Abstract
BackgroundPlotless density estimators are those that are based on distance measures rather than counts per unit area (quadrats or plots) to estimate the density of some usually stationary event, e.g. burrow openings, damage to plant stems, etc. These estimators typically use distance measures between events and from random points to events to derive an estimate of density. The error and bias of these estimators for the various spatial patterns found in nature have been examined using simulated populations only. In this study we investigated eight plotless density estimators to determine which were robust across a wide range of data sets from fully mapped field sites. They covered a wide range of situations including animal damage to rice and corn, nest locations, active rodent burrows and distribution of plants. Monte Carlo simulations were applied to sample the data sets, and in all cases the error of the estimate (measured as relative root mean square error) was reduced with increasing sample size. The method of calculation and ease of use in the field were also used to judge the usefulness of the estimator. Estimators were evaluated in their original published forms, although the variable area transect (VAT) and ordered distance methods have been the subjects of optimization studies.ResultsAn 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.ConclusionPlotless 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.
Highlights
Plotless density estimators are those that are based on distance measures rather than counts per unit area to estimate the density of some usually stationary event, e.g. burrow openings, damage to plant stems, etc
Instead natural populations tend to occur as a mixture of spatial patterns at various levels of intensity and grain
Interpretation of the performance of estimators based on relative root mean square error (RRMSE) (Table 4) and relative bias (RBIAS) (Table 5) was undertaken for estimators that were ranked highly by [1] (Table 1) for the natural and simulated data sets described in Tables 2 and 3
Summary
Plotless density estimators are those that are based on distance measures rather than counts per unit area (quadrats or plots) to estimate the density of some usually stationary event, e.g. burrow openings, damage to plant stems, etc. 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. Some plotless density estimators are better at handling departures from randomness due to the intensity and grain of the overall spatial pattern
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