The most efficient sampling technique in terms of time spent out of the three tested during this exercise was systematic at 12hrs 35m. Following this was Haphazard at 13hrs 6m and lastly random at 13hrs 18m.
For my data and calculations, random sampling was the most accurate for common species, which I sampled as Eastern Hemlock and Sweet birch as they were the most abundant in actual data.
For rare species (Striped Maple, White Pine), my most accurate sampling strategy was systematic.
Systematic Sampling:
Common species:
Eastern Hemlock: (344.0-469.9)/469.9*100= 26.8%
Sweet Birch: (176.0-117.5)/117.5*100 = 49.8%
Rare Species:
White pine (8.0-8.4)/8.4*100 = 4.8%
Striped Maple: (12.0-17.5)/17.5*100=31.4%
For the rarest species, the white pine it actually improved in this case.
Random Sampling:
Common Species:
Eastern Hemlock: (416.0-469.9)/469.9*100=11.6%
Sweet Birch: (100.0-117.5)/117.5*100=14.9%
Rare Species:
White Pine:(8.4-0)/0*100=840%
Striped Maple:(20.0-17.5)/17.5*100=14.2%
For the rarest species, the white pine, accuracy declined.
Haphazard (chose the densest looking areas):
Common species:
Eastern Hemlock (620.0-469.9)/469.9*100=32%
Sweet Birch: (176.0-117.5)/117.5*100=49.8%
Rare Species:
White Pine: (8.0-8.4)/8.4*100=4.76%
Striped Maple: (44.0-17.5)/17.5*100=151.4%
The rarest species, White pine increased, however the plot I chose looked abundant in tree species, so me choosing that location would be likely to have more of a rare species because of my selection methods.
For this sampling study, 25 plots seems to be an accurate number in certain sampling methods, and more so for common species than rare, as rare seem to have larger discrepancies. I think that with higher amount of sample plots, and random selection, the data could potentially be more accurate.
I am always surprised how close the sampling times are. Good insights, rare species are harder to capture and depending on what you want to know you need to adjust your sampling strategy and number of samples.