Biodiversity is declining at an alarming rate (1–5), requiring more than ever to be carefully measured in different ecosystems. Traditionally the focus when measuring biodiversity was on taxonomical diversity, e.g. species richness or evenness. However, such an approach has been criticized for its inability to bring a mechanistic understanding of the effects that species composing the community have on ecosystem functioning (1, 6, 7). As an alternative, measuring functional diversity was suggested, whereby one measures the functional traits defined as characteristics of an organism's phenotype that affect its performance (8), on the one hand, and that shape ecosystem-level processes, on the other hand (9–12). Although focusing on functional diversity is appealing, quantifying it remains difficult.
The main challenge when measuring functional diversity relates to the choice of the functional traits to measure. There are too many traits to measure them all, and efforts are limited, thus usually only a subset of possible traits are measured (13, 14), often those that are rather easy to get (15, 16). However, the measured traits must properly capture the effects of an organism on ecosystem functioning and its own fitness. As one way of dealing with this, Hodgson et al. (1999) introduced the idea of soft and hard traits, where the former ones are relatively easy and quick to quantify, while the latter ones are meaningful but hard to measure. Ideally, we would measure hard traits (e.g. metabolic or physiological traits) when quantifying functional diversity, but since they are by definition difficult to measure, one could instead measure soft traits (e.g. leaf area) that are assumed to be linked to these hard traits. Such use of the soft traits as proxies for hard ones is promising but it is based on a strong assumption: hard and soft traits must be tightly connected. Another implicit assumption lurking behind the most common correlation measurement methods (e.g. Pearson correlation, PCA) is that the relationship between soft and hard traits is monotone and linear (Fig. 1). However, these assumptions are rarely checked.
Here we address the assumptions underlying the soft/hard framework by focusing on Tetrahymena thermophila, a ciliate unicellular that has widely been studied as a model system in cellular and molecular biology for more than 80 years (17–19) and in ecology and evolutionary biology for over a decade. Over these years, numerous studies provided a lot of information about T. thermophila metabolism (18, 20–23), reproduction (24–26), movement (27, 28) and morphology (19), allowing us to carefully assert the functional traits for this species. Based on the literature and our own existing data, we have chosen the following six functional traits of T. thermophila cells: two morphological traits (cell size and shape), two movement traits (movement speed and trajectory tortuosity), oxygen consumption and population growth rate. In our experimental microcosm system, these traits vary in their measurement difficulty and functional meaningfulness: easy (morphological traits), intermediate (movement traits) and hard (oxygen consumption and population growth rate).
The morphological traits are considered functional because they relate to the resource use of T. thermophilla. Indeed, an increase in cell size is often a consequence of resource accumulation. These resources could then be mobilized if environmental conditions become harsh. For example, when oxygen is present, the cells will produce and accumulate glycogen using a part of this oxygen (22, 23, 29); glycogen is here used as a storage for energy and can be used to produce ATP (i.e. energy needed for the cell survival) through fermentation when oxygen is lacking (20), allowing cells to survive for few hours without breathing. The shape of the cell is an indicator of wellness for T. thermophila (30). When the environment is stressful, T. thermophila cells tend to adopt a rounder shape, possibly because they exhaust all their metabolites (e.g. glycogen) in reserves to survive until the environmental conditions become suitable again. We classify these two morphological traits as “easy” since they remained indirect proxies of glycogen accumulation or wellness, and their quantification in our system only requires a snapshot picture of cells.
The two movement traits are expected to play a major role in resource foraging, hence survival, reproduction, and dispersal strategy (31, 32). Swimming fast gives the advantage of quickly exploring spaces, allowing the cells to potentially find a better environment, at the cost of the energy needed to move and the risk of exhausting themselves to death. The same reasoning applies to the trajectory linearity, since a tortuous trajectory could enhance local foraging by maximizing resources exploited in the neighborhood, while a straight trajectory will allow access to distant patches with possible better resources and escaping harsh local conditions. We considered these two movement traits as having an “intermediate” level both regarding the measurement difficulty, since measuring them requires recording a video with trajectories of moving cells, but also functionally, since they directly impact the foraging abilities of T. thermophila cells.
Regarding the last group of traits, oxygen consumption is a direct proxy of the cell metabolic rate, and one of the major factors driving protist community structure (33). Population growth rate is directly proportional to the individual clonal cell reproduction rate and is the main driver of biomass production, which is often used as a proxy for ecosystem functioning or species wellness (34–39). These two traits are the most difficult to measure in our microcosm system because they cannot be measured from a snapshot data recording (picture or video), which is possible to acquire even in the field, but instead involve a time series of measurements using specialized equipment in the lab. However, they are also more directly connected to the ecological parameters of the population (i.e. metabolism and biomass production), making their estimation very desirable in functional diversity studies. Thus, according to the soft/hard framework (40), if we detect a significant relationship between these hard traits and the intermediate/easy ones, it would allow for indirect estimation of these hard traits based on snapshot picture or video measurements, which are even possible in the field.
Here, we measured the above-described six functional traits on 40 genetically distinct strains (i.e. clonally reproducing genotypes) of the protozoa T. thermophila, which differ in geographic origin and time since extraction from the field (Pennekamp et al., 2014). These strains were previously shown to exhibit clear differences in several life-history characteristics such as growth rate, maximum density, and survival under starvation conditions (Fjerdingstad et al., 2007; Pennekamp, 2014; Pennekamp et al., 2014); which have been demonstrated to be reliable phenotypic traits at the strain level because of the high repeatability of their measures through time (Chaine et al., 2009; Fjerdingstad et al., 2007; Pennekamp, 2014; Schtickzelle et al., 2009). The use of several strains gave us some intraspecific variation, without which it is impossible to establish trend between traits valid over the whole species.
We expect some of our chosen easy or intermediate traits to correlate with the hard traits. For example, cell shape and cell size could correlate with population growth rate as the faster a strain reproduces, the less time its cells have to accumulate resources, to become longer and larger. The oxygen consumption rate is also expected to correlate with both movement traits and population growth rate since these processes require energy, creating a complex relationship between three traits. Further, since bigger cells may have a higher metabolism, we also expect cell size and shape to be related to oxygen consumption. However, these examples are not exhaustive, and to account for any possible pattern, we looked first of all for general trend between all the traits through a PCA. Secondly, we used GAM to look at if the predictions were improved by considering possible non-linear or monotonous relationships between the traits. Specifically, we assessed the shapes (i.e. form and standard deviation, see Fig. 1) of the best fit, for all possible pairwise relationships between the six traits, regardless of the difficulty of taking measurement.