Abstract
Unveiling the factors and processes that shape the dynamics of host associated microbial communities (microbiota) under natural conditions is an important part of understanding and predicting an organism's response to a changing environment. The microbiota is shaped by host (i.e., genetic) factors as well as by the biotic and abiotic environment. Studying natural variation of microbial community composition in multiple host genetic backgrounds across spatial as well as temporal scales represents a means to untangle this complex interplay. Here, we combined a spatially-stratified with a longitudinal sampling scheme within differentiated host genetic backgrounds by reciprocally transplanting Pacific oysters between two sites in the Wadden Sea (Sylt and Texel). To further differentiate contingent site from host genetic effects, we repeatedly sampled the same individuals over a summer season to examine structure, diversity and dynamics of individual hemolymph microbiota following experimental removal of resident microbiota by antibiotic treatment. While a large proportion of microbiome variation could be attributed to immediate environmental conditions, we observed persistent effects of antibiotic treatment and translocation suggesting that hemolymph microbial community dynamics is subject to within-microbiome interactions and host population specific factors. In addition, the analysis of spatial variation revealed that the within-site microenvironmental heterogeneity resulted in high small-scale variability, as opposed to large-scale (between-site) stability. Similarly, considerable within-individual temporal variability was in contrast with the overall temporal stability at the site level. Overall, our longitudinal, spatially-stratified sampling design revealed that variation in hemolymph microbiota is strongly influenced by site and immediate environmental conditions, whereas internal microbiome dynamics and oyster-related factors add to their long-term stability. The combination of small and large scale resolution of spatial and temporal observations therefore represents a crucial but underused tool to study host-associated microbiome dynamics.
Highlights
Assessing the temporal and spatial stability of microbial communities is vital for understanding and predicting their response to disturbances (Shade et al, 2012) and their functioning in a changing environment
Controlled repeated-measures experiments in the laboratory (e.g., Shade et al, 2011; Berga et al, 2012; Lokmer and Wegner, 2015) are indispensable for a mechanistic understanding of how environmental factors affect microbial community dynamics, such results may not directly translate to natural conditions, which represent a blend of abiotic and biotic disturbances varying in their intensity, predictability, spatial scale and duration (Bender et al, 1984; Sousa, 1984; Paine et al, 1998; Berga et al, 2012)
Studying natural temporal variability of microbial communities represents a valuable complement to controlled experiments, as it provides an opportunity to estimate the effects of known environmental factors and disturbances, as well as to uncover yet unknown and potentially important determinants of community structure and dynamics (Shade et al, 2013; David et al, 2014; Faust et al, 2015)
Summary
Assessing the temporal and spatial stability of microbial communities is vital for understanding and predicting their response to disturbances (Shade et al, 2012) and their functioning in a changing environment This requires knowledge of the underlying disturbance-free community dynamics (Hunt and Ward, 2015). Controlled repeated-measures experiments in the laboratory (e.g., Shade et al, 2011; Berga et al, 2012; Lokmer and Wegner, 2015) are indispensable for a mechanistic understanding of how environmental factors affect microbial community dynamics, such results may not directly translate to natural conditions, which represent a blend of abiotic and biotic disturbances varying in their intensity, predictability, spatial scale and duration (Bender et al, 1984; Sousa, 1984; Paine et al, 1998; Berga et al, 2012). Longitudinal, individual-based, repeated-measure studies remain scarce (but see Pratte et al, 2015; Glasl et al, 2016)
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