*Bounty: 50*

*Bounty: 50*

I have repeated measures plant abundance data for 37 forest plots, across 80 years involving 50+ species of plants.

- The data are structured as:
`Columns`

= different species,`Rows`

= separate samples [Plot-Year combos],- Each
`cell`

= abundance (i.e., basal area) of the the given species in the given sample.

Simplified Example (from here):

`> abund.data Plot Year Sp1 Sp2 Sp3 Sp4 1 P1 1 1 2 0 0 2 P2 1 1 0 3 2 3 P3 1 0 2 1 0 4 P1 2 1 2 0 0 5 P2 2 1 0 3 2 6 P3 2 0 2 1 0`

I’ve calculated a Bray-Curtis dissimilarity (distance) matrix from these data.

```
Continuing the example:
library(ecodist)
distance(abun.data[,-c(1:2)], 'bray')
1 2 3 4 5
2 0.7777778
3 0.3333333 0.7777778
4 0.0000000 0.7777778 0.3333333
5 0.7777778 0.0000000 0.7777778 0.7777778
6 0.3333333 0.7777778 0.0000000 0.3333333 0.7777778
```

**I want to calculate the rate at which plots change in community composition over time.**

I had originally run a non-metric multidimensional scaling (NMDS) ordination and wanted to simply calculate changes in NMDS space.

- i.e., I wanted to create change vectors between plot points in subsequent years (I did so here) and then compare the lengths between years using some sort of regression….
`ChangeVectorLength ~ Time | Plot`

However, I don’t think this is valid because of the rank-oriented construction of NMDs ordination.

**Is there a way I could do something similar but using the “raw” distance (dissimilarity) values??**

- For example (using the example data above): I want to quantify how much the community of species (as a whole) in Plot
`P1`

has changed from Year`1`

to Year`2`

.- However, because the distance matrix represents — well — a matrix of pairwise distances bewteen all points, I’m not sure how to go about quantifying change in “distance space”