DescriptionHierarchical Approximate Bayesian Computation to Detect Community Response to Sea Level Change in the Hawaiian Archipelago. Methods that integrate population sampling from multiple taxa into a single analysis are a much needed addition to the comparative phylogeographic toolkit. Here we present a statistical framework for multi-species analysis based on hierarchical approximate Bayesian computation (hABC) for inferring community dynamics and concerted demographic response. Detecting community response to climate change is an important issue with regards to how species have and will react to past and future events. Furthermore, whether species responded individualistically or in concert is at the center of related questions about the abiotic and biotic determinants of community assembly. This method combines multi-taxon genetic datasets into a single analysis to determine the proportion of a contemporary community that historically expanded in a temporally clustered pulse as well as when the pulse occurred. We will apply this method to 59 species in the Hawaiian Archip ela! go in order to examine community response of coral reef taxa to sea-level change in Hawaii. The method can accommodate dataset heterogeneity such as variability in effective population size, mutation rates, and sample sizes across species and utilizes borrowing strength from the simultaneous analysis of multiple species. This hABC framework used in a multi-taxa demographic context can increase our understanding of the impact of historical climate change by determining what proportion of the community responded in concert or independently, and can be used with a wide variety of comparative phylogeographic datasets as biota-wide DNA barcoding data sets accumulate.
OrganizationIolani School
Sponsor Campus GridOSG Connect
Principal Investigator
Yvonne Chan
Field Of ScienceBiological Sciences