Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-24T20:11:03.643Z Has data issue: false hasContentIssue false
  • English
  • Français

Fine-scale phylogenetic diversity gradients support the Antarctic geothermal refugia hypothesis

Published online by Cambridge University Press:  07 May 2024

William S. Pearman Open the ORCID record for William S. Pearman [Opens in a new window] ,
Clare I.M. Adams ,
Maria Monteiro ,
Antonio Quesada Open the ORCID record for Antonio Quesada [Opens in a new window]  and
Ceridwen I. Fraser
William S. Pearman*
Affiliation:
Department of Marine Science, University of Otago, Dunedin, New Zealand
Clare I.M. Adams
Affiliation:
Department of Marine Science, University of Otago, Dunedin, New Zealand
Maria Monteiro
Affiliation:
Te Aka Mātuatua - School of Science, University of Waikato, Hamilton, New Zealand
Antonio Quesada
Affiliation:
Departamento de Biología, Universidad Autónoma de Madrid, Madrid, Spain
Ceridwen I. Fraser
Affiliation:
Department of Marine Science, University of Otago, Dunedin, New Zealand
*
[email protected]
Rights & Permissions [Opens in a new window]

Abstract

The possible role of geothermal areas, such as volcanoes, in fostering biodiversity in Antarctica has received considerable recent attention. Under a geothermal refugia hypothesis, diverse life could be supported near or at geothermal sites, and we should see decreasing diversity and/or patterns of nestedness moving away from ‘hotspots’. Although there is evidence that geothermal areas have played a role in the persistence of some terrestrial species through glacial periods in Antarctica, the spatial scales at which such refugia operate is not clear. We sampled sediment from a range of locations across volcanic Deception Island in the Maritime Antarctic and used eDNA metabarcoding approaches (targeting a region of the 28S marker) to assess patterns of diversity in relation to thermal gradients. We found that although colder sites harboured significantly greater taxonomic richness than warmer sites, phylogenetic diversity was lower at colder sites (i.e. taxa at colder sites tend to be more evolutionary close to each other). We infer that increased selective processes in low-temperature environments have reduced phylogenetic diversity, supporting a hypothesis of geothermal locations acting as refugia for diverse taxa, even on fine spatial scales, in cold-climate regions such as Antarctica.

Keywords

biodiversityDeception IslandeDNApolarsoilvolcano
Type
Biological Sciences
Information
Antarctic Science , Volume 36 , Issue 4 , August 2024 , pp. 206 - 213
Check for updates
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press on behalf of Antarctic Science Ltd

Introduction

Despite geological evidence of Antarctic glaciers extending onto the continental shelf at the Last Glacial Maximum (Anderson et al. Reference Anderson, Shipp, Lowe, Wellner and Mosola2002), molecular and biological evidence indicates that diverse terrestrial life in Antarctica has persisted for millions of years (Convey et al. Reference Convey, Gibson, Hillenbrand, Hodgson, Pugh, Smellie and Stevens2008, Green et al. Reference Green, Sancho, Türk, Seppelt and Hogg2011, Fraser et al. Reference Fraser, Nikula, Ruzzante and Waters2012), with relatively little colonization from outside of Antarctica (Convey et al. Reference Convey, Biersma, Casanova-Katny, Maturana, Oliva and Ruiz-Fernández2020).

There are several possible explanations for the persistence of terrestrial life in Antarctica through Pleistocene glaciations (Convey et al. Reference Convey, Stevens, Hodgson, Smellie, Hillenbrand and Barnes2009, Reference Convey, Biersma, Casanova-Katny, Maturana, Oliva and Ruiz-Fernández2020, Fraser et al. Reference Fraser, Nikula, Ruzzante and Waters2012). One is survival on ice-free nunataks - rocky patches poking through glaciers, particularly on mountain ridges and tops (Stevens & D'Haese Reference Stevens and D'Haese2014). Whereas some species can survive on nunataks, including some macrofauna (springtails: Stevens & D'Haese Reference Stevens and D'Haese2014, Collins et al. Reference Collins, Hogg, Convey, Barnes and McDonald2019), the extreme conditions of such refugia can support only a subset of Antarctic taxa (Sohlenius & Boström Reference Sohlenius and Boström2008, Convey et al. Reference Convey, Biersma, Casanova-Katny, Maturana, Oliva and Ruiz-Fernández2020). Another plausible explanation for persistence through glaciations is the presence of volcanic and radiogenic geothermal areas (Fraser et al. Reference Fraser, Terauds, Smellie, Convey and Chown2014) that can create both ice-free areas and subglacial caves (Fraser et al. Reference Fraser, Connell, Lee and Cary2018).

