Quadrats

11 Key excerpts on "Quadrats"

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  Plant Taxonomy And Biosystematics

  Classical And Modern Methods

  • Rana, T.S.(Authors)
  • 2020(Publication Date)
  • NEW INDIA PUBLISHING AGENCY (NIPA)

    (Publisher)

  In the real world, it is important to count organisms carefully and lay out plots accurately for good estimates of density. This is not a concern here however, because the computer will be laying out the plots and counting the plants. (ii) Total area sampled. In general, the more area sampled, the more precise the estimates will be, but at the expense of additional sampling effort. (iii) Dispersion of the population . Whether the population tends to be aggregated, evenly spaced, or randomly dispersed can affect precision. Note that the dispersion pattern of the same population may be different at different spatial scales (e.g., 1 x 1 m plots vs 100 x 100 m plots). (iv) Size and shape of Quadrats . The size and shape of the plots can affect sampling precision. Often, the optimal plot size and shape will depend on the dispersion pattern of the population. Sampling with Quadrats (plots of a standard size) can be used for most plant communities. A quadrat delimits an area in which vegetation cover can be estimated, plants counted, or species listed. Quadrats can be established randomly, regularly, or subjectively witin a study site. Since plants often grow in clumps, long, narrow plots often include more 438 Plant Taxonomy and Biosystematics species than square or round plots of equal area; especially if the long axis is established parallel to environmental gradients. However, accuracy may decline as the plot lengthens because, as the perimeter increases, the surveyor must make more subjective decisions about the placement of plants inside or outside the plot. Round Quadrats can be most accurate because they have the smallest perimeter for a given area. Round Quadrats are also simple to define in the field, requiring only a center stake and a tape measure (Cox 1990). The appropriate size for a quadrat depends on the items to be measured. If cover is the only factor being measured, size is relatively unimportant.

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  Landscapes and Labscapes

  Exploring the Lab-Field Border in Biology

  • Robert E. Kohler(Author)
  • 2010(Publication Date)
  • University of Chicago Press

    (Publisher)

  Assuming that the Quadrats have been lo-cated in representative parts of the formation being studied, the resulting table of frequencies will describe it as precisely as one wishes. 5 In practice, of course, it is not quite that simple. Knowing where to locate Quadrats to get biologically meaningful results requires skill and experi-ence. Deciding how big to make them and how many to do also requires judgment: smaller and fewer Quadrats are quicker to do, but more or larger ones give more precise results, and precision, even if not strictly required for the problem in hand, affords valuable epistemological cover. Pound and Clements’s original Quadrats were five meters square, but one-meter squares became the standard for grasses and forbs, two-meter squares for shrubs, and ten-to-fifty meter squares for trees. Where exactly in the lab-field border zone did the quadrat arise? The historian Ronald Tobey has pointed to the laboratory side, observing that quantification was much in fashion at the time in the up-and-coming botan-ical schools of the Middle West—especially in Charles Bessey’s school at the University of Nebraska, where Clements was a student. (Bessey looked 100 Chapter 4 4. Ronald C. Tobey, Saving the Prairies: The Life Cycle of the Founding School of American Plant Ecology, 1895–1955 (Berkeley: University of California Press, 1981), chap. 3. 5. Roscoe Pound and Frederic E. Clements, “A method of determining the abundance of secondary species,” Minnesota Geological and Natural History Survey Botanical Series 4 (1898): 19–24, pp. 20–22. to physiology to make botany scientific; ecology was too like children’s na-ture study for his comfort.) 6 However, a quantitative spirit was also abroad among field botanists at the time. Mass collecting was becoming standard practice in the late nineteenth century, mainly in zoology but also botany, and species descriptions commonly included abundances, which were a use-ful aid to collecting and identifying.

