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

Authors:
Open Access
Publisher website
Google Scholar
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.
Funding Information
  • University of Trieste (BACARO.FRA2015)

This publication has 44 references indexed in Scilit: