The three sampling strategies despite having a difference in the values that they posted for the population of the vegetation. They all intersected in the idea that some plants are well adapted to the coastal line while others were well adapted to the dry and hilly area around Prince Rupert Regional Hospital. The results in all three sampling strategies show that different vegetation has adapted differently based on the topography and salinity of the area. In this case, some vegetation and trees were densely populated along the coastal line while others had a low population along the sea while the population increased up the hill. For example, the water skunk cabbage had a large population along the coastal line. The population decreased gradually as one moved away from the sea line. This shows that this plant is well adapted to the marshy and saline environment along the coast. On the other hand, the trees showed a reduced population as one moved from the dry and hilly land down to the coastal line. The Monterey Cypress that grew very close to the shores of the ocean ended up drying. This shows that the Monterey Cypress is not well adapted to the saline environment and it does better in the hilly area near the Prince Rupert regional hospital. During the sample collection, three sampling strategies were used; simple random sampling, stratified sampling, and cluster sampling. Among the three, the sampling strategy with the fastest estimated sampling time was simple random sampling. This can be attributed to the simple nature of this sampling strategy and the simple way of selecting the sample. From the samples the percentage error of the different strategies ranged from 15% to 28% for common chokeberry and Banyan Tree which are the rarest species and also western skunk cabbage and black locust which were the most abundant species for the trees and vegetation respectively. The accuracy varied depending on the species abidance but it was not less than 12% and not more than 31% for all species. The most consistent and accurate sampling style was simple random sampling as it is simple and not biased during sampling since all samples are collected randomly and thus each has an equal chance of selection.
I am very confused by your post. This is the virtual forests exercise and not trying out sampling on your own site!
Thank you for helping me out in this course and guiding me to achieve best, I have revised my post, can you please have a look on this one.
Blog Post 4: Sampling Strategies
The three sampling strategies from the virtual sampling strategy, systematic, random sampling, and subjective sampling, had different values despite using the same area, the Snyder-Middleswarth Natural Area. They all intersected in the idea that some plants are more populated along the topographical gradient. Among the three sampling strategies, the sampling strategy with the fastest estimated sampling time was simple random sampling. This can be attributed to the simple nature of this sampling strategy and the simple way of selecting the sample. From the samples, the percentage error of the different strategies ranged from 9.5% for the simple random sampling to 13.7% for the systematic and 15.6% for the subjective sampling. However, the accuracy varied depending on the species abundance, but the variation in error was not less than 1% for all sampling strategies. Nevertheless, the most consistent and accurate sampling style was simple random sampling as it is simple and not biased during sampling since all samples are collected randomly, and thus, each has an equal chance of selection.