Philippa Kaur

Determination of the mtDNA D-loop haplotypes of indigenous chicken breeds and red junglefowl in Thailand
We determined the nucleotide sequences of the 780 bp fragments of the mtDNA D-loop region, including the hypervariable segment I, in 125 individuals from 10 indigenous chicken breeds (a list of breeds is shown in Table 1), and 279 red junglefowls from two subspecies (G. g. gallus and G. g. spadiceus) within 12 populations in Thailand. A total of 44 haplotypes with 62 variable sites, consisting of 26 singletons and 36 parsimony informative sites were identified (Supplementary Tables S1, S2; accession No. LC542982 to LC543385). Table 2 summarizes the details of the haplotypes found in the 10 indigenous chicken breeds and 12 red junglefowl populations, and Fig. 1 shows the composition of each haplogroup of indigenous chickens and two subspecies of red junglefowl. Forty-four haplotypes were temporally classified into eight common haplogroups; A, B, C, D, E, F, H, and J (Supplementary Figs. S1, S2), according to Liu et al.4 and Miao et al.5. In the present study, we treated haplogroups C and D as one unit (CD) as they were not clearly separated. In addition, haplogroup J was closely related to haplogroup CD. Haplogroups A, B, and E were predominant in the Thai indigenous chickens (Table 2), and their frequencies were almost the same (Fig. 1). Haplogroup CD was predominant in the G. g. spadiceus population, but rare in indigenous chickens. In the G. g. gallus population, the haplogroups B, CD, and E were detected at almost the same frequency; however, haplogroup A was not detected. The frequency of haplogroup J, which was mainly found in the Si Sa Ket population, was much higher in G. g. gallus compared with indigenous chicken and G. g. spadiceus populations (Fig. 1). BT, NK-W, NK-B, LHK, CH, PHD, Decoy, fighting chicken, and seven red junglefowl populations (Huai Sai [Ggg], Huai Sai [Ggs], Sa Kaeo, Chanthaburi, Khao Kho, Chaiyaphum, and Khok Mai Rua) each exhibited breed- or population-specific haplotypes (Supplementary Table S2): Hap_04 (BT); Hap_07 and 08 (NK-W); Hap_09 (NK-B); Hap_10 and Hap_11 (LHK); Hap_14 (CH); Hap_17 (PHD); Hap_18 to 20 (Decoy); Hap_29 to 33 (fighting chicken); Hap_21 (Huai Sai [Ggg]); Hap_22 (Huai Sai [Ggs]); Hap_24 and 25 (Sa Kaeo); Hap_27 and 28 (Chanthaburi); Hap_36 (Khao Kho); Hap_38 and 39 (Chaiyaphum); and Hap_43 and 44 (Khok Mai Rua). Twenty-nine out of 44 D-loop haplotypes which had not been previously deposited in GenBank, were newly identified in the present study.
Table 1 List of indigenous chicken breeds and red junglefowl populations examined in the present study.
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Table 2 Summary of haplogroups of mtDNA D-loop sequences and haplotypes that were found in 10 indigenous chicken breeds and 12 populations of two Gallus gallus subspecies in Thailand and their distribution.
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Figure 1

Composition ratio of haplogroups of the mtDNA D-loop sequences in chickens indigenous to Thailand, G. g. spadiceus, and G. g. gallus. The haplogroup names were conformed to those described by Miao et al.5 The numbers in parentheses indicate the number of individuals examined.

