Dear Mr. Saroj,

Please refer following papers for your study (Q).

http://forestry.oxfordjournals.org/content/72/4/313.full.pdf

https://www.google.lk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=5&cad=rja&ved=0CEgQFjAE&url=ftp%3A%2F%2Fftp.fao.org%2Fdocrep..%2Ffao%2F011%2Fak321e%2Fak321e00.pdf&ei=u-EGU8DFNamTiAehsYGIDA&usg=AFQjCNE2pOS0foDJBIOTIK3iwnzbA4fgwA&sig2=QK-15A0DZGGrkLq5qMmQjw&bvm=bv.61725948,d.aGc

http://www.environment.nsw.gov.au/resources/nature/BioMetric_Structure_as_index.pdf

Thank you S.Udagedara for the reply. Let me refer

Sooraj,

Working with biological assemblies is a matter of weigh pros and cons. There is no single metric (or index) that best describe a community, but using this type of approach you may gain in simplicity. Distance-based and multivariate approach, as Ana Bugnot said, is much better, but you still loosing information. In addition, there is a lot of data transformations to fine tune the analysis with your needs.

I don't want sound dizzy, but the first step is define what type of information is most valuable to you. Are you more interested in abundances per species, or just the composition?

Best,

Dear Mr. Saroj,

Through functional diversity

Please refer following document, this published in Spanish

http://www.nucleodiversus.org/uploads/file/Casanoves%20et%20al%202011%20Serie%20Tecnica%20CATIE.pdf

Dear Sooraj NP

I see you have already received a number of sensible suggestions.

I think to answer the question effectively would require you to be more specific regarding the purpose of the study; i.e. tell us "suitable and meaningful" for what?

No measure or index or multivariate approach can do everything for all cases, but many different answers will be more or less "suitable and meaningful" depending on your specific goals.

If you are exploring patterns within the data (simply to understand how communities vary) then I think Ana's suggestions are appropriate. Using a range of multivariate analyses and univariate summaries should help uncover the main patterns.

If you want to explore spatial patterns, or gradients, or partitioning across scales, or among trophic groups, etc. etc. there are various approaches to do those.

There are many many options depending on how deep you want to go (but it all depends WHY you want to make the analysis and what kind of data you have).

Good luck