VOLUME 13 TEACHING ISSUES AND EXPERIMENTS IN ECOLOGY
Kaitlin M. Bonner1, Arietta E. Fleming-Davies2, Kristine L. Grayson3, Alison N. Hale4, X. Ben Wu5, Samuel Donovan6
1Department of Biology, St. John Fisher College, Rochester, NY 14618
2Biology Department, University of San Diego, San Diego, CA 92104
3Department of Biology, University of Richmond, Richmond, VA 23173
4Department of Science and Research, Carnegie Museum of Natural History, Pittsburgh, PA 15213
5Department of Ecosystem Science and Management, Texas A&M University, College Station, TX 77843
6Department of Biological Sciences, University of Pittsburgh, Pittsburgh, PA 15260
Corresponding Author: Kaitlin Bonner (kbonner@sjfc.edu)
The field of ecological research is in the midst of a data revolution. Best practices in the curation, archiving, and dissemination of data have received much attention over the past decade (e.g., Reichman et al. 2011, Whitlock 2011, Hampton et al. 2013). One result of these efforts has been a dramatic increase in open access data. Primary data associated with published manuscripts are increasingly accessible through supplementary materials, university repositories, journal-specific archives (e.g., ESA Data Registry), or public repositories (e.g., Dryad Digital Repository). Large-scale monitoring data from sensor arrays, field stations, state or federal governmental databases, and citizen science projects can be accessed on the web. Other important data streams include the digitization of biodiversity data from museum and university collections (Page et al. 2015, Holmes et al. 2016).
The increased availability of research data coincides with calls for undergraduate education reform, including providing opportunities for students to gain experience with scientific practices and the development of quantitative competencies (AAAS 2011). Authentic inquiry that is rooted in real research datasets allows students to gain a range of skills related to quantitative competency in biology (Aikens and Dolan 2014). These skills include data cleaning, visualization, analysis, and interpretation. Data-centric authentic inquiry allows students to engage in the scientific process through framing questions and hypotheses, designing and carrying out investigations using research datasets, and analyzing data and interpreting their findings. The successful integration of data exploration into the classroom has the potential to play a major role in the quest for quantitative literacy in undergraduate students (e.g., Manduca and Mogk 2002, Trautmann and McLinn 2012, Langen et al. 2014).
While the technical aspects of accessing data no longer limit data-centric inquiry in undergraduate classrooms, other significant barriers remain and limit the potential educational impacts of teaching using research data (Henderson et al. 2011, Strasser and Hampton 2012). These challenges include the time and effort needed for data cleaning and curating, matching the learning outcomes of the class period to the data set, and determining the potential analyses and investigative directions. Publishing datasets that have already been curated for use in classrooms can connect learning objectives, instructional materials, and teaching notes to the data and make teaching with research data more practically accessible to larger numbers of classrooms.
The products shared in this Teaching Issues and Experiments in Ecology (TIEE) special issue emerged from a collaboration between the Ecological Society of America (ESA) and the Quantitative Undergraduate Biology Education and Synthesis (QUBES) project. QUBES is an NSF-funded virtual center that promotes faculty teaching scholarship and the integration of quantitative approaches across the undergraduate biology curriculum (Donovan et al. 2015). These special issue authors collaborated as part of a Faculty Mentoring Network (FMN) to prepare their dataset teaching modules for publication. The FMN was conceived and run by the Data Incubator Group (DIG), a recently funded project whose larger goal is to build collaborative conversations about teaching with quantitative data. In this FMN, faculty met online with leaders in pedagogy and discussed issues regarding best practices for data in the classroom, the benefits to using authentic data, where to find publicly available datasets and teaching resources, and approaches to student assessment. Participants brought a diversity of teaching experience and backgrounds, coming from 19 different institutions from across the United States and Canada, including Ph.D granting institutions, small liberal arts colleges, primarily undergraduate public institutions, community colleges, and museums. The FMN structure allowed us to build a faculty community of practice that generated the innovative educational scholarship shared here. This approach highlights our commitment to helping teaching faculty engage in scholarly projects and receive academic credit for their work. The efforts of this incredible group of faculty and data educators has resulted in a wide variety of unique dataset modules that span ecological inquiry (Table 1).
The DIG group is continuing to work towards expanding the adoption of data-centric teaching resources and facilitating large-scale collaboration on approaches to classroom implementation. We aim to expand the application of the open educational resources (OER) life cycle framework and encourage the community to share comments, questions, teaching notes, or modifications related to these materials. Please visit the QUBES site for this project to learn more: httpss://qubeshub.org/groups/data_incubator/tiee.