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The topologies of the Bayesian tree and the maximum-likelihood (ML) tree based on the HKY + G + I model of evolution, which were selected as the best-fit substitution model, were fundamentally similar. Although the Bayesian posterior probability of the internal nodes and the ML bootstrap values were relatively low due to the short internal branches (multifurcations) of the phylogenetic trees, the haplogroups A, B, F–I, K, Y, and Z were supported by a Bayesian posterior probability of greater than 0.97 (Supplementary Figs. S1, S2). Both The trees revealed that the D-loop sequences obtained in this study could be classified into six haplogroups: A, B, CD, E, F, and J, and a complex group of rare haplogroups (H, I, K, W, and X), except for an unclassified haplotype, Hap_38.
Six haplotypes from seven indigenous and two red junglefowl populations (LHK, CH, PHD, KP, BT, Decoy, fighting chicken, Huai Sai [Ggs], and Petchaburi) belonged to haplogroup A (Fig. 2a). Seven haplotypes from seven indigenous chicken breeds and five red junglefowl populations (LHK, CH, PHD, KP, Decoy, fighting chicken, DT, Sa Kaeo, Huai Sai [Ggg], Huai Sai [Ggs], Khao Kho, and Petchaburi) were classified into haplogroup B (Fig. 2b). Haplogroup CD contained 12 haplotypes, which were identified in two Thai indigenous chicken breeds (PHD and fighting chicken) and seven red junglefowl populations (Chanthaburi, Khok Mai Rua, Chaing Rai, Huai Sai [Ggs], Khao Kho, Chaiyaphum, and Huai Yang Pan) (Fig. 2c). Eight haplotypes in four indigenous chicken breeds (CH, BT, NK-W, and NK-B) and four red junglefowl populations (Roi Et, Khok Mai Rua, Chaiyaphum, and Huai Yang Pan) belonged to haplogroup E (Fig. 2e). Haplogroup F contained one haplotype, which was only found in two indigenous chicken breeds (LHK and PHD) (Fig. 2f). Eight haplotypes of haplogroup J (Hap_10, Hap_11, Hap_24 to 26, Hap_34, Hap_40, and Hap_42) were found in two indigenous chicken breeds (LHK and fighting chicken) and seven red junglefowl populations (Sa Kaeo, Chabthaburi, Si Saket, Roi Et, Khok Mai Rua, Chaing Rai, and Huai Yang Pan) (Fig. 2c). Only one haplotype of haplogroup H (Hap_05) was detected in two indigenous chicken breeds (BT and fighting chicken) (Fig. 2d). Hap_38, which was found in three individuals of the Chaiyaphum population, did not belong to any known haplogroups; however, the haplotype was more closely related to haplogroup CD than the other haplogroups (Fig. 2c; Supplementary Figs. S1, S2).
Figure 2

Locations of mtDNA D-loop haplotypes of Thai red junglefowl and indigenous chicken populations in the global chicken population network. (a) Haplogroup A. (b) Haplogroup B. (c) Haplogroups CD, Y, Z, J, and an unclassified haplotype, Hap_38. (d) Haplogroups H, I, K, X, and W. (e) Haplogroup E. (f) Haplogroup F. Haplotypes that were found in the present study and representative haplotypes reported by Miao et al.5 are shown by magenta and yellow circles, respectively. Black nodes are the inferred intermediate haplotypes. The number of bars on the lines, which link haplotypes, represent the number of nucleotide substitutions that occurred between the haplotypes for comparison.

