Sciences: Ecology

Department: Environmental Sciences

Supervising Faculty Member: Howard Epstein

Specialization: Ecology

Research Focus: One of the dominant controls on vegetation production in relatively dry ecosystems is soil water.  The texture of the soil (e.g. clay vs. sand) is an important factor for determining the amount of water that could be held within the rooting zone of plants and therefore used for photosynthesis - as opposed to being lost to deep drainage or evaporated.  One hypothesis in this regard (The Inverse Texture Hypothesis) states that sandy soils are better for keeping water available in relatively dry ecosystems and clay soils are better in wetter ecosystems.  However, this hasn't been very rigorously evaluated.  We would use a relatively sophisticated simulation model of soil water dynamics to examine the effects of soil water on plant productivity to try to provide evidence for or against this hypothesis.

Position Description: Students would be responsible for analyzing the data from the model output in rigorous yet creative ways for evaluating our hypotheses regarding soil texture and water availability.  Students would not be doing any coding or model simulations, but only working with the output.  Students would also gain familiarity with the relevant scientific literature on the topic, and could potentially even develop some greenhouse experiments to complement the modeling results.

Required skills: Most of the data are currently in Excel, so students should be familiar with Excel, although the use of more sophisticated data organizational and analysis tools (e.g. Matlab, R) would be great (not mandatory though)

Computer software: At least very familiar with Excel (see skills above)

Training/certification: No

What you will learn: Develop a relatively comprehensive understanding of plant-soil-atmosphere interactions within terrestrial ecosystems, particularly with regard to water balance; understand the concepts of a simulation model, and be able to assess model output within the context of the model itself; analyze model output, which includes evaluating the key variables, and develop graphs that clearly illustrate the main results.

Web site link to research: