Taxonomic and functional diversity covary in rock pool microalgal communities despite their different drivers.

Janne Soininen,Sonja Aarnio

doi:10.1002/ece3.7953

Abstract

Local biodiversity has traditionally been estimated with taxonomic diversity metrics such as species richness. Recently, the concept of biodiversity has been extended beyond species identity by ecological traits determining the functional role of a species in a community. This interspecific functional diversity typically responds more strongly to local environmental variation compared with taxonomic diversity, while taxonomic diversity may mirror more strongly dispersal processes compared with functional metrics. Several trait‐based indices have been developed to measure functional diversity for various organisms and habitat types, but studies of their applicability on aquatic microbial communities have been underrepresented. We examined the drivers and covariance of taxonomic and functional diversity among diatom rock pool communities on the Baltic Sea coast. We quantified three taxonomic (species richness, Shannon's diversity, and Pielou's evenness) and three functional (functional richness, evenness, and divergence) diversity indices and determined abiotic factors best explaining variation in these indices by generalized linear mixed models. The six diversity indices were highly collinear except functional evenness, which merely correlated significantly with taxonomic evenness. All diversity indices were always explained by water conductivity and temperature–sampling month interaction. Taxonomic diversity was further consistently explained by pool distance to the sea, and functional richness and divergence by pool location. The explained variance in regression models did not markedly differ between taxonomic and functional metrics. Our findings do not clearly support the superiority of neither set of diversity indices in explaining coastal microbial diversity, but rather highlight the general overlap among the indices. However, as individual metrics may be driven by different factors, the greatest advantage in assessing biodiversity is nevertheless probably achieved with a simultaneous application of the taxonomic and functional diversity metrics.

Highlights

Biodiversity patterns have been at the center of ecological research for decades
These physiological, morphological, and metabolic traits shared by polyphyletic groups of species typically respond more strongly to local environmental variation than conventional and more complex taxonomic diversity metrics (Mouchet et al, 2010; Reynolds et al, 2002; Rimet & Bouchez, 2012)
The exact definition and tools for quantification of functional diversity have been open to discussion ever since the functional approaches begin to gain foothold in ecology (Mouchet et al, 2010)

Summary

| INTRODUCTION

Biodiversity patterns have been at the center of ecological research for decades. Traditionally, local biodiversity has been quantified with taxonomic diversity metrics such as species richness S (the total number of species in a community), Shannon's diversity H (accounting both species richness and abundance in a community; Shannon, 1948), or Pielou's evenness J (the equality in abundance between species in a community constrained between 0 and 1; Pielou, 1966). Despite the indisputable role of these taxonomic diversity indices and their dominant usage in explaining patterns of local biodiversity (Cadotte et al, 2011), ecosystem functions such as productivity are extensively related to inherited traits determining the performance and functional role of a species in a community (Mason et al, 2005; McGill et al, 2006; Villéger et al, 2008). Low functional diversity reduces productivity and biodiversity functioning either through unutilization of available resources (FRic) or occupied niche space (FEve) in a community, or intense competition for resources through weakly differentiated niches (FDiv) (Mason et al, 2005) Together, these three indices quantify trait-level responses to environmental variation on a continuous scale, unlimited by the high level of detail required for taxonomic species identification (McGill et al, 2006; Violle & Jiang, 2009; Violle et al, 2014). We hypothesize that (H2a) the effects of local environmental variation on diatom diversity are better captured by the functional diversity indices compared with the taxonomic ones due to ecological adaptations, while (H2b) taxonomic diversity indices relate more strongly with spatial gradients than functional diversity indices as taxonomic metrics respond more to dispersal processes (Erős et al, 2009; Heino, 2008; Leibold & Chase, 2017)

| MATERIAL AND METHODS

Findings

| DISCUSSION

| CONCLUSIONS