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Divergence times for each haplotype were determined using BEAST analysis (Supplementary Table S2) and were 0.24–0.45 kilo years ago (KYA) for haplogroup A, 0.15–0.39 KYA for haplogroup B, and 0.14–0.37 KYA for haplogroup CD; the haplotypes of haplogroup E exhibited a wide range of divergent times, ranging from 0.12 to 0.70 KYA (0.41 KYA on average). One haplotype in haplogroups F and H had possibly diverged at 0.33 and 0.34 KYA, respectively. The divergence times of haplotypes in haplogroup J ranged from 0.10 to 0.60 KYA. Hap_38 exhibited a markedly earlier divergence time, which was estimated to be approximately 12,000 years ago (Supplementary Table S2; Supplementary Fig. S1).
Genetic diversity of mtDNA D-loop sequences
The number of D-loop haplotypes in each population (H) ranged from 1 (G. g. gallus population at Huai Sai and Si Sa Ket) to 10 (fighting chicken) (Table 3). Among the Thai indigenous chicken breeds, LHK, CH, PHD, KP, BT, Decoy and fighting chicken exhibited relatively higher genetic diversity (pi, 0.005 for KP and Decoy to 0.009 for LHK, PHD, and fighting chicken; Theta-w, 2.86 for KP to 8.30 for fighting chickens) than in NK-W, NK-B, and DT (pi, 0.001 for NK-W, NK-B, and DT; Theta-w, 0.35 for NK-B to 0.71 for NK-W) (Table 3). With regard to the red junglefowl populations, all populations excluding Si Sa Ket and Petchaburi exhibited similar levels of genetic diversity. The Petchaburi population had two haplotypes, and the genetic diversity was relatively low. The low genetic diversity in the Si Sa Ket population was attributed to the fact that all 30 examined individuals shared only one haplotype. Seven out of the 10 indigenous chicken breeds (LHK, CH, PHD, Decoy, fighting chicken, NK-W, and DT) exhibited negative Tajima’s D values, suggesting that the chickens were bred under purifying selection within each population; however, even though the Tajima’s D values of all populations were not statistically significant (p > 0.05).
Table 3 Genetic diversity of indigenous chicken breeds and red junglefowl populations estimated using of mtDNA D-loop sequences and 28 microsatellite markers.
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Phylogenetic relationships among mtDNA D-loop haplotypes in red junglefowl in Asia
Haplogroups A, B, CD, E, and J were frequently identified in red junglefowl in Thailand (Fig. 1; Supplementary Table S2). Haplotypes from Thailand, Vietnam, Laos, and Myanmar were located in internal nodes of the haplogroup A, B, CD, and E, and haplotypes from China were derived from the haplotypes in Southeast Asia (Fig. 3). Haplogroup J exclusively consisted of haplotypes from Thailand, Vietnam, and Cambodia. In haplogroup F, haplotypes from Cambodia exhibited the ancestral haplotypes of Chinese red junglefowl. Three haplotypes of red junglefowl from Indonesia were observed in haplogroup K. A small number of haplotypes from the other rare haplogroups G and W to Z were only observed in Chinese red junglefowl.
Figure 3

Median-joining haplotype network of mtDNA D-loop sequences of red junglefowl. The haplotypes are approximately subdivided into 12 haplogroups, A, B, CD, E, F, G, J, K, W, X, Y, and Z, and unclassified haplotypes (U) in this network, according to the haplotype classification by Miao et al.5 and the present study. The sizes of circles indicate relative frequencies of haplotypes, and the number of bars on the lines, which link haplotypes, represent the number of nucleotide substitutions that occurred between the haplotypes for comparison. Black nodes are the inferred intermediate haplotypes. The geographic origins of haplotypes or subspecies names are shown using circles with different colours. The numbers in parentheses after the location or subspecies names indicate the numbers of sequences used for analyses. Detailed information on the sequences obtained from the database are listed in Supplementary Table S7.

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Genetic characteristics of indigenous chicken breeds and red junglefowl estimated by 28 microsatellite DNA markers
Two-hundred and ninety-eight red junglefowls in two subspecies (G. g. gallus and G. g. spadiceus) from 12 populations and 138 chickens from 10 indigenous chicken breeds, were used for the genetic diversity analyses using 28 microsatellite markers (Table 1; Supplementary Table S3). The allelic richness (AR) values ranged from 1.40 for MCW103 to 1.93 for MCW0014 (1.77 on average) (Supplementary Table S3). Na ranged from 2.14 for MCW0103 to 7.59 for LEI0192 (2.86 on average). FIS varied from – 0.08 for LEI0166 to 0.54 for MCW0014 (0.05 on average). The FST and FIT values fell within the 0.07 (MCW0098) to 0.31 (MCW0247) range and 0.09 (MCW0123) to 0.63 (MCW0014) range, respectively (FST = 0.17, FIT = 0.22 on average). Two markers, MCW0222 and MCW0014, showed the null allele frequency across all populations (NAF) higher than 0.2 (Supplementary Table S3). Looking at each population, null allele frequencies higher than 0.2 were detected in seven, three, and three populations for MCW0014, MCW0222, and LEI0192, respectively, and in less than one or two populations for the other 11 markers (Supplementary Table S4).
Significant departures from Hardy–Weinberg equilibrium were observed for LEI0234, MCW0014, and MCW0123 in more than 10 populations (p  More

