Fig 1.
Schematic representation of a PCQM sample point with trees represented as circles, squares or triangles.
In this example squares are always the nearest to the sample point and represent trees measured for PCQM, followed by circles for PCQM2 and triangles for PCQM3.
Table 1.
Characteristics of simulated and empirical datasets having different spatial patterns.
Table 2.
Fig 2.
Box plot of the density (individuals ha–1) distribution of 1,000 simulations estimated with different methods and varying sample points (N) in a simulated population having a random spatial pattern with a density of 5000 individuals ha–1.
Boxes with white background represent densities based on corrected estimators and those with grey background represent densities based on published estimators. Methods: 1 = true density, 2 & 5 = PCQM1, 3 & 6 = PCQM2, 4 & 7 = PCQM3.
Table 3.
The relative root mean square error (RRMSE) and relative bias (RBIAS) with varying true density and “random” spatial pattern.
Table 4.
The relative root mean square error (RRMSE) and relative bias (RBIAS) with “aggregated” spatial pattern, varying aggregation radius (AR), varying aggregation intensity (AI) and a fixed true density of 3000 ha-1.
Table 5.
The relative root mean square error (RRMSE) and relative bias (RBIAS) with “regular” spatial pattern having varying repulsion distances (RD) and a fixed true density of 3000 ha-1.
Table 6.
The relative root mean square error (RRMSE) and relative bias (RBIAS) with “natural forests” having different true densities.
Fig 3.
Box plot of the density (individuals ha–1) distribution of 1,000 simulations estimated with different methods using varying sample points (N = 15 to 100) comparing the differences between the two estimators in three natural populations (site 1, site 2 and site 3).
In each sample size, boxes with white background represent corrected estimators and those with grey background represent published estimators (PCQM1, PCQM2 and PCQM3 from left to right in each scenario). The dotted horizontal line in each plot indicates the true density.