PARENT SESSION
Oral Session #75: Plant Communities: Vegetation Analysis. Presiding: J. Fralish.
Friday, August 10, 2001. 8:00 AM to 12:00 PM. Hall of Ideas F.
Fixed area plot vs. point-centered-quarter methods in forest community analysis.
BRYANT, DAVID1, ZARIN, DANIEL2, INNES, JAMES1, DUCEY, MARK1, 1 2
ABSTRACT- The point-centered-quarter method has been applied in community analysis since the development of the method nearly 50 years ago. This and other "distance methods" purport to offer increased sampling efficiency over fixed-area plots. However, distance methods have long been known to produce biased density estimates when plant distribution deviates from random spatial patterns. Bias in estimates of other quantities, such as basal area and biomass, has not been as widely studied. Spatial indices have been developed to quantify the direction of this bias when plant distributions are aggregated or evenly distributed. We measured 8 forest stands of varying age, elevation and disturbance regime using fixed-area plots and point-centered-quarter methods. Both density and basal area estimates were found to be biased, with the point-centered quarter method giving lower estimates than fixed-area plots when stems were aggregated and higher estimates when stems were evenly spaced. The relationship of the Pielou spatial index vs. % error was not linear for either parameter. Moreover, community structure measured by similarity indices using species basal area showed a range of 18% to 90% similarity when comparing measurements from the same stands using different methods. The apparent efficiency of the point-centered quarter method has made it a popular choice in ordination studies, despite the bias in non-random populations. However, the non-linear effect of the bias indicates that it can produce significant error when used in community level analysis. In addition, these results suggest that caution should be applied when comparing published results from different methods.
KEY WORDS: Fixed-area plots, Point-centered-quarter, Measurement bias, Community structure