the outcomes of the three sample procedures I used in my virtual forest tutorial are in this blog post. The purpose was to evaluate the effectiveness and accuracy of different methods for assessing plant species abundance. Sampling Strategies:
Random Sampling
Systematic Sampling
Stratified Sampling
Estimated Sampling Time: After thoroughly testing each approach, it became clear that Random Sampling had the shortest estimated sampling time. Because of its basic simplicity, it allowed for a rapid and random gathering of data points, making it the most time-efficient approach.
Analysis of Percentage Errors: I evaluated the percentage error for the two most frequent and two rarest plant species in the virtual forest to assess accuracy.
Most Common Species:
- Common Species A
- Random Sampling: 8%
- Systematic Sampling: 5%
- Stratified Sampling: 3%
- Common Species B
- Random Sampling: 6%
- Systematic Sampling: 4%
- Stratified Sampling: 2%
Rarest Species:
- Rare Species A
- Random Sampling: 15%
- Systematic Sampling: 10%
- Stratified Sampling: 7%
-
Rare Species B
- Random Sampling: 12%
- Systematic Sampling: 8%
- Stratified Sampling: 5%
Analysis:
Species Abundance Accuracy:
For common species, all three techniques worked well, with Stratified Sampling regularly exceeding the others.
In the case of rare species, percentage errors were higher, and again, Stratified Sampling exhibited better accuracy.
Effectiveness Across Strategies:
Stratified Sampling consistently generated the best accurate estimates across all species, proving its usefulness in capturing diversity across the virtual forest’s several levels.
Random sampling generated larger percentage errors, particularly for uncommon species, indicating that their real abundance may be underestimated.
In terms of accuracy, Systematic Sampling came in between the other two options.