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

Advanced Statistical Methods in Biometric Research.

1 Introduction and Overview.- 1.1 Objectives.- 1.2 Multivariate Statistics: An Ecological Perspective.- 1.3 Multivariate Description and Inference.- 1.4 Multivariate Confusion!.- 1.5 Types of Multivariate Techniques.- 1.5.1 Ordination.- 1.5.2 Cluster Analysis.- 1.5.3 Discriminant Analysis.- 1.5.4 Canonical Correlation Analysis.- 2 Ordination: Principal Components Analysis.- 2.1 Objectives.- 2.2 Conceptual Overview.- 2.2.1 Ordination.- 2.2.2 Principal Components Analysis (PCA).- 2.3 Geometric Overview.- 2.4 The Data Set.- 2.5 Assumptions.- 2.5.1 Multivariate Normality.- 2.5.2 Independent Random Sample and the Effects of Outliers.- 2.5.3 Linearity.- 2.6 Sample Size Requirements.- 2.6.1 General Rules.- 2.6.2 Specific Rules.- 2.7 Deriving the Principal Components.- 2.7.1 The Use of Correlation and Covariance Matrices.- 2.7.2 Eigenvalues and Associated Statistics.- 2.7.3 Eigenvectors and Scoring Coefficients.- 2.8 Assessing the Importance of the Principal Components.- 2.8.1 Latent Root Criterion.- 2.8.2 Scree Plot Criterion.- 2.8.3 Broken Stick Criterion.- 2.8.4 Relative Percent Variance Criterion.- 2.8.5 Significance Tests.- 2.9 Interpreting the Principal Components.- 2.9.1 Principal Component Structure.- 2.9.2 Significance of Principal Component Loadings.- 2.9.3 Interpreting the Principal Component Structure.- 2.9.4 Communality.- 2.9.5 Principal Component Scores and Associated Plots.- 2.10 Rotating the Principal Components.- 2.11 Limitations of Principal Components Analysis.- 2.12 R-Factor Versus Q-Factor Ordination.- 2.13 Other Ordination Techniques.- 2.13.1 Polar Ordination.- 2.13.2 Factor Analysis.- 2.13.3 Nonmetric Multidimensional Scaling.- 2.13.4 Reciprocal Averaging.- 2.13.5 Detrended Correspondence Analysis.- 2.13.6 Canonical Correspondence Analysis.- Appendix 2.1.- 3 Cluster Analysis.- 3.1 Objectives.- 3.2 Conceptual Overview.- 3.3 The Definition of Cluster.- 3.4 The Data Set.- 3.5 Clustering Techniques.- 3.6 Nonhierarchical Clustering.- 3.6.1 Polythetic Agglomerative Nonhierarchical Clustering.- 3.6.2 Polythetic Divisive Nonhierarchical Clustering.- 3.7 Hierarchical Clustering.- 3.7.1 Polythetic Agglomerative Hierarchical Clustering.- 3.7.2 Polythetic Divisive Hierarchical Clustering.- 3.8 Evaluating the Stability of the Cluster Solution.- 3.9 Complementary Use of Ordination and Cluster Analysis.- 3.10 Limitations of Cluster Analysis.- Appendix 3.1.- 4 Discriminant Analysis.- 4.1 Objectives.- 4.2 Conceptual Overview.- 4.2.1 Overview of Canonical Analysis of Discriminance.- 4.2.2 Overview of Classification.- 4.2.3 Analogy with Multiple Regression Analysis and Multivariate Analysis of Variance.- 4.3 Geometric Overview.- 4.4 The Data Set.- 4.5 Assumptions.- 4.5.1 Equality of Variance-Covariance Matrices.- 4.5.2 Multivariate Normality.- 4.5.3 Singularities and Multicollinearity.- 4.5.4 Independent Random Sample and the Effects of Outliers.- 4.5.5 Prior Probabilities Are Identifiable.- 4.5.6 Linearity 153.- 4.6 Sample Size Requirements.- 4.6.1 General Rules.- 4.6.2 Specific Rules.- 4.7 Deriving the Canonical Functions.- 4.7.1 Stepwise Selection of Variables.- 4.7.2 Eigenvalues and Associated Statistics.- 4.7.3 Eigenvectors and Canonical Coefficients.- 4.8 Assessing the Importance of the Canonical Functions.- 4.8.1 Relative Percent Variance Criterion.- 4.8.2 Canonical Correlation Criterion.- 4.8.3 Classification Accuracy.- 4.8.4 Significance Tests.- 4.8.5 Canonical Scores and Associated Plots.- 4.9 Interpreting the Canonical Functions.- 4.9.1 Standardized Canonical Coefficients.- 4.9.2 Total Structure Coefficients.- 4.9.3 Covariance-Controlled Partial F-Ratios.- 4.9.4 Significance Tests Based on Resampling Procedures.- 4.9.5 Potency Index.- 4.10 Validating the Canonical Functions.- 4.10.1 Split-Sample Validation.- 4.10.2 Validation Using Resampling Procedures.- 4.11 Limitations of Discriminant Analysis.- Appendix 4.1.- 5 Canonical Correlation Analysis.- 5.1 Objectives.- 5.2 Conceptual Overview.- 5.3 Geometric Overview.- 5.4 The Data Set.- 5.5 Assumptions.- 5.5.1 Multivariate Normality.- 5.5.2 Singularities and Multicollinearity.- 5.5.3 Independent Random Sample and the Effects of Outliers.- 5.5.4 Linearity.- 5.6 Sample Size Requirements.- 5.6.1 General Rules.- 5.6.2 Specific Rules.- 5.7 Deriving the Canonical Variates.- 5.7.1 The Use of Covariance and Correlation Matrices.- 5.7.2 Eigenvalues and Associated Statistics.- 5.7.3 Eigenvectors and Canonical Coefficients.- 5.8 Assessing the Importance of the Canonical Variates.- 5.8.1 Canonical Correlation Criterion.- 5.8.2 Canonical Redundancy Criterion.- 5.8.3 Significance Tests.- 5.8.4 Canonical Scores and Associated Plots.- 5.9 Interpreting the Canonical Variates.- 5.9.1 Standardized Canonical Coefficients.- 5.9.2 Structure Coefficients.- 5.9.3 Canonical Cross-Loadings.- 5.9.4 Significance Tests Based on Resampling Procedures.- 5.10 Validating the Canonical Variates.- 5.10.1 Split-Sample Validation.- 5.10.2 Validation Using Resampling Procedures.- 5.11 Limitations of Canonical Correlation Analysis.- Appendix 5.1.- 6 Summary and Comparison.- 6.1 Objectives.- 6.2 Relationship Among Techniques.- 6.2.1 Purpose and Source of Variation Emphasized.- 6.2.2 Statistical Procedure.- 6.2.3 Type of Statistical Technique and Variable Set Characteristics.- 6.2.4 Data Structure.- 6.2.5 Sampling Design.- 6.3 Complementary Use of Techniques.- Appendix: Acronyms Used in This Book.

