Project EDDIE Environmental Data-Driven Inquiry & Exploration) is a community effort aimed at developing teaching resources and instructors that address quantitative reasoning and scientific concepts using open inquiry of publicly available data. Project EDDIE modules are designed with an A-B-C structure to make them flexible and adaptable to a range of student levels and course structures.
Many species' life cycles are strongly influenced by temperature, but other cues, like day length and precipitation, can also trigger life cycle changes. 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: how is bumblebee phenology affected by climate, and are patterns in the phenology of an organism better explained at smaller scales?
Strengths of Module
Students should be able to clean and wrangle data, create and interpret figures, compare R2 values among bivariate relationships and think about data reliability.
What does success look like
Through this module, students should develop data analysis skills that help them to evaluate the relationship between a variety of climate-related environmental cues and a taxa's phenology. In the context of climate change, they will be able to make an argument using data about whether changing climates are likely to impact the phenology of a particular species of interest. They will compare the results using different subsets of a large dataset and make decisions about how to create subsets of data for the analyses they plan to complete. Students will able to compare the strength of the association between temperature/climate-related variables and phenology for different species. To achieve these goals, students will develop abilities to generate, read, and evaluate scatterplots and regressions between sets of variables.
Context for Use
Description and Teaching Materials
Why this Matters:
Quick outline/overview of the activities in this module
- Worksheet Part A: Orientation to phenology via the National Phenological Network website and critical thought of phenology, season and latitude.
- Worksheet Part B: Determine whether there is a detectable trend in bumblebee emergence date in the Spring over time for a continental and regional dataset.
- Worksheet Part C: Compare the date of bumblebee emergence with a variety of climate-related site traits, including: Winter and Spring max/min temperatures and precipitation using scatterplots and regressions. Then, exploration of smaller scale variation within a chosen climate variable via latitude, elevation, or state (USA).
Worksheet Part A
Students learn to define phenology and understand how phenological data are collected. Then, they evaluate the role that phenology plays in human society and begin to think about the broad roles of climate on phenological behavior.
Worksheet Part B
In this section, students manipulate data to make and interpret scatterplot graphs and regressions about the phenology of bumblebees across time for a large dataset and a provided subset (Minnesota) of the dataset. They will make interpret and compare the biological patterns in both datasets, and evaluate their confidence in their conclusions given the nature of the available data.
Worksheet Part C
In activity B, students tested change over time, it is uncertain if changing climate is responsible for the patterns observed. In activity C, students use environmental data recorded for each site to identify how much variation in bumblebee emergence phenology is explained by other climate-related variables. Students are required to justify their chosen climate variable. Additionally, students are asked to explore smaller scale sources of variation within relationship of bumblebee phenology and their climate variable of choice.
- Phenology Data Exercise (handout, Microsoft Word 2016 and PDF)
- phenology_data_student.xlsx (Microsoft Excel 2016)
- pre-lab_presentation_v2 (Power Point 2016, not specifically used but kept as a resource from original module)
Teaching Notes and Tips
Workflow of this module:
- This module is designed to be completed in a 2.5 hour laboratory session or asynchronously
- Give students digital access to handout and dataset when they arrive to class
- Instructor gives brief PowerPoint presentation with background material (not used). Instructor may need to introduce concepts of phenology, climate vs weather or summary statistics depending on the level of the students
- Students can then work through the module activities.
Notes on the student handout:
The handout is meant to increased in difficulty and complexity from one question to the next. Students should easily be able to complete Part A without assistance. The final parts of Part B and most of Part C will likely require guidance from the instructor, depending on the skill level of students with Excel.
Potential pre-class readings:
Instead of attaching one specific paper for pre-reading, I suggest that instructors find a very recent paper that discusses insect/plant phenology and global change. Their are many wonderful papers on bee phenology that are continually being added to the scientific literature.
Measures of Student Success
Success is shown if:
- Students can clean, manipulate and subset data from the NPN network before visualizing or analyzing data trends.
- Students can create, compare, and interpret scatterplots and correlations between pairs of variables.
- Students can articulate the relationship between temperature, environmental cues, and a taxon's phenology, and use these relationships to predict how the taxon will be affected by climate change.
For this module I combined the pre-class handout and the student handout part A & B into one exercise. I stripped away the questions asking student to hand-draw figures and instead focus more on working specifically with the data. I did not ask the students to subset specific years of phenology data (with clearer climate patterns) because I disagree with that premise. I do ask students to explore smaller scale sources of variation within the broad climate dataset (state, elevation, latitude).
Cite this work
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