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A minor focus in the MEC Lab is the refinement or development of quantitative tools for ecological use. See some examples below of tools refined or developed by our lab.
Automated Content Analysis
Automated Content Analysis
Dr. Nunez-Mir introduced automated content analysis (ACA) to the broader ecological and evolutionary biology community in an article published in Methods in Ecology and Evolution, and follow-up blogpost in the journal’s Methods.blog. Automated content analysis (ACA) refers to a suite of text-mining, machine-learning algorithms that use probabilistic models to identify and quantify the concepts and themes discussed in a body of literature. We have used ACA to explore a variety of questions, such as “are sociecological challenges being addressed in forestry research?” and “how have research themes shifted in the past four decades of ecological research?”
BattyCoda
BattyCoda
Together with the UIC Salles Bat Lab, we present BattyCoda—a flexible, open-source tool for annotating and classifying bat communication calls. Until now, there was no software designed to label different types of social communication calls or syllables within a species’ vocal repertoire. BattyCoda fills that gap by enabling researchers to train classifiers on small datasets (typical for communication calls) and to supervise suggested labels, improving both the speed and consistency of annotations. This helps expand acoustic libraries and makes it easier to study the rich vocal behavior of bats.
Learn more about this exciting new tool here.
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