Download Laboratory Exercise - Yale Peabody Museum of Natural History

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
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
Morphological variation in the littorine
snail, Littorina saxatilis
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
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)
Shell Morphometrics - Littorina
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
Sample worksheet for collection of data*
* Refer to Worksheet_snails.docx file
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)
Measuring with pencil mark on paper held
against image on computer screen
Compare measurement to scale bar
(8.5 mm in this example)
Alternative measurement technique:
Calipers, reversed to hide numbers (avoids confusion)
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
Example of Data Capture
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)
Analysis – Null Hypothesis
Null Hypothesis: all population means are the same
(no statistical difference between populations)
H0: μ1 = μ2 = μ3
Alternative Hypothesis:
H1: at least one of the means are different.
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.
Begin typing formula in cell directly below column of numbers
we wish to average. Use no spaces, and finish with open
parenthesis – “(“
Highlight column of numbers and end in a closed
parenthesis (no spaces). Press enter key.
When enter key is pressed, the mean will appear below the
column of data in the cell where the formula was written.
With insert tab active,
select insert chart function
Select cells
Select cells
From drop down menu select chart type
Completed chart alongside data. Note icons to right of
chart can be activated to control formatting options.
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.
On the Data tab, highlight and click “Data Analysis”
Select Anova: Single Factor and click “ok”
Input data and click “ok”
Data Analysis Output
Default will produce difficult to interpret p value, change
format of cell from “general” to “number”
Data Analysis Output
This number is compared to critical cutoff for
significance, typically .05
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
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.
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)
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