Kurt J. Leonard

Similarity coefficients for molecular markers in studies of genetic relationships between individuals for haploid, diploid, and polyploid species

Determining true genetic dissimilarity between individuals is an important and decisive point for clustering and analysing diversity within and among populations, because different dissimilarity indices may yield conflicting outcomes. We show that there are no acceptable universal approaches to assessing the dissimilarity between individuals with molecular markers. Different measures are relevant to dominant and codominant DNA markers depending on the ploidy of organisms. The Dice coefficient is the suitable measure for haploids with codominant markers and it can be applied directly to (0,1)‐vectors representing banding profiles of individuals. None of the common measures, Dice, Jaccard, simple mismatch coefficient (or the squared Euclidean distance), is appropriate for diploids with codominant markers. By transforming multiallelic banding patterns at each locus into the corresponding homozygous or heterozygous states, a new measure of dissimilarity within locus was developed and expanded to assess dissimilarity between multilocus states of two individuals by averaging across all codominant loci tested. There is no rigorous well‐founded solution in the case of dominant markers. The simple mismatch coefficient is the most suitable measure of dissimilarity between banding patterns of closely related haploid forms. For distantly related haploid individuals, the Jaccard dissimilarity is recommended. In general, no suitable method for measuring genetic dissimilarity between diploids with dominant markers can be proposed. Banding patterns of diploids with dominant markers and polyploids with codominant markers represent individuals’ phenotypes rather than genotypes. All dissimilarity measures proposed and developed herein are metrics.

COMPETITION AND DENSITY‐DEPENDENT FITNESS IN APLANT PARASITIC FUNGUS

Inter- and intrastrain competitive interactions and their effects on fitness were quantified for coexisting strains of Puccinia graminis f.sp. tritici (Pgt) on wheat leaves. Urediniospores of two strains were inoculated onto leaves singly and in a 1:1 mixture over a range of inoculum densities. Per-leaf relationships among inoculum density, uredinial formation, and urediniospore production were quanitified and mathematically modeled. From the single-strain data, values of carrying capacity for uredinia on leaves, infection efficiency of urediniospores, maximum sporulation capacity on leaves, and sporulation efficiency of uredinia were estimated for both strains. From the mixed-strain data, interstrain competitive effects of each strain on the other’s uredinial formation and urediniospore production were evaluated. Although one strain had competitive advantages in both uredinial formation and urediniospore production, the other strain was able to dominate in mixture due to its substantially higher carrying capacity, maximum sporulation capacity, and infection efficiency. This illustrates that in coexisting strains or species, competitive advantages do not necessarily translate into an advantage in fitness. The methods of competition analysis have potential applications for the study of pathogen populations, as well as other systems of coexisting organisms.

Relationship between phyllosphere population sizes of Xanthomonas translucens pv. translucens and bacterial leaf streak severity on wheat seedlings

ABSTRACT The relationship between leaf-associated population sizes of Xanthomonas translucens pv. translucens on asymptomatic leaves and subsequent bacterial leaf streak (BLS) severity was investigated. In three experiments, X. translucens pv. translucens was spray-inoculated onto 10-day-old wheat seedlings over a range of inoculum densities (10(4), 10(5), 10(6), 10(7), and 10(8) CFU/ml). Lesions developed most rapidly on plants inoculated with higher densities of X. translucens pv. translucens. Leaf-associated pathogen population sizes recovered 48 h after inoculation were highly predictive of BLS severity 7 days after inoculation (R(2) = 0.970, P < 0.0001). The relationship between pathogen population size on leaves and subsequent BLS severity was best described by the logistic model. Leaf-associated X. translucens pv. translucens population size and BLS severity from a particular pathogen inoculum density often varied among experiments; however, the disease severity level caused by a particular leaf-associated X. translucens pv. translucens population size was not significantly different among experiments. Biological and disease control implications of the X. translucens pv. translucens population size-BLS severity relationship are discussed.

