A good way to start looking at abundances especially with community data (lots of 0's) are MDS plots. Look into Primer, it is a very easy program to use and can run a variety of statistical tests looking at similarity, evenness, etc.

Thanks Dean

I had thought of MDS ordination scaling with Bray curtis similarity measures. What else can I do with presence/absence data of the plots distributed over the intertidal zone?

I think that depending on what your question(s) is will determine what path you take. Visually, MDS plots work well for me. You can code by tidal height(zone), season, taxonomic group, etc all using presence/absence data.

Thanks for the paper Dean.

I don't know what's the purpose of your research, but with such data and to understand the ecological relations, the first thing I'd be doing would be to seek correlations with weather, temperatures, and any other factor that may influence the abundance of the benthic fauna.

hope you will find most of the statistical analysis in PAST package

I suggest principal Component Analysis (PCA) rightly applied could give u some important insight into the group of variables (both water quality and atmospheric variables) that account for the greatest amount of variations for both spatial and temporal scales.

Perhaps look into coefficients of variation, it should be able to tell you about the magnitude of variation within a variable month to month and year to year and its relationship to a predictor.

http://en.wikipedia.org/wiki/Coefficient_of_variation

Thanks all for the suggestions.

Hi.

It depend of your final goal, but classical multivariate analysis would be a good way to have a global view of your data. nMDS and cluster analysis should be a start. Then you can perform a PCA then a CCA.

To identify species assemblages and target species, the SIMPER method is highly relevant.

You can use it with PRIMER or R (vegan package I think).

Thanks Pierrick

Thanks all for your suggestions and help. I have figured out high densities of a few species affecting my overall data. Please give your views on this.

If you have environmental data I would recommend a (detrended) canonical correspondence analysis ((D)CCA).

You might also model single species or overall numbers by Generalised Additive Modeling (GAM) or other kinds of models, in order to get the factors that explain the distributional pattern in the most reasonable way. Do this with R (package mgcv).