metamds | currently read-only ; see signac | BPM library

Here's a workaround to plot a very similar plot using ggplot2.The trick was to get the structure of the stressplot(example_NMDS) and extract the data stored in that object. I used the tidyverse package that includes ggplot and other packages such as tidyr that contains the pivot_longer function.

I'm not super clear on your question, but I think what you are asking is how you can plot the sizes of the different species as well as the richness. I'll give an answer to that.

By the way, you can read in the data much easier using data.table::fread. As in:

You need to provide the argument choices = , see help page :

According to the documentation (see ?envfit) "The printed output of continuous variables (vectors) gives the direction cosines which are the coordinates of the heads of unit length vectors." Further it explains that "In plot these are scaled by their correlation (square root of the column ‘r2’) so that “weak” predictors have shorter arrows than “strong” predictors. You can see the scaled relative lengths using command scores." The last piece of information is confirmed at the end of the documentation which says "The results can be accessed with scores.envfit function which returns ... the fitted vectors scaled by correlation coefficient". So the difference is correlation, and you should use the results extracted by scores. The directly accessed direction cosines will draw unit-length arrows (like you should see) irrespective of the strength of the variable.

The conventional plot function in vegan can select variables by permutation P-values, but geom_text and geom_segment have no idea of doing this. You should only pass those rows that you want to plot, and remove the other scores.

Based on the comments, 'Y' is created as a list from splitting the 'Z' by the 'site' column

Here's a simple approach with writing a custom function using substitute:

You have some rows in NMDS that contain all 0 values which apparently doesn't work with metaMDS.

You can remove rows containing all values == 0 using dplyr:

No they do not give the same result as these are doing two quite different things. In this answer I have explained how envfit() works. wascores() takes the coordinates of the points in the nmds space and computes the mean on each dimension, weighting observations by the abundance of the species at each point. Hence the species score returned by wascores() is a weighted centroid in the NMDS space for each species, where the weights are the abundances of the species. envfit() fits vectors that point in the direction of increasing abundance. This implies a plane over the NMDS ordination where abundance increase linearly from any point on the plane as you move parallel to the arrow, whereas wascores() are best thought of as optima, where the abundance declines as you move away from the weighted centroid, although I think this analogy is looser than say with a CA ordination.

The issue about being optimal or not, is an issue if you passed in standardised data; as the answer you linked to shows, this would imply negative weights which doesn't work. Typically one doesn't standardise species abundances — there are transformations that we apply like converting to proportions, square root or log transformations, normalizing the data to the interval 0-1 — but these wouldn't give you negative abundances so you;re less likely to run into that issue.

envfit() in an NMDS is not necessarily a good thing as we wouldn't expect abundances to vary linearly over the ordination space. The wascores() are better as they imply non-linear abundances, but they are a little hackish in NMDS. ordisurf() is a better option in general as it adds a GAM (smooth) surface instead of the plane implied by the vectors, but you can't show more than one or a few surfaces on the ordination, whereas you can add as many species WA scores or arrows as you want.

The basic issue here is the assumption that envfit() and wascores() should give the same results. There is no reason to assume that as these are fundamentally different approaches to computing "species scores" for NMDS and each comes with it's own assumptions and advantages and disadvantages.

I probably shouldn't have said "standardised" in that answer.

For each column (variable) in varechem and the first two axes of the ordination (choices = 1:2), the linear model is: