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Our goals are to study moth communities and
teach how to collect, analyze and present data.

Catocala ultronia (Hübner, 1823), Ultronia Underwing


The data collected by Mothing supports inquiry based learning and teaching quantitative methods to high school through graduate level students. Participants can compare their own data with those of others, learning data management, graphing, statistical analysis, and, for the most advanced students, modelling. The original data are automatically updated nightly and presented in individual albums (use 'Find' feature) and collectively in the Results section. Thus, we make all photographs, identifications, and associated data available within a day of submission to everyone via the web.

Here we provide faculty and teachers suggested questions that they and their students can answer with the data. These assignments range of difficulty. They start with simple graphing for high school biology and environmental science classes. The more complex questions should probably only be attempted by college seniors and graduate students with a bent for computer programming and fitting data to biological and statistical models. Everyone should read all questions, especially the last ones.

Potential assignments:

  1. Graph the number of moths photographed in each month from Table 1. Which month of the year has the greatest diversity? Is this the same across years?

  2. Based on Table 2, which moth species have the longest flight period? Are these the most common species based on the number of photographs taken?

  3. Compare the detailed flight times of two or more species using the 'plot=3' feature. How do your species differ from each other across years or across sites? For example, how many generations a year do the four species in this graph have? Change the scientific names in the graph's URL address to look at additional species.

  4. Make a pie chart comparing the species at your site with those collected during the same months at the 275 Blue Heron Drive site in Clarke County, Georgia (Table 2). Have the chart represent the proportions of species at both sites, at just your site, and just in Clarke County. Discuss how the following might explain your results: (1) differences in sampling effort and number of moths photographed between the sites, (2) local differences in the habitat and plant communities surrounding the sites, (3) the geographical location of the sites with regard to possible regional, latitudinal, and elevational differences, (4) local weather patterns at the two sites, and (5) other factors that mighted have affected the results.

  5. In terms of the number of moth species photographed each night, which phase of the moon has the greatest diversity? Does this differ across months? Table 4 has the lunar cycle; tabulate the number of species recorded each night from the individual records.

  6. Graph the seasonality (phenology) of a species, comparing it across years (or across sites when we get time-series data from additional sites)? Is the species flying during the same period across years (sites)? Explain what might cause the species seasonality to differ across years and sites. How might Altanta's "heat-island" affect moth seasonality relative to moths at generally cooler, rural sites?

    [Starting fall 2011, Jonathan Lochamy's students propose to monitor sites in Atlanta; John Pickering's, around Athens.]

  7. Choose the four most photographed species (say species A, B, C, D) that flew in April, 2010. Make a time-series graph to compare when they were photographed in April and May in 2010 versus 2011. Did species A, B, C, and D fly earlier, later, or on the same dates in 2011 as compared with 2010? Did the relative order of their (first, median, mode (peak or peaks), mean and last) recorded dates change across years or were they the same? For example, if species A's peak flew before species B's in 2010 did it do so in 2011, or did B fly before A. If the order were ABCD in 2010 and DCBA in 2011, then there would be a complete reversal in the species phenology. Why might this happen?

    When we get plant data up, we plan to allow students to compare, contrast, and explain insect and plant phenologies across sites and years. Consider how insect versus plant development times and activity levels may responded differently to photoperiod and temperature regimes. Under what circumstances is it most likely that climate changes could affect pollination efficiency by disrupting the seasonal synchrony between flowers and their pollinators.

  8. And for the serious number crunchers, after we have more site-year comparisons, model how temperature, rainfall, photo period, lunar cycle, and latitude predict the date when a species will start flying each year and the date it will stop flying. How does your model incorporate the effect of temperature on generation time? If there were an average increase of 5 degrees Centigrade throughout the year, would it predict an addition generation, assuming that the moth's host plants were available?

  9. Why are geometrids apparently more common than noctuids? Answer: because they are easier to identify! Which raises all the issues of data quality, assumptions, and scientific ethics. Something that everyone should understand. This is real science. No cheating (unless you are trying to advance your career at a high powered university like Harvard). Don't regurgitate. Learn to think, question, and give thoughtful, creative answers.

  10. What would you ask of these data? And we have the beginning of independent research, discovery, and a wonderful life solving mysteries.

Contact information

  • If you need technical support or have questions, please contact:
    Nancy Lowe -- email: nancy@discoverlife.org -- telephone: USA-404-272-4526

Updated: 1 January, 2012
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