7,839 research outputs found
Distribution of variation over populations
No full textUnderstanding the significance of the distribution of genetic or phenotypic variation over populations is one of the central concerns of population genetic and ecological research. The import of the research decisively depends on the measures that are applied to assess the amount of variation residing within and between populations. Common approaches can be classified under two perspectives: differentiation and apportionment. While the former focuses on differences (distances) in trait distribution between populations, the latter considers the division of the overall trait variation among populations. Particularly when multiple populations are studied, the apportionment perspective is usually given preference (via F ST/G p. indices), even though the other perspective is also relevant. The differences between the two perspectives as well as their joint conceptual basis can be exposed by referring them to the association between trait states and population affiliations. It is demonstrated that the two directions, association of population affiliation with trait state and of trait state with population affiliation, reflect the differentiation and the apportionment perspective, respectively. When combining both perspectives and applying the suggested measure of association, new and efficient methods of analysis result, as is outlined for population genetic processes. In conclusion, the association approach to an analysis of the distribution of trait variation over populations resolves problems that are frequently encountered with the apportionment perspective and its commonly applied measures in both population genetics and ecology, suggesting new and more comprehensive methods of analysis that include patterns of differentiation and apportionment
The Analysis of Association Between Traits When Differences Between Trait States Matter
No full textBecause of their elementary significance in almost all fields of science, measures of association between two variables or traits are abundant and multiform. One aspect of association that is of considerable interest, especially in population genetics and ecology, seems to be widely ignored. This aspect concerns association between complex traits that show variable and arbitrarily defined state differences. Among such traits are genetic characters controlled by many and potentially polyploid loci, species characteristics, and environmental variables, all of which may be mutually and asymmetrically associated. A concept of directed association of one trait with another is developed here that relies solely on difference measures between the states of a trait. Associations are considered at three levels: between individual states of two variables, between an individual state of one variable and the totality of the other variable, and between two variables. Relations to known concepts of association are identified. In particular, measures at the latter two levels turn out to be interpretable as measures of differentiation. Examples are given for areas of application (search for functional relationships, distribution of variation over populations, genomic associations, spatiogenetic structure)
Linking Diversity and Differentiation
No full textGenerally speaking, the term differentiation refers to differences between collections for the distribution of specified traits of their members, while diversity deals with (effective) numbers of trait states (types). Counting numbers of types implies discrete traits such as alleles and genotypes in population genetics or species and taxa in ecology. Comparisons between the concepts of differentiation and diversity therefore primarily refer to discrete traits. Diversity is related to differentiation through the idea that the total diversity of a subdivided collection should be composed of the diversity within the subcollections and a complement called “diversity between subcollections”. The idea goes back to the perception that the mixing of differentiated collections increases diversity. Several existing concepts of “diversity between subcollections” are based on this idea. Among them, β-diversity and fixation (inadvertently called differentiation) are the most prominent in ecology and in population genetics, respectively. The pertaining measures are shown to quantify the effect of differentiation in terms of diversity components, though from a dual perspective: the classical perspective of differentiation between collections for their type compositions, and the reverse perspective of differentiation between types for their collection affiliations. A series of measures of diversity-oriented differentiation is presented that consider this dual perspective at two levels of diversity partitioning: the overall type or subcollection diversity and the joint type-subcollection diversity. It turns out that, in contrast with common notions, the measures of fixation (such as FST or GST ) refer to the perspective of type rather than subcollection differentiation. This unexpected observation strongly suggests that the popular interpretations of fixation measures must be reconsidered
Model-based analysis of latent factors
No full textThe detection of community or population structure through analysis of explicit cause–effect modeling of given observations has received considerable attention. The complexity of the task is mirrored by the large number of existing approaches and methods, the applicability of which heavily depends on the design of efficient algorithms of data analysis. It is occasionally even difficult to disentangle concepts and algorithms. To add more clarity to this situation, the present paper focuses on elaborating the system analytic framework that probably encompasses most of the common concepts and approaches by classifying them as model-based analyses of latent factors. Problems concerning the efficiency of algorithms are not of primary concern here. In essence, the framework suggests an input–output model system in which the inputs are provided as latent model parameters and the output is specified by the observations. There are two types of model involved, one of which organizes the inputs by assigning combinations of potentially interacting factor levels to each observed object, while the other specifies the mechanisms by which these combinations are processed to yield the observations. It is demonstrated briefly how some of the most popular methods (Structure, BAPS, Geneland) fit into the framework and how they differ conceptually from each other. Attention is drawn to the need to formulate and assess qualification criteria by which the validity of the model can be judged. One probably indispensable criterion concerns the cause–effect character of the model-based approach and suggests that measures of association between assignments of factor levels and observations be considered together with maximization of their likelihoods (or posterior probabilities). In particular the likelihood criterion is difficult to realize with commonly used estimates based on Markov chain Monte Carlo (MCMC) algorithms. Generally applicable MCMC-based alternatives that allow for approximate employment of the primary qualification criterion and the implied model validation including further descriptors of model characteristics are suggested
- …