Multivariate statistics for wildlife and ecology research

A fundamental question related to ecological and genetic aspects of dispersal is: What are the mechanisms for the evolution of dispersal? In recent years a prodigious number of mathematical models have described the evolution of dispersal. We believe that it is a propitious time to examine these models with respect to their underlying assumptions (Table 1), critical parameters, and predictions. Our objective is to elucidate common themes as well as differences in these models, and to present them in a manner comprehensible to individuals who lack extensive mathematical training, so that others might be encouraged to perform critical experiments in the field. We conclude the review with a summary of empirical work that uses data on birds

https://www.jstor.org/stable/pdfplus/2097033.pdf

Evolution of Dispersal: Theoretical Models and Empirical Tests Using Birds and Mammals

Publisher Summary Ordination implies an abstract space in which the entities form a constellation. In the Bray–Curtis ordination, the entities are samples and the attributes are species values in those samples. The aim of this method is to (1) calculate a distance matrix, (2) select two reference points (either real or synthetic samples) for determining direction of each axis, and (3) project all samples onto each such axis by their relationship to the two reference points. There are two major problems common to all ordination techniques, which include a function of the β-diversity or heterogeneity of the data set—that is, how different the samples are from one another. All ordinations distort the original multivariate data set and information is inevitably lost. Distortion in ordination has two kinds of consequences. The first is compressing and stretching distances in the ordination, compared with the original distance measures and relative to one another. The second consequence is the curvature of environmental axes, and this relates to Orloci's types A and C. Some of the alternatives to Bray–Curtis ordination are principal component analysis, reciprocal averaging, and iterative-stress minimization techniques.

