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Large Underestimation of Intraspecific Trait Variation and Its Improvements

Intraspecific trait variation (ITV) is common feature of natural communities and has gained increasing attention due to its significant ecological effects on community dynamics and ecosystem functioning. However, the estimation of ITV per se has yet to receive much attention, despite the need for ac...

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Autores principales: Yang, Jing, Lu, Jiahui, Chen, Yue, Yan, Enrong, Hu, Junhua, Wang, Xihua, Shen, Guochun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7031497/
https://www.ncbi.nlm.nih.gov/pubmed/32117390
http://dx.doi.org/10.3389/fpls.2020.00053
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author Yang, Jing
Lu, Jiahui
Chen, Yue
Yan, Enrong
Hu, Junhua
Wang, Xihua
Shen, Guochun
author_facet Yang, Jing
Lu, Jiahui
Chen, Yue
Yan, Enrong
Hu, Junhua
Wang, Xihua
Shen, Guochun
author_sort Yang, Jing
collection PubMed
description Intraspecific trait variation (ITV) is common feature of natural communities and has gained increasing attention due to its significant ecological effects on community dynamics and ecosystem functioning. However, the estimation of ITV per se has yet to receive much attention, despite the need for accurate ITV estimation for trait-based ecological inferences. It remains unclear if, and to what extent, current estimations of ITV are biased. The most common method used to quantify ITV is the coefficient of variation (CV), which is dimensionless and can therefore be compared across traits, species, and studies. Here, we asked which CV estimator and data normalization method are optimal for quantifying ITV, and further identified the minimum sample size required for ±5% accuracy assuming a completely random sample scheme. To these ends, we compared the performance of four existing CV estimators, together with new simple composite estimators, across different data normalizations, and sample sizes using both a simulated and empirical trait datasets from local to regional scales. Our results consistently showed that the most commonly used ITV estimator (CV (1)= σ(sample)/μ(sample)), often underestimated ITV—in some cases by nearly 50%—and that underestimation varies largely among traits and species. The extent of this bias depends on the sample size, skewness and kurtosis of the trait value distribution. The bias in ITV can be substantially reduced by using log-transforming trait data and alternative CV estimators that take into consideration the above dependencies. We find that the CV(4) estimator, also known as Bao's CV estimator, combined with log data normalization, exhibits the lowest bias and can reach ±5% accuracy with sample sizes greater than 20 for almost all examined traits and species. These results demonstrated that many previous ITV measurements may be substantially underestimated and, further, that these underestimations are not equal among species and traits even using the same sample size. These problems can be largely solved by log-transforming trait data first and then using the Bao's CV to quantify ITV. Together, our findings facilitate a more accurate understanding of ITV in community structures and dynamics, and may also benefit studies in other research areas that depend on accurate estimation of CV.
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spelling pubmed-70314972020-02-28 Large Underestimation of Intraspecific Trait Variation and Its Improvements Yang, Jing Lu, Jiahui Chen, Yue Yan, Enrong Hu, Junhua Wang, Xihua Shen, Guochun Front Plant Sci Plant Science Intraspecific trait variation (ITV) is common feature of natural communities and has gained increasing attention due to its significant ecological effects on community dynamics and ecosystem functioning. However, the estimation of ITV per se has yet to receive much attention, despite the need for accurate ITV estimation for trait-based ecological inferences. It remains unclear if, and to what extent, current estimations of ITV are biased. The most common method used to quantify ITV is the coefficient of variation (CV), which is dimensionless and can therefore be compared across traits, species, and studies. Here, we asked which CV estimator and data normalization method are optimal for quantifying ITV, and further identified the minimum sample size required for ±5% accuracy assuming a completely random sample scheme. To these ends, we compared the performance of four existing CV estimators, together with new simple composite estimators, across different data normalizations, and sample sizes using both a simulated and empirical trait datasets from local to regional scales. Our results consistently showed that the most commonly used ITV estimator (CV (1)= σ(sample)/μ(sample)), often underestimated ITV—in some cases by nearly 50%—and that underestimation varies largely among traits and species. The extent of this bias depends on the sample size, skewness and kurtosis of the trait value distribution. The bias in ITV can be substantially reduced by using log-transforming trait data and alternative CV estimators that take into consideration the above dependencies. We find that the CV(4) estimator, also known as Bao's CV estimator, combined with log data normalization, exhibits the lowest bias and can reach ±5% accuracy with sample sizes greater than 20 for almost all examined traits and species. These results demonstrated that many previous ITV measurements may be substantially underestimated and, further, that these underestimations are not equal among species and traits even using the same sample size. These problems can be largely solved by log-transforming trait data first and then using the Bao's CV to quantify ITV. Together, our findings facilitate a more accurate understanding of ITV in community structures and dynamics, and may also benefit studies in other research areas that depend on accurate estimation of CV. Frontiers Media S.A. 2020-02-13 /pmc/articles/PMC7031497/ /pubmed/32117390 http://dx.doi.org/10.3389/fpls.2020.00053 Text en Copyright © 2020 Yang, Lu, Chen, Yan, Hu, Wang and Shen http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Plant Science
Yang, Jing
Lu, Jiahui
Chen, Yue
Yan, Enrong
Hu, Junhua
Wang, Xihua
Shen, Guochun
Large Underestimation of Intraspecific Trait Variation and Its Improvements
title Large Underestimation of Intraspecific Trait Variation and Its Improvements
title_full Large Underestimation of Intraspecific Trait Variation and Its Improvements
title_fullStr Large Underestimation of Intraspecific Trait Variation and Its Improvements
title_full_unstemmed Large Underestimation of Intraspecific Trait Variation and Its Improvements
title_short Large Underestimation of Intraspecific Trait Variation and Its Improvements
title_sort large underestimation of intraspecific trait variation and its improvements
topic Plant Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7031497/
https://www.ncbi.nlm.nih.gov/pubmed/32117390
http://dx.doi.org/10.3389/fpls.2020.00053
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