Under the geothermal refugia hypothesis, we would expect species richness to be higher in or near warmer areas. The geothermal refugia hypothesis is well supported by modelling and species richness data (Fraser et al. Reference Fraser, Terauds, Smellie, Convey and Chown2014) and eDNA evidence of plants and animals in or around subglacial geothermal caves (Fraser et al. Reference Fraser, Connell, Lee and Cary2018). Furthermore, higher biodiversity associated with geothermally warmed areas has been reported in the South Sandwich Islands, particularly for floral diversity (Convey & Lewis Smith Reference Convey and Lewis Smith2006). Until recently, however, the evolutionary relationships between species in purported refugia has been largely neglected; new work suggests than when ancient refugia are small, modern phylogenetic diversity is lower, indicating diversification out from the refugia (Barthelemy et al. Reference Barthelemy, Fortunel, Jaunatre and Munoz2021). Investigating these relationships can provide insights into the processes that structure Antarctic biodiversity.

Deception Island is an active volcano in Antarctica and is an ideal place to test the geothermal refugia hypothesis on fine spatial scales, with numerous thermal gradients found on the island. Deception Island also experiences high levels of tourism (Dibbern Reference Dibbern2010) and is the subject of considerable anthropogenic pressure (Carvalho-Silva et al. Reference Carvalho-Silva, Rosa, Pinto, Silva, Henriques, Convey and Câmara2021). Studies have been examining biodiversity across the island using metabarcoding approaches (Rosa et al. Reference Rosa, da Silva, Ogaki, Pinto, Stech and Convey2020, Carvalho-Silva et al. Reference Carvalho-Silva, Rosa, Pinto, Silva, Henriques, Convey and Câmara2021, de Souza et al. Reference de Souza, Lirio, Coria, Lopes, Convey and Carvalho-Silva2022) but have not tested the influence of thermal gradients around fumaroles. We explore the role of soil temperature in structuring eukaryotic communities on Deception Island to test the geothermal refugia hypothesis using DNA metabarcoding of eukaryotes on small spatial scales (metres).

We examine both taxonomic (the number of amplicon sequence variants (ASVs)/taxa present within a community) and phylogenetic diversity (the average evolutionary distance between taxa within a community). These diversity metrics can inform our understanding of the role of geothermal refugia in evolution, as taxonomic diversity helps to show the ecological structure of a community, whereas phylogenetic diversity can enable understanding of the evolutionary processes (e.g. diversification). For example, following glacial thawing, organisms may diversify out of geothermal refugia, and lineages that successfully diversify from initial founders of new areas would thus dominate the taxonomic structure of the community. Therefore, although a geothermal ‘hotspot’ may have similar levels of species richness to nearby ‘coldspots’, the phylogenetic diversities may be drastically different. In this contribution, we analyse this important issue in geothermal areas.

Methods

Sampling took place in February 2016 on Deception Island (62.95°S, 60.65°W), Maritime Antarctica. Approximately 30 ml of surface soil, at 0–4 cm depth, was collected for each sample using autoclave-sterilised steel spatulas and sterile Falcon tubes. Surface soil temperature was measured for each sample using a temperature probe (ATP Folding 115 mm Probe thermometer) to measure temperature in the soil surface layer (upper centimetres). Sampling locations were selected to be centred on geothermal hotspots such as fumaroles (where present), or - at sites where such hotspots were not clearly discernible - samples were haphazardly taken from areas of soils of different surface temperatures. Multiple samples were taken from each site, with 63 samples taken in total from nine sites: Crater Lake, Telefon Bay, Sea Lion Beach, Southeast Point, Obsidian Cove, Whalers Bay, Fumarole Bay, Stonethrow Ridge and Pendulum Cove (at the latter four sites, samples were taken of both beach and coastal vegetated sediments; Fig. 1). Samples were stored at -20°C (in transit) and then at -80°C.

Figure 1. Sampling sites on Deception Island, Maritime Antarctica. Site names are abbreviated to exclude Bay, Cove, Ridge, etc. The numbers in parentheses indicate the number of samples for which data were obtained and analysed in this study.