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  Practical Field Ecology

  A Project Guide

  • C. Philip Wheater, James R. Bell, Penny A. Cook(Authors)
  • 2020(Publication Date)
  • Wiley-Blackwell

    (Publisher)

  Figure 3.5 illustrates these two systems. Vegetation can be extremely patchy. When this occurs, you should try to sample about 5% of the habitat, using a large number of small Quadrats (of a size suitable for the size of the species being examined) rather than using a few large Quadrats.

  Figure 3.5 Using random numbers to identify a position in a sampling grid. Either (a) each block is allocated a number (the shaded cell is block 14) or (b) each block is identified by coordinates (the shaded block is at 4, 9).

  Quadrat shape

  Most researchers use square Quadrats, although circular or oblong ones may be useful in certain situations. The reason for this is related to two characteristics of differently shaped Quadrats:

  1. Circular Quadrats may be chosen where edge effects are important. The ratio of the length of the edge to the area inside the quadrat changes with the shape so that circular Quadrats have a smaller edge effect than square ones of the same area, with oblong shapes having the greatest edge effect (Figure 3.6 ). Edge effects can lead to errors in counting since every plant or animal at the edge of a quadrat can be identified as being either in or out of the sampling frame. If all organisms touching the edge are counted as being inside, this may overestimate the cover. Conversely, ignoring all those on the edge may result in an underestimate. One of two strategies are usually employed to avoid these problems:
     • counting only those individuals with more than 50% of their area within the quadrat and ignoring those where more than 50% lies outside;
     • counting all those touching only two of the sides (say the top and right‐hand side) and ignoring those touching the other two sides.
  2. Long, thin frames are better than circular or square ones of the same area at covering habitat heterogeneity, since they tend to cross more patches (which is one reason for using transects with Quadrats – see p. 104).

  Figure 3.6 Comparison of the perimeter to area ratios of circular, square, and oblong Quadrats. Note that all the Quadrats have an area of 0.25 m

  2

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  Field Methods in Marine Science

  From Measurements to Models

  • Scott Milroy(Author)
  • 2022(Publication Date)
  • Garland Science

    (Publisher)

  Figure 3.2 ). Similarly, any quadrat can be used to define a fixed-area grid, containing any number of equidistant points depending on the desired scale of measurement.

  However, as an interesting consequence of geometry, it is best to maximize the “area-to-perimeter” ratio of the quadrat, so that irregularly shaped elements are more likely to be contained within the area of the quadrat (rather than falling along the edge of the quadrat). Deciding whether an element should be counted as “in” or “out” of the quadrat can occasionally lead to a positive bias in the data, sometimes called the edge effect. We can generally minimize this bias by choosing quadrat dimensions that maximize the quadrat area and minimize the quadrat perimeter (edge). This is done simply by using Equation 3.6 to analyze the area-to-perimeter (A:P) ratio:

  Quadrat     A : P   =   L ⋅ W 2 L   +   2 W

  (3.6)

  Figure 3.2 A nested quadrat, when placed over the substrate, serves as a versatile instrument for conducting a plot census. This device is especially versatile in the field, as various plot methods can be employed using either the entire quadrat area or a specified number of randomly selected sub-Quadrats. Note that the same device can also be used to gather gridded, point-intercept data at each of the “cross-hairs.” (Courtesy of the Griffith School of Environment & Australian Rivers Institute, —Coast & Estuaries. Brisbane Australia.)

  Using this simple calculation, it is easy to demonstrate that not all Quadrats are created equal. As an example, let us assume we wish to employ a quadrat of fixed area, such as 4 m2 . Let us now compare the A:P

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  Measurements for Terrestrial Vegetation

  • Charles D. Bonham(Author)
  • 2013(Publication Date)
  • Wiley-Blackwell

    (Publisher)

  Assessing Carbon Stocks and Modeling Win–Win Scenarios of Carbon Sequestration Through Land-Use Changes . Food and Agriculture Organization of the United Nations: Rome.) Nested Quadrats are illustrated in Figure 4.4 .

  Quadrat dimensions	Use of quadrat in measurements and sampling
  10 × 10 m	Morphometric measurements of the tree layer. Measurements of trunk and canopy of trees and large deadwood. Identification of tree species and individual organisms within a species for biodiversity assessment. Site measurements and observations for land degradation assessment.
  5 × 5 m	Study of the shrub layer. Morphometric measurements of the shrub layer. Measurements of stem and canopy and small deadwood. Identification of shrub species and individual shrub organisms within species for biodiversity assessment.
  1 × 1 m	Sampling of biomass of herbaceous species and grasses, above-ground and roots, litterfall and debris for drying and weighing to determine live and dead biomass. Counting of herbaceous species and number of individuals within species.
  The design of nested Quadrats of different sizes obeys requirements for measuring and counting vegetation of different sizes and strata, and for collecting debris and litter for estimation of biomass. The above Table 4.1 indicates the designated use for each quadrat.

  Figure 4.4 Nested Quadrats recommended by FAO (2010).

  4.5 On-ground large-scale spatial analyses

  Spatial patterns in plant species and their associated environment result in spatial heterogeneity in plant communities. The sampling design used to obtain measures will determine the degree of spatial effects detected among sampling units. Measures obtained tend to be more alike as the sampling units are located closer together than when the units are placed farther apart. A spatial auto-correlation index is one method to estimate spatial effects on the results of sampling. It follows that when biomass of a plant species is spatially correlated, then biomass of the species can be predicted at other locations in the sample area.

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  Monitoring Plant and Animal Populations

  A Handbook for Field Biologists

  • Caryl L. Elzinga, Daniel W. Salzer, John W. Willoughby, James P. Gibbs(Authors)
  • 2009(Publication Date)
  • Wiley-Blackwell

    (Publisher)

  The quadrat length should be determined by the size of the area that you are working in and the spatial distri- bution of the species you are counting. You want to avoid getting many sampling units with zeros, so you want your Quadrats to be long enough to incorporate several dumps. You also do not want your Quadrats so long that you have to count thousands of individuals-the time in- volved and the potential measurement error associated with counting that many individuals would be too great. Box 8.3 gives a procedure for comparing the efficiency of different density quadrat sizes and shapes through pilot sampling. 4 CHAPTER 8: SAMPLING DESIGN I 115 116 I MEASURING AND MONITORING PLANT AND ANIMAL POPULATIONS 4 Other Sampling Units Your prime design objective when selecting a sampling-unit size and shape is to try to reduce the variability between sampling units while maintaining a size and shape that is practical in the field. Many of the design principles described for density Quadrats are applicable to other types of sampling units. Transects should be long enough to intersect clumps of the target species and should be oriented to include as much of the gradient variation as possible. Plots for visually esti- mating cover or measuring biomass are typically quite small and often square or rectangular, be- cause it is difficult to estimate cover, to clip vegetation, or to estimate biomass in large or long plots. These small Quadrats can be arranged, however, along a transect, with the transect, not the Quadrats, treated as the sampling unit. This design is really a two-stage sampling design, with the transects serving as the primary sampling units and the Quadrats serving as secondary sampling units.

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  The Conservation Handbook

  Research, Management and Policy

  • William J. Sutherland(Author)
  • 2008(Publication Date)
  • Wiley-Blackwell

    (Publisher)

