Principles of Vegetation Measurement & Assessment
and Ecological Monitoring & Analysis


Veg Sampling
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Sampling Designs

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Readings & Instructions

  1. Read the following on-line information to gain a basic understanding of Random, Systematic, Selected (or Subjective), and Stratified Sampling:
    • Allocation of Sample Units - Sampling Designs
  2. Read Chapter 7 (pages 97-141) "Sampling Design" of Monitoring and Measuring Plant Populations.

What this Reading Covers

Sampling Schemes

Once a researcher or manager has decided "what" to sample, then "where" to sample must be determined.  Three basic sampling schemes exist: Subjective (or Selected), Systematic, Random, and Stratified.

  • Subjective - Selectively place a set of plots or plants (a sample) in specific areas that meet research interests or management objectives.

    • Select sites that are considered representative of change or responsive to management.
    • May or may not reflect the larger area. How well the sampel reflects the larger area depends on judgment of person selecting sites.
    • Difficult or impossible to make statistical inferences about a whole pasture, park, watershed, or management unit. However, use of subjective sampling may be very effective in determining if management strategies are working to meet management goals.


    Monitoring “key areas” or “critical areas” is a type of subjective sampling.

    Key Areas are a portion of land which, because of its location, grazing or browsing value, or topography, serves as an indicator of land conditions, trend, or degree of seasonal use by animals. These Key Areas are considered indicators of what is happening on a larger area as a result of on-the-ground management actions.

    Critical Areas are units that contain unique or special values such as:
      - fragile watersheds
      - sage grouse nesting grounds
      - riparian areas
      - habitats with rare plants


  • Systematic - Placing plots or selecting plants for research or monitoring by systematically and regularly spacing plots according to a predetermined grid.
    • Rapid and easy to use in the field.
    • Assures good distribution and uniform coverage of the target population.
    • Limitations in statistical analysis because units in the sample are not independent of one another. For example, if plots are placed every intersection in a ½-mile grid, once the first sample is selected, the location of all other plots in the sample is known. In other words, the location of all plots is dependent on the location of the first plot of the placement of grid.


  • Random - the location of plots or areas to be studied are selected in a way that each sampling unit (i.e., plot or plant) is selected completely at random and any potential area or plot to be studied has an equal chance of being selected.
    • All observations in the sample are independent so normal (i.e., parametric) statistics can be applied with confidence.
    • The best way to select a random sample is generally to apply a grid (with two coordinates) to the total area of interest.
    * A pair of random numbers can then be selected for the x- and y-coordinates on the grid.
    * One could also number all the intersections on the grid and select a random number from 1 to the total number of intersections.
    * Random numbers can be selected in several ways:
         roll dice
         put all units or coordinates in a hat and draw a number
         use a random numbers table in a statistics book
         ▫ create a list of random numbers with the “random” feature in a spreadsheet (like excel)
    • One problem with random sampling is that it may result in poor distribution of sampling units as the units that are randomly selected may not be evenly distributed across the landscape or target population. Plant populations and habitats are rarely distributed evenly and randomly across the landscape, therefore a sample of random units may or may not represent the landscape.


  • Stratification - If there are aspects of the landscape that will clearly result in differences among the plots sampled it is often good to stratify the area and sample within these sub-units. For example, if a pasture has 3 major ecological sites that vary in biomass productivity, the proportion (%) of samples examined in each ecological site could be based on the proportion of the total area occupied by each site.
    • Stratification often overcomes the problem of poor sample distribution.
    • At least 2 sample units (preferably 3 or more) must be drawn from each sub-unit to determine the variation within each sub-unit.
    • Can yield information about variation among and within sub-units.
    • The samples within each sub-unit can be applied in a random fashion to create a “Stratified Random” sample, or systematically to create “Stratified Systematic” sample, or subjectively to create a “Stratified Subjective” sample.


  • Several other sampling approaches exist such as paired sampling and cluster sampling. More information about statistical terms related to sampling can be found at: STEPS- Statistics Glossary.

Selecting Study Areas

Appropriate locations of study sites is crucial to success of an inventory or monitoring program.  Selection should be documented. In other words, explains somewhere in your survey notes, why and how you selected the sites for evaluation.  The site selection should clearly reflect the management or monitoring objectives. Criteria used for selecting sites are generally based on:

  • soils

  • habitat type

  • seral state of plant community

  • topography

  • location of water, fences, & natural boundaries

  • areas of animal concentration

  • kinds of statistical comparisons or interpretations intended

    Remember: Study sites must always be clearly mapped and documented.

Summary Questions

  1. When should random sampling be used over systematic sampling schemes?
  2. Why are key areas used so frequently in management and seldom used in scientific investigations?

Advanced Questions:

  1. In assignment 1, you were asked to find an example of a sampling protocol. In the example you found, who were samples placed on the landscape? Random, Subjective, Stratified, or something else.

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