Biogeographic survey of soil bacterial communities across Antarctica

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dc.contributor.author Varliero, Gilda
dc.contributor.author Lebre, Pedro H.
dc.contributor.author Adams, Byron
dc.contributor.author Chown, Steven L.
dc.contributor.author Convey, Peter
dc.contributor.author Dennis, Paul G.
dc.contributor.author Fan, Dandan
dc.contributor.author Ferrari, Belinda
dc.contributor.author Frey, Beat
dc.contributor.author Hogg, Ian D.
dc.contributor.author Hopkins, David W.
dc.contributor.author Kong, Weidong
dc.contributor.author Makhalanyane, Thulani Peter
dc.contributor.author Matcher, Gwynneth
dc.contributor.author Newsham, Kevin K.
dc.contributor.author Stevens, Mark I.
dc.contributor.author Weigh, Katherine V.
dc.contributor.author Cowan, Don A.
dc.date.accessioned 2025-02-17T07:30:26Z
dc.date.available 2025-02-17T07:30:26Z
dc.date.issued 2024-01-12
dc.description AVAILABILITY OF DATA MATERIAL : The datasets generated and/or analyzed during the current study are available in the National Center for Biotechnology Information (NCBI) repository under the accession PRJNA699250, PRJNA504513, PRJEB55870, PRJNA630822, PRJEB29853, PRJNA868884, PRJNA781801, PRJNA765625, PRJEB63224, PRJEB55309, PRJNA678471, PRJEB11689, PRJNA684555, PRJNA721735, PRJNA359740, and SRP067111 and also at https://data.bioplatfor ms.com/organization/australian-microbiome. The R scripts used for the analysis of the sequencing data can be found on the GitHub page https://github.com/gvMicroarctic/AntarcticBiogeographyPaper. en_US
dc.description SUPPLEMENTARY FIGURES: FIGURE S1. Analysed islands part of the Antarctic Conservation Biogeographic Regions (ACBRs). Geographic positions of islands included in the ACBR classification proposed by Terauds and Lee (2016) for ACBR1 and ACBR3 (A), ACBR 4 (B), ACBR 12 (C), ACBR 9 (D), ACBR 7 and ACBR16 (E-F), and ACBR 6 (G). FIGURE S2. Antarctic Conservation Biogeographic Regions (ACBR) unclassified islands (AUI). Sample locations are indicated by white dots. FIGURE S3. Bacterial Shannon diversity trends across ACBRs. Shannon diversity calculated using the genus dataset (A). Significant Tukey’s statistical tests (p < 0.01) for Shannon diversity calculated at the genus-level (B). White dots correspond to non-significant Tukey’s statistical tests (p ≥ 0.01). ACBRs from 1 to 16 correspond to those described from Terauds and Lee (2016). ACBR 1: North-east Antarctic Peninsula; ACBR 3: North-west Antarctic Peninsula; ACBR 4: Central South Antarctic Peninsula; ACBR 6: Dronning Maud Land; ACBR 7: East Antarctica; ACBR 8: North Victoria Land; ACBR 9: South Victoria Land; ACBR 10: Transantarctic Mountains; ACBR 12: Marie Byrd Land; ACBR 16: Prince Charles Mountains. AUI: ACBR unclassified islands. FIGURE S4. Correlations between number of genera and bioclimatic variables. Pearson’s correlations between number of genera (i.e., richness) and BIO1 (A), BIO2 (B), BIO4 (C), BIO5 (D), BIO10 (E), BIO12 (F), BIO14 (G), BIO15 (H), BIO17 (I), BIO18 (J), SWE (K), distance to ocean (L) and elevation (M). BIO1: mean annual air temperature, °C; BIO2: mean diurnal air temperature range, °C; BIO4: temperature seasonality,°C/100; BIO5: mean daily maximum air temperature of the warmest month, °C; BIO10: mean daily mean air temperatures of the warmest quarter, °C; BIO12: annual precipitation, kg m- 2; BIO14: precipitation in the driest month, kg m- 2; BIO15: precipitation seasonality, %; BIO17: mean monthly precipitation in the driest quarter, kg m- 2; BIO18: mean monthly precipitation in the warmest quarter, kg m- 2; SWE: snow water equivalent, kg m- 2; Distance to ocean: km; Elevation: m. FIGURE S5. Correlations between Shannon diversity and bioclimatic variables. Pearson’s correlations between Shannon diversity and BIO1 (A), BIO2 (B), BIO4 (C), BIO5 (D), BIO10 (E), BIO12 (F), BIO14 (G), BIO15 (H), BIO17 (I), BIO18 (J), SWE (K), distance to ocean (L) and elevation (M). BIO1: mean annual air temperature, °C; BIO2: mean diurnal air temperature range, °C; BIO4: temperature seasonality, °C/100; BIO5: mean daily maximum air temperature of the warmest month, °C; BIO10: mean daily mean air temperatures of the warmest quarter, °C; BIO12: annual precipitation, kg m- 2; BIO14: precipitation in the driest month, kg m- 2; BIO15: precipitation seasonality, %; BIO17: mean monthly precipitation in the driest quarter, kg m- 2; BIO18: mean monthly precipitation in the warmest quarter, kg m- 2; SWE: snow