Lab 3. Sequential sampling plan for the gypsy moth

   1. Save the file "[1]gmegg90.txt" in the student's folder or any other temporary
   folder.

   2. Open the file from the Microsoft Excel. This file contains data on sampling
   gypsy moth egg masses in West Virginia, near Morgantown. X and Y are longitude
   and latitude UTM-coordinates (Universal Transversal Mercator), zone 17, are
   measured in meters. E is the number of egg masses found in a 0.01-ha sampling
   plot. Data was taken from a rectangle area of 4 x 20 km.

   Sampling methods. Each plot was a circle with the radius of 5.6 m. All new gypsy
   moth egg masses encountered within a circle were counted. Egg masses located
   within taller portions of a tree were counted using binoculars. New egg masses
   can be distinguished from old egg masses by their color and texture. Old egg
   masses are soft and have a gray/white color whereas new egg masses are hard and
   have a brown color to them. If both old and new egg masses were present in the
   plot, then the proportion of new egg masses was calculated from egg masses that
   can be reached from the ground. Egg masses located in the crown of the trees
   cannot be visually separated into new and old. Thus, the total number of egg
   masses was counted and then multiplied by the proportion of new egg masses.

   3. Plot the location of traps. Subdivide the area into 2 x 2 km blocks (note: use
   FLOOR function to convert real numbers to integers). In each block estimate the
   average population density, SD, SE, and confidence intervals. Compare average
   population densities in different blocks. Show significance of differences using
   ANOVA. According to the Fisher's method, the difference between 2 means is
   significant if it is greater than

                                     [eqlab3.gif]

   where t is the critical t-value of Student's distribution (usually, t=2), MSE is
   the mean squared error (take it from ANOVA), and ni and nj are sample sizes.

   Use log-transformation (log(N+1)) to make uniform variances. Compare ANOVA
   results before and after log-transformation. Did the log-transformation improve
   the analysis?

   4. Predict defoliation in the next year in these 20 blocks. Use the model of
   Gansner et al. (1985, North. J. Appl. For. 2: 78-79):

                                     [eqgans.gif]

   5. Estimate aggregation indexes: CD and mean crowding for 20 blocks.

   6. Estimate density-invariant characteristics of spatial distribution: Taylor's
   power law, and the mean crowding regression. What can you tell about the spatial
   distribution of egg masses?

   7. Develop a sequential sampling plan to reach the accuracy of 10, 20, and 30%.
   8. Develop a sequential sampling plan to determine which areas to spray by
   pesticides in order to prevent defoliation. Use the following parameters:
   Low population threshold = 1200 egg masses per hectare
   High population density = 3500 egg masses per hectare
   Alpha (risk of not spraying a high population) = 0.05
   Beta (risk of spraying a low population) = 0.1
   Estimate parameter k using the method of moments from field data. Use only those
   blocks where the average density was > 1000 egg masses per hectare. Estimate the
   average k-value for these blocks.

   9. Write a lab report using the Microsoft Word. Optional: Convert your report
   into the HTML language. Incorporate cross-references to gypsy moth WWW-sites,
   pictures, etc.

   Use this [2]checklist to improve your lab reports!

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     ____________________________________________________________________________

   [6]Alexei Sharov 12/4/98

References

   1. http://alexei.nfshost.com/PopEcol/lab3/gmegg90.txt
   2. http://alexei.nfshost.com/PopEcol/lab3/cheklist.html
   3. http://alexei.nfshost.com/PopEcol/lab2/lab2.html
   4. http://alexei.nfshost.com/PopEcol/popecol.html
   5. http://alexei.nfshost.com/PopEcol/lab4/lab4.html
   6. http://alexei.nfshost.com/~sharov/alexei.html