Large Underestimation of Intraspecific Trait Variation and Its Improvements

Abstract

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 = s sample /m 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.

Experimentally induced drought and growing season stage modulate community-level functional traits in a temperate grassland

“…Furthermore, whether differences in community-level functional traits are driven by changes in community composition, intraspecific variation, or both, remains unknown. Failure to account for intraspecific variation is one reason why functional trait ecology has fallen short of fulfilling its predictive potential (Shipley et al, 2016; Yang et al, 2020). Indeed, functional traits vary significantly within species (Violle et al, 2012; Siefert et al, 2015; Moran et al, 2016), potentially shifting mean community trait values even if species composition remains unchanged (Pichon et al, 2022; Bricca et al, 2022).…”

A protocol for reproducible functional diversity analyses

“…If summarising or comparing univariate trait characteristics is the principal goal of the study, then raw trait data can often be used without any data transformation. The most common examples of a univariate metric that uses raw trait data are the coefficient of variation (standard deviation divided by mean relationship; Yang et al 2020) and the community‐weighted mean (Garnier et al 2004, Lavorel et al 2008), which summarises the mean trait value of all individuals or species in the population or assemblage.…”

Sources and consequences of mismatch between leaf disc and whole‐leaf leaf mass per area (LMA)

“…The extent of intraspecific variation (ITV) was calculated using Bao's coefficient of variation (CV) estimator (Yang et al, 2020):…”

“…Trait variance explained by intraspecific trait variance within and among communities for LMA can be similar to or greater than that explained by interspecific trait variation (e.g., Messier et al, 2010;Kichenin et al, 2013;Fajardo and Siefert, 2018). The coefficient of variation (CV) is often used to quantify the extent of ITV for each species (Yang et al, 2020;Westerband et al, 2021). Sources of ITV for whole-leaf samples are variation among individuals within the same species and variation among leaves within the same individuals (Messier et al, 2010(Messier et al, , 2017).…”