water equivalent, kg m- 2; Distance to ocean: km; Elevation: m. FIGURE S6. Bioclimatic variables. Selected bioclimatic variables and characteristics for each ACBR and for ACBR unclassified islands (AUI): BIO1 (mean annual air temperature) (A), BIO2 (mean diurnal air temperature range) (B), BIO4 (temperature seasonality) (C), BIO10 (mean daily mean air temperatures of the warmest quarter) (D), BIO12 (annual precipitation amount) (E), BIO15 (precipitation seasonality) (F), BIO18 (mean monthly precipitation amount of the warmest quarter) (G), SWE (snow water equivalent) (H), elevation (I), distance from coast (J) and distance from ocean (K). All bioregions were reported except from H-J where AUI was excluded by data representation. ACBR 1: North-east Antarctic Peninsula; ACBR 3: North-west Antarctic Peninsula; ACBR 4: Central South Antarctic Peninsula; ACBR 6: Dronning Maud Land; ACBR 7: East Antarctica; ACBR 8: North Victoria Land; ACBR 9: South Victoria Land; ACBR 10: Transantarctic Mountains; ACBR 12: Marie Byrd Land; ACBR 16: Prince Charles Mountains. AUI: ACBR unclassified islands. FIGURE S7. Correlations between bacterial community composition and geographic distance, bioclimatic data, elevation, distance to coast and ocean. Relation between Bray-Curtis dissimilarity matrix performed on genus dataset and Euclidean distance matrix calculated for the entire dataset on geographic sample location (A) and on bioclimatic data (B), for the island dataset on geographic sample location (C) and on bioclimatic data (D), for the mainland dataset on geographic sample location (E), on bioclimatic data (F), on elevation (G), on distance to coast (H) and on distance to ocean (I). Bioclimatic data: BIO1, BIO2, BIO4, BIO5, BIO10, BIO12, BIO14, BIO15, BIO17, BIO18 and SWE associated to each sample. FIGURE S8. Variation partitioning performed for entire dataset and single ACBRs. Variation partitioning analyses performed on geography (distance) and bioclimatic variables for the entire dataset (A), AUI (B), ACBR 3 (C), ACBR 6 (D), ACBR 7 (E), ACBR 8 (F), ACBR 9 (G), ACBR 10 (H), ACBR 16 (I), only island samples (J), and only mainland samples (K). In addition to geography (distance) and bioclimatic variable, elevation and distances from coast and ocean were taken in consideration for variation partitioning performed only on mainland samples. ACBR 1: North-east Antarctic Peninsula; ACBR 3: North-west Antarctic Peninsula; ACBR 4: Central South Antarctic Peninsula; ACBR 6: Dronning Maud Land; ACBR 7: East Antarctica; ACBR 8: North Victoria Land; ACBR 9: South Victoria Land; ACBR 10: Transantarctic Mountains; ACBR 12: Marie Byrd Land; ACBR 16: Prince Charles Mountains. AUI: ACBR unclassified islands. FIGURE S9. Sample clustering at bioclimatic, bacterial community and geographic level. Tanglegram performed between dendrograms created using geography and bacterial community datasets (A) and bioclimatic and bacterial community datasets (B). Geography: geographical distances between samples in the form of latitude and longitude information; Bacterial community: Hellinger-transformed community at genus-level; Bioclimatic data: BIO1, BIO2, BIO4, BIO5, BIO10, BIO12, BIO14, BIO15, BIO17, BIO18 and SWE associated to each sample. ACBR 1: North-east Antarctic Peninsula; ACBR 3: North-west Antarctic Peninsula; ACBR 4: Central South Antarctic Peninsula; ACBR 6: Dronning Maud Land; ACBR 7: East Antarctica; ACBR 8: North Victoria Land; ACBR 9: South Victoria Land; ACBR 10: Transantarctic Mountains; ACBR 12: Marie Byrd Land; ACBR 16: Prince Charles Mountains. AUI: ACBR unclassified islands. FIGURE S10. ACBR 7 bacterial composition. PCoA where only samples collected from ACBR 7 were collected and are colored in blue if from Vestfold hill region, and in red if from Windmill island region. FIGURE S11. dbRDA performed on only island samples or mainland samples. Distance-based redundancy analysis (dbRDA) performed on Hellinger transformed genus dataset and standardized bioclimatic variable dataset for only island samples (n = 142) (A) and only mainland samples (n = 846) (B). BIO2: mean diurnal air temperature range; BIO4: temperature seasonality; BIO10: mean daily mean air temperatures of the warmest quarter; BIO15: precipitation seasonality; BIO18: mean monthly precipitation amount of the warmest quarter; SWE: Snow water equivalent. FIGURE S12. Predictors of the dominant community distribution across Antarctica. Mean decrease accuracy associated to each