1. Laboratory Exercise Using Digital Images of Littorine Snails from the Peabody Museum Developed from a workshop held at the Yale Peabody Museum, July 2014 Sponsored by the National Science Foundation (Award #1203483) and hosted by the Division of Invertebrate Zoology in conjunction with Peabody Museum Public Education

2. The Value of Museum Collections for Research Natural history museums are filled with specimens, often large series of the same species from a variety of habitats, localities, and time periods Scientists can use suites of specimens to analyze features that may help detect variation When variation is detected, scenarios can be proposed to explain the observed variation, and these can include evolutionary processes, e.g. natural selection This exercise will demonstrate how museum “specimens” (in this case standardized images) can be used to analyze variation

3. Morphological variation in the littorine snail, Littorina saxatilis

4. What we will be doing From a priori groupings (example, geography) we suspect variation in quantifiable (i.e. measurable) characters Use morphometrics to detect variation in quantitative characters Simple statistics can evaluate if variation is “real,” i.e. statistically significant

5. General Data Gathering Measure and record width (SW) of the shell Measure and record height of spire (HT) of the shell Measure and record shell thickness (SLF) Enter raw values from data sheet into Excel; distinguish individual shells by referencing catalog number (derived from filename)

6. Shell Morphometrics - Littorina

7. Data Capture Students, working in teams, should first enter raw data manually on the data worksheet provide, see next panel (Littorina_worksheet1.docx) Data are next consolidated into an Excel spreadsheet (Littorina_worksheet2.xls) Note that all measures are recorded individually, before the ratios are derived; allows for auditing of data and results

8. Sample worksheet for collection of data* * Refer to Worksheet_snails.docx file

9. Measurement Techniques Use marks on paper held to screen image to measure a distance Compare distance to scale bar; small gradations are millimeters Record length to nearest 0.5 millimeter Most accurate measure will be from the edge of one gradation to another (black edge to black edge)

10. Measuring with pencil mark on paper held against image on computer screen

11. Compare measurement to scale bar (8.5 mm in this example)

12. Alternative measurement technique: Calipers, reversed to hide numbers (avoids confusion)

13. Rules to Remember To get accurate results one must be consistent in measuring Strive for perpendicular measures Lengths and widths should reflect absolute longest/widest distances

14. Example of Data Capture

15. Presentation and Analysis Step by Step The next several panels show how to prepare a simple bar graph of the results followed by an example of how to evaluate the data in Excel* using a test known as Single Factor ANOVA. *Be sure to activate the Data Analysis plugin is activated (see file Littorina_instructions.docx for directions)

16. Analysis – Null Hypothesis Null Hypothesis: all population means are the same (no statistical difference between populations) H 0 : μ 1 = μ 2 = μ 3 Alternative Hypothesis: H 1 : at least one of the means are different.

17. To begin, we may wish to prepare a simple representation of our data. In this example we will compute mean (average) values and present them with a simple bar graph.

18. Begin typing formula in cell directly below column of numbers we wish to average. Use no spaces, and finish with open parenthesis – “(“

19. Highlight column of numbers and end in a closed parenthesis (no spaces). Press enter key.

20. When enter key is pressed, the mean will appear below the column of data in the cell where the formula was written.

21. Select cells With insert tab active, select insert chart function

22. From drop down menu select chart type

23. Completed chart alongside data. Note icons to right of chart can be activated to control formatting options.

24. We see that the population means appear to differ. However, we must do an analysis to test whether these differences reflect statistical significance. The next series of panels will show one way to analyze population means for three or more groups.

25. On the Data tab, highlight and click “Data Analysis”

26. Select Anova: Single Factor and click “ok”

27. Input data and click “ok”

28. Data Analysis Output Default will produce difficult to interpret p value, change format of cell from “general” to “number”

29. Data Analysis Output This number is compared to critical cutoff for significance, typically.05

30. Statistical Conclusion Analysis indicates p = 0.00000349 which is much smaller that the cutoff value of.05 Null hypothesis, i.e. there are no population differences, is rejected At least one population is significantly different from the others

31. Biological Context Note the average values of the ratio of shell height to shell width. As the number decreases to zero, a stouter shell is indicated. In this example the European population has the stoutest shell.

32. Topics to Consider Knowing that populations vary should lead to questions about what might influence the variation Isolation by natural barrier or transplant (invasive species) over time may result in natural, detectable variation A causative factor such as predation pressure may be driving the variation, in effect an evolutionary process (selection)

33. Suggested Reading Seeley, R. H. 1986. Intense natural selection caused by a rapid morphological transition in a living marine snail. Proc. Natl. Acad. Sci USA 83:6697-6901. Link to Reference: http://www.pnas.org/content/83/18/6897.full.pdf