Peter T. Boag

Bio: Peter T. Boag is an academic researcher from Queen's University. The author has contributed to research in topics: Population & Reproductive success. The author has an hindex of 53, co-authored 123 publications receiving 12117 citations. Previous affiliations of Peter T. Boag include Trent University & McGill University.

Unrepeatable repeatabilities: a common mistake

Abstract: -Repeatability is a useful tool for the population geneticist or genetical ecologist, but several papers have carried errors in its calculation We outline the correct calculation of repeatability, point out the common mistake, show how the incorrectly calculated value relates to repeatability, and provide a method for checking published values and calculating approximate repeatability values from the F ratio (mean squares among groups/ mean squares within groups) Received 6 February 1986, accepted 25 August 1986 REPEATABILITY is a measure used in quantitative genetics to describe the proportion of variance in a character that occurs among rather than within individuals Repeatability, r, is given by: r = (VG + VEg)/ VP, (1) where VG is the genotypic variance, VEg the general environmental variance, and Vp the phenotypic variance (Falconer 1960, 1981) In addition to its use in assessing the reliability of multiple measurements on the same individual, repeatability may be used to set an upper limit to the value of heritability (Falconer 1960, 1981) and to separate, for instance, the effects of "self" and "mate" on a character such as clutch size (van Noordwijk et al 1980) Repeatability is therefore a useful statistic for population geneticists and genetical ecologists Recently, we have noticed an increasing number of published papers and unpublished manuscripts in which repeatability was miscalculated Our purpose is fivefold: (1) to outline the correct method of calculating repeatability; (2) to point out a common mistake in calculating repeatability; (3) to show how much this mistake affects values of repeatability; (4) to provide a quick way of checking published estimates, and to calculate an approximate value of repeatability from published F ratios and degrees of freedom; and (5) to make recommendations for authors, referees, editors, and readers to prevent the promulgation and propagation of incorrect repeatability values in the literature CALCULATION OF REPEATABILITY Repeatability is the intraclass correlation coefficient (Sokal and Rohlf 1981), which is based on variance components derived from a one-way analysis of variance (ANOVA) The intraclass correlation coefficient is given by some statistical packages; otherwise it can be calculated from an ANOVA ANOVA is described in most statistics textbooks (eg Sokal and Rohlf 1981; Kirk 1968 gives a detailed treatment of more complex designs of ANOVA), so we will not repeat it here, but give the general form of the results from such an analysis in Table 1 Repeatability, r, is given by r = sA / (S + SA)' (2) where S2A is the among-groups variance component and s2 is the within-group variance component These variance components are calculated from the mean squares in the analysis of variance as:

Preservation of avian blood and tissue samples for DNA analyses

Abstract: A problem frequently faced by researchers involved in collecting tissues for DNA isolation is the preservation of samples in the field prior to and during their transportation to the laboratory. Prevention of DNA degradation is usually achieved through freezing. As this is not always practical, we have tested the efficiency of chemical solutions containing high concentrations of salts (e.g., NaCl, EDTA, and diaminocyclohexanetetraacetate) and detergent at preserving DNA in bird tissue and blood samples stored at ambient temperature for extended periods of time. For blood samples, we recommend the use of a buffer that lyses the cells and nuclei and contains 0.01 M Tris, 0.01 M NaCl, 0.01 M EDTA, and 1% n-lauroylsarcosine, Ph 7.5. Tissue samples are best preserved as small pieces in a saline solution made of 20% dimethyl sulfoxyde, 0.25 M EDTA, saturated with NaCl, pH 8.0. DNA extracted from samples preserved in these solutions for up to 24 weeks was compared with DNA recovered from tissue samples stored at...

Intense Natural Selection in a Population of Darwin's Finches (Geospizinae) in the Galápagos

Abstract: Survival of Darwin's finches through a drought on Daphne Major Island was nonrandom. Large birds, especially males with large beaks, survived best because they were able to crack the large and hard seeds that predominated in the drought. Selection intensities, calculated by O'Donald's method, are the highest yet recorded for a vertebrate population.

Realized reproductive success of polygynous red-winged blackbirds revealed by DNA markers.

