocol for incorporating habitat suitability as anoccupancy criterion in null models. Our approach involves modeling species presenceor absence as a function of environmental conditions, and using the estimatedsite-speciﬁc probabilities of occurrence as the likelihood of species occupancy of a siteduring the generation of ‘‘null communities’’. We validated this approach by showingthat type I error is not affected by the use of probabilities as a site occupancycriterion and is robust against a variety of predictive performances of the species-en-vironmental models. We describe the expected differences when contrasting classicaland the environmentally constrained null models, and illustrate our approach with adata set of Dutch dune hunting spider assemblages. An environmentally constrainedapproach to null models will provide a more robust evaluation of species associationsby facilitating the distinction between mutually exclusive processes that may shapespecies distributions and community assembly.

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Environmentally constrained null models: site suitability as occupancy criterion

Ecologists often ‘standardize’ data through the use of ratios and indices. Such measures are employed generally to remove a ‘size effect’ induced by some relatively uniteresting variable. The implications of using the resultant data in correlation and regression analyses are poorly recognized. We show that ratios and indices often provide surprising and ‘spurious’ results due to their unusual properties. As a solution, we advocate the use of randomization tests to evaluate hypotheses confounded by ‘spurious’ correlations. In addition, we emphasize that identifying the appropriate null correlation is of utmost importance when statistically evaluating ratios, although this issue is frequently ignored.

The spectre of 'spurious' correlations.

Matrix comparison tests (i.e., Mantel's test or quadratic assignment) are employed with increasing frequency to measure the concordance between genetic, behavioural, morphological, ecological, and ...

Donald A. Jackson

Papers

Torturing data for the sake of generality: How valid are our regression models?

Environmentally constrained null models: site suitability as occupancy criterion

The spectre of 'spurious' correlations.

Are probability estimates from the permutation model of Mantel's test stable?

Addressing the removal of rare species in multivariate bioassessments: The impact of methodological choices