User: | Open Learning Faculty Member:
The largest issue found with my sampling strategy would be time constraint. Due to the large number of samples which I wanted to collect in order to create as accurate of a representation as possible, I had to be sure to schedule a full afternoon and evening to be out in the field.
Additionally, occasional thick vegetation growth slowed down data collection due to the method used; pacing at a set compass bearing meant that I had to go through the thick vegetation (being careful to cause little damage/disturbance) rather than around it in order to have the correct number of randomized paces.
The data collected was not very surprising. The only piece that surprised me was how little of the Knapweed grew in the canopied forest; while visual observations suggested this, the samples had virtually no occurrence of the Knapweed.
I do not plan on modifying my approach to collecting data. While significant amount of time is required for my approach, when considering the number of samples taken it is fairly efficient. I think that my data collection methods align well with the hypothesis that I am trying to test.
EDIT: The sampling design was changed to a randomized transect method. Ten transects were randomly chosen using a randomizer app on my cell phone. Transects were sampled every 10 metres for the presence/absence of Knapweed and the cover type at the sample point. This method took roughly 1/3 the time previous method required and provided more sound data to analyze. Similar difficulties with thick bush areas were encountered with this method, however they were not insolvable instances. Similar patterns were noticed with collecting data where Knapweed had a low frequency in the canopied area and a higher frequency in the open grassland. Further statistical analysis is required for the data.
