Meeting Abstract
S8.8 Wednesday, Jan. 6 Phylogeny and Ecology of Jellyfish (Scyphozoa) Mass Occurrences DAWSON, M.N*; BAYHA, K.M.; GOMEZ DAGLIO, L.E.; COLLINS, A.G.; University of California, Merced; University of California, Merced; University of California, Merced; Smithsonian Institution mdawson@ucmerced.edu
Most investigations of the mass occurrences of jellyfish continue to focus on the ecological responses of jellyfish to environmental change. Yet still, after two decades of ecological study, “management actions appear to be difficult and uncertain” (Richardson et al. 2009). This difficulty may arise in part because the evolutionary context for modern-day ecological responses are rarely considered. Prior phylogenetic analyses of Scyphozoa have offered a glimpse of the possible value of this perspective: transitions in the evolution of jellyfish mass occurrences – from aggregations to blooms and swarms – coincided with the evolution of a subset of life-history and morphological characters. The inference that these phenotypic characteristics cause or permit mass occurrences, however, remains preliminary due to incomplete taxon sampling, low resolution in some parts of the tree, and a somewhat limited phenotypic character matrix. Here, we revisit the question of “what is behind the evolution of mass occurrences?” using complete family-level taxonomic coverage of Scyphozoa, more rigorous phylogenetic analyses with additional loci, and a more extensive phenotypic dataset (ca. 200 characters). This analysis provides greater confidence in inferences of the evolution of mass occurrences, but for many taxa the phenotypic data remain too sporadic, or simply unavailable in the literature, to fully answer our question. Renewed effort to gather geographically extensive ecological, ethological, morphological, and physiological data describing all stages of all species of Scyphozoa, identified accurately and placed phylogenetically, is a logical and imperative next step to increase understanding of the evolution of jellyfish mass occurrences, thereby enabling development of a more predictive framework.
