As part of the “DIG into Data for the Biology Classroom” faculty mentoring network that ran in spring 2017, we created classroom modules based on authentic data sets. This network was the result of a partnership between the Ecological Society of America and the NSF-funded virtual center Quantitative Undergraduate Biology Education and Synthesis (QUBES). In Fall 2018, a second faculty mentoring network created adaptations of the dataset modules published in TIEE.
Students use small mammal data from the National Ecological Observatory Network to understand necessary steps of data management from data collection to data analysis by estimating small mammal population sizes using the Lincoln-Peterson model.
Students investigate the role of olfaction and infochemicals on bird foraging behavior through three different quantitative modules. In this module students create graphs, generate statistics, draw conclusions, and apply their knowledge.
Identifying, mapping and understanding the magnitude and the spatial prevalence of the Sexually Transmitted Diseases (STDs) such as chlamydia and gonorrhea diseases. Analyzing the spatial trends of the disease patterns at National, State and...
Resources @ ESA - TIEE, Disease ecology, fmn, DIG, human ecology and behavior, bacterial infections, spatial and temporal spread of disease, Geographic Information System (GIS), introductory, Lab, Online course, Teaching material, Lecture, Undergraduate, Advanced, More than 1 hour
Use a well-resolved food web database from a freshwater wetland ecosystem in central California to explore how parasites influence community properties and learn how food webs are constructed and analyzed.
Students evaluate weather and phenology data sets spanning 40+ years from a high-elevation field station in the Colorado Rocky Mountains, to test predictions about the effects of climate change on migrating and over-wintering animal and plant.
Students investigate the role of olfaction and infochemicals on bird foraging behavior through two different quantitative activities where they generate hypotheses, create figures, conduct data analyses, and draw conclusions.
Students use vegetation structure data from the National Ecological Observatory Network to understand necessary steps of data management from data collection to data analysis by correlating vegetation biomass across nine forest sites to climate metrics.
Students explore findings from long-term studies of migrating and hibernating animals at high elevation. Students then use this understanding to explore the phenology of species and possible mismatches for species that interact.
Students combine long-term observational data sets and experimental warming in an alpine meadow in Gothic, CO to test, evaluate, and interpret ecological data focusing on phenological variation and species interactions in a changing climate
Students generate predictions and test three hypotheses about how biodiversity is affected by urbanization over time using citizen science generated bird count data and land use data from 13 locations in Florida over a 10 year time span
Students evaluate long term (100+ years) trends in temperature and precipitation, and then isolate a shorter time span (20 years) in which to evaluate the correlation between spring temps and the earliest reported calling dates for MN frogs and toads
This module explores the effects of host density on contact rates and transmission of pathogens or mutualist symbionts using a hands-on simulation, a computer agent-based model in NetLogo, and an authentic ecological dataset.
This dataset of fungal communities associated with the leaves an endemic Hawaiian tree provides students opportunities to explore relationships between environmental variables and microbial community composition.
This module provides a framework for upper-level ecology students to learn about limiting factors for stream fish diversity, using data from Shenandoah National Park and gaining skills in predictive, regression-based modeling.
Jennifer Doherty, Gideon Dunster, Horacio de la Iglesia
Students use sleep and exam data from Introductory Biology students to investigate the question: Does a circadian rhythm, the sleep-wake cycle, influence an important mammalian adaptation, the ability to learn in an information-rich environment?