How to analyse community composition in relation to environmental variables with nMDS?

I have a big data set (over 1000 observations) with abundances of over 60 species at 15 different sites over two years. Each site was divided into 30 sampling points and these were each sampled four times (replicates). I also have environmental data for each site but this data was only measured once so I don't have any replicates as I do with the abundance data.

I want to find out if there is a difference in community composition between sites and how it is related to the environmental data. I will use a Non-Metric Multidimensional Scaling (nMDS).

Question 1: Do I need to test data for normality first? If so, how for this kind of data?

When I tried to run nMDS it took the row numbers as sites and I ended up with over 800 points for sites but I just want to have one point for each pair of site-year.

Question 2: Do I need to average my abundance data for the sampling points and replicates at each site before nMDS?

Question 3: How can I incorporate the environmental data into my nMDS?

Any help would be very appreciated as I am quite confused!

Many thanks,

• About your question on the 4 replicates per site : if I understand you well, you have acquired 4 datasets over two years. Why not make 4 nmds analysis and see/discuss if the results vary from one date to the others ? – Rodolphe Jun 9 '20 at 11:22
• Hi Rodolphe, thanks for your suggestion. I am interested in the spatial differences as well as the temporal ones. I have four replicates at each sampling point (30) within each site (15) for each year ( 2 years). So I wish to combine the replicates and sampling points to represent each site in order to be able to determine whether the community composition differs between each site and each year. – claire Jun 9 '20 at 11:27
• Hi. Ok thanks for clarifying. what was your objective when doing 4 replicates at each sampling point ? – Rodolphe Jun 9 '20 at 17:56
• Why using a non-metric multidimensional scaling? You should consider other multivariate approaches, like Redundancy Analysis (RDA) or Canonical Correspondance Analysis. These ordination methods are constrained analyses that combine ordination and regression: the ordination of the response matrix (i.e. species abundance) is constrained to be linearly related to the explanatory matrix (i.e. environmental parameters), unlike PCA or nMDS. You'll find information about ordination methods here, in the Numerical Ecology book and in many papers. – Circus pygargus Jun 9 '20 at 19:27
• Rodolphe, the replicates were done to increase accuracy and confidence surrounding the values – claire Jun 12 '20 at 14:25

If you want to produce an NMDS plot with one point for each site, you will first need to pool your sampling points to produce a single community for each site. You could produce separate plots like this for each year, or have them all on the same plot e.g. plot1_year1, plot1_year2 etc...

Alternatively, you could keep your data having one row for each sampling point. You could then plot all of the sampling points, and give each point a colour corresponding to which site it is from. This will allow you to visualise whether sampling points from the same site cluster together. Check out vignettes from the R package vegan for examples of how to do this.

I'm not clear on what the point of the replication was... Maybe just pool your replicates to give a single row per sampling point.

It sounds like sampling intensity was identical between sampling points and sites, but you might want to think about this to make sure.

Once you have some NMDS plots, you can fit your environmental variables on to them using the envfit function. This function can be used to test whether the correlations are significant using permutations - the data does not need to be normal.

If you want to test for effect of specific environmental variables, you will need to take into account spatial autocorrelation - sites that are far apart are likely to differ more in community composition and environmental variables than sites that are close together. To take this into account you can use partial mantel tests. In a similar way to how your community data is transformed into a distance matrix for NMDS, you need to construct a distance matrix for your sites based on geographic distance. The partial mantel test can then partial out the effect of geographic distance to show whether your environmental variables are still important.

You could also carry out exploratory partial mantel analysis, assessing the independent importance of matrices of related environmental variables with effects of other matrices removed. This involves sequentially testing the importance of each variable on community composition once the effects of remaining matrices are partialled out from the analysis.