Biomedical subjects

Richard Gomulkiewicz

Publications and source records attributed to Richard Gomulkiewicz.

6 recordsLinked to original sources

PubMed2006

Relating environmental variation to selection on reaction norms: an experimental test.

Theoretical models predict that selection on reaction norms should depend on the relative frequency of environmental states experienced by a population. We report a laboratory experimental test of this prediction for thermal performance curves of larval growth rate in Pieris rapae in relation to their thermal environment. We measured short-term relative growth rate (RGR) for each individual at a series of five temperatures, and then we assigned individuals randomly to warm or cool selection treatments, which differ in the frequency distributions of environmental temperatures. Selection gradient analyses of two independent experiments demonstrated significant positive selection for increasing RGR, primarily through its effects on survival to adulthood and on development rate. In both the warm and cool selection treatments, the magnitude of directional selection on RGR was consistently greater at lower (suboptimal) temperatures than at higher temperatures; differences in selection between the treatments did not match model predictions. The temporal order and duration of environmental conditions may affect patterns of selection on thermal performance curves and other continuous reaction norms, complicating the connections between variation in environment, phenotype, and fitness.

Animals↗

PubMed2006

Source-sink dynamics of virulence evolution.

To understand the evolution of genetic diversity within species--bacterial and others--we must dissect the first steps of genetic adaptation to novel habitats, particularly habitats that are suboptimal for sustained growth where there is strong selection for adaptive changes. Here, we present the view that bacterial human pathogens represent an excellent model for understanding the molecular mechanisms of the adaptation of a species to alternative habitats. In particular, bacterial pathogens allow us to develop analytical methods to detect genetic adaptation using an evolutionary 'source-sink' model, with which the evolution of bacterial pathogens can be seen from the angle of continuous switching between permanent (source) and transient (sink) habitats. The source-sink model provides a conceptual framework for understanding the population dynamics and molecular mechanisms of virulence evolution.

Adaptation, Biological↗

PubMed2005

Probability of fixation in a heterogeneous environment.

We investigate the probability of fixation of a new mutation arising in a metapopulation that ranges over a heterogeneous selective environment. Using simulations, we test the performance of several approximations of this probability, including a new analytical approximation based on separation of the timescales of selection and migration. We extend all approximations to multideme metapopulations with arbitrary population structure. Our simulations show that no single approximation produces accurate predictions of fixation probabilities for all cases of potential interest. At the limits of low and high migration, previously published approximations are found to be highly accurate. The new separation-of-timescales approach provides the best approximations for intermediate rates of migration among habitats, provided selection is not too intense. For nonzero migration and relatively strong selection, all approximations perform poorly. However, the probability of fixation is bounded above and below by the approximations based on low and high migration limits. Surprisingly, in our simulations with symmetric migration, heterogeneous selection in a metapopulation never decreased-and sometimes substantially increased-the probability of fixation of a new allele compared to metapopulations experiencing homogeneous selection with the same mean selection intensity.

Alleles↗

PubMed2003

Coevolution in temporally variable environments.

Many potentially mutualistic interactions are conditional, with selection that varies between mutualism and antagonism over space and time. We develop a genetic model of temporally variable coevolution that incorporates stochastic fluctuations between mutualism and antagonism. We use this model to determine conditions necessary for the coevolution of matching traits between a host and a conditional mutualist. Using an analytical approximation, we show that matching traits will coevolve when the geometric mean interaction is mutualistic. When this condition does not hold, polymorphism and trait mismatching are maintained, and coevolutionary cycles may result. Numerical simulations verify this prediction and suggest that it remains robust in the presence of temporal autocorrelation. These results are compared with those from spatial models with unrestricted movement. The comparisons demonstrate that gene flow is unnecessary for generating empirical patterns predicted by the geographic mosaic theory of coevolution.

Animals↗

PubMed2003

Coevolution in variable mutualisms.

Many mutualistic interactions are probably not mutualistic across all populations and years. This article explores consequences of this observation with a series of genetic models that consider how variable mutualisms coevolve. The first models, previously introduced in a general coevolutionary context, consider two coevolving species whose fitness interactions change between beneficial and antagonistic in response to independent spatial or temporal variation in the abiotic or biotic environment. The results demonstrate that both temporal and spatial variability in fitness interactions can cause partner species with tightly matched traits favored by unconditional mutualisms to be vulnerable to evolutionary invasion by alternative types. A new model presented here shows that an additional mutualistic species can have a similar effect and can even cause fitness interactions between the other two species to evolve. Under some conditions, the pairwise interactions can change unidirectionally from mutualistic to antagonistic, with virtually no evolutionary change in either partner species. In other cases, fitness interactions between the species pair can oscillate between mutualism and antagonism as a result of coevolution in the third species. Taken as a whole, these theoretical results suggest that many features of mutualistic coevolution can best be understood by considering spatial, temporal, and community-dependent patterns of fitness interactions.

Biological Evolution↗

PubMed2000

Hot Spots, Cold Spots, and the Geographic Mosaic Theory of Coevolution.

Species interactions commonly coevolve as complex geographic mosaics of populations shaped by differences in local selection and gene flow. We use a haploid matching-alleles model for coevolution to evaluate how a pair of species coevolves when fitness interactions are reciprocal in some locations ("hot spots") but not in others ("cold spots"). Our analyses consider mutualistic and antagonistic interspecific interactions and a variety of gene flow patterns between hot and cold spots. We found that hot and cold spots together with gene flow influence coevolutionary dynamics in four important ways. First, hot spots need not be ubiquitous to have a global influence on evolution, although rare hot spots will not have a disproportionate impact unless selection is relatively strong there. Second, asymmetries in gene flow can influence local adaptation, sometimes creating stable equilibria at which species experience minimal fitness in hot spots and maximal fitness in cold spots, or vice versa. Third, asymmetries in gene flow are no more important than asymmetries in population regulation for determining the maintenance of local polymorphisms through coevolution. Fourth, intraspecific allele frequency differences among hot and cold spot populations evolve under some, but not all, conditions. That is, selection mosaics are indeed capable of producing spatially variable coevolutionary outcomes across the landscapes over which species interact. Altogether, our analyses indicate that coevolutionary trajectories can be strongly shaped by the geographic distribution of coevolutionary hot and cold spots, and by the pattern of gene flow among populations.

antagonism↗