Quantitative estimation of phenotypic plasticity: bridging the gap between the evolutionary concept and its ecological applications

Abstract

Summary1 Global change and emerging concepts in ecology and evolution are leading to a growing interest in phenotypic plasticity (PP), the environmentally contingent trait expression observed in a given genotype. The need to quantify PP in a simple manner in comparative ecological studies has resulted in the prevalence of various indices instead of the classic approaches, i.e. a comparison of slopes in the norms of reactions (trait vs. environment plots). 2 The objectives of this study were: (i) to review the most common methods for quantitative estimation of PP; (ii) to apply them to a specific case study of growth and shoot-root allocation responses to irradiance in seedlings of four woody species grown at 1%, 6%, 20% and 100% full sunlight; and (iii) to propose new methods of estimating PP. 3 The 17 different plasticity indices analysed rendered disparate results, with cross-overs in species PP rankings. Statistical comparisons of PP among species were not possible with most of the indices due to the lack of confidence intervals. The non-linear responses of the traits made the use of the slope of the reaction norm to quantify PP unrealistic, and raised awareness on values derived from studies that consider just two environments. 4 We propose an alternative approach to quantify PP based on phenotypic distances among individuals of a given species exposed to different environments, which is summarized in a relative distance plasticity index (RDPI) that allows for statistical comparisons of PP between species (or populations within species). RDPI was significantly correlated with 12 out of the 17 PP indices analysed. An index including the environmental range leading to the different phenotypes (environmentally standardized plasticity index, ESPI), and thus expressing plasticity per unit of environmental change, is also proposed. 5 The new indexes can statistically segregate and unambiguously rank species according to their PP, which can foster a better understanding of plant ecology and evolution, particularly when common protocols are used by different investigators.

Traits correlate with invasive success more than plasticity: A comparison of three Centaurea congeners

“…We used total biomass to calculate simplified relative distance plasticity indexes (RDPI S ), which are specifically designed to allow for statistical testing among different populations and species (Valladares, Sanchez‐Gomez, & Zavala, 2006). RDPI ranges from 0 (absence of plasticity) to 1 (maximum plasticity).…”

Latitudinal and longitudinal clines of phenotypic plasticity in the invasive herb Solidago canadensis in China

“…Although PPI quantifies the magnitude of plasticity very well, it does not consider the direction of the plastic response. Therefore, we also calculated a directional plasticity index as (x0-xi)/x0 (Valladares et al 2006). However, as the results were very similar (compare Tables 1, 2 with ESM Appendices 2 and 3), we only represent the results of PPI in the main text, as the climatic variability hypothesis makes no clear predictions regarding the direction of plasticity.…”

“…The PPI was calculated as: (max(x0, xi)-min(x0, xi))/max(x0, xi), where x0 and xi stand for the mean values of the control and the drought or high-temperature treatments, respectively. Max(x0, xi) is the larger value of x0 and xi, and min(x0, xi) the smaller value of x0 and xi (Cheplick 1995;Valladares et al 2006). PPI per population was calculated as the mean of the PPI values of the genotypes in each population.…”

Functional response of Quercus robur L. to taproot pruning: a 5-year case study

“…The coefficient of variability, defined as the ratio of the standard deviation to the mean (Valladares et al 2006), was estimated for each container and was included in the analyses to enable variation between the seedlings in each experimental treatment to be estimated. All of the statistical analyses were performed using Statistica 8.0 software (Dell, Round Rock, TX, USA).…”