Community Sampling and Measurements

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CHAPTER 3 Community Sampling and Measurements
Tables, Figures, and Equations
From McCune, B. J. B. Grace. 2002. Analysis
of Ecological Communities. MjM Software Design,
Gleneden Beach, Oregon http//www.pcord.com
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Table 3.1. Cutoff points for cover classes.
Question marks for cutoff points represent
classes that are not exactly defined as
percentages. Instead, another criterion is
applied, such as number of individuals. Cutoffs
in parentheses are additional cutoffs points used
by some authors.
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Figure 3.1 Expected percent frequency of
presence in sample units (SU) as a function of
density (individuals/SU).
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Relative density of species j is the proportion
of the p species that belong to species j
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These relative measures are commonly expressed as
percents, by multiplying the proportions by 100,
for example
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Importance values are averages of two or more of
the above parameters, each of which is expressed
on a relative basis. For example, a measure
often used for trees in eastern North American
forests is IV (Relative frequency relative
dominance relative density) / 3
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Table 3.2. Example of identical importance values
representing different community structures.
Species 1 Species 2
Relative Density 42 8
Relative Dominance 10 44
Sum 52 52
IV 26 26
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Box 3.1. Example of stand description, based on
individual tree data from fixed-area plots. The
variance-to-mean ratio, V/M, is a descriptor of
aggregation, values larger than one indicating
aggregation and values smaller than one
indicating a more even distribution than random.
The variance and mean refer to the number of
trees per plot. IV and other measures are
defined in the text.
Raw data for three tree species in each of four
0.03 hectare plots. Each number represents the
diameter (cm) of an individual tree.
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Box 3.1, cont. Frequencies, counts, total basal
areas, stand densities, and stand basal areas.
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Box 3.1, cont. Relative abundances, importance
values, and variance statistics.
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Table 3.3. Average accuracy and bias of
estimates of lichen species richness and gradient
scores in the southeastern United States.
Results are given separately for experts and
trainees in the multiple-expert study. Extracted
from McCune et al. (1997). N sample size.
Deviation from expert Deviation from expert Deviation from expert Deviation from expert
Activity N Species richness Species richness Score on climatic gradient Score on climatic gradient Score on air quality gradient Score on air quality gradient
of expert Bias Acc. Bias Acc. Bias
Reference plots 16 61 -39 4.4 2.4 11.1 -10.5
Multiple-expert study, experts 3 95 -5 3.6 3.6 4.7 -4.7
Multiple-expert study, trainees 3 54 -46 8.0 8.0 5.0 -5.0
Certifications 7 74 -26 2.7 2.4 2.1 -2.1
Audits 3 50 -50 10.3 3.7 6.0 2.7
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If Sobs is the observed number of species, xobs
is the observed value of variable x, and xtrue is
the true value of parameter x, then
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Table 3.4. Raw data for two-dimensional example
of accuracy and bias, plotted in Figure 3.2.
Person x y Person x y
1 3.0 5.2 4 0.9 -0.4
1 4.0 5.1 4 -0.6 1.1
1 4.0 2.6 4 -1.1 -2.0
2 0.9 -0.7 5 -2.7 -1.0
2 0.1 -2.9 5 -3.1 -1.2
2 0.4 -2.9 5 1.3 -1.9
3 0.0 0.0
3 0.6 0.3
3 -2.1 -1.6
Figure 3.2. Two-dimensional example of accuracy
and bias. Each person (1, 2,.. ,5) aims at the
center (0,0), representing the true value.
Deviations are measured in two dimensions, x and
y.
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Table 3.5. Inaccuracy and bias for
two-dimensional example (Fig. 3.2).
Inaccuracy
(Ave. distance Average bias Average bias
Person to 0,0) x y
1 5.75 3.67 4.30
2 2.32 0.47 -2.17
3 1.10 -0.50 -0.43
4 1.51 -0.27 -0.43
5 2.84 -1.50 -1.37
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Table 3.6. Tradeoffs between few-and-large and
many-and-small sample units.
Few-and-large Many-and-small
Bias against cryptic species Higher. There is a hazard that some species, particularly cryptic species, are inadvertently missed by the eye. Lower. Small sample units force the eye to specific spots, reducing inadvertent observer selectivity in detection of species.
Degree of visual integration High. The use of visual integration over a large area is an effective tool against the normally high degree of heterogeneity, even in "homogeneous" stands. Low. Minimal use is made of integrative capability of eye, forcing the use of very large sample size to achieve comparable level of representation of the community.
Inclusion of rare to uncommon species High. Visual integration described above results in effective "capture" of rare species in the data. Low. Unless sample sizes are very large, most rare to uncommon species are missed.
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Table 3.6. (cont.)
Accuracy of cover data on common species Lower. Cover classes in large sample units result in broadly classed cover estimates with lower accuracy and precision than that compiled from many small sample units. Higher. More accurate and precise cover estimates for common species.
Bias of cover estimates for rare species High (overestimated). Low.
Sampling time Varies by complexity and degree of development of vegetation. No consistent difference from many-and-small. Varies by complexity and degree of development of vegetation. No consistent difference from few-and-large.
Analysis time Faster. With a single large plot, data entry at site level leads directly to site-level analysis. Slower. Point data or microplot data require initial data reduction (by hand, calculator, or computer) to site-level abundance estimates.
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Table 3.6. (cont.)
Analysis options Estimates of within-site variance are poor or impossible, restricting analyses to individual sites as sample units. Within-site variance estimates are possible as long as sample units are larger than points.
Recommendations The extreme case (single large plot) is most useful with extensive (landscape level) inventory methods. In many cases it is better to compromise with a larger number of medium-sized sample units. The extreme case (point sampling) is most useful when rare to uncommon species are of little concern and accurate estimates are desired for common species. In most cases a compromise by using a smaller number of larger sample units is better.
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The following formula rescales aspect to a scale
of zero to one, with zero being the coolest slope
(northeast) and one being the warmest slope
(southwest).
where q aspect in degrees east of true north.
A very similar equation but ranging from zero to
two, was published by Beers et al. (1966).
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The plane-corrected distance D' for a distance D
on an angle of S above the horizontal is D'
D/cos S.