A comparison of plotless density estimators using Monte Carlo simulation on totally enumerated field data sets

An estimator that was a compound of three basic distance estimators was found to be robust across all spatial patterns for sample sizes of 25 or greater. The same field methodology can be used either with the basic distance formula or the formula used with the Kendall-Moran estimator in which case a reduction in error may be gained for sample sizes less than 25, however, there is no improvement for larger sample sizes. The variable area transect (VAT) method performed moderately well, is easy to use in the field, and its calculations easy to undertake.Plotless density estimators can provide an estimate of density in situations where it would not be practical to layout a plot or quadrat and can in many cases reduce the workload in the field.Plotless density estimators are those that based on distance measures rather than counts per unit area (quadrats or plots) to estimate the density of some fixed event, e.g. burrow openings, damage to plant stems, etc. Plotless density estimators can provide an estimate of density in situations where it would not be practical to layout a plot or quadrat, e.g. difficult terrain, crops, situations where a low impact is required. These techniques make certain assumptions about the spatial distribution of the event that in the worst case assume that the event is randomly distributed, a situation that occurs infrequently in nature. Other techniques permit greater degrees of non-randomness. It is important therefore to understand when a certain plotless density estimator is robust to departures from non-randomness.An evaluation of which plotless density estimator (PDE) is suitable for a given field situation requires examination of fully enumerated field populations and is ideally suited to computer simulation. Inferences about PDEs using simulated populations [1] are limited because field data rarely consists of a single type of spatial pattern. Instead natural populations tend to occur as a mixture of spatial patterns at various levels