GSA Connects 2021 in Portland, Oregon

Paper No. 138-2
Presentation Time: 8:20 AM


LOPERA CONGOTE, Laura1, QUANG, Hung Ha1, MCGLUE, Michael2, STREIB, Laura C.3, LYON, Eva4 and STONE, Jeffery5, (1)Indiana State University, department of Earth and Environmnetal Systems, 600 Chesnut st, Terre Haute, IN 47807, (2)Department of Earth and Environmental Sciences, University of Kentucky, Lexington, KY 40506, (3)Geosciences, Georgia State University, PO Box 4105, Atlanta, GA 30302, (4)Department of Earth and Environmental Sciences, University of Kentucky, Lexington, KY 40508, (5)Earth and Environmental Systems, Indiana State University, Terre Haute, IN 47809

The aim of paleoecological reconstructions from lacustrine sediments is to understand the past climatic and hydrologic dynamics that have altered lake ecosystems through time. Diatoms are commonly used as indicators in paleoecology because they preserve well in the sediments and respond to changing environmental conditions with great sensitivity. In this context, interpretations based on diatom assemblages from cores often lack a sense of the extant spatial distribution of diatoms within lake floor sediments and, hence, may not fully reflect changes in environmental variables that may be driving their distribution. To resolve this question, we applied geographic information systems (GIS) and a series of statistical techniques to unveil the relationship between diatoms and environmental factors such as carbon, grain size/ energy of the system and nitrogen from a montane lake (June Lake) in the Sierra Nevada, California, USA. Diatom distribution was plotted in QGIS using heatmaps based on the relative abundance of fossil diatoms from grab samples collected from the sediment-water interface. A suite of elemental and stable isotope geochemical parameters and grain size were measured from the same grab samples. These variables were plotted in QGIS using an inverse-distance weighting interpolation (IDW). To help understand the relationship among the diatoms and the environmental variables, a multivariate regression tree was performed. With this data we expect to create a model that helps to predict the major controls on diatom distribution within June Lake, in order to provide better inferences in our reconstruction of past environmental conditions. In addition, this approach may allow us to make better decisions regarding the optimal location for collecting longer sediment cores within the lake.