Variability of functional traits and their syndromes in a freshwater fish species (Phoxinus phoxinus): The role of adaptive and nonadaptive processes

Abstract

Functional traits can covary to form “functional syndromes.” Describing and understanding functional syndromes is an important prerequisite for predicting the effects of organisms on ecosystem functioning. At the intraspecific level, functional syndromes have recently been described, but very little is known about their variability among populations and—if they vary—what the ecological and evolutionary drivers of this variation are. Here, we quantified and compared the variability in four functional traits (body mass, metabolic rate, excretion rate, and boldness), their covariations and the subsequent syndromes among thirteen populations of a common freshwater fish (the European minnow, Phoxinus phoxinus ). We then tested whether functional traits and their covariations, as well as the subsequent syndromes, were underpinned by the phylogenetic relatedness among populations (historical effects) or the local environment (i.e., temperature and predation pressure), and whether adaptive (selection or plasticity) or nonadaptive (genetic drift) processes sustained among‐population variability. We found substantial among‐population variability in functional traits and trait covariations, and in the emerging syndromes. We further found that adaptive mechanisms (plasticity and/or selection) related to water temperature and predation pressure modulated the covariation between body mass and metabolic rate. Other trait covariations were more likely driven by genetic drift, suggesting that nonadaptive processes can also lead to substantial differences in trait covariations among populations. Overall, we concluded that functional syndromes are population‐specific, and that both adaptive and nonadaptive processes are shaping functional traits. Given the pivotal role of functional traits, differences in functional syndromes within species provide interesting perspectives regarding the role of intraspecific diversity for ecosystem functioning.

Environmental effects on the covariation among pace‐of‐life traits

“…It is noteworthy, however, that the patterns of covariation may differ when examining the syndromes among and within populations and species (e.g. Debecker et al, 2016;Debecker & Stoks, 2019;Delahaie et al 2018; see also Raffard, Cucherousset, Prunier, Loot, Santoul, & Blanchet, 2019) due to adaptive or random processes (Raffard et al, 2019). The application of the predictions of the POLS framework at different hierarchical level (across species/populations, within populations) have been discussed in detail elsewhere (Araya-Ajoy, Bolstad, Brommer, Careau, Dingemanse, & Wright 2018; see also Mathot & Frankenhuis, 2018).…”

Stable resource polymorphism along the benthic littoral–pelagic axis in an invasive crayfish

“…“Littoral reliance” was square‐root‐transformed to improve the fit of the model. We computed 95% confidence interval (CI 95%) for P ST , using bootstrapping procedure (Raffard et al, ), while CI 95% for F ST was implemented in FSTAT. All statistical analyses were performed using R v.3.4.4 (R Core Team, ).…”

Intraspecific diversity loss in a predator species alters prey community structure and ecosystem functions

“…We selected ten populations from a large river basin in southern France (the Garonne catchment) based on a priori knowledge to maximise both genetic and functional differentiations among populations (Fourtune et al 2018; Raffard et al 2019a) (Fig. S1).…”

“…S1). Specifically, the ten selected populations displayed a high level of genetic differentiation (mean pairwise Fst = 0.133, range = 0.029-0.320) and greatly varied in the number of alleles they harbour (from 5.470 alleles in average for the less diverse population to 10.176 alleles for the most diverse population) according to data measured using seventeen microsatellite markers (from Fourtune et al 2018; Prunier et al 2019; Raffard et al 2019a). Moreover, we selected five populations mainly composed of small-bodied adults (mean body mass ± standard error (SE) = 1.03 g ± 0.02) and five populations mainly composed of large-bodied adults (mean body mass ± SE = 3.06 g ± 0.07) (Fig.…”

“…S2a). These differences in adult body mass are due to selective pressures from the local environment such as mean water temperature; the higher the mean water temperature, the smallest the adult body mass due to increased metabolic rate and an accelerated pace-of-life (Raffard et al 2019a). Finally, populations are geographically distant one from each other, and they will hereafter be each considered as functionally and genetically unique entity, and intraspecific richness will be manipulated by varying the number of populations in independent mesocosms (see details below).…”