Ice environment
Physicochemical analyses of individual ice blocks were conducted to observe eventual differences that could be attributed to spatially related gradual freezing–melting and fresh ice deposition, and to characterize the habitat that enables long-term survival of ice microbiota. All ice samples contained low concentrations of salts, indicating that they originated from recent clean snow. Concentrations of anions in the upper layers, Ice-1 and Ice-2, were similar. However, the bottom layer Ice-3 had distinctly higher electrical conductivity (EC), hardness and alkalinity, less nitrate, and more sulphate. This could indicate that this ice stratum includes a higher proportion of percolation water, which contains more ions than rain and snow as shown by the differences between the percolation water from the cave Planinska jama (that was used for preparing growth media) and the ice, as shown in Table 1. Total organic carbon (TOC) concentrations in the ice were in a range typical of karst streams22, and above the minimum values reported for surface streams, i.e. 0.1–36.6 mg/l23, indicating a significant input of organic matter for the underground ecosystem. TOC indicates an available in situ source of carbon for the ice microbiome. Nitrogen expressed as nitrate did not exhibit high values in ice samples (Table 1). In this respect, a parallel can be drawn with karst sediments, where microbes are commonly limited more by carbon and phosphorus than by nitrogen24.
Table 1 Characteristics of ice samples from Paradana.
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Besides EC and temperature, pH and dissolved oxygen are additionaly two influential parametres that can affect the abundance and taxonomic structure of microbial communities. pH was found to drive the shift in the community structure not only in habitats such as freshwater, marine sediments or soils but also in cold habitats as Antarctic soils25. In the current samples, the pH effect on the microbial community structure is less evident because all the values are rather similar (Table 1). Cave ice habitats with incoming waterflow are probably not oxygen depleted; on the contrary, for example in Antarctic lakes, glacial meltwater inflow is responsible for oxygen supersaturation26.
Isotopically, the Ice-3 stratum was significantly lighter than the stratum represented by Ice-1 and Ice-2 (Table 1). Correlation of δ2H and deuterium excess did not indicate any effect of kinetic fractionation during water freezing. Thus, intersection of the freezing-line determined by stable isotopes in samples Ice-1 to Ice-3 (δ2H = 6.48δ18O + 2.88) with the local meteoric-water line (LMWL) constructed for the precipitation station at Postojna (Supplementary Fig. S1) (δ2H = 7.95 δ18O + 12.13), provided the δ18O value − 6.3‰ for the original water before freezing. It represents relatively enriched water, but such a value is not uncommon in daily precipitation in Slovenia27. The ice lake in Paradana is presumably formed by the refreezing of water from melting snow accumulated during the winter months20, with some contribution of water dripping from the cave ceiling. November and December 2015 had only a few days with precipitation in Postojna (5 and 4, respectively). However, January and February 2016 had 12 and 20 days with precipitation and monthly totals were high, 152 mm and 312 mm, respectively. The air temperature data adjusted for the elevation difference between Postojna and the Trnovski gozd karst plateau (about 600 m) indicate that about one third of the precipitation in January and one half in February probably fell as snow. The rest was probably a mixture of solid and liquid precipitation, but heavy rains could have occurred as well (e.g. about 55.5 mm of precipitation was measured in Postojna on February 8–9, with mean daily air temperatures between 8 °C and 9 °C). Isotopic composition of precipitation varied significantly between and also during individual events. It is known that snow cover can preserve the isotopic composition of the original snowfalls for long periods28. However, individual snowfalls can mix at the entrance of the cave and the isotopic composition of snow accumulated in the cave can also be influenced by thaws caused by temporary increases of air temperature or rainfall. The isotopic composition of snowmelt water that eventually refreezes in the cave is therefore the result of many processes. Further research with better temporal and spatial resolution of samples and sampling of snowmelt water would be needed to improve knowledge on the dynamics and sources of ice formation. LMWLs known from the literature for other precipitation stations in Slovenia, i.e. Kozina, Portorož and Ljubljana that are given in Supplementary Fig. S1 provided δ18O values for the original water, which we consider too high (− 3.0‰ for LMWL from Portorož, − 3.8‰ for LMWL from Ljubljana and − 5,1‰ for LMWL from Kozina). Postojna is the closest precipitation station to the Paradana and the data on isotopic composition of precipitation cover the period of ice sampling (Supplementary Fig. S2). Therefore, the LMWL at Postojna could be the best representation of the isotopic composition of precipitation supplying water to the Paradana Ice Cave (after considering the elevation difference between the two sites, which is about 600 m).