bioclimatic variable (A). Number of taxa associated to the best predictor for each taxon distribution (predictor with highest %lncMSE) related to random forest analysis (B). BIO2: mean diurnal air temperature range; BIO4: temperature seasonality; BIO10: mean daily mean air temperatures of the warmest quarter; BIO15: precipitation seasonality; BIO18: mean monthly precipitation amount of the warmest quarter; SWE: Snow water equivalent. Figure S13. Relative abundance of dominant genera that were not selected by random forest model (variance explained < 30%). Only samples sequenced with V3-V4 and V4 16S rRNA primers were used for this analysis to ensure the best taxonomic consistency between samples (Varliero et al., 2023). Dominant genera were defined as those with a relative abundance of > 1% in at least one sample that were present in at least 10% of samples. Correspondingly, this approach included samples from AUI and ACBRs 1, 3, 4, 8, 9, 10 and 12. BIO2: mean diurnal air temperature range; BIO4: temperature seasonality; BIO10: mean daily mean air temperatures of the warmest quarter; BIO15: precipitation seasonality; BIO18: mean monthly precipitation amount of the warmest quarter; SWE: Snow water equivalent. SUPPLEMENTARY TABLES: TABLE S1. Specifics for all analysed datasets. The total number of samples was 1164, whereas the number of samples passing all the quality steps was 988. *ACBRs from 1 to 16 correspond to those described from Terauds and Lee (2016), "AUI" stands for “ACBR unclassified islands” and represents islands associated with the Antarctic mainland not included in the ACBR classification, and sub- and peri-Antarctic islands. **years correspond to Austral summers except from when specified otherwise. ***number of samples after a cutoff of 5000 reads per sample was applied. TABLE S2. Sample specifics. BIO1: mean annual air temperature, °C; BIO2: mean diurnal air temperature range, °C; BIO4: temperature seasonality, °C/100; BIO5: mean daily maximum air temperature of the warmest month, °C; BIO10: mean daily mean air temperatures of the warmest quarter, °C; BIO12: annual precipitation, kg m- 2; BIO14: precipitation in the driest month, kg m- 2; BIO15: precipitation seasonality, %; BIO17: mean monthly precipitation in the driest quarter, kg m- 2; BIO18: mean monthly precipitation in the warmest quarter, kg m- 2; SWE: snow water equivalent, kg m- 2; Distance to coast: km; Distance to ocean: km; Elevation: m. ACBRs from 1 to 16 correspond to those described from Terauds and Lee (2016). ACBR 1: North-east Antarctic Peninsula; ACBR 3: North-west Antarctic Peninsula; ACBR 4: Central South Antarctic Peninsula; ACBR 6: Dronning Maud Land; ACBR 7: East Antarctica; ACBR 8: North Victoria Land; ACBR 9: South Victoria Land; ACBR 10: Transantarctic Mountains; ACBR 12: Marie Byrd Land; ACBR 16: Prince Charles Mountains. AUI: ACBR unclassified islands. TABLE S3. Paramenters used in the dada2 function filterAndTrim() in each dataset. All the other options were set to default except for truncQ which was set to 0. TABLE S4. Number of reads at each step of the 16S rRNA gene processing pipeline for all datasets. *counts reported as read pairs. TABLE S5. Taxonomic relative abundance at phylum- (A), class- (B), order- (C) and family-level (D). TABLE S6. Relative abundance and taxonomy associated to genus dataset. TABLE S7. Analyses of variance (ANOVA) performed on bioclimatic variables, elevation and sample distance from coast/ocean. BIO1 (mean annual air temperature, °C), BIO2 (mean diurnal air temperature range, °C), BIO4 (temperature seasonality,°C), BIO5 (mean daily maximum air temperature of the warmest month, °C), BIO10 (mean daily mean air temperatures of the warmest quarter, °C), BIO12 (annual precipitation amount, kg m- 2), BIO14 (precipitation amount of the driest month, kg m- 2), BIO15 (precipitation seasonality, kg m- 2), BIO17 (mean monthly precipitation amount of the driest quarter, kg m- 2), BIO18 (mean monthly precipitation amount of the warmest quarter, kg m- 2) and SWE (snow water equivalent, kg m- 2). TABLE S8. Statistics from dbRDA (A-B) and variation partitioning (C-G). A and B only performed on bioclimatc varaibles selected by interactive dbRDA selection. Statistics from function varpart() with X1 as bioclimac dataset and X2 as geography (C) and individual statistical tests using anova.cca(): geography without controlling for environmental variables (D), environmental variables without controlling geography (E, geography alone (F) and environmental variables alone (G). TABLE S9. Indicator taxa across ACBRs and AUI at genus-level (LEfSe analysis based on Kruskal–Wallis p < 0.01). en_US
