A COMPARISON OF PLOTLESS DENSITY ESTIMATORS USING MONTE-CARLO SIMULATION

We conducted an extensive simulation study to compare the performance of a large group of plotless density estimators (PDEs) to obtain clarification of their relative performance in a diversity of sampling situations. The PDEs studied included well-known ones from the literature plus some extensions and modifications introduced here. The simulations cover 96 combinations of 6 spatial patterns, 4 sample sizes, and 4 population densities. We made comparisons within classes of similar estimators, and we indicate the best-performing PDEs out of the complete set studied. Over all spatial patterns, the angle-order estimator with measurements to the third-closest individual in each quadrant had the lowest relative root-mean-squared error (RRMSE), followed by the same estimation method with measurements to the second closest individual in each quadrant. Also performing well were the variable area transect, the ordered distance estimator using the third closest individual, and an extension of the Kendall-Moran estimator that searches for the second nearest neighbor and pools search areas from all sample points. Opinions and recommendations are given as to which PDEs perform well enough and are practical enough to deserve strong consideration for use in the field.