Complexity vs linearity: relationships between functional traits in a protist

Background: The mechanisms underlying the relationship between biodiversity and ecosystem functioning are still poorly understood. Although species richness is commonly used as a biodiversity measure, recent studies showed that functional diversity, i.e. the diversity of functional traits, might be a better proxy. Functional traits are defined as phenotypic traits that affect an organism's performance and shape ecosystem-level processes. The main challenge when using those traits to quantify biodiversity is to choose which ones to measure, since effort and money are limited. As one way of dealing with this, Hodgson et al. (1999) introduced the idea of two types of traits, with soft traits that are easy and quick to quantify, and hard traits that are directly linked to ecosystem functioning but difficult to measure. If a link exists between the two types of traits, then one could use soft traits as a proxy for hard traits for a quick but meaningful assessment of biodiversity. However, this framework is based on two assumptions: (1) hard and soft traits must be tightly connected to allow reliable prediction of one using the other; (2) the relationship between traits must be monotone and linear to be detected by the most common statistical techniques (e.g. GLM, PCA). Results Here we addressed those two assumptions by focusing on six functional traits of the protist species Tetrahymena thermophila , which vary both in their measurement difficulty and functional meaningfulness. They were classified as: easy traits (morphological traits), intermediate traits (movement traits) and hard traits (oxygen consumption and population growth rate). We were able to detect a high number (> 60%) of non-linear relationships between the traits, which can explain the low number of significant relationships found using PCA and GLM analysis. In the end, these analyses did not detect any relationship strong enough to predict one trait using another, but that does not imply there are none. Conclusions Our results highlighted the need for more complex statistical analyzes than the ones commonly used by the scientific community, to account for all the factors that might blur the relationships between traits (e.g. plasticity, non-linearity), and state about the soft/hard framework.