DNA was extracted using MoBio PowerSoil kit (Qiagen), and DNA was amplified using universal 28S primer C1' (forward; D'Haese Reference D'Haese2002) and C2 (reverse; Le et al. Reference Le, Lecointre and Perasso1993). Primers were modified to include Ion Torrent sequencing barcodes. Sequencing was carried out on an Ion 318v2 chip using the Ion PGM Sequencing Kit on an Ion Torrent PGM DNA sequencer at the Waikato DNA Sequencing Facility; no DNA was detected via qubit fluorometry (high-sensitivity assay) in DNA extraction controls, nor was amplification detected via gel electrophoresis in either DNA extraction or polymerase chain reaction (PCR) controls after library preparation; therefore, following practices at the time (Fraser et al. Reference Fraser, Connell, Lee and Cary2018, Monteiro et al. Reference Monteiro, Marshall, Hawes, Lee, McDonald and Cary2022), they were not sequenced.

Amplicon sequence analysis

Following DNA sequencing, libraries were demultiplexed using cutadapt (v. 4.4; Martin Reference Martin2011) with a minimum length of 300 base pairs and with reads being flanked by the forward and reverse primers to ensure that the resultant libraries only represented full-length amplicons. Primer sequences were then trimmed, and the libraries were processed into ASVs using DADA2 (Callahan et al. Reference Callahan, McMurdie, Rosen, Han, Johnson and Holmes2016). In DADA2, following developer guidance, we truncated reads at the first point of an expected error score < 2 and trimmed the first 15 bases off the reads followed by a truncation length of 285 bases. Error profiling and dereplication followed DADA2 default settings; however, for ASV inference we explicitly modified the HOMOPOLYMER_GAP_PENALTY to -1 and BAND_SIZE to 32, following developer guidance for Ion Torrent sequencing data. Following ASV inference, we rarefied data at a value of 1700, which resulted in 4 (out of 43) samples being removed from further analyses.

Soil surface temperatures ranged from -1°C to 57.6°C (median: 4.5°C, standard deviation: ±14.6°C). Phylogenetic relationships between ASVs were inferred using MAFFT (v. 7.505; Katoh & Standley Reference Katoh and Standley2013) to align sequences, with a maximum of 10 iterations, and then FastTree (v. 2.1.11; Price et al. Reference Price, Dehal and Arkin2010) to generate a phylogeny using a generalized time-reversible (GTR) model. ASVs were classified using the Ribosomal Database Project (RDP) classifier in DADA2 using the SILVA 138.1 database, with a minimum of 75 bootstraps required for taxonomic classification.

Analysis of data was conducted using R (v. 4.2.1; R Core Team 2020), and estimates of taxonomic richness were produced using the estimate_richness function in the phyloseq R package (McMurdie & Holmes Reference McMurdie and Holmes2013). Modified permutational multivariate analyses of variance (PERMANOVAs) were conducted using the vegan (Oksanen et al. Reference Oksanen, Blanchet, Kindt, Legendre, O'Hara and Simpson2010) R package using the adonis2 function, with temperature treated as a continuous variable. Non-metric multidimensional scaling was performed using Bray-Curtis distance in the phyloseq R package. Selective processes were inferred using the iCAMP.cm R package, with the site as a meta-community group for randomizations (Ning et al. Reference Ning, Yuan, Wu, Zhang, Guo and Zhou2020). Null-model inference was performed with 1000 randomizations using the confidence significance index and beta mean nearest taxon distance (MNTD) as the form of phylogenetic distance. Temperatures were binned into high (> 10°C), medium (4–10°C) and low (< 4°C) categories for iCAMP.cm analyses. Taxonomic classifications of ASVs were made using the assignTaxonomy function in the phyloseq R package, which utilizes the RDP naïve Bayes classifier (Wang et al. Reference Wang, Garrity, Tiedje and Cole2007) using the SILVA 138.1 LSURef_NR99 taxonomic database (Yilmaz et al. Reference Yilmaz, Parfrey, Yarza, Gerken, Pruesse and Quast2014).

Phylogenetic diversity within temperature classes was calculated as MNTD within communities using the mntd function in the picante R package (Kembel et al. Reference Kembel, Cowan, Helmus, Cornwell, Morlon and Ackerly2010). This metric was chosen because it does not scale with species richness and thus is useful for comparing phylogenetic diversity across temperature classes.

Linear regressions were conducted using the lm function in base R, with the protected status of different sampling sites determined based on the Antarctic Specially Protected Areas (ASPA) 140 classifications from the Antarctic Protected Areas (APA) database.

Results

Sequencing summary

In total, 43 out of 62 samples were retained following sequencing. These samples had an average of 9745 reads (minimum: 3 reads, maximum: 22 785 reads). Following rarefaction, 39 samples were retained, with a total of 1902 ASVs (Fig. 1 indicates numbers of samples per site retained after quality control). Nineteen samples failed library preparation, either as a result of failed DNA extractions or PCR; therefore, these samples were not sequenced.

