Using NSF's NEON Data in an Undergraduate Ecology CURE on the Ecological Impacts of Global Climate Change
Jennifer Kovacs, Agnes Scott College
We live in a time where we can see a very real need for a basic understanding of ecological terminology, concepts, and methodologies to improve public policy and other ecological problem-solving decisions, especially in light of global climate change. Across the field, there is a major push to incorporate computational thinking and an understanding of human social systems throughout the science curriculum. In ecology and other STEMM fields, basic programming and coding skills have become essential and marketable, as has the ability to mine and analyze large data sets.
In this semester-long CURE, students individually develop and answer their own ecological research question using a selection of publicly available datasets from the expansive NSF NEON data repository. Generally, at the beginning of the course the instructor selects several data products from a specific geographic region. After gaining familiarity with the NEON project through videos, a NEON data tutorial, and a case study, students also use these curated NEON data products to begin forming their independent research projects. Most students ultimately incorporate other data products either from NEON or other databases into their final research projects.
Students use mostly R to download, wrangle, and analyze their data. The instructor assumes no prior knowledge of R or coding at the beginning of the course. Throughout the semester, students complete mini-assignments and tutorials which introduce them to the necessary coding skills to download, clean, analyze, and visualize their chosen data products. Additionally, students are provided with a wide range of free resources, including videos, tutorials, and the free online textbook Passion Driven Statistics to help them master the skills they need to complete their individual research projects.
During weekly in-class one-on-one meetings with the instructor, students work to identify, collect, and analyze data that would address an existing hypothesis/ problem in the field of ecology and global climate change. Ultimately, students present their findings to the larger campus community during the annual undergraduate research day at our institution.
- Generate a testable hypothesis with specific predictions and design an investigation using publicly available data.
- Analyze and visualize data from a publicly available database to test current ecological hypotheses using R.
- Communicate findings to a broader audience.
- Determine the effects of changes in land use on biodiversity across a range of ecosystems
- Investigate and draw conclusions about how climate change can impact biodiversity and/or phenological timing in biological systems
This course was designed as an upper-level biology majors elective course with a lab and lecture section. It was intended to be a face-to-face course, but due to being taught during the COVID-19 pandemic shutdowns, the course was moved to an online asynchronous format with 11 students.
In the asynchronous format, students watched short lecture videos, answered online discussion writing prompts, and completed and submitted case studies and coding tutorials throughout the semester. Students also met for 30 minutes once per week one-on-one with the instructor to discuss the progress of their individual projects. Students completed a weekly progress report and check-in on the class Slack channel prior to their weekly one-on-one instructor meetings.
Students were not required to have any prior coding knowledge, and students were given the choice of coding in either R or Python.
In future semesters with the return to face-to-face instruction, I plan on retaining the short video lectures and coding tutorials to be completed outside of class. In-class time will be used for case studies, live coding, and troubleshooting. Additionally, I plan to make the progress reports into short student presentations throughout the semester, and to have all students code in R.
Target Audience:Upper Division
CURE Duration:A full term
Students are responsible for generating and completing their own research projects over the course of the semester. There are no exams in this course and the majority of the grade is the research project.
The project is scaffolded over the course of the semester with online discussion prompts focused on specific parts of the research process. For example early discussion prompts focus on identifying interesting research variables and crafting a testable hypothesis. Later discussion prompts and coding tutorials focus on generating data visualizations which address the student's hypothesis and predictions.
In order to make the process of crafting a research question and identifying a dataset to use to address that research question, I provide students with a curated and already downloaded (and stacked) set of selected NEON data products. For beginning users especially, the breadth and depth of the data products available through the NEON project can be overwhelming. NEON has a network of over 80 field stations (both aquatic and terrestrial) and collects hundreds of data products including species counts and measurements, air temperature, water quality, and soil measurements. However, I've found that providing students with a few smaller data products allows them to become familiar with the data products and their structure.
For example, for the Spring 2021 course, I provided students with following data products from the terrestrial field stations at Disney Wilderness Preserve (DSNY- Florida), Ordway-Swisher Biological Station (OSBS -Florida), Jones Ecological Research Center (JERC- Georgia), and Mountain Lake Biological Station (MLBS, Virginia): Beetle Fall Traps, Bird Counts, Small Mammal Traps, Mosquito Counts, Mosquito Pathogens, and Phenology.
