After completing the virtual forest tutorial, I had some pretty unexpected results with regard to percent error. I had expected systematic sampling to have the greatest accuracy, but that was not the case.
The two most common species were the Eastern Hemlock and Sweet Birch.
Eastern Hemlock Percent Error:
Systematic: 49.8%
Randomized: 34.5%
Haphazard: 64.3%
Sweet Birch Percent Error:
Systematic: 69.4%
Randomized: 21.7%
Haphazard: 56.6%
The two most rare species were Striped Maple and White Pine:
Striped Maple Percent Error:
Systematic: 100% (were not sampled)
Randomized: 14%
Haphazard: 100% (were not sampled)
White Pine Percent Error:
Systematic: 100% (were not sampled)
Randomized: 4.8%
Haphazard: 5.2%
In terms of species abundance, there was a clear difference in accuracy measures between species that were more common, versus those who were rare. While the common species still varied from 20-70%, they still had much greater accuracy than rare species which evoke a more probable all-or-none accuracy measure. There is a greater likelihood that you will sample the species that are abundant in the population as opposed to the rare ones which you may completely miss.
To my surprise, this happened with the two most rare species when systematic sampling was used, and for one of the two species when haphazard sampling was used. Overall, it appears as though the random sampling method was the most accurate, as it had the lowest percent error for all four of the species analyzed.
There wasn’t much difference between the percent errors of systematic versus haphazard sampling either which was also surprising. I had expected haphazard to be the least accurate by a significant degree, but it turned out that with my sample, systematic and haphazard sampling were pretty much neck and neck for all of the species except for the White Pine, where haphazard sampling was actually more accurate than systematic sampling.
Although systematic sampling theoretically took less time (12h 38min) than randomized and haphazard (both 13h 11min), it seems that the time-accuracy trade-off is well worth it. Of course, it is worth noting that if I redid the tutorial with a different sample, it is possible that the systematic sampling accuracy could have been much better. However, this tutorial was a bit reassuring that haphazard sampling may not be all that bad for accuracy because I do believe haphazard sampling is what makes most sense for my own field research project, and I will likely take that route for my sampling methods with point count data.