Evolutionary history of the burnet moth genus Zygaena Fabricius, 1775 (Lepidoptera: Zygaenidae) inferred from nuclear and mitochondrial sequence data: phylogeny, host-plant association, wing pattern evolution and historical biogeography : EVOLUTIONARY HISTORY OF THE GENUS ZYGAENA

The recent development of Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) techniques has facilitated the exploration of parameter-rich evolutionary models. At the same time, stochastic models have become more realistic (and complex) and have been extended to new types of data, such as morphology. Based on this foundation, we developed a Bayesian MCMC approach to the analysis of combined data sets and explored its utility in inferring relationships among gall wasps based on data from morphology and four genes (nuclear and mitochondrial, ribosomal and protein coding). Examined models range in complexity from those recognizing only a morphological and a molecular partition to those having complex substitution models with independent parameters for each gene. Bayesian MCMC analysis deals efficiently with complex models: convergence occurs faster and more predictably for complex models, mixing is adequate for all parameters even under very complex models, and the parameter update cycle is virtually unaffected by model partitioning across sites. Morphology contributed only 5% of the characters in the data set but nevertheless influenced the combined-data tree, supporting the utility of morphological data in multigene analyses. We used Bayesian criteria (Bayes factors) to show that process heterogeneity across data partitions is a significant model component, although not as important as among-site rate variation. More complex evolutionary models are associated with more topological uncertainty and less conflict between morphology and molecules. Bayes factors sometimes favor simpler models over considerably more parameter-rich models, but the best model overall is also the most complex and Bayes factors do not support exclusion of apparently weak parameters from this model. Thus, Bayes factors appear to be useful for selecting among complex models, but it is still unclear whether their use strikes a reasonable balance between model complexity and error in parameter estimates.

The evolution of aposematism, a phenomenon where prey species conspicuously advertise their unprofitability to predators, is puzzling. How did conspicuousness evolve, if it simultaneously increased the likelihood of an inexperienced predator to detect the prey and presumably kill it? Antiapostatic selection, where rare prey is predated relatively more often, is considered as another major difficulty for aposematism to evolve. However, the risk of being conspicuous in low frequencies has not been experimentally tested. We designed an experiment to test how frequency (4%, 12%, 32%) of conspicuous aposematic prey and its dispersion type (solitary vs. aggregated) affect an initial predation risk of the prey and in avoidance learning of predators. Wild great tits (Parus major) were predators on artificial prey in a "novel world." The relative mortality of aposematic prey was antiapostatic, thus the frequency-dependent predation was most severe at low frequencies. In all frequencies, aggregated aposematic prey survived better than solitary prey. Surprisingly, learning was not determined by a fixed number of unpalatable prey eaten, but at low frequencies fewer aposematic individuals eaten generated predators' avoidance learning. However, per-capita risk for the prey remained highest at low frequencies. Our results underscore the problems of initial evolution of rare conspicuous morphs. Aggregated prey suffered less from predation, indicating selective advantage of aggregation over solitary living for a conspicuous individual.