Abstract
Abstract Three methods for estimating crown cover of woody vegetation were compared, in three mapped field sites and one theoretical map of randomly distributed individuals, by computer simulated sampling. The effect of plant size on the performance of the methods was investigated by varying the size of individuals in the theoretical map. Using the line‐intercept and point‐sampling techniques according to standard procedures, the net crown cover of a species is estimated and overlapping areas are not recognized. The variable plot or Bitterlich gauge technique estimates the total crown cover of individuals, and areas of overlap are thus recorded more than once. Consequently, Bitterlich gauge estimates will always be greater whenever individuals intermingle or overtop one another. Line‐intercept and point‐sampling techniques produced highly variable estimates when plants were small, but were not prone to greater variability when plant distribution departed from randomness. The Bitterlich gauge estimates were no more variable with small than with large plants, but were affected by the degree of plant aggregation. The problem is overcome by selecting a sample point spacing and a half‐angle for the gauge that optimizes plant counting. The advantages of the variable plot method over the other two are discussed in terms of speed and operator differences, and it is recommended as a monitoring technique for woody vegetation in arid rangelands.
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Claims 9
Claim · model-written summary
Line-intercept and point-sampling estimate net species crown cover and do not recognize overlap.
Verbatim
e0“Using the line‐intercept and point‐sampling techniques according to standard procedures, the net crown cover of a species is estimated and overlapping areas are not recognized.”
Claim · model-written summary
The Bitterlich gauge estimates total individual crown cover, counting overlapping areas more than once.
Verbatim
e0“The variable plot or Bitterlich gauge technique estimates the total crown cover of individuals, and areas of overlap are thus recorded more than once.”
Claim · model-written summary
Bitterlich gauge estimates exceed the other estimates when individuals intermingle or overtop one another.
Verbatim
e0“Consequently, Bitterlich gauge estimates will always be greater whenever individuals intermingle or overtop one another.”
Claim · model-written summary
Line-intercept and point-sampling estimates were highly variable for small plants.
Verbatim
e0“Line‐intercept and point‐sampling techniques produced highly variable estimates when plants were small”
Claim · model-written summary
Line-intercept and point-sampling variability did not increase when plant distribution departed from randomness.
Verbatim
e0“but were not prone to greater variability when plant distribution departed from randomness.”
Claim · model-written summary
Bitterlich gauge variability was no greater for small than large plants.
Verbatim
e0“The Bitterlich gauge estimates were no more variable with small than with large plants”
Claim · model-written summary
Bitterlich gauge estimates were affected by plant aggregation.
Verbatim
e0“The Bitterlich gauge estimates were no more variable with small than with large plants, but were affected by the degree of plant aggregation.”
Claim · model-written summary
Selecting point spacing and gauge half-angle to optimize counting overcomes the aggregation problem.
Verbatim
e0“The problem is overcome by selecting a sample point spacing and a half‐angle for the gauge that optimizes plant counting.”
Claim · model-written summary
The variable plot method is recommended to monitor woody vegetation in arid rangelands.
Verbatim
e0“it is recommended as a monitoring technique for woody vegetation in arid rangelands.”