These are the selected modules for this Faculty Mentoring Network (FMN). Note that these modules are being offered as a preview for the EDDIE FMN. They have all been piloted at least once by their authors, but are still undergoing final review and revisions. During the Spring 2021 semester, participants will adapt and implement portions from one of the following into their classrooms. These modules cover a range of earth and environmental science topics, so please select the activities that will fit best into your course curriculum. All EDDIE modules are built with an A-B-C structure that makes them flexible and adaptable to a range of student levels and course structures. Below are overview descriptions of the available modules. Additional details for each module can be provided upon request.
Quick List of Modules (more details for each below):
- Paleoclimate and Ocean Biogeochemistry
- Wind and Ocean Ecosystems
- Sustainability Metrics
- Remote Sensing of Plants and Topography in R
- Plate Tectonics: GPS Data, Boundary Zones, and Earthquake Hazard
- Climate Drivers of Phenology
- Phenology trends and climate change in Minnesota
Paleoclimate and Ocean Biogeochemistry
This module guides students in an examination of how surface ocean productivity relates to global climate on glacial-interglacial timescales and how the availability of ocean nutrients can be correlated with changes in productivity. The overarching question the module helps students answer is: How does primary productivity influence global climate?
In Part A, students reflect on how nitrogen and phosphorous are distributed globally, and how patterns of primary productivity compare with those nutrient patterns.
In Part B, students use statistical analysis to examine the influence of dust-borne iron on carbon export in two ocean regions.
In Part C, students choose a data set to investigate the relationship between ocean carbon export and climate, formulate a hypothesis to test using that data set, and share their findings with peers who chose a different data set.
This module takes approximately 90 minutes to complete, and was designed for use as part of an undergraduate course on Earth's Climate System. The module might also be appropriate for use in courses that have a focus on oceanography, marine biogeochemistry, paleoclimatology or paleoceanography. Students should have experience plotting quantitative data sets - both time series and cross plots- (in any software environment), and students need to know how to perform a linear regression and interpret it. Module Activities A and B are designed to be completed individually; Activity C should be completed in collaborative team.
Wind and Ocean Ecosystems
This module introduces students to the concepts of Ekman transport, eastern boundary currents, and upwelling, while learning how to find a location on a map using latitude and longitude, how to build and interpret a wind rose plot in Excel, and how to access and view relevant data from oceanographic satellites.
In Part A, students find latitude and longitude of buoys from the NOAA National Data Buoy Center in the Northwest Atlantic Ocean and plot the buoys on a worksheet.
In Part B, students create a wind rose plot in Excel of wind direction and strength of a specific buoy in the Gulf Stream.
In Part C, students identify an upwelling event with buoy water temperature data, create wind rose plots of upwelling and non-upwelling events.
In Part D, students visualize upwelling with a choropleth map time series, look at MODIS chlorophyll data of upwelling and non-upwelling events.
Depending on the class environment, students may need to complete pre-work of creating an account with ArcGIS, online, outside of class. This module was piloted in an environmental statistics class in a computer lab as a means of teaching map and wind rose plot interpretation. It was taught over two 3-hour computer lab periods, which included students writing a lab report with 'results' and 'discussion' sections, with appropriate figures referenced, after completing the activity worksheets themselves.
In this module, students use an analytical framework with publicly available data to formulate questions, analyze data, and report metrics of sustainability.
In Part A, students learn to navigate the Gapminder tool, identify components of a graph, and interpret a graph under the IPAT analytical framework.
In Part B, students explore sustainability metrics by framing a sustainability question, building a graph, interpreting results, and communicating findings with peers.
In Part C, students formulate their own question about sustainability, download datasets from the Gapminder Tool website, compare and contrast sustainability metrics for a specific country over time, and reflect on strengths and limitations of datasets and IPAT framework for quantifying sustainability.
This module is intended for an upper-level undergraduate course in sustainability, environmental studies, systems thinking, natural resources consumption, or any interdisciplinary course that includes these concepts. Students are expected to be familiar with the three facets of sustainability and to have some experience with reading and interpreting graphs. This module was designed to be implemented in two 1.25-hour classroom sessions, but could be adapted to be completed in one 2.5 - 3 hour lab session.
