GSA Annual Meeting in Indianapolis, Indiana, USA - 2018

Paper No. 73-9
Presentation Time: 10:00 AM


FINNEGAN, Seth, Department of Integrative Biology & Museum of Paleontology, University of California, Berkeley, Valley Life Sciences Building #4780, Berkeley, CA 94720-4780, SAUPE, Erin E., Department of Earth Sciences, University of Oxford, Oxford, OX1 3AN, United Kingdom, RIDGWELL, Andy, Earth Sciences, University of California, Riverside, 900 University Ave., Riverside, CA 92521 and QIAO, Huijie, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen W Rd, Chaoyang District, Beijing, 100101, China

Many marine mass extinction events coincide with correlated changes in temperature and seasonality, oxygenation, productivity, and seawater chemistry. Consequently, it is often unclear which specific environmental changes were responsible primarily for causing species extinctions. Selectivity patterns can, in principle, help to fingerprint the principal drivers of extinction, but a posteri interpretation of selectivity predictions is complicated by uncertainty about what selectivity patterns are expected under a given global change scenario. We introduce a simulation framework to evaluate expected selectivity patterns under different global change scenarios. We begin by using global circulation models and the cGENIE Earth system model to simulate global geographic and bathymetric distribution and seasonal variations of seawater temperature, productivity, dissolved oxygen, and pH under a range of assumptions about atmospheric CO2and O2concentrations. We select a model to represent a hypothetical global starting state, and use niche modeling to simulate the geographic distributions of species with a wide range of niche parameters, dispersal abilities, and centers of origin in this world. We then select an alternative global state model and predict the geographic and bathymetric distributions of the previously simulated species in this new global state. Species that are not able to disperse to cells that have environmental conditions within their niche parameters become extinct. This approach allows us to generate expected extinction selectivity patterns for a variety of hypothetical global state transitions, and to determine whether and how they differ from one another. Although necessarily simplistic, our simulation framework has potential to aid in solving the inverse problem of inferring extinction drivers from patterns of extinction selectivity. We illustrate our approach by comparing predicted and observed selectivity patterns during the Late Ordovician Mass Extinction.