Plant species communities (Preston Montford NVC)

This dataset gives plant species communities for ten samples taken during National Vegetation Surveys (NVC) in Shropshire, UK. Each sample is based on five “standard” NVC quadrats. The data show Constancy (number of quadrats present) and Cover (maximum Domin abundance) for each species.

Table 1. Plant community NVC samples. Preston Montford 2007. Single NVC sample from larger dataset.

Site	Species	Const	Cover
ML1	Achillea millefolium	5	6
ML1	Centaurea nigra	5	4
ML1	Lathyrus pratensis	5	5
ML1	Leucanthemum vulgare	5	5
ML1	Lotus corniculatus	5	7
ML1	Plantago lanceolata	5	5
ML1	Prunella vulgaris	3	1
ML1	Ranunculus acris	3	4
ML1	Trifolium repens	5	8
ML1	Trifolium pratense	5	7
ML1	Cynosurus cristatus	5	5
ML1	Festuca rubra	2	7
ML1	Cirsium arvense	5	5
ML1	Deschampsia flexuosa	5	6
ML1	Holcus lanatus	2	4

Download

You can download the dataset as a TXT file using this link: <Plant-community-PM.txt>. The file is a Tab delimited file and will open in a text editor or a spreadsheet.

There are several columns:

Site: the site name, ML (meadow lower), MU (meadow upper), PU (pond upper), PL (pond lower), SL (set-aside lower) and SU (set-aside upper). Each site name also has a suffix denoting its “group”, so there may be more than one “survey” for a site.
Species: the botanical species name as a scientific binomial.
Const: the constancy, the number of quadrats (out of 5) each species was present.
Cover: the cover as the maximum Domin score for the quadrats in which the species was present.

Usage

You can use these data to practice/illustrate various topics:

Pivot Tables (e.g. rearrange the data in community layout).
Diversity and species richness.
NVC analysis (e.g. identify the NVC community types from the samples).
Plant community analysis (e.g. Ordination, Twinspan).
Dissimilarity.

Keywords:

Plant community, vegetation, NVC, National Vegetation Survey, quadrat, Domin, constancy, cover, Pivot Table, ordination, dissimilarity

Examples

The following examples will give you a few ideas about how you might explore or use these data.

Pivot Tables

You can use a Pivot Table to help check the species names as well as rearranging the “recording layout” of the original data into a “community layout”. If you plan to use methods of ordination or even just similarity/dissimilarity you will want to “convert” the constancy and cover measurements to a single value. One way to do this is to create an “importance” score such as cover / constancy x 5. The 5 is used because each sample is based on 5 quadrats.

You could create a new column in the data or try a Calculated Field directly from the Pivot Table. If you are using R you can create this calculated value directly as party of an xtabs() function when you rearrange the data into community layout.

Diversity and species richness

You can calculate species richness easily from a Pivot Table. Diversity indices such as Shannon entropy or Simpson’s D index, need a bit more work in the spreadsheet.

You can also import the data into R (or some other software tool) to explore the community data in many ways. Here are Shannon diversity indices for example:

ML1   ML2   MU1   MU2   PL2   PU2   SL1   SL2   SU1   SU2
2.592 2.711 2.933 2.496 2.465 2.276 2.700 2.973 3.190 3.131

NVC analysis

You can explore the NVC classification of the samples in various ways. The data are already set out by constancy and cover, so you might look at the NVC books directly. Alternatively, software such as MAVIS can calculate community labels and other statistics.

Plant community analyses

There are a number of ways to explore these data, such as:

Similarity and dissimilarity: calculate how “close” samples are to one another. This similarity can be visualised with a dendrogram. There are many similarity/dissimilarity metrics that can be used.
Ordination: explore the multivariate data by reducing the “dimensions”. Methods such as Principal Coordinates or Nonmetric multidimensional scaling can achieve this. The results can be visulaised in 2-dimensional scatter plots.
Twinspan: a method of ordination that looks at species “indicators”.

Graphics

There are various ways to visualise these data, the most useful would be:

Dendrogram: of dissimilarity between samples (to show relationships between samples).

Bray-Curtis dissimilarity metric used to generate a hierarchical cluster dendrogram.

Ordination scatter plots: represent the samples as points in a two-dimensional space.

Principal Coordinates (PCO) plot of plant community data. Species plotted as “+”.

References

Undergraduate field projects (2007), SXF216 Environmental Science, Open University, Preston Montford Field Centre.