Sampling intraspecific variability in leaf functional traits: Practical suggestions to maximize collected information

Abstract

The choice of the best sampling strategy to capture mean values of functional traits for a species/population, while maintaining information about traits’ variability and minimizing the sampling size and effort, is an open issue in functional trait ecology. Intraspecific variability (ITV) of functional traits strongly influences sampling size and effort. However, while adequate information is available about intraspecific variability between individuals (ITVBI) and among populations (ITVPOP), relatively few studies have analyzed intraspecific variability within individuals (ITVWI). Here, we provide an analysis of ITVWI of two foliar traits, namely specific leaf area (SLA) and osmotic potential (π), in a population of Quercus ilex L. We assessed the baseline ITVWI level of variation between the two traits and provided the minimum and optimal sampling size in order to take into account ITVWI, comparing sampling optimization outputs with those previously proposed in the literature. Different factors accounted for different amount of variance of the two traits. SLA variance was mostly spread within individuals (43.4% of the total variance), while π variance was mainly spread between individuals (43.2%). Strategies that did not account for all the canopy strata produced mean values not representative of the sampled population. The minimum size to adequately capture the studied functional traits corresponded to 5 leaves taken randomly from 5 individuals, while the most accurate and feasible sampling size was 4 leaves taken randomly from 10 individuals. We demonstrate that the spatial structure of the canopy could significantly affect traits variability. Moreover, different strategies for different traits could be implemented during sampling surveys. We partially confirm sampling sizes previously proposed in the recent literature and encourage future analysis involving different traits.

A Leaf Selfie: Using a Smartphone to Quantify Leaf Vulnerability to Hydraulic Dysfunction

“…More recently, psychrometer-based estimates of leaf osmotic potential (π), followed by calculations based on known general regression models between π and TLP [28], or on corrections based on leaf dry matter content [29], have allowed for fast and reliable estimates of TLP. These new approaches are supporting the introduction of this parameter in ecological studies involving large numbers of species or samples at different spatial scales [30][31][32].…”

Leaf Canopy Layers Affect Spectral Reflectance in Silver Birch

“…However, for broadleaf species, within-individual variation in SLA can be substantial. Petruzzellis et al [10] found that in Quercus ilex, the change in SLA related to light availability decreasing from the upper to the lower canopy and from outer to inner part of the canopy can be as much as 43% of the total intraspecific variation. SLA responds well to varying microenvironments within a forest and is directly influenced by the light intensity in the medium and large gaps compared to small gaps [21], which may partly explain the lack of directional SLA variation in the lower canopy in silver birch.…”

Plant–environment interactions through a functional traits perspective: a review of Italian studies

“…The increased interest payed to the inclusion of intraspecific variability in ecological studies raised also the question whether different spatial levels could account for different proportion of traits' variability. As an example, Petruzzellis et al (2017) compared variability patterns across multiple spatial scale of one morphological (SLA) and one physiological (leaf osmotic potential, π) functional trait in a population of Q. ilex. They found that the variability of SLA was mainly spread within individuals, while the variability of π was much higher between rather than within individuals of the same species.…”

“…About 500 species whose traits measurements have been published in papers considered in this review are not available in TRY. Moreover, 113 species among these (8% of the Intraspecific Trait Variability, which has a strong effect on the sampling size and effort (Petruzzellis et al 2017), is still scarcely considered at population and community-level, while intraspecific variability is often considered in ecophysiological studies. Moreover, we underline the need of approaches evaluating trait-environment relationships at broad spatial and temporal scales, possibly resulting from the collaboration of several research groups at national level, as well as analysis of traits variations along ecological gradients, in order to make predictions about land use and climate change impacts.…”