Abstract: Hypervariable genetic markers, including a novel locus-specific marker detected by a mouse major histocompatibility complex probe, reveal that multiple paternity is common in families of polygynous red-winged blackbirds (Agelaius phoeniceus). Almost half of all nests contained at least one chick resulting from an extra-pair fertilization, usually by a neighboring male. Genetically based measures of reproductive success show that individual males realize more than 20% of their overall success from extra-pair fertilizations, on average, and that this form of mating behavior confounds traditional measures of male success. The importance of alternative reproductive tactics in a polygynous bird is quantified, and the results challenge previous explanations for the evolution of avian polygny.

Female eavesdropping on male song contests in songbirds.

Abstract: Male song reflects the quality of the singer in many animals and plays a role in female choice of social and copulation partners. Eavesdropping on male-male vocal interactions is a means by which females can compare different males' singing behavior directly and make immediate comparisons between

Human biochemical genetics

Abstract: 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

The measurement of selection on correlated characters

Abstract: Natural selection acts on phenotypes, regardless of their genetic basis, and produces immediate phenotypic effects within a generation that can be measured without recourse to principles of heredity or evolution. In contrast, evolutionary response to selection, the genetic change that occurs from one generation to the next, does depend on genetic variation. Animal and plant breeders routinely distinguish phenotypic selection from evolutionary response to selection (Mayo, 1980; Falconer, 1981). Upon making this critical distinction, emphasized by Haldane (1954), precise methods can be formulated for the measurement of phenotypic natural selection. Correlations between characters seriously complicate the measurement of phenotypic selection, because selection on a particular trait produces not only a direct effect on the distribution of that trait in a population, but also produces indirect effects on the distribution of correlated characters. The problem of character correlations has been largely ignored in current methods for measuring natural selection on quantitative traits. Selection has usually been treated as if it acted only on single characters (e.g., Haldane, 1954; Van Valen, 1965a; O'Donald, 1968, 1970; reviewed by Johnson, 1976 Ch. 7). This is obviously a tremendous oversimplification, since natural selection acts on many characters simultaneously and phenotypic correlations between traits are ubiquitous. In an important but neglected paper, Pearson (1903) showed that multivariate statistics could be used to disentangle the direct and indirect effects of selection to determine which traits in a correlated ensemble are the focus of direct selection. Here we extend and generalize Pearson's major results. The purpose of this paper is to derive measures of directional and stabilizing (or disruptive) selection on each of a set of phenotypically correlated characters. The analysis is retrospective, based on observed changes in the multivariate distribution of characters within a generation, not on the evolutionary response to selection. Nevertheless, the measures we propose have a close connection with equations for evolutionary change. Many other commonly used measures of the intensity of selection (such as selective mortality, change in mean fitness, variance in fitness, or estimates of particular forms of fitness functions) have little predictive value in relation to evolutionary change in quantitative traits. To demonstrate the utility of our approach, we analyze selection on four morphological characters in a population of pentatomid bugs during a brief period of high mortality. We also summarize a multivariate selection analysis on nine morphological characters of house sparrows caught in a severe winter storm, using the classic data of Bumpus (1899). Direct observations and measurements of natural selection serve to clarify one of the major factors of evolution. Critiques of the "adaptationist program" (Lewontin, 1978; Gould and Lewontin, 1979) stress that adaptation and selection are often invoked without strong supporting evidence. We suggest quantitative measurements of selection as the best alternative to the fabrication of adaptive scenarios. Our optimism that measurement can replace rhetorical claims for adaptation and selection is founded in the growing success of field workers in their efforts to measure major components of fitness in natural populations (e.g., Thornhill, 1976; Howard, 1979; Downhower and Brown, 1980; Boag and Grant, 1981; Clutton-Brock et

AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons

Abstract: We briefly outline the information-theoretic (I-T) approaches to valid inference including a review of some simple methods for making formal inference from all the hypotheses in the model set (multimodel inference). The I-T approaches can replace the usual t tests and ANOVA tables that are so inferentially limited, but still commonly used. The I-T methods are easy to compute and understand and provide formal measures of the strength of evidence for both the null and alternative hypotheses, given the data. We give an example to highlight the importance of deriving alternative hypotheses and representing these as probability models. Fifteen technical issues are addressed to clarify various points that have appeared incorrectly in the recent literature. We offer several remarks regarding the future of empirical science and data analysis under an I-T framework.