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

A DNA test to sex most birds.

Summary
1The role of phenotypic plasticity in evolution has historically been a contentious issue because of debate over whether plasticity shields genotypes from selection or generates novel opportunities for selection to act. Because plasticity encompasses diverse adaptive and non-adaptive responses to environmental variation, no single conceptual framework adequately predicts the diverse roles of plasticity in evolutionary change.
2Different types of phenotypic plasticity can uniquely contribute to adaptive evolution when populations are faced with new or altered environments. Adaptive plasticity should promote establishment and persistence in a new environment, but depending on how close the plastic response is to the new favoured phenotypic optimum dictates whether directional selection will cause adaptive divergence between populations. Further, non-adaptive plasticity in response to stressful environments can result in a mean phenotypic response being further away from the favoured optimum or alternatively increase the variance around the mean due to the expression of cryptic genetic variation. The expression of cryptic genetic variation can facilitate adaptive evolution if by chance it results in a fitter phenotype.
3We conclude that adaptive plasticity that places populations close enough to a new phenotypic optimum for directional selection to act is the only plasticity that predictably enhances fitness and is most likely to facilitate adaptive evolution on ecological time-scales in new environments. However, this type of plasticity is likely to be the product of past selection on variation that may have been initially non-adaptive.
4We end with suggestions on how future empirical studies can be designed to better test the importance of different kinds of plasticity to adaptive evolution.

/pdf/adaptive-versus-non-adaptive-phenotypic-plasticity-and-the-55680hih4y.pdf

Adaptive versus non‐adaptive phenotypic plasticity and the potential for contemporary adaptation in new environments

In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.

Evolution and the Theory of Games

Repeatability (more precisely the common measure of repeatability, the intra-class correlation coefficient, ICC) is an important index for quantifying the accuracy of measurements and the constancy of phenotypes. It is the proportion of phenotypic variation that can be attributed to between-subject (or between-group) variation. As a consequence, the non-repeatable fraction of phenotypic variation is the sum of measurement error and phenotypic flexibility. There are several ways to estimate repeatability for Gaussian data, but there are no formal agreements on how repeatability should be calculated for non-Gaussian data (e.g. binary, proportion and count data). In addition to point estimates, appropriate uncertainty estimates (standard errors and confidence intervals) and statistical significance for repeatability estimates are required regardless of the types of data. We review the methods for calculating repeatability and the associated statistics for Gaussian and non-Gaussian data. For Gaussian data, we present three common approaches for estimating repeatability: correlation-based, analysis of variance (ANOVA)-based and linear mixed-effects model (LMM)-based methods, while for non-Gaussian data, we focus on generalised linear mixed-effects models (GLMM) that allow the estimation of repeatability on the original and on the underlying latent scale. We also address a number of methods for calculating standard errors, confidence intervals and statistical significance; the most accurate and recommended methods are parametric bootstrapping, randomisation tests and Bayesian approaches. We advocate the use of LMM- and GLMM-based approaches mainly because of the ease with which confounding variables can be controlled for. Furthermore, we compare two types of repeatability (ordinary repeatability and extrapolated repeatability) in relation to narrow-sense heritability. This review serves as a collection of guidelines and recommendations for biologists to calculate repeatability and heritability from both Gaussian and non-Gaussian data.

Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists.

This paper reviews the range expansion of Brachinus sclopeta (Fabricius, 1792) and describes the discovery of an apparently established population of B. sclopeta on an organic farm in southern England.

Range expansion of Brachinus sclopeta (Fabricius, 1792) (Coleoptera: Carabidae) in Southern England

/pdf/orphilus-aegeanus-coleoptera-dermestidae-orphilinae-a-new-25oysi12.pdf

Graham J. Holloway

Papers

Range expansion of Brachinus sclopeta (Fabricius, 1792) (Coleoptera: Carabidae) in Southern England

Orphilus aegeanus (Coleoptera, Dermestidae, Orphilinae): a new species from Greece and Turkey.

SHORT COMMUNICATION Host selection behaviour by adults of legume and cereal breeding populations of Sitophilus oryzae (Coleoptera: Curculionidae)