Evaluation of plotless density estimators in different plant density intensities and distribution patterns

Abstract

Choosing appropriate estimator that provides an accurate and precise prediction of plant population’s density is vital specifically when different density intensities and distribution patterns are concerned. Therefore, the efficiency of plotless plant density estimators for the various spatial patterns found in nature have been examined using a simulated population based on an observed population of Astragalus microcephalus in a semi-arid environment.We first surveyed the density of A. microcephalus in the field to have an estimation of the real density of the species (control method). Then a simulation scheme in three density intensities (low (mean−SD), moderate (equal to mean) and high (mean + SD)) and three distribution patterns (random, regular and aggregated) was drawn. Seven distance-based plant estimators were applied to evaluate their efficiency in the three density intensities and also distribution patterns within eight 40 × 100 m sampling units of the simulated scheme (repeats). The predictive precision and accuracy of the estimators in various density intensities and distribution patters were evaluated using the ideal point error-index and comparing the estimators predicted values with the controls (real densities). Angle Order (AO) and Third Closest Individual (TCI) in regular, TCI and Point Centered Quarter (PCQ) in random and AO in aggregate distribution pattern was the best plotless density estimators of plant populations. Overall, TCI, AO and PCQ were the most accurate and precise estimators of density among the seventh studied estimators in different density intensities and distribution patterns. Using these two estimators is recommended to achieve an unbiased estimation of plant population’s density.

Keywords

• Vegetation
• Plant population
• Distance-based methods
• Plant estimators
• Distribution pattern
• Simulation