dc.description.abstract BACKGROUND : Antarctica and its unique biodiversity are increasingly at risk from the effects of global climate change and other human influences. A significant recent element underpinning strategies for Antarctic conservation has been the development of a system of Antarctic Conservation Biogeographic Regions (ACBRs). The datasets supporting this classification are, however, dominated by eukaryotic taxa, with contributions from the bacterial domain restricted to Actinomycetota and Cyanobacteriota. Nevertheless, the ice-free areas of the Antarctic continent and the sub-Antarctic islands are dominated in terms of diversity by bacteria. Our study aims to generate a comprehensive phylogenetic dataset of Antarctic bacteria with wide geographical coverage on the continent and sub-Antarctic islands, to investigate whether bacterial diversity and distribution is reflected in the current ACBRs. RESULTS : Soil bacterial diversity and community composition did not fully conform with the ACBR classification. Although 19% of the variability was explained by this classification, the largest differences in bacterial community composition were between the broader continental and maritime Antarctic regions, where a degree of structural overlapping within continental and maritime bacterial communities was apparent, not fully reflecting the division into separate ACBRs. Strong divergence in soil bacterial community composition was also apparent between the Antarctic/ sub-Antarctic islands and the Antarctic mainland. Bacterial communities were partially shaped by bioclimatic conditions, with 28% of dominant genera showing habitat preferences connected to at least one of the bioclimatic variables included in our analyses. These genera were also reported as indicator taxa for the ACBRs. CONCLUSIONS : Overall, our data indicate that the current ACBR subdivision of the Antarctic continent does not fully reflect bacterial distribution and diversity in Antarctica. We observed considerable overlap in the structure of soil bacterial communities within the maritime Antarctic region and within the continental Antarctic region. Our results also suggest that bacterial communities might be impacted by regional climatic and other environmental changes. The dataset developed in this study provides a comprehensive baseline that will provide a valuable tool for biodiversity conservation efforts on the continent. Further studies are clearly required, and we emphasize the need for more extensive campaigns to systematically sample and characterize Antarctic and sub-Antarctic soil microbial communities. en_US
dc.description.department Biochemistry, Genetics and Microbiology (BGM) en_US
dc.description.librarian am2024 en_US
dc.description.sdg SDG-15:Life on land en_US
dc.description.sponsorship The South African NRF SANAP program; NERC core funding to the British Antarctic Survey’s “Biodiversity, Evolution and Adaptation” Team; École Polytechnique Fédérale de Lausanne; Swiss Polar Institute; Ferring Pharmaceuticals through the Antarctic Circumnavigation Expedition and the Swiss National Science Foundation (SNSF). en_US
dc.description.uri https://microbiomejournal.biomedcentral.com/ en_US
dc.identifier.citation Varliero, G., Lebre, P.H., Adams, B. et al. 2024, 'Biogeographic survey of soil bacterial communities across Antarctica', Microbiome, vol. 12, no. 9, pp. 1-22. https://DOI.org/10.1186/s40168-023-01719-3. en_US
dc.identifier.issn 2049-2618
dc.identifier.other 10.1186/s40168-023-01719-3
dc.identifier.uri http://hdl.handle.net/2263/100968
dc.language.iso en en_US
dc.publisher BioMed Central en_US
dc.rights © The Author(s) 2024. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License. en_US
dc.subject Antarctic conservation en_US
dc.subject Biogeographic regions (ACBRs) en_US
dc.subject Antarctic soil microbiome en_US
dc.subject Biogeography en_US
dc.subject Microbial diversity en_US
dc.subject Regionalization en_US
dc.subject Soils en_US
dc.subject Bioclimatic variables en_US
dc.subject SDG-15: Life on land en_US
dc.subject Antarctic conservation biogeographic regions (ACBRs) en_US
dc.title Biogeographic survey of soil bacterial communities across Antarctica en_US
dc.type Article en_US


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