When analysed in more detail, results obtained using the approach described above (to calculate the isotopic composition of the water that formed the sampled ice) also revealed the sensitivity of the constructed LMWL, the length of data series and extreme values. This is illustrated by records of isotopically very light precipitation in November and December 2015 (δ18O − 17.6‰ and − 14.2 δ18O, respectively). Although such isotopically light precipitation occurred in just two of the 27 months of the observation period, the two values changed the LMWL intercept significantly. However, because they did occur, they cannot be disregarded in the LMWL construction. Daily precipitation data indicate that in both cases monthly values were influenced dominantly by precipitation that fell during just one day (precipitation on those days represented almost the entire monthly precipitation). The LMWL intercept at Postojna without those two months would be 8.3, i.e. closely similar to values in Ljubljana and Kozina. Long-term data from Ljubljana show that the δ18O value of monthly precipitation was lower than − 16.0‰ (values around − 14.0‰ were quite abundant until 1986 and after 2004) in only 5 months in the years 1981–2010. Thus, precipitation with notable isotopically light values, as observed in Postojna between 21 and 23 November 2015 (92% of the precipitation fell on 22 November) appears to be rare in the study area. Nevertheless, it was observed, and it influenced the intercept of LMWL significantly.
It is worth noting that the δ18O values of Ice-1 and Ice-2 are higher than those reported for the Paradana Ice Cave by Carey et al.20. Deuterium excess is also significantly higher than the mean value reported for samples from different depths of ice by Carey et al.20. The difference in δ18O values could be related to different sampling sites. Carey et al.20 sampled the wall ice, whereas the samples collected during this study represent the frozen lake. Investigation of the difference in deuterium levels would be especially interesting. It could point at the input (either by overland flow from the cave entrance or by percolation from the vadose zone) of water from the autumn/winter months, with precipitation from the Eastern Mediterranean air masses having particularly high d-excess (up to 22‰). The Western Mediterranean air masses have d-excess of about 14‰, whereas air masses from the Atlantic have values of only about 10‰29. Late autumn to early winter precipitation in Slovenia (October to December) regularly exhibits high d-excess27. Unfortunately, the available data are insufficient to support analysis of the reason for high deuterium excess of the ice in detail. Study samples also display far lower concentrations of chloride, sulphate and nitrate than samples collected by Carey et al.20.
Concentration of microbes in cave ice
The upper ice stratum represented by Ice-1 and Ice-2 had comparable microbial load expressed in total ATP concentration and total cell counts, whereas the Ice-3 block exhibited significantly higher values (Table 1). Interestingly, the total cell counts of microorganisms in the ice samples was similar (4.67 × 104–15.15 × 104) to that recorded in the Pivka River (SW Slovenia) at the ponor connecting to the karst underground, i.e. 4.29 × 104–12.38 × 104, 30. A large proportion (51.0–85.4%) of entrapped microbes in the ice were viable, showing that they were able to survive ice formation and melting, or even several freezing–melting cycles. A relatively high cell viability can be linked to the availability of compatible solutes, indicated by correspondingly high TOC (Table 1). Not only do sugars and polyols increase microbial resistance to freezing, they can also be used inside the cell as carbon and nitrogen sources31. Higher concentration of salts in Ice-3 block was accompanied by the highest total cell counts and percentage of viable cells (Table 1). In ice from Scărişoara Cave total cell counts varied from 0.84 × 103 to 3.14 × 104 cells/ml with corresponding viability from 28.2 to 84.9%, but no correlation was observed between the ice age (0–13,000 years BP) or depth (0–25 m) and the total number of cells or viability14.
