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I am replicating another study in which trees were assigned to a class based on diameter. The most representative species of each class was identified for each plot sampled.

The classes are sapling (2.5-8 cm), pole (>8-18 cm), mature (>18-28 cm) and large (>28cm).

For our study, we recorded the species and diameter at breast height (dbh) for every tree > 1.5m in height and >1.8 cm dbh within a 100m2 circular plot. There were 100+ circular plots sampled.

We also visually estimated and recorded which species were most representative of each class to determine if a visual estimation was an acceptable surrogate for diameter derived classification.

I would like to use the tree species and diameter data we sampled to assign the most representative species in each diameter class for each plot. In addition to replicating the previous study, I would like to compare the species assigned based on the diameter distributions to the visual assignments we estimated in the field.

My question: What is the best method for identifying the most represented member of each class? I am currently considering selecting the species for each class either by stem density (a count of each species in each class) or by volume (basal area).

Is one of these methods (density vs basal area) generally preferred or more accepted? Is there a different method I should use?

I have also posted a R specific question with example data regarding how to accomplish the calculation here...

https://stackoverflow.com/q/29927772/4200318

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  • $\begingroup$ Also, I wasn't sure on the most appropriate tags or title for this question. I am welcome to any suggestions and I will change accordingly. Thanks! $\endgroup$ – GNG Apr 28 '15 at 19:33

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