I provide these data products as stacked .csv files along with their meta-data files so that students can begin looking at the data using Excel or Google Sheets and can begin interacting with the data even before they begin coding. Here is a NEON tutorial for downloading and beginning to work with NEON data. Later in the semester, I provide the code to students so that they can do that themselves going forward.
As the semester progresses and students begin forming and re-forming their research project and its scope, they often include other NEON data products or even data from other sources. Since many of my student projects focus on climate change, data products measuring temperature and precipitation are often incorporated, though I rarely start with these data products since they are large and a bit unwieldy to wrangle for beginners.
Students present their work at the annual interdisciplinary undergraduate research day at the end of the semester. This event is open to the general public and is well-attended by students and professors across the campus (no classes are held that day). Additionally, students have presented their work at other venues during the summers and semesters following the completion of the course including regional ecology and evolution conferences. The goal of this CURE is for students to have a scientific presentation of their own that they can share at conferences, with prospective employers or graduate schools, and with other interested parties, including those in the broader community. At the end of the semester, the research is the students' own.
Core Competencies: Analyzing and interpreting data, Asking questions (for science) and defining problems (for engineering), Using mathematics and computational thinking
Nature of Research:Basic Research
Tasks that Align Student and Research Goals
Student Goals ↓
Complete Case Study #1 looking at the effects of racist mortgage lending practices, known as "redlining," on biodiversity in urban areas (see resources on social justice in STEM available on the QUBES hub linked below). Part of this assignment includes proposing extensions of this research.
Complete the NEON Phenology tutorial using data downloaded from the NEON site. A large part of this tutorial is dedicated to understanding what variables are measured at NEON field sites and how that data can be used.
- Complete Case Study #1 looking at the effects of racist mortgage lending practices, known as "redlining", on biodiversity in urban areas (see resources on social justice in STEM available on the QUBES hub linked below).
- Use ArcGIS to visually display biodiversity data geographically.
- Use provided R code to perform data analysis on their biodiversity data and visualize the resulting statistics.
- Complete discussion prompts through their online textbook Passion Driven Statistics along with class example code.
Complete the NEON Phenology tutorial using data downloaded from the NEON site. This tutorial is based on raw data downloaded directly from NEON through their API. A fair amount of this tutorial is dedicated to cleaning up the raw data. This involves multiple rounds of data visualization using R.
Present research projects at the annual undergraduate research day at the end of the semester.
- Case Study #1: QUBES resources for Redlining and Biodiversity-- this link takes you to the QUBES website. You may need a (free) account to fully access these resources. This is the page for a short workshop that was held in the summer of 2021 during the QUBES BIOME summer meeting. There is a handout for the worksheet which is located under the "File Contents" tab that includes directions to using the free version of ArcGIS online and other data visualization tools to explore the effects of redlining on urban ecology and human health. There are also links to other developed educational resources located at the end of the handout document.
- Case Study #2: NEON tutorial on Phenology
- Textbook: Passion Driven Statistics- free on-line textbook
- Syllabus for asynchronous online Spring 2021 course offering (Acrobat (PDF) 126kB Jan14 22)
Student participation was tracked throughout the course through contributions to the weekly discussion prompts, as well as weekly progress reports. No tests were given for the class, but rather the majority of the grade for the course was for the final research presentation (either as a poster or oral presentation).
This course was taught by a single instructor without a TA.
Jennifer Kovacs, Agnes Scott College
I designed this CURE around the idea that students could do authentic ecological research investigating the impacts of climate change on a variety of ecosystems using large publicly available datasets. In this course, students develop and refine their own research questions, evaluate current ecological hypotheses, test predictions, and present their research at an undergraduate research day, while all developing the skills to wrangle, clean, and analyze real-world large dataset(s) made publicly available through NSF's NEON project (https://www.neonscience.org/).
Advice for Implementation
I highly recommend choosing a subset of NEON data to present to the students during their research question development. I chose data that came from field stations that were in our region of the US, as well as data products that I thought students would be interested in (like mosquitos and small mammals).
Using CURE Data
All student projects have resulted in student presentations at undergraduate research days. Two current students have opted to continue their projects as independent study projects.
Data Carpentry for Ecology
NEON site and tutorials
Joel's online coding videos and assignments