Sampling Strategies using Snyder-Middleswarth Natural Area

Using the virtual forest tutorial in the Snyder-Middleswarth Natural Area I used the following sampling techniques:

Random sampling (distance)= I used the option which generated a random list of 50 points. Using this it ensured equal chances for all locations in the simulated forest. It sampled 200 plants, it recognized 7 species with a sampling time of 9 hours and 58 minutes.   

Systematic sampling (distance)= I decided to use 50 locations to sample and I distributed them evenly across the simulated forest, In the middle of each gradient I tried sampling every 5th location. It sampled 200 plants, it recognized 7 species with a sampling time of 9 hours and 16 minutes.   

Haphazard sampling (distance)= 50 points were chosen arbitrarily. It sampled 199 plants, it recognized 6 species with a sampling time of 10 hours and 0 minutes.   

Systematic sampling had the fastest sampling time of 9 hours and 16 minutes. The two most common species were Eastern Hemlock and Red Maple while the two least common species were Striped Maple and White Pine. This are the % error between different strategies:

The accuracy of sampling strategies demonstrates variation based on species abundance. Common species such as Eastern Hemlock and Red Maple exhibit relatively low percentage errors across all three sampling methods, indicating good accuracy. Conversely, rarer species like Striped Maple and White Pine tend to yield higher errors with random sampling, suggesting potential limitations in accuracy for less common species.

Systematic sampling stands out for its consistent and lower percentage errors across all species, showcasing its potential to maintain accuracy even in if there are varying species abundances. Both haphazard sampling and systematic sampling perform well across species, with the added advantage of systematic sampling being the fastest in terms of sampling time.

In summary, the accuracy of sampling strategies is influenced by species abundance, with systematic sampling demonstrating a more reliable and consistent performance across different abundances.