Taxonomic structure of communities

Although many ASVs could not be confidently assigned to taxonomic groups (1055 out 2484 ASVs at the class level, 575 at the order level, 341 at the family level and 354 at the genus level; due to taxonomic ambiguities, some taxa classified at lower levels remained unclassified at higher levels), we found that the dominant class across all temperature groups was Glissomonadida, a class of flagellated protists. High- and medium-temperature communities were more diverse at the class level (Fig. 2) and had higher abundances of plant and algal communities. Conversely, low-temperature communities had higher abundances of ciliates (subphylum: Intramacronucleata) and mobile animals (arachnids and tardigrades). Springtails (subclass: Collembola) and mosses (order: Bryales) were also found in low-temperature communities (13 and 19 samples, respectively) but were rarer in medium- (3 and 5 samples, respectively) and high-temperature (2 and 1 samples, respectively) communities.

Figure 2. Stacked abundance bar plots of the taxonomic composition of samples (ordered in terms of temperature from high to low, with broad temperature classes specified).

Community diversity is associated with temperature and location

We observe that as temperature increases, the taxonomic richness of the community declines (Fig. 3a & Table I), whereas the MNTD increased with temperature (Fig. 3b & Table II). The protected status of the area was a significant variable for predicting species richness, with higher richness observed in protected areas (147 vs 95 ASVs), but this was non-significant with regards to MNTD.

Figure 3. a. Taxonomic richness declines with temperature. b. Mean nearest taxon distance increases with temperature. Lines represent linear models, shading represents the 95% confidence intervals.

Table I. Summary output of the linear regression of taxonomic richness, temperature and protected status.

Residual standard error: 33.59 on 36 degrees of freedom.

Multiple R 2: 0.52, adjusted R 2: 0.5.

F-statistic: 19.83 on 36 and 1 degrees of freedom, P < 0.0001.

Table II. Summary output of the linear regression of mean nearest taxon distance to temperature and protected area status.

Residual standard error: 0.06 on 36 degrees of freedom.

Multiple R2: 0.58, adjusted R2: 0.56.

F-statistic: 24.85 on 36 and 1 degrees of freedom, P < 0.0001.

Low-temperature samples typically clustered closely, regardless of the site of origin (Fig. 4), while medium- and high-temperature samples were much more dispersed and exhibited greater clustering based on sites. These results were supported by PERMANOVAs (Table III), which revealed that although both site of collection and zone (i.e. habitat type within zone) were significantly associated with community structure, temperature was also a significant factor.

Figure 4. Non-metric multidimensional scaling (nMDS) of community types on Deception Island, Antarctica. The dashed line indicates the ellipse for low-temperature communities, the dotted line indicates the ellipse for medium-temperature communities and the solid line indicates the ellipse for high-temperature communities. Ellipses were based on 95% confidence using the stat_ellipse function in ggplot. The stress value for this nMDS was 0.13.

Table III. Permutational multivariate analysis of variance (PERMANOVA) results for amplicon sequence variants based on site, zone (subsite) and temperature at site of collection. Results are based on 999 permutations.

Selective processes change with temperature

Null-model analyses to disentangle the relative contributions of deterministic and stochastic processes in shaping community assembly revealed that the influence of drift was greatest in high-temperature communities (Fig. 5). However, low temperatures made the greatest contribution to selection (specifically selection imposed by homogeneous conditions).

Figure 5. Relative contributions of different ecological factors shaping community assembly across different temperature groups. Gradient colour lines are approximately indicative of changes in temperature.

Discussion

Our findings indicate that although cold zones on Deception Island, Antarctica, harbour significantly more taxa than geothermal hotspots on fine spatial scales, these cold zones are of comparatively low phylogenetic diversity (Fig. 3). Our data provide support for the geothermal refugia hypothesis through null-model analyses, revealing the increased selective processes in low-temperature environments and reduced phylogenetic diversity (Figs 3 & 5).

