Project 1D: Forest Ecology Data

Overview

This data set contains information on the types and sizes of trees in an old-growth beech-maple forest in northeastern Ohio.

Details

The study described here was carried out at the James H. Barrow Field Station (owned and maintained by Hiram College in northeastern Ohio) during the summer of 1993. In this study, an extensive survey of the forest was performed to assess the diversity and distribution of tree species that occupy the forest. The forest was subdivided into a collection of 50m by 50m plots, and within each of these 67 plots, four 10m by 10m sub-plots were randomly selected to be surveyed. For each surveyed sub-plot, all tree species occurring within the sub-plot were counted and recorded. The diameter at breast height (dbh) was measured for any tree that was larger than 3cm dbh.

In this project, we will consider the data on the frequency of each of the tree species in each plot. The data set has each species as in one row, with the 67 plots as the columns. The values of each variable are the number of trees of that species observed in the plot.

Data Description

Variable	Description
Species	Scientific name of the tree species (column 1)
X100E.0N - X50E.50N	Data for each plot (67 columns)

forest_data = read.table("data/forest_frequency_t.txt", header = TRUE)
head(forest_data)

Data Files

• forest_frequency_t.txt

Objectives

We will consider the number of trees of each species in each plot. The goal is to find the distributions that fit these data. In particular, consider species 6 (C. ovata) - 28 (S. albidum).

• Provide numerical and graphical summaries of the number of trees in each plot for each of the species mentioned above.
  
• Consider fitting some of the known distributions you’ve discussed in class, both this semester and last semester, to this variable. For each distribution you consider, explain how you are estimating the relevant parameters (e.g., are you using the MLEs? MOMs? etc.). Provide a table of the parameter estimates for each species for the distribution that you find that fits the data best.
• Provide some assessment of fit of the distributions you consider in part (2) to the data. For example, q-q plots are useful for this purpose.
• Make some conclusions about modeling this variable.