Acknowledgements:
We would like to thank Chris Beck and Teresa Mourad for supporting the vision for this special issue and the work of the FMN. We are grateful for the time and expertise shared with our group by Tom Langen, Melissa Aikens, Megan Jones, Chris Beck, Elizabeth Schultheis, and Melissa Kjelvik. Many thanks to Gaby Hamerlinck and Hayley Orndorf for logistical support, particularly with the QUBES website. Thank you to Lily Thompson and Sara Bartelli for editorial assistance. This work was funded by National Science Foundation grants DUE 1446269, DUE 1446258, and DUE 1446284 to the QUBES project and DBI 1730122 to the DIG project. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Table 1. The data modules in this special issue, their ecological topics, key features and quantitative skills, and the sources of data (updated as modules are published and available in this special issue)
| Module | Authors and Institutions | Ecological context | Key features | Data Source |
| Painting turtles: an introduction to species distribution modeling in R | Anna Carter, Iowa State University | Species distributions; habitat and climate suitability | Big data; analysis in R; interpretation of mapped data; adaptable to any organism | Global Biodiversity Information Facility (httpss://www.gbif.org/) |
| Effects of rapid climate change on phenology at high altitudes | Carrie Wu and Amy Ellwein, University of Richmond and Rocky Mountain Biological Laboratory | Climate change; phenology; species interactions; trophic mismatch | Long-term data series; linear regression; recreate and expand published figures | The Biology of Climate Change from Digital RMBL (https://www.digitalrmbl.org/case-studies/bcc_background/); Inouye et. al. 2000 |
| Parasites – They’re what’s for dinner: Investigating the role of parasites in aquatic food webs. | Sarah A. Orlofske, University of Wisconsin, Stevens Point | Food web ecology, wetland communities, parasitism, disease ecology, complex life cycles, network modeling | Data interpretation, metadata evaluation in Excel | Preston, D.L., S.A. Orlofske, J.P. McLaughlin, P.T.J. Johnson. 2012. Food web including infectious agents for a California freshwater pond. Ecology 93:1760. Ecological Archives E093-153. |
| Investigating Leaf Litter Decomposition and Invertebrate Communities in Streams | Alida Janmaat, University of Fraser Valley | species tolerance curves, diversity indices, litter decomposition rates, invasive species, and freshwater ecology | Data manipulation and visualization with spreadsheets, data interpretation | Data were obtained from a classroom-based undergraduate research project developed and taught by A.F. Janmaat. |
| Environment-Richness Relationships in Ephemeral Pond Plants and Animals | Amanda Little, University of Wisconsin-Stout | Species richness, environmental variation, aquatic ecology | Data visualization with spreadsheets or R; data interpretation | Author |
| Investigating the Sexually Transmitted Disease (STD) Ecology Using Geographic Information Systems (GIS) | Maruthi Sridhar Balaji Bhaskar, Jason A. Rosenzweig, and Shishir Shishodia, Texas Southern University | Disease ecology, Human ecology and behavior, Bacterial infections, Spatial and Temporal spread of disease, Geographic Information Systems (GIS) | Data manipulation in GIS software, data interpretation | Atlas Plus CDC (Center for Disease Control and Prevention) Data: httpss://gis.cdc.gov/GRASP/NCHHSTPAtlas/main.html, GIS Data – HGAC (Houston Galveston Area Council): https://www.h-gac.com/rds/gis-data/gis-datasets.aspx |
| The Effect of Climate Change on Butterfly Phenology | Debra Linton, Anna Monfils, Molly Phillips, and Elizabeth R. Ellwood, Central Michigan University, Florida Museum of Natural History, Florida State University | Butterfly, climate change, interspecific interactions, natural history collections, phenology | Data interpretation in Excel | iDigBio portal of natural history collections records for butterfly specimen data (www.idigbio.org/portal/search), Environment and Climate Change – Canada for surface air temperature data of British Columbia (www.ec.gc.ca/dcchaahccd/default.asp?lang=en&n=1EEECD01-1 |
| The nose knows: How tri-trophic interactions and natural history shape bird foraging behavior | Kaitlin M. Bonner and Gregory B. Cunningham, St. John Fisher College | Foraging, trophic cascades, behavioral ecology, Antarctic food webs | Data manipulation and analysis in Excel, data and regression analysis in ToolPak, data interpretation | Bonadonna, F., Caro, S., Jouventin, P., and G. A. Nevitt. 2006. Evidence that blue petrel, Halobaena caerulea, fledglings can detect and orient to dimethyl sulfide. Journal of Experimental Biology 209:2165-2169. Cunningham, G.B., S. Leclaire, C. Toscani, and F. Bonadonna. 2017. Responses of king penguin Aptenodytes patagonicus adults and chicks to two food-related odours. Journal of Avian Biology 48:235-242 |
| Data Management using NEON's Small Mammal Data | Jim McNeil and Megan Jones, Smithsonian-Mason School of Conservation, George Mason University, Smithsonian Conservation Biology Institute, and National Ecological Observatory Network | Species abundance and distributions, small mammal trapping | Data management, big data, mark-recapture analysis | National Ecological Observatory Network (NEON) |
References
Aikens, M.L. and E.L. Dolan. 2014. Teaching quantitative biology: goals, assessments, and resources. Molecular Biology of the Cell 25: 3478–3481.