Bray-curtis ordination: an effective strategy for analysis of multivariate ecological data

Food Habits of Sympatric Peromyscus leucopus and Peromyscus maniculatus

In a study of habitat exploitation in a forest-floor small mammal community, we have collected habitat and population data for Peromyscus leucopus (white-footed mouse), Ochrotomys nuttalli (golden mouse) and Tamias striatus (eastern chipmunk). Using multiple regression analysis, we estimate the effects of habitat selection and competition on the local distributions of these species during three seasons. Each of the partial regression coefficients relating the density of an "independent" species to the density of the "dependent" species is negative. This result indicates that competition (i.e., mutual inhibition) is pervasive among these species. Competitive ability and habitat selectivity both increase in the order PeromyscusTamiasOchrotomys. Peromyscus is a poorly competitive habitat generalist, Ochrotomys is a strongly competitive habitat specialist, and Tamias is intermediate in both respects. The competitive hierarchy is stable between seasons. These results both confirm the conclusions reached in previous studies of this small mammal community and suggest the design of experiments to further clarify the mode and consequences of interaction between these species.

https://www.jstor.org/stable/info/3544642

Competition and habitat selection in a forest-floor small mammal fauna

This study tests two predictions about habitat use by the small mammal species of Assateague Island, the largest of the Virginia barrier islands: (1) habitat segregation between species should be conspicuous, with centers of species abundance located in different plant associations, and (2) competition should exert a strong influence on habitat use by each species, with the effects of the other species at least as great as the effects of habitat structure. Prediction 1 was supported by analyses of mammal species abundances (based on live-trapping) in five vegetation types. Habitat segregation was pronounced and species' distributions reflected coarse-grained habitat patchiness. However, pre? diction 2 was not supported by analyses of the independent effects of competition and habitat structure on the local abundance of each species. Despite the apparent ubiquity of competition, all of the interaction coefficients were <c 1.00 in absolute value, suggesting that the competition was relatively "weak." Futhermore, for most species the effects of other species were found to be relatively less important than those of habitat structure. Only Mus musculus, and to a much lesser extent Zapus hudsonius, appear to be affected more strongly by competition. These findings impugn an earlier suggestion that competition plays a significant role in promoting the strong positive relationship between rodent diversity and habitat complexity on the Virginia barrier islands. Although no data on turnover rates are available, there is no compelling reason to invoke a dynamic equilibrium to explain the biogeography of small mammals on the Virginia barrier islands. The number and combination of species found on an island may simply represent a sample drawn from the pool of available species, weighted primarily by island area and habitat complexity and by the colonizing abilities of the species.

Habitat Use by Insular Small Mammals: Relative Effects of Competition and Habitat Structure

Rodent populations were censused on nine Virginia barrier islands during the summers of 1975 and 1977. The five species observed include Microtus pennsylvanicus, Mus musculus, Ory- zomys palustris, Rattus norvegicus, and Sciurus carolinensis. The number of rodent species ranged from zero on a small, low-lying, sandy island to five on a large, elevated, forested island. Rodent species richness (diversity) increased directly with island area, elevation, vegetation height, and habitat complexity (i.e., the number of distinct woody plant associations). Because of strict zonation of plant associations, increased habitat complexity represents increased availability of exploitable patches of habitat. As expected from studies of mainland rodent communities, this patchiness appears to promote increased species diversity. Each species exhibits an "exploitation specialty" involving little spatial overlap with other species. Although no direct tests have been conducted, we speculate that the rodent communities of these islands are at equilibrium. Low rates of colonization and high rates of extinction interact to produce low species diversity and a steep species-area curve.

https://www.jstor.org/stable/pdfplus/1937154.pdf

Ecological Correlates of Insular Rodent Diversity

Stable URL:http://links.jstor.org/sici?sici=0030-1299%28199402%2969%3A1%3C107%3AOMAHTF%3E2.0.CO%3B2-2Oikos is currently published by Nordic Society Oikos.Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available athttp://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtainedprior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content inthe JSTOR archive only for your personal, non-commercial use.Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained athttp://www.jstor.org/journals/oikos.html.Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printedpage of such transmission.The JSTOR Archive is a trusted digital repository providing for long-term preservation and access to leading academicjournals and scholarly literature from around the world. The Archive is supported by libraries, scholarly societies, publishers,and foundations. It is an initiative of JSTOR, a not-for-profit organization with a mission to help the scholarly community takeadvantage of advances in technology. For more information regarding JSTOR, please contact support@jstor.org.http://www.jstor.orgTue Jul 17 16:34:31 2007

Raymond D. Dueser

Papers

Food Habits of Sympatric Peromyscus leucopus and Peromyscus maniculatus

Competition and habitat selection in a forest-floor small mammal fauna

Habitat Use by Insular Small Mammals: Relative Effects of Competition and Habitat Structure

Ecological Correlates of Insular Rodent Diversity

Of mice and habitats: tests for density-dependent habitat selection