Prepared by Liam Butler & Roy Sanderson
The standard vegetation quadrat is probably the most common method to survey plant communities, and thousands of scientific papers have been published in which some field data was collected via quadrats. Their popularity arises from their simplicity, low cost and portability. They can be adapted for the habitat being studied, e.g. with one side less than 1 m long for bryophytes (mosses), through to 10 m and above for woods and forests. Commonly used sizes are 1 m × 1 m or 2 m × 2 m quadrats. Typically, the percentage cover of all the plant species within a quadrat is estimated, often by visual estimation. Other common quadrat recording methods include semi-quantitative scales, e.g. DAFOR, in which species abundance is coded into ‘dominant’, ‘abundant’, ‘frequent’, ‘occasional’ and ‘rare’. Quadrat data can be summarised into quadrats × species tables that contain the abundances of all the species. Often ecologists wish to understand which quadrats are most similar to each other in their species composition or how species respond to environmental or management factors. Well-established statistical methods, such as multivariate ordination, have been developed that allow simultaneous analysis of the entire quadrats × species table.
Can we obtain additional valuable data from vegetation quadrats? The standard survey method does not provide any information on spatial patterns of vegetation within the quadrat. Do some species occur mainly in clumps? What are the sizes, numbers, shapes, or areas of those clumps for each species? How do the spatial patterns respond to environment or management? We tested a simple ‘add-on’ to conventional quadrat surveys that provides this information. Our research was undertaken at an upland sheep-grazed heather moorland / acid grassland in Redesdale in North East England. We used 1 m × 1 m quadrats, sub-divided by wires at 10 cm intervals into 100 ‘sub-quadrat’ cells. After we had completed the percentage cover survey, we then recorded the dominant and sub-dominant species that occurred in each sub-quadrat. This was a very rapid process, adding only a few additional minutes to the time taken to survey each quadrat.
We calculated number, area and shape of patches for each species, to create six quadrat x species tables (3 each for dominant and sub-dominant) for subsequent analysis. We used multivariate generalised linear models to determine the relationships between the patch metrics for the overall vegetation community and each species, with the environment and management. Location of drainage ditches, sheep tracks and soil conditions all affected patch structure.
As would be expected, the sub-quadrat datasets contained fewer species than the percentage cover estimates, since only dominant or sub-dominant species were recorded. Nevertheless, community-level analysis via multivariate ordination indicated that the sub-quadrat data was broadly in agreement with the percentage cover in terms of quadrat compositional similarity. However, it also provided additional insights into patch structure, and potential environmental drivers. Our findings suggest that sub-quadrats could easily be used by field ecologists, with little additional resource or time costs.
This is a plain language summary for the paper of Butler and Sanderson published in Applied Vegetation Science (https://doi.org/10.1111/avsc.12610).