Massasoit Community College’s native pollinator research team has a new way for anyone to follow our research with up-to-date pollinator data from our six test sites in Plymouth County. We recommend this data dashboard as a learning tool for teachers as well as a way for interested scientists to access high-quality monitoring data on local native bees. The database started back in 2016. The app has results from bi-weekly sampling data from each subsequent year.
The research team is made up of Massasoit faculty, staff, and students who design and implement all aspects of this research project. Our students collect and prepare the data that populates the database viewed by this dashboard. The dashboard itself was designed and created by a Massasoit STEM student named Alexander VanHelene and was created in the R statistical programming language.
Launch the app!
Learn to use the app with this example experiment
Before we can start our experiment we have to decide what we are curious about. For this experiment, I want to know more about how our wild bee population is affected by the weather. Our wild bee app supports mean temperature, growing degree days, and mean precipitation as independent variables. But I am going to narrow my question to just one of these and ask
“How does precipitation affect ground-nesting bee abundance?”
Now that I have a question it is time to make a hypothesis, an educated guess as to what will happen. Maybe ground-nesting bee abundance will be lower in months with higher precipitation because the rain might flood their burrows. Leading to the hypothesis
“Bee abundance will be lower when average precipitation is higher.”
Fortunately, we have already collected 6 years of bee data for the wild bee app. So all we have to do to test our hypothesis is to use the filters under the “Visualize Trends” option in the bee app’s sidebar. Each filter helps us narrow down our research question until it is specific enough to run an analysis of the data. Some important terms to know regardless of your question are “independent variable” and the “dependent variable”.
Our independent variable is our input and we want to see how changes to our independent variable affect the dependent variable our output. For our hypothesis, the average monthly precipitation is our independent variable and monthly bee abundance will be our dependent variable
All the options used in this experiment can be seen here. For “Which bees do you want to analyze?” clicking the ground nesting button will check off all the bees I have selected.
And now we finally get to see the results of our experiment. Once you have finished the filters about your question the bee app will ask you which type of graph you would like to use. For this question, I will use the correlation graph because I am trying to see if the variables of average precipitation and ground-nesting bee abundance are correlated (related).
This graph plots a data point for each month and site and then creates a trend line to show how one variable changes with the other. An important number to note is the R2 value at the top of the graph. It is a number between 0 and 1 showing how well our data fits the trend line, with 1 being a perfect fit. Our R2 value is 0.087 so our correlation is not very strong. We can also see that there is a negative correlation between bee abundance and mean precipitation, due to the downward trend line.
So now we just have to sum up what we learned. Due to the negative correlation between precipitation and ground-nesting bee abundance, we can tell that precipitation might reduce bee abundance when sampling. However, because the correlation was weak monthly precipitation likely isn’t a major factor. So perhaps instead of something catastrophic like rain flooding their burrows, more ground-nesting bees decide to stay in their nests or fly shorter distances leading to collecting less when sampling.
Now it is your turn next time you’re curious about bees try exploring your question with the bee app.