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

So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Human biochemical genetics

Summary
1. Here, I present a new, multifunctional phylogenetics package, phytools, for the R statistical computing environment.

2. The focus of the package is on methods for phylogenetic comparative biology; however, it also includes tools for tree inference, phylogeny input/output, plotting, manipulation and several other tasks.

3. I describe and tabulate the major methods implemented in phytools, and in addition provide some demonstration of its use in the form of two illustrative examples.

4. Finally, I conclude by briefly describing an active web-log that I use to document present and future developments for phytools. I also note other web resources for phylogenetics in the R computational environment.

/pdf/phytools-an-r-package-for-phylogenetic-comparative-biology-yli0omoidd.pdf

phytools: an R package for phylogenetic comparative biology (and other things)

Evolution of Protein Molecules

Genetics and analysis of quantitative traits

Summary
1. A common procedure in the regression analysis of interspecies data is to first test the independent and dependent variables X and Y for phylogenetic signal, and then use the presence of signal in one or both traits to justify regression analysis using phylogenetic methods such as independent contrasts or phylogenetic generalized least squares.

2. This is incorrect, because phylogenetic regression assumes that the residual error in the regression model (not in the original traits) is distributed according to a multivariate normal distribution with variances and covariances proportional to the historical relations of the species in the sample.

3. Here, I examine the consequences of justifying and applying the phylogenetic regression incorrectly. I find that when used improperly the phylogenetic regression can have poor statistical performance, even under some circumstances in which the type I error rate of the method is not inflated over its nominal level.

4. I also find, however, that when tests of phylogenetic signal in phylogenetic regression are applied properly, and in particular when phylogenetic signal in the residual error is simultaneously estimated with the regression parameters, the phylogenetic regression outperforms equivalent non-phylogenetic procedures.

/pdf/phylogenetic-signal-and-linear-regression-on-species-data-413qclrfmm.pdf

Phylogenetic signal and linear regression on species data

Ar ecent advance in the phylogenetic comparative analysis of continuous traits has been explicit, model-based measurement of "phylogenetic signal" in data sets composed of observations collected from species related by a phylogenetic tree. Phylogenetic signal is a measure of the statistical dependence among species' trait values due to their phylogenetic relationships. Although phylogenetic signal is a measure of pattern (statistical dependence), there has nonetheless been a widespread propensity in the literature to attribute this pattern to aspects of the evolutionary process or rate. This may be due, in part, to the perception that high evolutionary rate necessarily results in low phylogenetic signal; and, conversely, that low evolutionary rate or stabilizing selection results in high phylogenetic signal (due to the resulting high resemblance between related species). In this study, we use individual-based numerical simulations on stochastic phylogenetic trees to clarify the relationship between phylogenetic signal, rate, and evolutionary process. Under the simplest model for quantitative trait evolution, homogeneous rate genetic drift, there is no relation between evolutionary rate and phylogenetic signal. For other circumstances, such as functional constraint, fluctuating selection, niche conservatism, and evolutionary heterogeneity, the relationship between process, rate, and phylogenetic signal is complex. For these reasons, we recommend against interpretations of evolutionary process or rate based on estimates of phylogenetic signal. (Comparative method; evolutionary lability; functional constraint; genetic drift; niche conservatism; quantitative characters; phylogenetics.)

/pdf/phylogenetic-signal-evolutionary-process-and-rate-21jjrzwfh9.pdf

Phylogenetic Signal, Evolutionary Process, and Rate

Phylogenetic methods for the analysis of species data are widely used in evolutionary studies. However, preliminary data transformations and data reduction procedures (such as a size-correction and principal components analysis, PCA) are often performed without first correcting for nonindependence among the observations for species. In the present short comment and attached R and MATLAB code, I provide an overview of statistically correct procedures for phylogenetic size-correction and PCA. I also show that ignoring phylogeny in preliminary transformations can result in significantly elevated variance and type I error in our statistical estimators, even if subsequent analysis of the transformed data is performed using phylogenetic methods. This means that ignoring phylogeny during preliminary data transformations can possibly lead to spurious results in phylogenetic statistical analyses of species data.

/pdf/size-correction-and-principal-components-for-interspecific-1zj2u3sg68.pdf

Size-correction and principal components for interspecific comparative studies

The pace of phenotypic diversification during adaptive radiation should decrease as ecological opportunity declines. We test this prediction using phylogenetic comparative analyses of a wide range of morphological traits in Greater Antillean Anolis lizards. We find that the rate of diversification along two important axes of Anolis radiation—body size and limb dimensions—decreased as opportunity declined, with opportunity quantified either as time elapsed in the radiation or as the diversity of competing anole lineages inferred to have been present on an island at different times in the past. Most previous studies of the ecological opportunity hypothesis have focused on the rate of species diversification; our results provide a complementary perspective, indicating that the rate of phenotypic diversification declines with decreasing opportunity in an adaptive radiation.

/pdf/ecological-opportunity-and-the-rate-of-morphological-1nqmwechhs.pdf

Liam J. Revell

Papers

phytools: an R package for phylogenetic comparative biology (and other things)

Phylogenetic signal and linear regression on species data

Phylogenetic Signal, Evolutionary Process, and Rate

Size-correction and principal components for interspecific comparative studies

Ecological opportunity and the rate of morphological evolution in the diversification of Greater Antillean anoles.