1,721,102 research outputs found
Adaptation to spatially heterogeneous modifying and adaptive environments
No full textThe development of an individual's phenotype is influenced by environmental factors (the modifying environment) which may differ from those factors (the adaptive environment) that decide on the adaptational value of the developed phenotype. The shapes of adaptationally optimal norms of reaction are therefore essentially determined by associations between these two environmental components together with the degree of adaptational sensitivity of the developed phenotypes. Two complementary aspects of optimality are accounted for: (a) environments can be optimal for a given norm of reaction and (b) norms of reaction can be optimal for a given environment. The results are obtained for random distribution of genotypes over environmental conditions and under the physiologically reasonable premise that fitness is a function of the costs of modification and adaptation. It turned out that the associations of adaptive and modifying environments are the primary sources of adaptational optimization. More specifically, it is shown that (i) independence between the two environmental components constitutes an adaptationally optimal environment only for norms of reaction in Which all phenotypes are adaptively insensitive; (ii) if costs of modification do not depend on the environment, and if the two environmental components are not associated, adaptationally optimal norms of reaction can always be realized through phenogenetic invariance; (iii) as a rule, adaptively sensitive phenotypes developed under strong environmental associations necessitate phenogenetic plasticity for the optimal norm of reaction; (iv) a norm of reaction which is adaptationally optimal in its adaptationally optimal environment can always be realized through phenogenetic invariance, if costs of modification do not vary with the environment. These results reveal an important role of patterns of adaptive sensitivity of phenotypes, which may even surpass that of shapes of norms of reaction in adaptational processes. (C) 2000 Academic Press
A model for the determination of the variance in genetic relationship among offspring from open-pollinated plant populations
No full textContinuing a study of the average coefficient of kinship and inbreeding among offspring from specified mother plants belonging to a population of monoecious, diploid seed-plants, the variances of these coefficients have been computed. The variance in kinship was considered among offspring from a single mother plant and between offspring from two different mother plants. Special interest has been paid to the role played by the rate of self-fertilization and the effective size of neighbourhood and common neighbourhood. The graphical representation of some numerical examples indicates that it is impossible to predict general tendencies which hold true for the behaviour of all three types of variance if they are regarded as functions of the rate of self-fertilization; only the coefficient of inbreeding and kinship among offspring from the same parent showed similar tendencies. The influence of the effective size of neighbourhood and common neighbourhood on the respective variances proved to be of minor importance
Die Varianz der relativen Allelhäufigkeit in einer Population konstanter endlicher Größe
No full text
The isolation approach to hierarchical clustering
No full textThe idea of clusters being internally cohesive and externally isolated is consistently developed into a principle of hierarchical clustering. The principle rests on defining for each subset of at least two objects its degree of internal and external differentiation. Subsets with larger external than internal differentiation are considered as isolated groups, and it is shown that the resulting collection of all isolated groups forms an encaptic (hierarchical) structure. In an encaptic structure any two groups are either completely disjoint or one is included (nested) in the other. The clustering principle is applicable to arbitrary symmetrical difference measures between objects, does not have to rely on any special agglomerative or divisive type of clustering algorithm, always produces unambiguous results (including arbitrary numbers of tied objects), and is straight-forward to interpret. For example, the existence of "solitary" objects (which do not belong to any cluster) indicates that clustering may not be possible for all objects. At the extreme, this situation may result in complete inhibition of clustering, in which case the objects are evenly dispersed. External as well as internal degrees of differentiation strictly increase with (encaptic) levels of hierarchy, and equal levels of hierarchy are characterized by equal external but not necessarily internal degrees of differentiation. Further desirable features revealed by the joint consideration of external and internal differentiation are elaborated. The significance of the unrestricted choice of difference measures allowed by the isolation principle is demonstrated for a basic problem of phylogenetic reconstruction
The relationship between the concepts of genetic diversity and differentiation
No full textDiversity as a measure of individual variation within a population is widely agreed to reflect the number of different types in the population, taking into account their frequencies. In contrast, differentiation measures variation between two or more populations, demes or subpopulations. As such, it is based on the relative frequencies of types within these subpopulations and, ideally, measures the average distance of subpopulations from their respective lumped remainders. This concept of subpopulation differentiation can be applied consistently to a single population by regarding each individual as a deme (subpopulation) of its own, and it results in a measure of population differentiation δ T which depends on the relative frequencies of the types and the population size. δ T corresponds to several indices of variation frequently applied in population genetics and ecology, and it verifies these indices as measures of differentiation rather than diversity. For any particular frequency distribution of types, the diversity ν is then shown to be the size of a hypothetical population in which each type is represented exactly once, i. e. for which δ T =1. Hence, the diversity of a population is its differentiation effective number of types. This uniquely specifies the link between the two concepts. Moreover, ν again corresponds to known measures of diversity applied in population genetics and ecology. While population differentiation can always be estimated from samples, the diversity of a population, particularly if it is large, may not be. In such cases, it is recommended that population differentiation is estimated and the corresponding sample diversity merely computed. Finally, a solution to the problem of measuring multi-locus diversities is provided
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