The field of ecology is approaching new horizons through the work and innovation done at the Dietze research group here at Boston University. The development of computational and statistical models, built with analyzing vast amounts of ecological data in mind, has recast the ecological sciences as a predictive, informative endeavor, particularly in the face of climate change and its dynamic effects on ecosystems and biological systems.
This summer internship dives right into the process of developing and implementing ecological models through computer programming languages. I joined this internship on the basis of my experience in mathematics, computing, and the biological sciences. I enrolled in BI 303: Evolutionary Ecology as well as MA 226: Differential Equations this past Spring semester, and the interdisciplinary connections between the two classes were obvious and manifold. Dynamical mathematical models are abundantly used in ecological and biological contexts to make predictions and establish theories about ecological systems. Last Fall semester I enrolled in CS 111: Introduction to Computer Science I, where I was introduced to the Python coding language. All of these experiences have inadvertently helped prepare me for the work in the internship. In the development of computational ecological models, the Dietze lab uses the statistical computing language of R, in addition to many others. Seeking to broaden my knowledge of coding languages, and given my classroom experience, this opportunity seemed a natural fit.
As you would expect, a typical day in the lab is dedicated to coding, using the software version control platform of GitHub. Many other software components are used in developing the various ecological analyzers. One such project has been PEcAn.
|Picture 1. Associate Professor Mike Dietze of the Earth and Environment Department delivers a seminar on the "emerging imperative" of ecological forecasting, which seeks to synthesize vast amounts of existing ecological data into coherent, analytical, statistical forms that can then be used to make predictions (21 June 2017).|
The PEcAn Project
A large project of the Dietze lab has been PEcAn, which stands for the "Predictive Ecosystem Analyzer". http://pecanproject.github.io
It's goals, as established by the lab, includes:
"Climate change science has witnessed an explosion in the amount and types of data that can be brought to bear on the potential responses of the terrestrial carbon cycle and biodiversity to global change. Many of the most pressing questions about global change are not necessarily limited by the need to collect new data as much as by our ability to synthesize existing data. This project specifically seeks to improve this ability. Because no one measurement provides a complete picture, multiple data sources must be integrated in a sensible manner. Process-based models represent an ideal framework for integrating these data streams because they represent multiple processes at different spatial and temporal scales in ways that capture our current understanding of the causal connections across scales and among data types. Three components are required to bridge this gap between the available data and the required level of understanding: 1) a state-of-the-art ecosystem model, 2) a workflow management system to handle the numerous streams of data, and 3) a data assimilation statistical framework in order to synthesize the data with the model."
Screenshot 1. A workflow log of various model runs on the PEcAn web interface.
Screenshot 2. A typical R Studio working environment within the PEcAn development process.
By: Elias Kastritis
Mathematics and Philosophy double major