A New Type of Sample Plot that Is Particularly Useful for Sampling Small Clusters of Objects

Abstract

Abstract The sampling method presented here uses a new type of plot, with surprising characteristics. Although useful in sampling any grouping of objects, it is illustrated here by correctly sampling small patches of trees remaining after harvest. Small patches are difficult to sample unbiasedly because of the “edge effect𠇍 caused by sampling with fixed or variable plots. By this we do not refer to a biological effect on trees near an edge, but to a common sampling bias that distorts the selection probabilities of those trees. Groups of objects that are small and irregular are very prone to this “edge effect bias𠇍 when using typical sampling systems. “Sector plots𠇍 will allow the user to sample the trees in a small patch, and the method can also be extended to small trees planted outside the patch after harvesting, or to solitary “dispersed𠇍 trees outside the patch but within the harvested area. The method can unbiasedly select sample trees along an irregular border. The method eliminates all bias from edge effect. It can balance the tree selection on opposite sides of the patch, and the pivot point of the plot can be arbitrarily placed by the sampler in any convenient location without introducing any bias to the selection process. This new plot shape might be described as “a constrained angle shape with variable area, randomly oriented around an arbitrary sample point, which selects objects with equal probabilities.𠇍