Spatial and environmental variables structure sponge symbiont communities

Abstract

Understanding the maintenance and origin of beta diversity is a central topic in ecology. However, the factors that drive diversity patterns and underlying processes remain unclear, particularly for host‐prokaryotic associations. Here, beta diversity patterns were studied in five prokaryotic biotopes, namely, two high microbial abundance (HMA) sponge taxa (Xestospongia spp. and Hyrtios erectus), one low microbial abundance (LMA) sponge taxon (Stylissa carteri), sediment and seawater sampled across thousands of kilometres. Using multiple regression on distance matrices (MRM), spatial (geographic distance) and environmental (sea surface temperature and chlorophyll α concentrations) variables proved significant predictors of beta diversity in all five biotopes and together explained from 54% to 82% of variation in dissimilarity of both HMA species, 27% to 43% of variation in sediment and seawater, but only 20% of variation of the LMA S. carteri. Variance partitioning was subsequently used to partition the variation into purely spatial, purely environmental and spatially‐structured environmental components. The amount of variation in dissimilarity explained by the purely spatial component was lowest for S. carteri at 11% and highest for H. erectus at 55%. The purely environmental component, in turn, only explained from 0.15% to 2.83% of variation in all biotopes. In addition to spatial and environmental variables, a matrix of genetic differences between pairs of sponge individuals also proved a significant predictor of variation in prokaryotic dissimilarity of the Xestospongia species complex. We discuss the implications of these results for the HMA‐LMA dichotomy and compare the MRM results with results obtained using constrained ordination and zeta diversity.

The impact of space, host dissimilitude and environment on prokaryotic communities of golf ball sponges

“…These microbial communities often develop into stable, host-speci c assemblages, exerting a profound in uence on host tness and ful lling vital ecological functions (Thompson et al 2015). In a previous studies of the Xestospongia species complex (Swierts et al 2018; Cleary et al 2022), we showed that variation in prokaryotic dissimilarity was primarily associated with geography with minor contributions of host dissimilitude and environmental parameters (sea surface temperature and chlorophyl a concentrations). Likewise, other studies have highlighted the importance of space and environment in structuring microbial communities in a range of disparate habitats (Hanson et al 2012;Clark et al 2021…”

“…In barrel sponges (Xestospongia spp. ), this component explained 3% of the variation in prokaryotic composition using log-transformed data (Cleary et al 2022). There did, however, appear to be scale dependence in Xestospongia spp.…”

“…That being said, the results largely align with a previous study of the sponge species complex Xestospongia spp. sampled over a similarly large spatial scale encompassing IndoPaci c and Caribbean sites(Cleary et al 2022). Future studies of other sponge species complexes should help to shed greater light on the importance of space, host dissimilitude and environment in structuring prokaryotic communities.…”

Microbe-assisted seedling crop improvement by a seaweed extract to address fucalean forest restoration

“…The interactions between host and microbiome can also play a crucial role in development and welfare in the early life stage, as has been shown for corals and sponges (Taylor et al, 2007;Cleary et al, 2022;Peixoto et al, 2022;Thatcher et al, 2022). In the marine field, microbe-assisted crop improvement is still in its infancy compared to agriculture, where plants benefit from mixtures of diverse microbes to protect them from pests or pathogens, improve nutrient supply and directly promote growth through microbial antagonistic interactions (Berg et al, 2021).…”

Prokaryotic communities of the French Polynesian sponge Dactylospongia metachromia display a site-specific and stable diversity during an aquaculture trial

“…Following db-RDA, variance partitioning analyses(Borcard et al 1992;Cleary et al 2022) were conducted using the varpart() function from the "vegan" package for both the farming and the restricted biogeographical datasets. These analyses were performed to assess the relative percentage of the variance of the β-diversity explained by the selected environmental parameters, using the formula (~ temperature, ~ salinity, ~ PO 4 3− + NO x + Si[OH] 4 ) for both datasets.…”