Mon, Aug 15, 2022: 4:00 PM-4:15 PM
518A
Background/Question/MethodsEarly exposure to research is well established as a potent pedagogical tool and has prompted efforts to provide undergraduate research experiences. However, many undergraduate research opportunities fail to provide a realistic glimpse into the scientific method. Fortunately, data dashboards provide an accessible, scalable platform where students can work through the scientific method from experimental design to data analysis and final conclusions. The Massasoit Community College Native Bee Data Dashboard website provides easy access to a rich ecological data set including the results of an ongoing native bee monitoring study. Users can explore bee data from six sites in Plymouth County Massachusetts, sampled bi-weekly from 2016-2020. Bee data can be compared to NOAA weather data, sampling-site landscape attributes, or time. The guided data selection menu allows students and researchers to design and complete a hypothesis-driven analysis from start to finish. Additionally, users are prompted to evaluate their hypotheses using built-in statistical tests. Here we present an analysis of five years of abundance and richness data. Yearly bee abundance and richness from 2016-2020 were evaluated to determine if the Plymouth County bee community is in decline.
Results/ConclusionsThe data dashboard provided evidence for fluctuations in bee abundance, but did not indicate a declining trend. Bee abundance declined by 13% from 2016 to 2018, but rebounded by 2020 to 100.06% of the 2016 value, showing year-to-year variability but no overall change in abundance over five years (p > 0.05). Bee richness declined by 6% from 2016 to 2018, but recovered by 2020 to 102% of the 2016 value (p < 0.05). These analyses showcase the potential of the data dashboard to track changes in the native bee community. The data dashboard was coded using the R programming language, and hosted as a web app with the package R Shiny. The accompanying learning activity is a series of questions that guide the student through the scientific method by utilizing different app features. Users can select specific bee genera, and compare their abundance or richness to biologically relevant independent variables. The data dashboard queries data and evaluates hypotheses with graphs, repeated measures ANOVAs, and linear regressions. The guided learning activity and data dashboard work in synergy to create an accessible research experience for students and access to important native bee abundance and richness information to any interested party.
Results/ConclusionsThe data dashboard provided evidence for fluctuations in bee abundance, but did not indicate a declining trend. Bee abundance declined by 13% from 2016 to 2018, but rebounded by 2020 to 100.06% of the 2016 value, showing year-to-year variability but no overall change in abundance over five years (p > 0.05). Bee richness declined by 6% from 2016 to 2018, but recovered by 2020 to 102% of the 2016 value (p < 0.05). These analyses showcase the potential of the data dashboard to track changes in the native bee community. The data dashboard was coded using the R programming language, and hosted as a web app with the package R Shiny. The accompanying learning activity is a series of questions that guide the student through the scientific method by utilizing different app features. Users can select specific bee genera, and compare their abundance or richness to biologically relevant independent variables. The data dashboard queries data and evaluates hypotheses with graphs, repeated measures ANOVAs, and linear regressions. The guided learning activity and data dashboard work in synergy to create an accessible research experience for students and access to important native bee abundance and richness information to any interested party.