Spatial autocorrelation of microbial communities atop a debris-covered glacier is evidence of a supraglacial chronosequence

Abstract

Although microbial communities from many glacial environments have been analyzed, microbes living in the debris atop debris-covered glaciers represent an understudied frontier in the cryosphere. The few previous molecular studies of microbes in supraglacial debris have either had limited phylogenetic resolution, limited spatial resolution (e.g. only one sample site on the glacier) or both. Here, we present the microbiome of a debris-covered glacier across all three domains of life, using a spatially-explicit sampling scheme to characterize the Middle Fork Toklat Glacier's microbiome from its terminus to sites high on the glacier. Our results show that microbial communities differ across the supraglacial transect, but surprisingly these communities are strongly spatially autocorrelated, suggesting the presence of a supraglacial chronosequence. This pattern is dominated by phototrophic microbes (both bacteria and eukaryotes) which are less abundant near the terminus and more abundant higher on the glacier. We use these data to refute the hypothesis that the inhabitants of the glacier are randomly deposited atmospheric microbes, and to provide evidence that succession from a predominantly photosynthetic to a more heterotrophic community is occurring on the glacier.

Island Biogeography of Cryoconite Hole Bacteria in Antarctica's Taylor Valley and Around the World

“…We sampled at multiple nested spatial scales within each of the three Taylor Valley glaciers (Figure 1), and we expected the spatial structure of cryoconite hole bacterial communities to be fine-scale, occurring at spatial scales on the order of tens or hundreds of meters. Spatial structuring at small spatial scales (< 1 km) is often found in biogeographic analyses of supraglacial microbial communities, like in debris atop debris-covered glaciers (Franzetti et al, 2013;Darcy et al, 2017). But in the present study, this was not the case, as very little spatial structuring was observed for each of those three glaciers when analyzed separately (all r M values < 0.09).…”

“…But in the present study, this was not the case, as very little spatial structuring was observed for each of those three glaciers when analyzed separately (all r M values < 0.09). This contrasts with other supraglacial microbial biogeography, such as that of debris-covered glaciers, where strong spatial structuring of microbial communities is observed at scales <1 km (Franzetti et al, 2013;Darcy et al, 2017). However, is assumed that spatial structuring on debris-covered glaciers is caused by biogeochemical gradients over space, rather than dispersal limitation.…”

“…Understanding the extent to which these "natural microcosms" are indeed islands within glaciers is important to the study of cryoconite holes and to their use as model systems for studying microbial community assembly. This is because biogeographic structure of microbial comminutes is a result of community assembly processes (Nemergut et al, 2013;Darcy et al, 2017), and theory for community assembly in island systems is well developed (Rominger et al, 2016). Here, we investigate the extent to which bacterial communities living within cryoconite holes match two predictions made by island biogeographic theory (MacArthur and Wilson, 1967;Nekola and White, 1999).…”

“…In the latter case, distant islands may be less likely to have similar environments than islands that are close together, and species distributions are often functions of their environment, even for bacteria (Fierer and Jackson, 2006;Nemergut et al, 2013). Although this type of spatial pattern is not diagnostic for islands since many non-island study systems exhibit strong spatial autocorrelation (Robeson et al, 2011;Darcy et al, 2017), it is expected that islands communities will be strongly spatially autocorrelated.…”

Fungal communities living within leaves of native Hawaiian dicots are structured by landscape‐scale variables as well as by host plants

“…In order to test and visualize the spatial structuring of FEF beta diversity, we constructed a geographic semivariogram (Bachmaier & Backes, 2008) See Robeson et al (2011) for a thorough discussion of the applicability of semivariograms in this context, models that can be fitted to them and what hypotheses they can be used to test. Distance classes in the semivariogram were constructed using a sliding window method, where each class shared two-thirds of its points with the previous class (Darcy, King, Gendron, & Schmidt, 2017). A logistic curve was fit to semivariance values and mean geographic distances within semivariogram distance classes using the Nelder-Mead algorithm, minimizing the sum of squared differences in beta diversity semivariance.…”