Trait dimensions in bacteria and archaea compared to vascular plants

Abstract

Bacteria and archaea have very different ecology compared to plants. One similarity, though, is that much discussion of their ecological strategies has invoked concepts such as oligotrophy or stress tolerance. For plants, so‐called ‘trait ecology’—strategy description reframed along measurable trait dimensions—has made global syntheses possible. Among widely measured trait dimensions for bacteria and archaea three main axes are evident. Maximum growth rate in association with rRNA operon copy number expresses a rate‐yield trade‐off that is analogous to the acquisitive–conservative spectrum in plants, though underpinned by different trade‐offs. Genome size in association with signal transduction expresses versatility. Cell size has influence on diffusive uptake and on relative wall costs. These trait dimensions, and potentially others, offer promise for interpreting ecology. At the same time, there are very substantial differences from plant trait ecology. Traits and their underpinning trade‐offs are different. Also, bacteria and archaea use a variety of different substrates. Bacterial strategies can be viewed both through the facet of substrate‐use pathways, and also through the facet of quantitative traits such as maximum growth rate. Preliminary evidence shows the quantitative traits vary widely within substrate‐use pathways. This indicates they convey information complementary to substrate use.

Trait-trait relationships and tradeoffs vary with genome size in prokaryotes

“…Recent publications claimed that trait dimensions that are apparent among heterotroph prokaryotes are due to different physiological constraints and tradeoffs not directly comparable to those of autotroph plants (Malik et al, 2020;Westoby et al, 2021a). Here we show that physiological constraints and tradeoffs differ even within the microbial cosmos, which prevents a globally consistent assignment of microbial traits in agreement with the CSR or YAS frameworks.…”

“…Even though specific CSR or YAS trait attributions may not generally be applicable for all prokaryotes, they should be applicable with adapted trait associations for communities harboring species within certain ranges of genome sizes, such as aquatic or soil communities. We argue that, beside potential differences between heterotrophs and autotrophs outlined elsewhere (Malik et al, 2020;Westoby et al, 2021a), disturbances and productivity gradients are, analogous to plants, the main drivers for microbial community dynamics. To understand the ecology of microbes and make predictions about their dynamics it is consequently essential to combine different trait dimensions, concerning their response to disturbances and nutrient availability.…”

Embracing mountain microbiome and ecosystem functions under global change

“…We advocate integrating elevational gradients with the emerging trait‐based approaches to advance future mountain microbial ecology by explicitly developing new approaches to infer trait values, applying a quantitative strategy concept to select informative traits and subsequently estimating functional diversity indices to assess community assembly and predict functional reorganisation of microbes under global change. However, simplifying the complexity of microbial traits is required but challenging (Westoby et al ., 2021). The complexity of hyperdimensional traits can be qualitatively simplified by microbial life history strategies such as oligotrophy–copiotrophy (Fierer et al ., 2007), competitor–stress tolerator–ruderal (C‐S‐R) (Krause et al ., 2014) and growth yield–resource acquisition–stress tolerance (Y‐A‐S) strategies (Malik et al ., 2020).…”

Climate-Driven Legacies in Simulated Microbial Communities Alter Litter Decomposition Rates

“…More generally, our study, together with recent vegetation modeling (e.g., Wang et al, 2018;Rüger et al, 2020), suggests model predictions could be improved by considering the multi-dimensional nature of trait tradeoffs in microbiomes and the biosphere in general. Vegetation modeling is already moving in this direction (e.g., Kraft et al, 2015;Bruelheide et al, 2018;Weigelt et al, 2021), and microbiome studies are catching up (Westoby et al, 2021). Still, there remains the challenge of incorporating these multidimensional tradeoffs into ecosystem and Earth system models (e.g., Fiedler et al, 2021;Terrer et al, 2021) while avoiding the computational expense of simulating microbial and vegetation composition locally.…”