Bias and error in using survey records for ponderosa pine landscape restoration
Public land survey records are commonly used to reconstruct historical forest structure over large landscapes. Reconstruction studies have been criticized for using absolute measures of forest attributes, such as density and basal area, because of potential selection bias by surveyors and unknown measurement error. Current methods to identify bias are based upon statistical techniques whose assumptions may be violated for survey data. Our goals were to identify and directly estimate common sources of bias and error, and to test the accuracy of statistical methods to identify them. Location
Forests in the western USA: Mogollon Plateau, Arizona; Blue Mountains, Oregon; Front Range, Colorado. Methods
We quantified both selection bias and measurement error for survey data in three ponderosa pine landscapes by directly comparing measurements of bearing trees in survey notes with remeasurements of bearing trees at survey corners (384 corners and 812 trees evaluated). Results
Selection bias was low in all areas and there was little variability among surveyors. Surveyors selected the closest tree to the corner 95% to 98% of the time, and hence bias may have limited impacts on reconstruction studies. Bourdo’s methods were able to successfully detect presence or absence of bias most of the time, but do not measure the rate of bias. Recording and omission errors were common but highly variable among surveyors. Measurements for bearing trees made by surveyors were generally accurate. Most bearings were less than 5° in error and most distances were within 5% of our remeasurements. Many, but not all, surveyors in the western USA probably estimated diameter of bearing trees at stump height (0.3 m). These estimates deviated from reconstructed diameters by a mean absolute error of 7.0 to 10.6 cm. Main conclusions
Direct comparison of survey data at relocated corners is the only method that can determine if bias and error are meaningful. Data from relocated trees show that biased selection of trees is not likely to be an important source of error. Many surveyor errors would have no impact on reconstruction studies, but omission errors have the potential to have a large impact on results. We suggest how to reduce potential errors through data screening.