Faculty of SITE Northern Territory University Flora & Fauna Survey Techniques
what is flora and fauna survey?
why survey?
factors to consider
preparing to sample
flora survey techniques
vegetation descriptions
sampling concepts
fauna survey techniques
analysing data
presenting data

Sampling concepts - sampling criteria

The form of the sample unit must be decided during the planning stages of the survey or monitoring program as the choice of sample unit will depend on the aims. Remember that precision and accuracy of the estimate will be affected by sampling criteria choice.

Sampling criteria include


Sizetop of page
How large a quadrat should be used?

Quadrat size depends on the morphology of the species, the homogeneity of vegetation and the local conditions of the study site.

  • small quadrats (e.g. 1 metre squared; with a side scale of 1:2, e.g. 0.71 m x 1.41 m) for small plants
  • large quadrats (e.g. 10 m2; 2.24 m x 4.47 m) for scrub and woodland and more widely spaced individuals.

The quadrat should be slightly larger than the 'minimal area' of that vegetation. It may be necessary to undertake a pilot study to determine the appropriate quadrat size.

Shape top of page
What shape of quadrat should be used?

The more edge to the quadrat per unit area, the more possibility of miscounting individuals. The ratio of length of edge to inside area in a circle is less than that for a square. That of a square is less than that of a rectangle (i.e. edge length:area = circle<square<rectangle).

The effects of your choice on the accuracy of your sample is species and habitat specific. The choice of quadrat shape may be guided by the patterns within the vegetation being sampled, or by the logistics of sampling. For example, when sampling diverse low heath, it may be easier to keep track of your data taking in a long and narrow quadrat, than in a square one.

Numbertop of page
How many quadrats will be used?

The decision of how many quadrats to sample requires balancing the (a) need to include enough quadrats to capture the true variabiltiy within the vegetation being sampled with (b) the available labour, time and cost. The actual number you should include in your sample depends on the variability between qudrats (i.e. within the area being sampled). Your sample should include enough quadrats to capture the true variabiltiy within the vegetation being sampled.

A plot of the running mean and standard error (a measure of variability about the mean) of your results against the number of quadrats sampled will provide an indication of whether you have captured that variability. For example, you will have collected data from sufficient quadrats when the standard error begins to stabilise.

A decisions about how many qudrats to sample may also be based upon the total area sampled. Often 30% of the total area is assumed to capture a representative sample. The variance within a sample would be less than the variance between samples but what is acceptable will depend on the degree of heterogeneity of the encountered species and the degree of accuracy required.

The most effective and efficient way of optimising your sample size is to aim to maximise the degrees of freedom of your analysis. Refer to notes for SBI209 Topics 4-6 for examples of the use of degrees of freedom and how these effect confidence in your conclusions, based on sample sizes.

Arrangementtop of page
How will the quadrats be oriented?
Where will the quadrats be placed in the study area?
Quadrat arrangement can be

  • Selective - quadrats are arranged subjectively to include representative areas or areas with some special feature, such as the species under study.
  • Random - each sample by definition must have an equal chance of being chosen. Samples may be positioned by using pairs of random numbers as distances along two axes positioned at right angles to each other. Many statistical tests assume that data have been randomly collected.
  • Regular (systematic) - samples positioned using a grid, or transects arranged linearly and contiguously are examples of systematic sampling. This method is often used in the study of pattern. It may be useful as a preliminary method of identifying variability in an area, or for mapping. This method, however does not account for sub-areas within the study area. Hence some sub-areas may be undersampled and some over sampled. Limited statistical tests are available. It can give a very distorted result if the area is not homogenous.
  • Restricted random (partial random) - area under study is subdivided and then each subdivision is sampled at random.
  • Stratified - stratified sampling involves dividing the area of study into relatively homogenous sub-areas and then sampling each of these at a frequency according to its area.

For more information
Please view


"Plant biodiversity in Natural, mixed-species Forests and Silvicultural plantations in the vicinity of Fundy National Park".
Case study within the Greater Fundy Ecosystem Research Project by Cam Andrea Veinotte, Bill Freedman and Wolfgang Maass (1996).

This study aims to examine the effect of clear cutting and plantation establishment on the region's floral biodiversity. Details are provided of the sampling regime which included, within each stand, 30 quadrats of 1m2 established along a transect, in order to sample the ground vegetation present within the site.


5.2 (iii)

Please read this case study and identify what sampling methodology they have used.

We will discuss this during out next online tutorial.


Errorstop of page
Errors are deviations of observations from true population values. For example

  1. Bias - refers to deviations in a constant direction.
    Bias may result from
    • inaccurate measuring instruments
    • incorrect technique
    • consistent misapplication of technique or sampling design
    • consistent misidentification.
  2. Indeterminate errors - deviations are equally likely to be in both directions. This may result from
    • personal factors such as fatigue, inattention, response to weather
    • sampling errors - because only a fraction of the whole target area is measured, the mean value of sampled data will always differ from the true mean of the population.

A sampling strategy with simple stratification on vegetation types is likely to bias sampling against the floristically-rich ecotonal areas where major vegetation types overlap. A systematic method such as Gradsect avoids this bias.

Gillison & Brewer (1985) showed that gradsects capture more information than randomly placed quadrats of similar length. This paper details a gradsect survey in NSW which aimed to determine the type and range of eucalypt and rainforest communities present, and to use the results to provide a stratified sample for a fauna survey to establish the correlation between fauna and floristic composition.

Bias due to lack of accessibility to certain grid points was made explicit by restricting sampling to within 0.5 km of tracks accessible to 4WD vehicles.

For more information
Please read

Please continue to read your textbook,
pages 111-138.

Reading 5
Goldsmith et al. (1976) for the criteria for selecting quadrat size, placement, orientation etc. This reading will also assist you to consider the concept of 'minimal area'.

Further Readings

Sampling Methods/Criteria

(1) Shifley, S.R. and Schlesinger, R.C. (1994) 'Sampling guidelines for old-growth forests in the Midwest, USA' Natural Areas Journal 14 (4): 258-268.
In this study the relation between sampling intensity in old forests and precision of estimates for
(i) measures of stand density
(ii) woody debris, and
(iii) species richness/diversity, were examined. Effects of plot size are discussed.

(2) Menges, E.S. and Gordon, D.R. (1996) 'Three levels of monitoring intensity for rare plant species' Natural Areas Journal 16 (3) 227-237.
Three hierarchical levels of sampling are used.
Level one focuses on species occurrence (presence/absence).
Level two involves a quantitative assessment of abundance or condition, often in terms of percent cover, density or frequency.
Level three involves demographic monitoring of marked individuals.
The three levels can be efficiently nested.

(3) Overton, W.S. and Stehman, S.V. (1996) 'Desirable design characteristics for long-term monitoring of ecological variables' Environmental and Ecological Statistics 3 (4) : 349-361.
This paper discusses the difference in demands that a sampling strategy for long-term monitoring has compared to a survey designed for a single time period.

(4) Johnson, R.R. and Higgins, K.F. (1998) 'Bias in quadrat-derived estimates of number of prairie wetlands' Wetlands 18:3.
This study aimed to evaluate the magnitude of bias in estimates of the number of temporary, seasonal, semipermanent, and total wetlands from samples of square quadrats of different sizes.

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