Evaluation of distance methods for estimating population density in Populus euphratica Olivier natural stands (case study: Maroon riparian forests, Iran)

Abstract

Basiri R., Moradi M., Kiani B., Maasumi Babaarabi M. (2018): Evaluation of distance methods for estimating population density in Populus euphratica Olivier natural stands (case study: Maroon riparian forests, Iran). J. For. Sci., 64: 230-244.The aim of this study was to determine the performance of distance methods in terms of accuracy, precision, bias, consumed time and sampling efficiency in the Maroon riparian forests, Iran. 40 estimators were used to evaluate the density of Populus euphratica Olivier trees in pure and mixed stands. Fifty quadrates (30 × 30 m) were established in each stand. To evaluate the accuracy, precision, bias, consumed time and efficiency of sampling techniques, relative root mean square error -RRMSE (%), coefficient of variation -CV (%), relative bias -RBIAS (%), t × RBIAS 2 , t × E 2 , where t is study time and E (%) is sampling error at a confidence level of 95%, and efficiency ratio between method j and k (Ef jk ) were used. A compound of three basic distance estimators sampling method and n-tree were the best in both stands according to all criteria for density estimation. Moreover, variable area transect by Parker (g = 3) and quadrat method were the best methods for density estimation only in pure stand, while the angle order-point-centred quarter method was superior in mixed stand. Regarding to the results, we recommend the use of compound of three basic distances (BDAV3) and basic distance-nearest neighbour (BDNN2) for density estimation of P. euphratica stands in riparian forests.

Performance assessment of two plotless sampling methods for density estimation applied to some Alpine forests of northeastern Italy

“…Therefore, the effectiveness of a plotless density estimator must be evaluated considering its performance in non-random patterns and especially in clustered patterns, which are more frequent in real forests (Engeman et al 1994). Many studies confirmed the robustness of PCQM towards the bias related to non-random patterns (Engeman et al 1994, White et al 2008, Khan et al 2016, Basiri et al 2018, Jamali et al 2020. Additionally, in our findings the PCQM provided estimates comparable to PSM.…”

“…Furthermore, in our results, the plot-based estimates do not differ from those of PCQM, in terms of both accuracy and precision. Despite the higher field efforts required, due to the identification of the four equal-angular sectors, the PCQM proved to be the most effective method for a variety of forests (Ahmed & Ogden 1987, Kumarathunge et al 2011, Khan et al 2016, Basiri et al 2018, Jamali et al 2020.…”