title: Small Sample Sizes Yield Biased Allometric Equations in Temperate Forests.
abstract: Accurate quantification of forest carbon stocks is required for constraining the global carbon cycle and its impacts on climate. The accuracies of forest biomass maps are inherently dependent on the accuracy of the field biomass estimates used to calibrate models, which are generated with allometric equations. Here, we provide a quantitative assessment of the sensitivity of allometric parameters to sample size in temperate forests, focusing on the allometric relationship between tree height and crown radius. We use LiDAR remote sensing to isolate between 10,000 to more than 1,000,000 tree height and crown radius measurements per site in six U.S. forests. We find that fitted allometric parameters are highly sensitive to sample size, producing systematic overestimates of height. We extend our analysis to biomass through the application of empirical relationships from the literature, and show that given the small sample sizes used in common allometric equations for biomass, the average site-level biomass bias is ~+70% with a standard deviation of 71%, ranging from -4% to +193%. These findings underscore the importance of increasing the sample sizes used for allometric equation generation.
authors.affiliation: Department of Geographical Sciences, University of Maryland, College Park, 2181 Lefrak Hall, University of Maryland, College Park, MD, 20742.|Biospheric Sciences Lab, Code 618, NASA Goddard Space Flight Center, Greenbelt, MD 20771.
authors.forename: L
authors.initials: L
authors.lastname: Duncanson
authors.affiliation: AMSC, Department of Mathematics, University of Maryland, College Park, 0209 Mathematics Building, University of Maryland, College Park, MD, 20742.
authors.forename: O
authors.initials: O
authors.lastname: Rourke
authors.affiliation: Department of Geographical Sciences, University of Maryland, College Park, 2181 Lefrak Hall, University of Maryland, College Park, MD, 20742.
authors.forename: R
authors.initials: R
authors.lastname: Dubayah
mesh_terms: D015982:Bias; D018533:Biomass; D065928:Forests; D015233:Models, Statistical; D018401:Sample Size; D014481:United States