Under a geothermal refugia hypothesis, we would expect to see lower MNTD at lower temperatures as a result of recent diversification, whereas warmer sites would be expected to exhibit higher MNTD values. Following declines in ice and snow coverage, areas surrounding geothermal hotspots would become open to colonization, and some taxa from hotspots could diversify in these regions with reduced competition; by contrast, within-hotspot diversification would remain limited by competition. As a result, the hotspots would be highly phylogenetically diverse, as observed in other types of refugia (Mastrogianni et al. Reference Mastrogianni, Kallimanis, Chytrý and Tsiripidis2019), whereas ‘coldspots’ would exhibit low phylogenetic diversity. Our analyses supported this hypothesis, suggesting that low-temperature communities are typically more phylogenetically clustered than warmer communities. Such results are further supported by the high taxonomic richness of low-temperature communities, suggesting that diversification for cold-adapted taxa might be a reasonable explanation.

Joint consideration of both the phylogenetic structure and taxonomic richness of communities on Deception Island suggests that hotspots might be a source of diversity in relatively inhospitable environments. In Antarctica, where one of the dominant selective pressures might be temperature (Bendia et al. Reference Bendia, Lemos, Mendes, Signori, Bohannan and Pellizari2021), warmer spots may act as reprieves from such harsh selective pressures (McGaughran et al. Reference McGaughran, Laver and Fraser2021). As a result, neutral processes dominate warmer environments, enabling greater diversity by virtue of reduced selection. Such warmer environments are also likely to persist through glacial cycles, and thus, even as the surrounding environments become increasingly inhospitable to life as ice cover increases, these hotspots remain isolated islands of diversity ready to be colonized when ice coverage decreases (Convey & Lewis Smith Reference Convey and Lewis Smith2006, Fraser et al. Reference Fraser, Terauds, Smellie, Convey and Chown2014). Our results highlight the importance of considering the phylogenetic structure of communities when testing for refugia, as traditional approaches that focus on gradients of intraspecific diversity or species richness (Hewitt Reference Hewitt1999, von Cräutlein et al. Reference von Cräutlein, Leinonen, Korpelainen, Helander, Väre and Saikkonen2019) might not be adequate on small scales or in extreme environments. Similar results have also been observed when examining the phylogenetic structure of cold and dry communities in Norway, where post-colonization diversification results in higher levels of relatedness or neoendemism (Mienna et al. Reference Mienna, Speed, Bendiksby, Thornhill, Mishler and Martin2020).

Nevertheless, alternative explanations to the geothermal refugia hypothesis exist, and as a result further hypothesis testing is necessary. One possible alternative explanation is that the high levels of tourism to Deception Island relative to the rest of Antarctica might result in high levels of species invasions from warmer, inhabited regions (McCarthy et al. Reference McCarthy, Peck and Aldridge2022). Such invaders may preferentially inhabit warmer zones (Hughes et al. Reference Hughes, Pertierra, Molina-Montenegro and Convey2015) and/or may be more likely to be deposited there by tourists, increasing the phylogenetic diversity of warm sites relative to colder zones. Future research should aim to better quantify the biodiversity in such locations and to explore the use of phylogenetic metrics alongside species richness to better understand the geothermal refugia hypothesis.

Supplementary material

To view supplementary material for this article, please visit https://doi.org/10.1017/S0954102024000099.

Data availability

DNA sequencing data, ASV tables and the metadata associated with this paper are available at doi.org/10.6084/m9.figshare.25293172.

Acknowledgements

We are grateful to the Spanish Polar Programme for logistical support with fieldwork and to the 2015–2016 Spanish polar expeditioners for creating a valuable and memorable field experience. Geoff Kay assisted with sample collection in the field, and Luis Pertierra provided advice on sampling and analysis. We thank Charles Lee and Craig Cary for advice and support with laboratory work and data generation. Sampling permission was provided by the Department of the Environment (Australian Antarctic Division) of the Australian Government (ATEP 14-15-FRASER-DECEPTION IS-Var1). The authors wish to acknowledge the use of New Zealand eScience Infrastructure (NeSI) high-performance computing facilities, consulting support and/or training services as part of this research. We also wish to thank the two anonymous reviewers, whose comments helped to improve this paper.

Financial support

Fieldwork was supported by an Antarctic Science Bursary that enabled travel for CIF to join the expedition to Deception Island, and by the provision of Antarctic kit to CIF by the Australian Antarctic Division via AAS Project 4370. Genetic work was funded by an ARC Discovery Early Career Research Award to CIF (DE140101715), and the analysis was supported by a RSNZ Rutherford Discovery Fellowship (RDF-UOO1803). CIF is also supported by the MBIE New Zealand Antarctic Science Platform (ANTA1801). New Zealand's national facilities are provided by NeSI and funded jointly by NeSI's collaborator institutions and through the Ministry of Business, Innovation & Employment's Research Infrastructure programme (https://www.nesi.org.nz).

