Adjusting Forest Density Estimates for Surveyor Bias in Historical Tree Surveys

Abstract

The U.S. General Land Office surveys, conducted between the late 1700s to early 1900s, provide records of trees prior to widespread European and American colonial settlement. However, potential and documented surveyor bias raises questions about the reliability of historical tree density estimates and other metrics based on density estimated from these records. In this study, we present two complementary approaches to adjust density estimates for possible surveyor bias. We addressed the problem of surveyor bias of density estimates by simulating the effects of (1) rank of selected trees (compared to assuming the nearest trees were selected) and (2) specific surveyor bias in selection of (a) quadrant location, (b) quadrant configuration, (c) azimuth, and (d) combined species and diameter. We then developed regression equations to calculate adjustment quotients for these biases, making the adjustment quotients transferable to any similar datasets. For the rank-based approach, an unvarying rank of 2 (selection of the second nearest tree instead of always the nearest tree) decreased density estimates to about 25 to 45% of the actual density, depending on number of trees per survey point, resulting in corrected density estimates that are 2.2 to 4 times greater than uncorrected density estimates. However, constant selection of the second nearest tree did not occur; varying ranks decreased density estimates to around 55 to 65% of the density, resulting in corrected density estimates about 1.5 to 1.8 times greater than uncorrected values. For the bias-based approach, depending on the specific General Land Office dataset, bias for tree species and diameter alone may decrease density estimates by about 35%. Quadrant configuration and azimuth preference may decrease density estimates by about 15% each. The quadrant location bias has negligible effects on the density estimates. The overall density estimates may be about 35 to 55% of the actual density and correction of the density estimate will approximately double the value. These methods can provide a range of estimates, from low values of uncorrected density to high values of corrected density, about the amount that varying surveyor bias may have decreased density estimates for any areas where bias is detected (i.e., non-random frequencies) in point-centered quarter surveys. Adjustments will increase reliability of historical forest density estimates and their applications for restoration.