The problem of character weighting in cladistic analysis is revisited. The finding that, in large molecular data sets, removal of third positions (with more homoplasy) decreases the number of well supported groups has been interpreted by some authors as indicating that weighting methods are unjustified. Two arguments against that interpretation are advanced. Characters that collectively determine few well-supported groups may be highly reliable when taken individually (as shown by specific examples), so that inferring greater reliability for sets of characters that lead to an increase in jackknife frequencies may not always be warranted. But even if changes in jackknife frequencies can be used to infer reliability, we demonstrate that jackknife frequencies in large molecular data sets are actually improved when downweighting characters according to their homoplasy but using properly rescaled functions (instead of the very strong standard functions, or the extreme of inclusion ⁄ exclusion); this further weakens the argument that downweighting homoplastic characters is undesirable. Last, we show that downweighting characters according to their homoplasy (using standard homoplasy-weighting methods) on 70 morphological data sets (with 50-170 taxa), produces clear increases in jackknife frequencies. The results obtained under homoplasy weighting also appear more stable than results under equal weights: adding either taxa or characters, when weighting against homoplasy, produced results more similar to original analyses (i.e., with larger numbers of groups that continue being supported after addition of taxa or characters), with similar or lower error rates (i.e.

One of the lasting controversies in phylogenetic inference is the degree to which specific evolutionary models should influence the choice of methods. Model‐based approaches to phylogenetic inference (likelihood, Bayesian) are defended on the premise that without explicit statistical models there is no science, and parsimony is defended on the grounds that it provides the best rationalization of the data, while refraining from assigning specific probabilities to trees or character‐state reconstructions. Authors who favour model‐based approaches often focus on the statistical properties of the methods and models themselves, but this is of only limited use in deciding the best method for phylogenetic inference—such decision also requires considering the conditions of evolution that prevail in nature. Another approach is to compare the performance of parsimony and model‐based methods in simulations, which traditionally have been used to defend the use of models of evolution for DNA sequences. Some recent papers, however, have promoted the use of model‐based approaches to phylogenetic inference for discrete morphological data as well. These papers simulated data under models already known to be unfavourable to parsimony, and modelled morphological evolution as if it evolved just like DNA, with probabilities of change for all characters changing in concert along tree branches. The present paper discusses these issues, showing that under reasonable and less restrictive models of evolution for discrete characters, equally weighted parsimony performs as well or better than model‐based methods, and that parsimony under implied weights clearly outperforms all other methods.

Genetic variation at 33 protein loci was investigated in 41 wild brown trout populations from four river basins in Galicia (northwest Spain) to analyse the amount and distribution of genetic diversity in a marginal area, located in the distribution limit of the anadromous form of this species. The genetic diversity detected within populations (H between 0 and 6%) lies within the range quoted for this species in previous reports. The Mino, the most southern river basin analysed, showed a significantly lower genetic diversity and the highest genetic differentiation among the river basins studied. The hierarchical gene diversity analysis showed high population differentiation in a restricted area (GST = 27%), mostly due to differences among populations within basins (GSC = 22%). The reduction of GST observed when the isolated samples were excluded from the analysis (GST = 17%) showed the importance of habitat fragmentation on the heterogeneity detected. Gene flow among populations was comparatively evaluated by three indirect methods, which in general revealed low figures of absolute number of migrants per generation, slightly higher than 1. The gene flow among basins reflected a positive relationship with geographical distance. This trend was confirmed by the significant correlation observed between geographical and genetic distances, including all population pairs, which suggests a component of isolation by distance in brown trout genetic structure. Nevertheless, the nonsignificant intrabasin correlation demonstrates the complexity of genetic relationships among populations in this species. The model of genetic structure in brown trout is discussed in the light of the results obtained.