The media types used in this study differed in their ability to stimulate the growth of colonies. In general, nutrient-poor media and low temperatures resulted in higher colony counts in all samples. This phenomenon has been reported previously in cave microbiology, but was not correlated with phylogenetic diversity of microbes obtained on the growth media32. After 28 days of incubation, samples grown on the oligotrophic medium with percolation water (PWA) and cultivated at 10 °C produced the highest colony counts (Table 2). In context this indicates that cave percolation water contains soluble compounds that are not present in tap water and which support the growth of cave-ice microorganisms. With respect to individual samples, the highest colony counts were found in the Ice-3 sample, i.e., 167.37‰ of all cell biomass, determined by flow cytometry (Table 2), and this sample also contained the highest concentration of nutrients (Table 1). Cultivable anaerobic bacteria and fungi were detected in all the ice samples (Table 2).
Table 2 Colony counts (colony-forming units—CFU/ml) and their proportion to total cell counts determined by flow cytometry (‰) at different cultivation conditions and media.
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Communities in the ice blocks differed in the representation of r-strategists, with their predominance in the Ice-1, and a big difference between Ice-1 and Ice-2, the two ice samples from the same stratum. Interestingly, a more-uniform community structure in terms of r-strategists was displayed in ice block Ice-2–Ice-3 (Table 1). R-strategists commonly dominate in uncrowded and unstable habitats where resources are temporarily abundant and available; with development of a community, r-strategists are gradually replaced by the slow-growing equilibrium K-strategists33.
Cultivation on different media showed that the ice contained metabolically diverse microorganisms, aerobic and anaerobic bacteria and fungi. Two species of yellow-green algae were also recovered in cultures from samples Ice-2 and Ice-3. The two cultivated species, Chloridella glacialis and Ellipsoidion perminimum (for identification see Supplementary Fig. S3), were also found in green ice from Antarctica34. It is known from results of previous studies that algae in ice can survive and even grow under such adverse conditions34,35,36. They can also be well adapted to low light and low water temperature; for example they can thrive under ice- and snow-cover where the available photosynthetic photon flux density is only around the photosynthetic compensation point37. In these terms, and particularly in ice caves with available light, algae and cyanobacteria should not be overlooked as an important part of the ice microbial community. Interestingly, in Himalayan-type glaciers, the algae-rich layers in ice cores were suggested as providing accurate boundary markers of annual layers38. It remains unclear whether algae can be applied similarly as boundary markers in cave ice. Their existence is already known from some caves, for example in Hungary in a small ice cave colonizing surfaces of the ice39, Romania in Scarişoara Ice Cave at the ice/water interface40 and in New Mexico, USA, in Zuni Ice Cave giving the distinctive greenish patina of the layered ice35.
Bacterial community structure
Previous study of ice from the Paradana Ice Cave showed that it probably originates from local rainfall that reaches the cave as drip water after dissolving bedrock while percolating from the surface, and from snow that includes dust particles20. Thus, the largely impacted cave ice in Paradana has different sources, each bringing along a diverse and adaptable microbiota. 16S metagenomic analysis was conducted to describe the taxonomic composition of bacteria found in different ice blocks. Quality filtration of sequence readings gave a total number of 120,381 sequences in the three studied samples (Table 3). The number of operational taxonomic units (OTUs) varied from 185 in Ice-2 to 304 in Ice-1. This pattern was in alignment with values of alpha diversity parameters: extrapolated richness (Chao1), abundance-based coverage estimator (ACE) and Shannon index (Table 3). The rarefaction curves indicated that the diversity had been sampled sufficiently (Supplementary Fig. S4).
Table 3 Number of reads, OTUs, taxon richness and diversity indexes for cave ice samples.
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A Venn diagram of the distribution of 441 distinct OTUs found in the three studied samples is presented in Fig. 1. Observations showed that 119 OTUs (28.3%) occurred in all three samples and can be interpreted as “a core microbiome”. Three of these OTUs dominated microbial communities in individual samples (relative abundance range 14.5–56.5%) and corresponded to the members of the genera Pseudomonas, Lysobacter, and Sphingomonas, as discussed below. These were followed in abundance by Polaromonas, Flavobacterium, Rhodoferax, Nocardioides, and Pseudonocardia (relative abundance range 3.3–6.9%). Another 35 OTUs had relative abundance above 0.5% and the remaining 76 OTUs had relative abundance below 0.5%. The unique OTUs probably contribute to the variability due to internal variations within the ice block caused by incoming snow or the freezing of percolation water. For example, samples Ice-2 and Ice-3 were cut from the same ice block in a vertical ice profile, but differed in their content of dark, particulate, organic inclusions.
Figure 1

