Data Tables
The following images are selections from my data tables.
Resin Duct Data
Annual Growth Data
Monoterpene Data
Diterpene Data
Non-Structural Carbohydrates Data
Variables
The aim of my research is to determine whether there is a relationship between anatomical defences and resin-based chemical defences, as such I cannot necessarily classify the data as response or predictor variables.
I essentially have two data sets: anatomical characteristics (a combination of resin duct data and annual growth data) as well as chemical characteristics (a combination of the monoterpene, diterpene and non-structural carbohydrate data).
The following variables are resin duct characteristics:
The following variables were used to represent radial growth:
The following variables are chemical characteristics:
I essentially have two data sets: anatomical characteristics (a combination of resin duct data and annual growth data) as well as chemical characteristics (a combination of the monoterpene, diterpene and non-structural carbohydrate data).
The following variables are resin duct characteristics:
- Resin Duct Production (no. yr-1) - number of resin ducts per 10 mm width in a given year on an increment core or wedge
- Total Resin Duct Area (mm2 yr-1) - sum of resin duct area per 10 mm width in a given year on an increment core or wedge
- Relative Resin Duct Area (% yr-1) - percent area occupied by resin ducts per year within the ring area for a given year on each increment core or wedge
- Resin Duct Size (mm2 yr-1) - mean size of resin ducts per 10 mm width in a given year on an increment core or wedge
- Resin Duct Density (no. mm-2 yr-1) - total number of resin ducts per year divided by the ring area (10 mm * ring width) for a given year
The following variables were used to represent radial growth:
- Ring Width (mm yr-1)
- Basal Area Increment (BAI, mm2 yr-1)
The following variables are chemical characteristics:
- Total Monoterpene Concentration (μg/mg) - sum of all monoterpenes per tree
- Total Diterpene Concentration (μg/mg) - sum of all diterpenes per tree
- Total Non-structural Carbohydrate Concentration (μg/mg) - sum of all non-structural carbohydrates per tree
- Total Terpene Concentration (μg/mg) - the sum of monoterpenes and diterpenes
histograms of the Data
Correlation Plots
To help visualize the data, I created the following correlation plots.
Univariate Correlation Test Results
Chemical Correlations
Total Terpene and Anatomical Characteristic Correlations
Total Non-structural Carbohydrate and Anatomical Characteristic Correlations
- Total Monoterpenes and Total Diterpenes: r = 0.84, p-value < 0.001
- Total Monoterpenes and Total Non-Structural Carbohydrates: r = -0.05, p-value = 0.14
- Total Diterpenes and Total Non-Structural Carbohydrates: r = 0.04 , p-value = 0.27
- Total Terpenes and Total Non-Structural Carbohydrates: r = 0.03, p-value = 0.46
Total Terpene and Anatomical Characteristic Correlations
- Total Terpenes and Resin Duct Production: r = 0.28, p-value < 0.001
- Total Terpenes and Relative Resin Duct Area: r = -0.22, p-value < 0.001
- Total Terpenes and Total Resin Duct Area: r = 0.35, p-value < 0.001
- Total Terpenes and Resin Duct Density: r = -0.32, p-value < 0.001
- Total Terpenes and Resin Duct Size: r = 0.12, p-value = < 0.001
- Total Terpenes and Ring Width: r = 0.56, p-value < 0.001
- Total Terpenes and Basal Area Increment: r = 0.58, p-value < 0.001
Total Non-structural Carbohydrate and Anatomical Characteristic Correlations
- Total Non-structural Carbohydrate and Resin Duct Production: r = 0.07, p-value = 0.05
- Total Non-structural Carbohydrate and Relative Resin Duct Area: r = 0.03, p-value = 0.37
- Total Non-structural Carbohydrate and Total Resin Duct Area: r = 0.13, p-value < 0.001
- Total Non-structural Carbohydrate and Resin Duct Density: r = -0.04, p-value = 0.19
- Total Non-structural Carbohydrate and Resin Duct Size: r = 0.14, p-value < 0.001
- Total Non-structural Carbohydrate and Ring Width: r = 0.18, p-value < 0.001
- Total Non-structural Carbohydrate and Basal Area Increment: r = 0.11, p-value < 0.001
Principle component analysis (PCA) Plots
To further visualize the data, I created various PCA plots of each data set as well as the combined data sets.
Checking for Errors
To check for errors in my data, I looked at the minimum and maximum values of the variables to see if data fell within an expected range. I also had a second and third person look over my data for any inconsistencies or inaccuracies. I also created histograms and pca plots of my data to transform data and remove outliers.