For my initial data collection, it was easy to measure my response variable of bird diversity. Since this was done by conducting the surveys from points already selected prior to data collection, I stood at each point for 15 minutes and recorded all bird species observed or heard. These bird species were recorded using the four letter species codes and were counted. I noticed that Black-capped Chickadees have been the most common bird observed but this is likely due to them being common and widespread during the winter months.
The challenges came when looking at my predictor variable of canopy cover. I had initially chose to measure canopy cover in three separate sites as a percentage of canopy cover. This ended up being difficult to define since this would be based off the leaf-off canopy cover. Instead, it was more effective to have an open and closed canopy site only and have this variable as a category rather than continuous. Closed canopy was based on very little sun light penetrating through. Since it is winter and surveys were conducted during leaf-off timing, Eastern White Cedar forest was selected as the closed canopy cover area. This has the most dense canopy during this time of year and since this area is free of human disturbance, it was the most effective to use as a predictor variable.
hello
some good observations
for sure bird presence/absence can be used to get (through analysis) diversity measurements (be sure to include in methods and results!)
canopy cover: yes can be tricky to do consistently, even with a densiometer
using discrete categories of open vs closed seems to be a good solution here
sometimes people use iphone apps or their hands to create a circle with which to view through, and then use a reference chart like: (see forest canopy cover)
http://hwwff.cce.cornell.edu/learning-printable9776.html?unit=2§ion=Site%20Assessment%20Part%20I%20%28Climate%29&subsection=Climate%20III&how-to=