I want to compare arthropod assemblages (with presence-absence data) found in several plant species belonging to a same genus.
As the sampling effort was not the same in some plant species, the resulting dendrograms are very skewed to the number of localites sampled.
I would like to know if there is a method to weight my data in order to get a more realistic interpretation of the relationship between host plants.
Do you know any statistical method that allows this kind of analysis?
Thanks in advance.
You could instead compare beta diversity of arthropod assemblages among plant species using your presence absence data, the most frequent kind of data to which such measures are applied. See: Journal of Animal Ecology 2003, 72, 367–382 , Measuring beta diversity for presence–absence data.
Or you could use Chi Squared tests, which are suitable for comparing observed versus expected results in data :)
Hi Daniel
If you have the data on which arthropod species are from which location, you could use rarefaction to rarefy to the smallest number of locations you have for a particular plant species. That is, if I sampled a smaller number of locations for plant species x, how many arthropod species would I expect on average?
This approach is commonly used for alpha-diversity patterns but not beta-diversity. However, in principle, it could be done by repeatedly subsampling the data and recalculating the dissimilarity matrix (I assume you are using dissimilarity because you mentioned dendrograms).
A much simpler alternative is to use Simpson's Dissimilarity. If increased sampling effort leads to more arthropod species per plant species (a very likely assumption), then Simpson's Dissimilarity is designed to correct for this difference in species richness between samples. In practice, I find it an over-correction but it might work fine for your data.
I forgot to mention previously that you can find discussion of the virtues of Simpson's Dissimilarity in papers by Andres Baselga, such as:
Article Partitioning the turnover and nestedness components of beta diversity
Dear Daniel, I agree with David that resampling would be a good way how to approach the problem. Alternatively, you may think about using similarity indices that are based on a null model and take different sample sizes into account (raup-crick).
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