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

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 patte...

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Main Authors: Hamid Jamali, Ataollah Ebrahimi, Elham Ghehsareh Ardestani, Fatemeh Pordel
Format: Article
Language:English
Published: Elsevier 2020-09-01
Series:Global Ecology and Conservation
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2351989419308856
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spelling doaj-3a2c3aaa1a09470db67cf42c8121b0e62020-11-25T03:34:22ZengElsevierGlobal Ecology and Conservation2351-98942020-09-0123e01114Evaluation of plotless density estimators in different plant density intensities and distribution patternsHamid Jamali0Ataollah Ebrahimi1Elham Ghehsareh Ardestani2Fatemeh Pordel3MSc in Rangeland Management, Department of Range and Watershed Management, Shahrekord University, IranDepartment of Range and Watershed Management, Shahrekord University, Iran; Corresponding author.Department of Range and Watershed Management, Shahrekord University, IranMSc in Rangeland Management, Department of Range and Watershed Management, Shahrekord University, IranChoosing 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.http://www.sciencedirect.com/science/article/pii/S2351989419308856VegetationPlant populationDistance-based methodsPlant estimatorsDistribution patternSimulation
collection DOAJ
language English
format Article
sources DOAJ
author Hamid Jamali
Ataollah Ebrahimi
Elham Ghehsareh Ardestani
Fatemeh Pordel
spellingShingle Hamid Jamali
Ataollah Ebrahimi
Elham Ghehsareh Ardestani
Fatemeh Pordel
Evaluation of plotless density estimators in different plant density intensities and distribution patterns
Global Ecology and Conservation
Vegetation
Plant population
Distance-based methods
Plant estimators
Distribution pattern
Simulation
author_facet Hamid Jamali
Ataollah Ebrahimi
Elham Ghehsareh Ardestani
Fatemeh Pordel
author_sort Hamid Jamali
title Evaluation of plotless density estimators in different plant density intensities and distribution patterns
title_short Evaluation of plotless density estimators in different plant density intensities and distribution patterns
title_full Evaluation of plotless density estimators in different plant density intensities and distribution patterns
title_fullStr Evaluation of plotless density estimators in different plant density intensities and distribution patterns
title_full_unstemmed Evaluation of plotless density estimators in different plant density intensities and distribution patterns
title_sort evaluation of plotless density estimators in different plant density intensities and distribution patterns
publisher Elsevier
series Global Ecology and Conservation
issn 2351-9894
publishDate 2020-09-01
description 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.
topic Vegetation
Plant population
Distance-based methods
Plant estimators
Distribution pattern
Simulation
url http://www.sciencedirect.com/science/article/pii/S2351989419308856
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AT elhamghehsarehardestani evaluationofplotlessdensityestimatorsindifferentplantdensityintensitiesanddistributionpatterns
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