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Carlos Garcia

Publications and source records attributed to Carlos Garcia.

2 recordsLinked to original sources

Steep difference between ecotypes and intermediate phenotype depression for shell trait integration in a marine snail hybrid zone.

Multivariate analyses of phenotypic integration for a set of characters provide information about biological systems that cannot be obtained in univariate studies of these characters. We studied phenotypic integration for seven shell measures across the phenotypic gradient in a hybrid zone of the marine snail Littorina saxatilis in Galicia, NW Iberia. We found clear differences in the degree of integration between the two ecotypes involved in the hybrid zone, likely related to differences in the strength of natural selection acting on the snails' shells in each ecotype's habitat. We found also evidence of a decrease in integration in the phenotypically intermediate, hybrid snails, consistent with hybridization resulting in a release of multivariate variation and increased evolvability. Across the phenotypic gradient, decreases in overall integration tended to be accompanied by increases in some measures of modularity, but the latter did nor reflect high correlation structure. The increases occurred only in a proportional sense, correlations among modules tending to decrease faster than within modules for low overall integration tiers. Integration analyses based on non partial and partial correlations tended to produce contrasting results, which suggested hierarchical sources of shell integration. Given that the two ecotypes could have differentiated in situ according to a parapatric model, our results would show that changes in integration can occur in a short evolutionary time and be maintained in the presence of gene flow, and also that this gene flow could result in the hybrid release of multi character variation.

Evolutionary Biology

BoCluSt: bootstrap clustering stability algorithm for community detection in networks

The identification of modules or communities of related variables is a key step in the analysis and modelling of biological systems. Many module identification procedures are available, but few of these can determine the module partitions best fitting a given dataset in the absence of previous information, in an unsupervised way, and when the links between variables have different weights. Here I propose such a procedure, which uses the stability under bootstrap resampling of different alternative module structures as a criterion to identify the structure best fitting to a set of variables. In its present implementation, the procedure uses linear correlations as link weights. Computer simulations show that the procedure is useful for problems involving moderate numbers of variables, such as those commonly found in gene regulation cascades or metabolic pathways, and also that it can detect hierarchical network structures, in which modules are composed of smaller sub modules. The procedure becomes less practical as the number of variables increases, due to increases in processing time.The proposed procedure may be a valuable and robust network analysis tool. Because it is based on comparing the amount of evidence for different module partitions structures, this procedure may detect the existence of hierarchical network structures.

Bioinformatics