Life history of Parnips and the evolutionary origin of gall wasps

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Abstract

By mechanisms that are still unknown, gall wasps (Cynipidae) induce plants to form complex galls, inside which their larvae develop. The family also includes inquilines (phytophagous forms that live inside the galls of other gall inducers) and possibly also parasitoids of gall inducers. The origin of cynipids is shrouded in mystery, but it has been clear for some time that a key group in making progress on this question is the ‘figitoid inquilines’. They are gall-associated relatives of cynipids, whose biology is poorly known. Here, we report the first detailed data on the life history of a figitoid inquiline, the genus Parnips. Dissections of mature galls show that Parnipsnigripesis a parasitoid of Barbotiniaoraniensis, a cynipid that induces single-chambered galls inside the seed capsules of annual poppies ( Papaverrhoeasand P.dubium). Galls with pupae of Parnipsnigripesalways contain the remains of a terminal-instar larva of B.oraniensis. The mandibles of the terminal-instar larva of P.nigripesare small and equipped with a single sharp tooth, a shape that is characteristic of carnivorous larvae. The weight of P.nigripespupae closely match that of the same sex of B.oraniensispupae, indicating that Parnipsmakes efficient use of its host and suggesting that ovipositing Parnipsfemales lay eggs that match the sex of the host larva. Dissection of young galls show that another species of Parnips, hitherto undescribed, spends its late larval life as an ectoparasitoid of Iraellahispanica, a cynipid that induces galls in flowers of annual poppies. These and other observations suggest that Parnipsshares the early endoparasitic-late ectoparasitic life history described for all other cynipoid parasitoids. Our findings imply that gall wasps evolved from parasitoids of gall insects. The original hosts could not have been cynipids but possibly chalcidoids, which appear to be the hosts of several extant figitoid inquilines. It is still unclear whether the gall inducers evolved rapidly from these ancestral parasitoids, or whether they were preceded by a long series of intermediate forms that were phytophagous inquilines.

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Bayesian phylogenetic analysis of combined data.

Johan Nylander, Fredrik Ronquist, John P. Huelsenbeck … (2004)

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.