Competing interests

The authors declare none.

Author contributions

AQ and CIF conceived the study and collected samples. MM performed the laboratory work. WSP and CIMA analysed the data. WSP wrote the manuscript with input from all authors.

References

Anderson, J.B., Shipp, S.S., Lowe, A.L., Wellner, J.S. & Mosola, A.B. 2002. The Antarctic Ice Sheet during the Last Glacial Maximum and its subsequent retreat history: a review. Quaternary Science Reviews, 21, 10.1016/S0277-3791(01)00083-X.CrossRefGoogle Scholar
Barthelemy, E., Fortunel, C., Jaunatre, M. & Munoz, F. 2021. Imprints of past habitat area reduction on extant taxonomic, functional, and phylogenetic composition. Frontiers in Ecology and Evolution, 9, 10.3389/fevo.2021.634413.CrossRefGoogle Scholar
Bendia, A.G., Lemos, L.N., Mendes, L.W., Signori, C.N., Bohannan, B.J.M. & Pellizari, V.H. 2021. Metabolic potential and survival strategies of microbial communities across extreme temperature gradients on Deception Island volcano, Antarctica. Environmental Microbiology, 23, 10.1111/1462-2920.15649.CrossRefGoogle ScholarPubMed
Callahan, B.J., McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson, A.J.A. & Holmes, S.P. 2016. DADA2: high-resolution sample inference from Illumina amplicon data. Nature Methods, 13, 10.1038/nmeth.3869.CrossRefGoogle ScholarPubMed
Carvalho-Silva, M., Rosa, L.H., Pinto, O.H.B., Silva, T.H.D., Henriques, D.K., Convey, P. & Câmara, P.E.A.S. 2021. Exploring the plant environmental DNA diversity in soil from two sites on Deception Island (Antarctica, South Shetland Islands) using metabarcoding. Antarctic Science, 33, 10.1017/S0954102021000274.CrossRefGoogle Scholar
Collins, G.E., Hogg, I.D., Convey, P., Barnes, A.D. & McDonald, I.R. 2019. Spatial and temporal scales matter when assessing the species and genetic diversity of springtails (Collembola) in Antarctica. Frontiers in Ecology and Evolution, 7, 10.3389/fevo.2019.00076.CrossRefGoogle Scholar
Convey, P. & Lewis Smith, R.I. 2006. Geothermal bryophyte habitats in the South Sandwich Islands, Maritime Antarctic. Journal of Vegetation Science, 17, 10.1111/j.1654-1103.2006.tb02474.x.CrossRefGoogle Scholar
Convey, P., Biersma, E.M., Casanova-Katny, A. & Maturana, C.S. 2020. Refuges of Antarctic diversity. In Oliva, M. & Ruiz-Fernández, J., eds, Past Antarctica. Cambridge, MA: Academic Press, 10.1016/B978-0-12-817925-3.00010-0.Google Scholar
Convey, P., Gibson, J.A.E., Hillenbrand, C.-D., Hodgson, D.A., Pugh, P.J.A., Smellie, J.L. & Stevens, M.I. 2008. Antarctic terrestrial life - challenging the history of the frozen continent? Biological Reviews, 83, 10.1111/j.1469-185X.2008.00034.x.CrossRefGoogle ScholarPubMed
Convey, P., Stevens, M.I., Hodgson, D.A., Smellie, J.L., Hillenbrand, C.-D., Barnes, D.K.A., et al. 2009. Exploring biological constraints on the glacial history of Antarctica. Quaternary Science Reviews, 28, 10.1016/j.quascirev.2009.08.015.CrossRefGoogle Scholar
D'Haese, C.A. 2002. Were the first springtails semi-aquatic? A phylogenetic approach by means of 28S rDNA and optimization alignment. Proceedings of the Royal Society of London. Series B: Biological Sciences, 269, 10.1098/rspb.2002.1981.CrossRefGoogle ScholarPubMed
de Souza, L.M.D., Lirio, J.M., Coria, S.H., Lopes, F.A.C., Convey, P., Carvalho-Silva, M., et al. 2022. Diversity, distribution and ecology of fungal communities present in Antarctic lake sediments uncovered by DNA metabarcoding. Scientific Reports, 12, 10.1038/s41598-022-12290-6.CrossRefGoogle ScholarPubMed
Dibbern, J.S. 2010. Fur seals, whales and tourists: a commercial history of Deception Island, Antarctica. Polar Record, 46, 10.1017/S0032247409008651.CrossRefGoogle Scholar
Fraser, C.I., Connell, L., Lee, C.K. & Cary, S.C. 2018. Evidence of plant and animal communities at exposed and subglacial (cave) geothermal sites in Antarctica. Polar Biology, 41, 10.1007/s00300-017-2198-9.CrossRefGoogle Scholar
Fraser, C.I., Nikula, R., Ruzzante, D.E. & Waters, J.M. 2012. Poleward bound: biological impacts of Southern Hemisphere glaciation. Trends in Ecology & Evolution, 27, 10.1016/j.tree.2012.04.011.CrossRefGoogle ScholarPubMed
Fraser, C.I., Terauds, A., Smellie, J., Convey, P. & Chown, S.L. 2014. Geothermal activity helps life survive glacial cycles. Proceedings of the National Academy of Sciences of the United States of America, 111, 10.1073/pnas.1321437111.Google ScholarPubMed
Green, T.G.A., Sancho, L.G., Türk, R., Seppelt, R.D. & Hogg, I.D. 2011. High diversity of lichens at 84°S, Queen Maud Mountains, suggests preglacial survival of species in the Ross Sea region, Antarctica. Polar Biology, 34, 10.1007/s00300-011-0982-5.CrossRefGoogle Scholar
