Virtual forest sampling

 

I selected Mohn Mill for my virtual forest sampling. Three techniques, Systematic, Random and Haphazard sampling techniques were used in the area-based method.

Which technique had the fastest estimated sampling time? 

Systematic sampling was the fastest as it used less time compared to other techniques.

Systematic: 11 hr 32 mins

Random: 12 hr 53 mins

Haphazard:  12 hrs 51 mins

Compare the percentage error of the different strategies

The most common species: Red maple

 Systematic: 1.97%

Random: 1.99%

Haphazard:  12.9%

The rarest: Sweet birch

Systematic: 0%

Random: 71.4%

Haphazard:  70%

The percentage error for rare species is quite high in some samples, no individuals were sampled in some of the samples and that’s why some results have 0% error

Did the accuracy change with species abundance? 

When sampling for density, accuracy changes with species abundance. Results from species with higher density are more accurate than those from species with lower density. To ensure accuracy, it is important to sample an adequate number of individuals from each species. Sampling from a greater number of sites also helps to increase accuracy.

Was one sampling strategy more accurate than another?

Systematic Sampling Strategy is the best sampling strategy for common species. A random sampling strategy is the most accurate sampling strategy for rare species Haphazard sampling strategy showed the most consistent results, as well as the most accurate data when compared to the other two sampling strategies. Therefore, the Haphazard Sampling Strategy is the most reliable when trying to accurately tracking species.

One thought to “Virtual forest sampling”

  1. I would not recommend including the graphs as is because I can not read them and you do not refer to them in your post. You are also supposed to look at the two most common and two rarest species, it is hard to draw conclusions from one species. I don’t see haphazard as the most accurate or consistent when looking at your results….

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