American Association for the Advancement of Science 2011. Vision and Change: A Call to Action, Final report. Washington, DC.
Donovan, S., C. Diaz Eaton, S.T. Gower, K.P. Jenkins, M.D. LaMar., D. Poli, R. Sheehy, and J.M. Wojdak. 2015. QUBES: a community focused on supporting teaching and learning in quantitative biology. Letters in Biomathematics 2: 46–55.
Hampton, S.E., C.A. Strasser, J.J. Tewksbury, W.K. Gram, A.E. Budden, A.L. Batcheller, C.S. Duke, and J.H. Porter 2013. Big data and the future of ecology. Frontiers in Ecology and the Environment 11: 156–162.
Henderson, C., A. Beach, and N. Finkelstein 2011. Facilitating change in undergraduate STEM instructional practices: An analytic review of the literature. Journal of research in science teaching 48: 952–984.
Holmes, M.W., T.T. Hammond, G.O.U. Wogan, R.E. Walsh, K. Labarbera, E.A. Wommack, F.M. Martins, J.C. Crawford, K.L. Mack, L.M. Bloch, and M.W. Nachman 2016. Natural history collections as windows on evolutionary processes. Molecular Ecology 25: 864–881.
Inouye D.W., B. Barr, K.B. Armitage, and B.D. Inouye 2000. Climate change is affecting altitudinal migrants and hibernating species. Proceedings of the National Academy of Sciences 97: 1630–1633.
Langen, T.A., T. Mourad, B.W. Grant, W.K. Gram, B.J. Abraham, D.S. Fernandez., M. Carroll, A. Nuding, J.K. Balch, J. Rodriguez, and S.E. Hampton 2014. Using large public datasets in the undergraduate ecology classroom. Frontiers in Ecology and the Environment 12: 362–363.
Manduca, C.A. and D.W. Mogk 2002. Using data in undergraduate science classrooms. Northfield Workshop Report. httpss://serc.carleton.edu/files/usingdata/UsingData.pdf
Page, L.M., B.J. McFadden, J.A. Fortes, P.S. Soltis, and G. Riccardi 2015. Digitization of biodiversity collections reveals biggest data on biodiversity. BioScience 65: 841–842.
Reichman, O.J., M.B. Jones, and M.P. Schildhauer 2011. Challenges and opportunities of open data in ecology. Science 331: 703–705.
Strasser, C.A. and S.E. Hampton 2012. The fractured lab notebook: undergraduates and ecological data management training in the United States. Ecosphere 3: 116.
Trautmann, N.M. and C.M. McLinn 2012. Using Online Data for Student Investigations in Biology and Ecology. In Informed Design of Educational Technologies in Higher Education: Enhanced Learning and Teaching: 80-100. IGI Global.
Whitlock, M.C. 2011. Data archiving in ecology and evolution: best practices. Trends in Ecology & Evolution 26: 61–65.
Kaitlin M. Bonner, Arietta E. Fleming-Davies, Kristine L. Grayson, Alison N. Hale, X. Ben Wu, and Samuel Donovan. November 2017, posting date. Bringing Research Data to the Ecology Classroom through a QUBES Faculty Mentoring Network Teaching Issues and Experiments in Ecology, Vol. 13: Commentary [online]. https://tiee.esa.org/vol/v13/issues/commentary.html