Remote Sensing of Plants and Topography in R
This module introduces students who are already familiar with remote sensing and R to doing quantitative analyses with large spatial data sets. Students will explore different possible abiotic drivers of plant growth, defined as greenness and height. In the final step, students will analyze data from around the United States and consider macroscale patterns of vegetation controls.
In Part A, students calculate slope and aspect from a DEM, map elevation, slope, aspect, NDVI, and vegetation height. Calculate northness (cosine of aspect). Based on visualization, generate hypotheses about the most likely driver(s) of vegetation growth.
In Part B, students calculate correlations between topographic variables and vegetation variables. At the end of this activity, students decide on one metric and gradient combination to explore across a set of NEON sites to determine to what extent the local pattern applies at large scales.
In Part C, each student chooses a NEON location, accesses data, and calculates correlations as had been done in Activity B. Then, the correlations chosen at the end of Activity B are mapped together (by hand or digitally) on a map of the U.S. that is accessible to all students, and macroscale patterns are considered qualitatively (as a group or as a written assignment).
This module was developed for an upper-level remote sensing class, where students have a lot of familiarity with gridded (raster) data sets and concepts like NDVI and digital elevation models. The class that the module was piloted in was a 15-person lab, with computers pre-loaded with R, RStudio, and the data. Students had some introduction to R. The labs were 2 hours each, and the expectation was that the module could be completed over the course of two lab sessions, with some of the answer writing done as homework, if necessary.
Plate Tectonics: GPS Data, Boundary Zones, and Earthquake Hazard
Students work with high precision GPS data to explore how motion near a plate boundary is distributed over a larger region and hypothesize the area over which boundary-related earthquake hazards might exist. Primary emphasis is strike-slip regions (examples from Alaska, Dominican Republic, and California).
Motivating Question: Is all motion on the plate boundary? How far away from the plate boundary might earthquakes occur?
In Part A, students understand the horizontal motion from just one GPS station
In Part B, students compare GPS data from pairs of stations near two different strike-slip regions (Alaska and Dominican Republic)
In Part C, students choose their own transect of 4-5 stations in California and eastern Nevada to look at how motions change away from the "plate boundary line" on the map.
In the optional Part D, students discuss velocities across a wider area of the western US and/or other applications of GPS for plate tectonic and earthquake issues.
This module was intended for introductory earth science students at the college or secondary level. It also works well for earlier-stage geology majors. Appropriate courses include: physical geology, natural hazards, plate tectonics, and structural geology. Data analysis works best if students have computers (individually or in small groups), although the module can also be done with provided graphs (which the instructor would have to print) instead of downloaded data, from which the students graph the data themselves in a spreadsheet program (ex. Excel or Sheets).
Climate Drivers of Phenology
Phenology is a way of recording the time when events, like bud break or insect emergence, occur, and these events can be important for everything from predicting the timing of disease or insect outbreaks to predicting the impacts of climate change on particular species. This activity explores the question: which species will be most affected by temperature changes, and how will changes in the phenology of one species affect its interaction with others as the climate warms.
In Part A, students determine whether there is a detectable trend in bumblebee emergence date in the Spring over time.
In Part B, students compare the date of bumblebee emergence with a variety of temperature-related site traits, including: latitude, elevation, and Winter and Spring max/min temperatures using scatterplots and regressions.
In Part C, students choose a species of interest, select data from the NPN network, and identify which variables are most predictive of emergence date. They then compare their results with those from other species to predict which species are likely to be most susceptible to temperature changes and to consider whether interacting species will respond asynchronously or in parallel.
Phenology trends and climate change in Minnesota
In this module, students will practice answering a specific question about how climate change has affected flowering date in American elm trees. After students learn to manipulate the elm data set, build graphs, and analyze the data with a regression, they can then practice on a species of their own interest. Students can then share their species' information with the class for a larger discussion about what types of species may be affected by climate change.
In Part A, students determine changes in flowering date for American elm in Ramsey Co, MN.
In Part B, students determine significance of changes in flowering date.
In Part C, students determine a phenophase ("event") pattern for a Minnesota organism of students' choice.
This module is designed for an introductory-level course and can be used in majors or non-majors courses. The exercise assumes no prior knowledge of spreadsheet manipulation, work with large data sets, nor extensive climate change knowledge. The module is designed for one class period of 65 min with an additional take home assignment.