  They are traditionally square but need not be. Circular Quadrats, consisting of a peg (or weight if underwater) and string/rope of known length, can provide a simple portable quadrat of known area and have the advantage of the smallest possible edge for a given area. Strip transects, in which the obser- ver moves a set distance by foot, vehicle, boat or plane and records the number of individuals that can be seen within a given distance of the transect, are really long rectangular Quadrats. If individuals close to the transect line are more likely to be detected then distance sampling methods (Section 4.7) should be used instead. Quadrats and strip transects are typically used when the species can be easily counted and is not disturbed by the observer. These are the commonest techniques for counting plants. They may also be used for counting signs such as dung or breeding sites. They can be used for mobile species as long as they can be counted before they move out of range. Some individuals will usually be missed. It is thus useful to attempt to estimate this bias by other means such as seeing how many of a known number are detected. Caughley (1974) reviewed 17 studies in which known numbers of large terrestrial mammals were counted in aerial transects and between only 23% and 89% were seen from the air. Strip transects have two advantages over square Quadrats. It is often more practical to create a single transect in thick vegetation than to ensure all of a large quadrat is visited. Secondly, strip transects are likely to include a range of habitats and so reduce the variation between Quadrats and thus increase the precision (Bormann 1953). Strip transects have the considerable disadvantage of a long perimeter so 42 Monitoring increasing inaccuracies due to deciding whether a particular individual is inside or not. 4.7 Distance sampling: line transects and point counts A line transect consists of counting individuals seen from a transect.

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  Vegetation Description and Data Analysis

  A Practical Approach

  • Martin Kent(Author)
  • 2011(Publication Date)
  • Wiley-Blackwell

    (Publisher)

  76 VEGETATION DESCRIPTION AND DATA ANALYSIS In conclusion, it is worth stressing that spatially biased or non-random data collection in inductive or observational research is entirely valid. However, such data should not then be ‘re- used’ within a hypothesis testing/deductive framework (see Chapter 1). Instead, new research data collected in a truly random manner should be used to answer the research question and test any newly generated hypotheses. Systematic sampling Systematic sampling involves the location of sampling points at regular or systematic intervals. The size of the sampling interval is extremely important and may be a fixed interval, such as 100 m, or a regular number of paces. If Quadrats are taken at fixed intervals, then care has to be taken that the sampling interval does not coincide with any pattern in the vegetation due either to the properties of plants themselves or to some regularly distributed environmental control. An example is where the floristics of an old meadow in England are being examined and past agricultural practice has left some form of ridge and furrow system, with damper conditions in the furrows giving rise to one set of species, and drier conditions on the ridges resulting in another. If the phase length of the ridge and furrow coincides with the sampling interval, only ridges or furrows could be selected. Transects The transect approach is very popular in vegetation work. A transect is a line along which samples of vegetation are taken and are usually set up deliberately across areas where there are rapid changes in vegetation and marked spatial environmental gradients. Most transects are thus biased in their location, although it is clearly possible to locate the start and end of a transect at random and then take samples along the line connecting the two points.

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  Sampling Strategies for Natural Resources and the Environment

  • Timothy G. Gregoire, Harry T. Valentine(Authors)
  • 2007(Publication Date)
  • Chapman and Hall/CRC

    (Publisher)

  For example, trees exceeding some threshold diameter will be sampled on 0.1 ha plots, whereas trees smaller than the threshold diameter will be sampled only if they occur on a 0.05 ha plot located at the same place in A . Another variant is the establishment of not one but a cluster of plots equidistant from a central sampling location. An example of a plot cluster appears in Figure 7.5. These variants notwithstanding, a distinguishing feature of plot sampling is that, with some exceptions, each element of the population is sampled with a probability proportional to the area of the plot in which it occurs. The exceptional elements are close to the boundary of A , where, it turns out, their inclusion probabilities are somewhat diminished. This interesting complication will be explained in detail in section 7.5. With the exception of these elements close to the boundary of A , plot sampling is an example of equal probability sampling with an areal frame. 207 208 SAMPLING WITH FIXED AREA PLOTS 10 m 5 m 1 m Trees Shrubs Herbs Figure 7.1 The layout of the nested plot from Ponce-Hernandez (2004). As described in Example 7.1, successively smaller vegetation, organisms, and features are measured on successively smaller plots. Example 7.1 In a manual prepared by the Food and Agriculture Organization of the United Nations (Ponce-Hernandez 2004), a nest of square plots, shown in Figure 7.1, was described for the sampling of aboveground biomass and assessment of land degradation. Morphometric measurements of the trees and large woody detritus are gath-ered over the entire 10 m 10 m quadrat. On this plot, too, tree species and in-dividual organisms within a species are recorded for the purpose of biodiversity assessment, as well as site measurements and observations for land degradation assessment. In addition to the measurements just noted, the shrub layer is also measured in the 5 m 5 m quadrat.