Hewitt, G.M. 1999. Post-glacial re-colonization of European biota. Biological Journal of the Linnean Society, 68, 10.1111/j.1095-8312.1999.tb01160.x.CrossRefGoogle Scholar
Hughes, K.A., Pertierra, L.R., Molina-Montenegro, M.A. & Convey, P. 2015. Biological invasions in terrestrial Antarctica: what is the current status and can we respond? Biodiversity and Conservation, 24, 10.1007/s10531-015-0896-6.CrossRefGoogle Scholar
Katoh, K. & Standley, D.M. 201). MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Molecular Biology and Evolution, 30, 10.1093/molbev/mst010.Google Scholar
Kembel, S.W., Cowan, P.D., Helmus, M.R., Cornwell, W.K., Morlon, H., Ackerly, D.D., et al. 2010. Picante: R tools for integrating phylogenies and ecology. Bioinformatics, 26, 10.1093/bioinformatics/btq166.CrossRefGoogle ScholarPubMed
Le, H.L.V., Lecointre, G. & Perasso, R. 1993. A 28S rRNA-based phylogeny of the gnathostomes: first steps in the analysis of conflict and congruence with morphologically based cladograms. Molecular Phylogenetics and Evolution, 2, 10.1006/mpev.1993.1005.CrossRefGoogle ScholarPubMed
Martin, M. 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.Journal, 17, 10.14806/ej.17.1.200.CrossRefGoogle Scholar
Mastrogianni, A., Kallimanis, A.S., Chytrý, M. & Tsiripidis, I. 2019. Phylogenetic diversity patterns in forests of a putative refugial area in Greece: a community level analysis. Forest Ecology and Management, 446, 10.1016/j.foreco.2019.05.044.CrossRefGoogle Scholar
McCarthy, A.H., Peck, L.S. & Aldridge, D.C. 2022. Ship traffic connects Antarctica's fragile coasts to worldwide ecosystems. Proceedings of the National Academy of Sciences of the United States of America, 119, 10.1073/pnas.2110303118.Google ScholarPubMed
McGaughran, A., Laver, R. & Fraser, C. 2021. Evolutionary responses to warming. Trends in Ecology & Evolution, 36, 10.1016/j.tree.2021.02.014.CrossRefGoogle ScholarPubMed
McMurdie, P.J. & Holmes, S. 2013. phyloseq: an R Package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE, 8, 10.1371/journal.pone.0061217.CrossRefGoogle Scholar
Mienna, I.M., Speed, J.D.M., Bendiksby, M., Thornhill, A.H., Mishler, B.D. & Martin, M.D. 2020. Differential patterns of floristic phylogenetic diversity across a post-glacial landscape. Journal of Biogeography, 47, 10.1111/jbi.13789.CrossRefGoogle Scholar
Monteiro, M.R., Marshall, A.J., Hawes, I., Lee, C.K., McDonald, I.R. & Cary, S.C. 2022. Geochemically defined space-for-time transects successfully capture microbial dynamics along lacustrine chronosequences in a polar desert. Frontiers in Microbiology, 12, 10.3389/fmicb.2021.783767.CrossRefGoogle Scholar
Ning, D., Yuan, M., Wu, L., Zhang, Y., Guo, X., Zhou, X., et al. 2020. A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming. Nature Communications, 11, 10.1038/s41467-020-18560-z.CrossRefGoogle ScholarPubMed
Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., O'Hara, R.B., Simpson, G.L., et al. 2010. Vegan: community ecology package. R package version 1.17-4. Retrieved from https://cran.r-project.org/web/packages/vegan/index.htmlGoogle Scholar
Price, M.N., Dehal, P.S. & Arkin, A.P. 2010. FastTree 2 - approximately maximum-likelihood trees for large alignments. PLoS ONE, 5, 10.1371/journal.pone.0009490.CrossRefGoogle ScholarPubMed
R Core Team. 2020. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/Google Scholar
Rosa, L.H., da Silva, T.H., Ogaki, M.B., Pinto, O.H.B., Stech, M., Convey, P., et al. 2020. DNA metabarcoding uncovers fungal diversity in soils of protected and non-protected areas on Deception Island, Antarctica. Scientific Reports, 10, 10.1038/s41598-020-78934-7.CrossRefGoogle ScholarPubMed
Sohlenius, B. & Boström, S. 2008. Species diversity and random distribution of microfauna in extremely isolated habitable patches on Antarctic nunataks. Polar Biology, 31, 10.1007/s00300-008-0420-5.CrossRefGoogle Scholar
Stevens, M.I. & D'Haese, C.A. 2014. Islands in ice: isolated populations of Cryptopygus sverdrupi (Collembola) among nunataks in the Sør Rondane Mountains, Dronning Maud Land, Antarctica. Biodiversity, 15, 10.1080/14888386.2014.928791.CrossRefGoogle Scholar
von Cräutlein, M., Leinonen, P.H., Korpelainen, H., Helander, M., Väre, H. & Saikkonen, K. 2019. Postglacial colonization history reflects in the genetic structure of natural populations of Festuca rubra in Europe. Ecology and Evolution, 9, 10.1002/ece3.4997.CrossRefGoogle ScholarPubMed
Wang, Q., Garrity, G.M., Tiedje, J.M. & Cole, J.R. 2007. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73, 10.1128/AEM.00062-07.CrossRefGoogle ScholarPubMed
Yilmaz, P., Parfrey, L.W., Yarza, P., Gerken, J., Pruesse, E., Quast, C., et al. 2014. The SILVA and ‘All-species Living Tree Project (LTP)’ taxonomic frameworks. Nucleic Acids Research, 42, 10.1093/nar/gkt1209.CrossRefGoogle Scholar
Figure 0

