The deluge of data from diverse fields, including genomics and environmental sensing, has created challenges and opportunities for undergraduate biology curricula. As biology students prepare for careers in science, biomedicine, and technology, they will need a more sophisticated understanding of data, algorithms, and computer science. In addition, students in bioinformatics will be required to link their understanding of algorithm development and analysis to disciplines not historically part of bioinformatics, including ecology and environmental science. In particular, development of new microelectronics, digital computation, and networking has created a data deluge in natural resources and ecology. With the promise of a future of inexpensive, spatially and temporally expansive data comes the challenges of managing, integrating, analyzing, and interpreting these data. Fortunately for ecologists, many of these challenges can be addressed by bioinformaticians who have traditionally been trained to apply computational techniques to data associated with biomolecules, including genomics, gene expression, structural biology, and other molecular applications. We have integrated bioinformatics instruction in two core ecology courses (Ecology and Conservation Biology) and integrated ecoinformatics instruction in two core bioinformatics courses (Computational Biology and Bioinformatics).
In Winter 2017, three students in the capstone Bioinformatics course undertook a project that required them to apply bioinformatics tools and techniques using genomic and ecological data. This original research allowed them to use key bioinformatics skills, including quantitative methods, data management, and placing their own work in the context of previous studies. Their study used a physiologically based species distribution model to assess the environmental controls over invasion success by grass species from Eurasia to North America and Australia. The students found that many of the skills developed in class projects related to genomic and transcriptomic data translated well to this project, including data management and programming. The collaboration between bioinformaticians and ecologists was engaging but occasionally challenging. Here we reflect on assignment development, implementation, and assessment. We suggest modifications to course and program curriculum to facilitate additional and expanded collaborations between bioinformatics and ecology.