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  Spatial Analysis

  A Guide For Ecologists

  • Mark R. T. Dale, Marie-Josée Fortin(Authors)
  • 2014(Publication Date)
  • Cambridge University Press

    (Publisher)

  5 Contiguous units analysis Introduction We will begin this chapter with a discussion of spatial analysis in one dimension; the basic form of data is a series of values, x 1 to x n , representing the density or stem counts of a particular species in a transect of n contigu- ous sampling units, usually Quadrats which represent the most common sampling method in plant ecology. The density will often be measured in per cent or may be expressed as a proportion, running from 0 to 1. In pres- ence : absence form, the data take only the values 0 for absence and 1 for presence. The spatial pattern of a single species is often studied using one of several methods that examine the effects of distance or block size on a calculated variance, with low variance indicat- ing similarity and high variance indicating dissimilarity. The distances that produce high and low values of the variance are closely related to the scales of pattern and the sizes of patches and gaps in the original data. The methods we describe in the sections that follow can also apply to density data, presence : absence, or to ‘count’ data such as the number of stems found in each quadrat; these include methods for studying the spatial relationship of pairs of species and for assessing multispecies pattern. Having begun with a single species in one dimension, we also look at methods for two or more spatial dimensions. To compare with variance-based approaches, we describe analysis tech- niques based on wave forms such as spectral analysis and wavelet analysis. It turns out these approaches are actually similar to the quadrat variance methods, and we conclude with a simplified summary comparison of the methods presented in Chapters 4 and 5. 5.1 Quadrat variance methods These methods have their origins in the examination of counts of events, such as plant stems, in sampling units, like Quadrats, for evidence of nonrandomness in their distribution among units.

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  Measurements from Maps

  Principles and Methods of Cartometry

  • D H Maling(Author)
  • 2016(Publication Date)
  • Butterworth-Heinemann

    (Publisher)

  Linear sampling units: (a) unrestricted random lines within the sampling frame; (b) systematic (equidistantly spaced) sampling points along a single line of random orientation. had been recorded in units of cubic metres per hectare. The different populations were created by arranging these cells in different patterns to simulate the different kinds of plantation which might be found in the field. Since each population comprised the same data, it was possible to examine the relative merits of different sampling strategies in great detail. The leader seeking an exhaustive analysis of the methods is recommended to study their work. As a less ambitious example, based upon real data, we quote an investigation by Haggett (1963). He devised an exercise in sampling to be carried out by students, which was based upon the extent of woodland, expressed as the percentage area within squares of the National Grid for a block of country measuring 100 χ 100 km of the borderland between England • Ξ • Ξ ϋ B 0 m FIG. 8.4. Area sampling units. Unrestricted random Quadrats of equal size within the sampling frame. 128 Measurements from Maps FIG. 8.5. Multi-stage sampling units. The primary units are the Quadrats, or squares of equal size which are randomly distributed within the sampling frame. The second-stage units are the randomly distributed points occurring within each quadrat. and Wales. For this investigation the sampling unit was the 5 χ 5 km grid square. Variation in the sampling pattern and the size of samples was made by selecting different members of these units from the population of 400 such units. The threefold subdivision of sampling units should be extended to include the concept of cluster sampling, which is normally associated with hierarchical multi-stage sampling. The term multi-stage means that the population is divided into a number of first-stage, or primary units which are sampled in the ordinary manner.

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