Figure 1. Sampling sites on Deception Island, Maritime Antarctica. Site names are abbreviated to exclude Bay, Cove, Ridge, etc. The numbers in parentheses indicate the number of samples for which data were obtained and analysed in this study.

Figure 1

Figure 2. Stacked abundance bar plots of the taxonomic composition of samples (ordered in terms of temperature from high to low, with broad temperature classes specified).

Figure 2

Figure 3. a. Taxonomic richness declines with temperature. b. Mean nearest taxon distance increases with temperature. Lines represent linear models, shading represents the 95% confidence intervals.

Figure 3

Table I. Summary output of the linear regression of taxonomic richness, temperature and protected status.

Figure 4

Table II. Summary output of the linear regression of mean nearest taxon distance to temperature and protected area status.

Figure 5

Figure 4. Non-metric multidimensional scaling (nMDS) of community types on Deception Island, Antarctica. The dashed line indicates the ellipse for low-temperature communities, the dotted line indicates the ellipse for medium-temperature communities and the solid line indicates the ellipse for high-temperature communities. Ellipses were based on 95% confidence using the stat_ellipse function in ggplot. The stress value for this nMDS was 0.13.

Figure 6

Table III. Permutational multivariate analysis of variance (PERMANOVA) results for amplicon sequence variants based on site, zone (subsite) and temperature at site of collection. Results are based on 999 permutations.

Figure 7

Figure 5. Relative contributions of different ecological factors shaping community assembly across different temperature groups. Gradient colour lines are approximately indicative of changes in temperature.

Supplementary material: File

Pearman et al. supplementary material 1

Pearman et al. supplementary material
Download Pearman et al. supplementary material 1(File)
File 3.5 KB
Supplementary material: File

Pearman et al. supplementary material 2

Pearman et al. supplementary material
Download Pearman et al. supplementary material 2(File)
File 224.3 KB
Supplementary material: File

Pearman et al. supplementary material 3

Pearman et al. supplementary material
Download Pearman et al. supplementary material 3(File)
File 224.3 KB