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

THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

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

Statistics for Spatial Data.

Best linear unbiased prediction (BLUP) is a standard method for estimating random effects of a mixed model. This method was originally developed in animal breeding for estimation of breeding values and is now widely used in many areas of research. It does not, however, seem to have gained the same popularity in plant breeding and variety testing as it has in animal breeding. In plants, application of mixed models with random genetic effects has up until recently been mainly restricted to the estimation of genetic and non- genetic components of variance, whereas estimation of genotypic values is mostly based on a model with fixed effects. This paper reviews recent developments in the application of BLUP in plant breeding and variety testing. These include the use of pedigree information to model and exploit genetic correlation among relatives and the use of flexible variance-covariance structures for genotype-by-environment interaction. We demonstrate that BLUP has good predictive accuracy compared to other procedures. While pedi- gree information is often included via the so-called numerator relationship matrix ðAÞ, we stress that it is frequently straightforward to exploit the same infor- mation by a simple mixed model without explicit reference to the A-matrix.

https://www.esalq.usp.br/departamentos/lce/arquivos/aulas/2012/LCE5872/Piepho%20et%20al.,%202008.pdf

BLUP for phenotypic selection in plant breeding and variety testing

Recent review articles in this journal have compared the relative merits of two prominent statistical models for analyzing yield-trial data: Additive main effects and multiplicative interaction (AMMI) and genotype main effects and genotype × environment interaction (GGE). This review addresses more than 20 issues that require clarification after controversial statements and contrasting conclusions have appeared in those recent reviews. The AMMI2 mega-environment display incorporates more of the genotype main effect and captures more of the genotype × environment (GE) interaction than does GGE2, thereby displaying the which-won-where pattern more accurately for complex datasets. When the GE interaction is captured well by one principal component, the AMMI1 display of genotype nominal yields describes winning genotypes and adaptive responses more simply and clearly than the GGE2 biplot. For genotype evaluation within a single mega-environment, a simple scatterplot of mean and stability is more straightforward than the mean vs. stability view of a GGE2 biplot. Diagnosing the most predictively accurate member of a model family is vital for either AMMI or GGE, both for gaining accuracy and delineating mega-environments.

Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations

Heritability is often used by plant breeders and geneticists as a measure of precision of a trial or a series of trials. Its main use is for computing the response to selection. Most formulas proposed for calculating heritability implicitly assume balanced data and independent genotypic effects. Both of these assumptions are often violated in plant breeding trials. This article proposes a simulation-based approach to tackle the problem. The key idea is to directly simulate the quantity of interest, e.g., response to selection, rather than trying to approximate it using some ad hoc measure of heritability. The approach is illustrated by three examples.

https://www.genetics.org/content/genetics/177/3/1881.full.pdf

Computing Heritability and Selection Response From Unbalanced Plant Breeding Trials

Designed experiments conducted by crop scientists often give rise to several random sources of variation. Pertinent examples are split-plot designs, series of experiments and repeated measurements taken on the same field plot. Data arising from such experiments may be conveniently analysed by mixed models. While the mixed model framework is by now very well developed theoretically, and good software is readily available, the technology is still under-utilized. The purpose of the present paper is, therefore, to encourage more widespread use of mixed models. We outline basic principles, which help in setting up mixed models appropriate in a given situation, the main task required from users of mixed model software. Several examples are considered to demonstrate key issues. The theoretical underpinnings are briefly sketched in so far as they are practically relevant for making informed use of mixed-model computer packages. Finally, a brief review is given of some recent methodological developments, which are of interest to the plant sciences. A German version of this paper is available from the corresponding author upon request.

A Hitchhiker's guide to mixed models for randomized experiments

All-pairwise comparisons among a set of t treatments or groups are one of the most frequent tasks in applied statistics. Users of statistical software are accustomed to the familiar lines display, in which treatments that do not differ significantly, are connected by a common line or letter. Availability of the lines display is restricted mainly to the balanced analysis of variance setup. This limited availability is at stark variance with the diversity of statistical methods and models, which call for multiple comparisons. This article describes a general method for graphically representing any set of t(t−1)/2 all-pairwise significance statements (p values) for t treatments by a familiar letter display, which is applicable regardless of the underlying data structure or the statistical method used for comparisons. The method reproduces the familiar lines display in case of the balanced analysis of variance. Its broad applicability is demonstrated using data from an international multienvironment wheat yie...

Hans-Peter Piepho

Papers

BLUP for phenotypic selection in plant breeding and variety testing

Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations

Computing Heritability and Selection Response From Unbalanced Plant Breeding Trials

A Hitchhiker's guide to mixed models for randomized experiments

An Algorithm for a Letter-Based Representation of All-Pairwise Comparisons