Hi all, I've got a question with FDis values from 9 mammal assemblages which range from 4.001 to 5.031 (mean ± SD = 4.690 ± 0.290). I'm using a set of 28 traits, mostly binary traits. As far as I know, these values appear to be very high. For the same assemblages, functional evenness ranged from 0.481 to 0.667 (mean ± SD = 0.557 ± 0.048) and Rao’s square entropy from 17.62 to 26.04 (mean ± SD = 23.150 ± 2.582).
Thank you in advance for any suggestions!
Dear Fabio,
What do you mean with very high? Compared to other research?
Unlike FEve, which is bounded between 0 and 1, FDis has no upper limit. Be aware that you cannot compare FDis values between two datasets because FDis is the average distance to the centroid of your dataset in the multidimensional trait space of you dataset.
How did you calculate the FDis metric? With the FD package in R? And why do you include so many traits? That is rarely a good choice.
FDi has no upper limit as Koenraad Van Meerbeek said and is an average distance, so no value can be too high. But it can handle any number of traits
Dear Koenraad and Andrew, thank you for your suggestion.
I'm using FD package to run the analyses and include a series of physical, dietary, behavioural traits that have been used in recent researches (e.g., carnivore = binary, insectivore = binary). Do you think that is a good idea to use the traits in another way (e.g., diet = categorical) to reduce the number of traits? On the other hand, I first thought that FDis values were high because in other research it never exceeds 1, but now I realise that this index can be constrained to a 0-1 range for convenience.
Thanks in advances!
I calculated FDis several times and there are often values that exceed 1. I don't see the need for constraining the values between 0 and 1.
The function dbFD uses principal coordinate analysis to construct the multidimensional trait space. If there are categorical variables, PCoA is based on the Gower dissimlarity matrix. I don't think it matters much whether you include those traits as binary or 1 categorical variable. You can compare the two and let us know.
The number of traits per se is not a problem. As mentioned above, you can include a large number of traits. However, you should only include the traits that are relevant for your research question (eg if you study the relationship between FD and a certain ecosystem function, you should only include the traits that are revelant for that ecosystem function. And you should not include all traits you have).
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