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Testing the Dutilleul syndrome: host use drives the convergent evolution of multiple traits in parasitic wasps.

Bernardo F SantosAdrien Perrard
Published in: Journal of evolutionary biology (2018)
Common life-history aspects among independent lineages often result in the repeated evolution of suites of adaptive traits, or 'syndromes'. Such syndromes can be key avenues to understand relationships between morphological and ecological traits, but are rarely tested due to insufficient trait shift repetitions. We use a hyperdiverse lineage to investigate the evolution of a syndrome. Cryptine ichneumonid wasps that parasitize insects concealed in hard substrates display several traits that are putative adaptations to that end. Using a phylogenetic framework from a combined multigene molecular and morphological data set with 308 cryptine species, we tested whether these traits were part of a morphofunctional syndrome related to host use. Ancestral state estimations show multiple origins for six investigated traits, which are correlated to each other and to the use of deeply concealed hosts, suggesting adaptation. Putatively adaptive traits showed a much stronger link among themselves than with an assemblage of 49 other morphological traits. However, estimation of the order of evolution in adaptive traits showed no structured pattern. The results indicate that the challenge of attacking deeply concealed hosts induced the repeated evolution of a 'Dutilleul syndrome', named after the 'walker-through-walls' character from French literature. They also point towards a dynamic scenario in the evolution of complex functional systems. These findings highlight the power of morphology to illuminate poorly known aspects of natural history, and how hyperdiverse lineages can be used to understand the evolution of complex traits.
Keyphrases
  • genome wide
  • dna methylation
  • gene expression
  • systematic review
  • risk assessment
  • climate change
  • artificial intelligence
  • deep learning
  • mass spectrometry
  • single molecule
  • high glucose
  • big data