Contamination, Error, and Nonspecific Molecular Tools

ABSTRACT Random amplified polymorphic DNA (RAPD) and amplified fragment length polymorphism (AFLP) are widely used in studies of genetic variation. Although it is recognized that contamination should be avoided in DNA samples, little is known about the potential hazards of low level bacterial contamination of samples from which DNA is extracted for RAPD or AFLP analyses. We found that contamination of Aphanomyces cochlioides cultures with a prokaryote at visibly undetectable levels markedly altered the results of RAPD and AFLP analyses. The contamination resulted in seven contaminant-specific RAPD products and in the suppression of eight products characteristic of uncontaminated A. cochlioides cultures. Prokaryote contamination resulted in 39 contaminant-specific AFLP products, but did not cause suppression of AFLP products. Comparing A. cochlioides samples with outgroup A. euteiches did not clearly indicate the presence of contaminant DNA, because uneven product suppression in RAPD analysis increased the apparent similarity between contaminated samples and A. euteiches and because a high proportion of the contaminant-specific amplified products comigrated with products from A. euteiches in both RAPD and AFLP analyses. Work with organisms that are prone to contamination should employ techniques such as restriction fragment length polymorphism or DNA sequence comparisons rather than relying solely on RAPD or AFLP analyses.

Constraints on the Use of de Wit Models to Analyze Competitive Interactions

The de Wit (5) replacement series is one of the most widely used designs for the study of competitive interactions in the ecological literature (2) and has been used in several recent studies of microbial interactions (1,4,14,17–19). In replacement series studies, the proportions of two microbial strains in a mixture are varied, while the total population density of the mixture is held constant, and the relative yields (RYs) of two strains as well as the relative yield total (RYT) for the two are analyzed as functions of the varying proportions of the strains. RY is the reproductive output of a strain in the mixture relative to its output alone at the same total population density, and RYT is the sum of the RYs of the two strains. The null hypothesis in the traditional interpretation of de Wit curves is that interand intrastrain competitive abilities are equal, so the expected RY of each strain in the mixture is equal to its proportion in the mixture. Thus, in the null hypothesis, the expected relationship between strain frequency and RY in the mixture is linear, and the expected RYT is 1.0 over all strain frequencies (Fig. 1). If a curve for a strain’s observed RY plotted against its frequency in mixture lies above the null straight line, the traditional interpretation is that this strain’s reproductive output per individual is more strongly inhibited by other individuals of the same strain than by individuals of the other strain. Conversely, a RY curve that falls below the null straight line is generally interpreted as indicating that this strain is more strongly inhibited by the other strain than by itself. There have been several criticisms of de Wit analysis. One is that typical de Wit replacement series involves only one total density, and, therefore, does not take into account the expected density dependence of competition (6,16). Also, in replacement series, effects of changes in the density of one strain are confounded by effects of changes in the density of the other, making it impossible to fully describe reproductive dynamics in mixture as a function of the densities of both strains (16). Recently, Newton (10) identified another limitation in using de Wit models to quantify competitive interactions. de Wit curves constructed for pathogens, or any organisms whose life cycles have multiple stages at which competitive interactions may occur, may not correspond to the traditional expectations for particular combinations of intraand interstrain interactions in a system. Specifically, the RY curves may deviate from the null lines as a result of differences in ecological traits other than competitive ability (e.g., carrying capacity, infection efficiency) among organisms in mixed populations, or they may deviate from the null lines in unexpected patterns when competitive differences do exist.

Quantifying Microbial Competition on Leaves

The concept of competition occupies a central place in theories of ecology and evolution. Over the last three decades, extensive studies of the role of competition in regulating the population dynamics of various organisms have been accompanied by the development of a rich body of theory relating to competition (Diamond, 1978; Grime, 1979; Tilman, 1982; Roughgarden, 1983). However, not all ecologists have accepted the supremacy of competition as a mechanism for regulating natural populations (Roughgarden, 1985; Connell, 1983; Connor and Simberloff, 1986; Goldberg and Barton, 1992). In response to the intense focus on competition by some ecologists, a vociferous debate has erupted about the significance of interspecific competition in natural communities (Lewin, 1983 a, Lewin, 1983 b). In recent years, researchers have provided strong evidence for the importance of disturbance, the physical environment, and extra-population movement (immigration and emigration) in determining the dynamics of specific populations (Dayton, 1971; Roughgarden, 1986). Today this debate continues, and the tension generated by the competing hypotheses has provided a fertile ground for both theory and experimentation.