Prokaryotic OTU distribution in cave ice. The Venn diagram indicates the number of distinct and shared OTUs in ice samples Ice-1, Ice-2 and Ice-3.

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Members of 29 bacterial phyla were detected in the cave ice microbiome (Fig. 2, Supplementary Fig. S5). All samples were dominated by Proteobacteria, with relative abundances of 79.1% in Ice-2, 65.5% in Ice-3 and 55.9% in Ice-1.
Figure 2

Relative abundance of phyla in the cave-ice samples. Phyla with relative abundance  1% of phylotypes in at least one sample and corresponded to Firmicutes, Cyanobacteria and Gemmatimonadetes. Phototrophic bacterial phylotypes belonging to Cyanobacteria were recovered from all three samples. They represented 1.3% of phylotypes in sample Ice-1, but only 0.6% and 0.3% in samples Ice-2 and Ice-3 respectively, from where algae, C. glacialis and E. perminimum, were obtained via cultivation.
Phyla whose relative abundance was less than 1% were grouped together and classified as “Rare phyla”. These phyla comprised 2.2%, 1.5% and 1.2% of Ice-1, Ice-2, and Ice-3, respectively. Their relative abundance is presented in Supplementary Fig. S5.
Among the 31 classes detected in this study, members of Gammaproteobacteria were most abundant and represented 20.1% (Ice-1), 45.3% (Ice-2) and 42.5% (Ice-3) of total detected phylotypes (Fig. 3A). This proteobacterial group was also most abundant in the ice from Scărişoara Cave14. Actinobacteria represented the second most abundant group of phylotypes, with its relative abundances declining from 30.8% in Ice-1 to 26.2% in Ice-3 and 11.7% in Ice-2. Other notably abundant classes were Alpha- and Betaproteobacteria, whose abundances ranged from 9.6 to 26.3% and from 6.9 to 12.3%, respectively.
Figure 3

Heat-map analysis of the relative abundance of members of cave-ice prokaryotic communities at class (A) and genus (B) levels in Ice-1, Ice-2 and Ice-3. Phylotypes whose relative abundances at class level were  More

Construction of RUGs from rumen sequencing data
We produced 979G of Illumina sequencing data from 4 cows, 2 sheep, 4 red deer and 2 reindeer samples, then performed a metagenomic assembly of single samples and a co-assembly of all samples. This created a set of 391 dereplicated genomes (99% ANI (average nucleotide identity)) with estimated completeness ≥ 80% and estimated contamination ≤ 10% (Fig. 1). 284 of these genomes were produced from the single-sample assemblies and 107 were produced from the co-assemblies. 172 genomes were > 90% complete with contamination  90% complete with More

Tested animals
Altogether 73 individuals from seven species of subterranean rodents differing in body mass, phylogenetic relatedness, and sociality were studied (Table 1). All animals were adult non-breeders, or their breeding history was unknown in solitary species, but none of them showed signs of recent breeding, which may theoretically influence measured parameters. For the purpose of this study, we used the following taxa. African mole-rats (Bathyergidae): the social Ansell’s mole-rat Fukomys anselli (Burda, Zima, Scharff, Macholán & Kawalika 1999) occupies the miombo in a small area near Zambia’s capital Lusaka; another social species of the genus Fukomys is named here as Fukomys “Nsanje” because founders of the breeding colony were captured near town Nsanje in south Malawi. Although we used name Fukomys darlingi (Thomas 1895) for mole-rats from this population in previous studies (e.g.38,49), its taxonomic status is still not resolved; the social common mole-rat Cryptomys hottentotus hottentotus (Lesson, 1826) occurs in mesic and semi-arid regions of southern Africa; the solitary Cape dune mole-rat Bathyergus suillus (Schreber, 1782) inhabits sandy soils along the south-western coast of South Africa; and the solitary Cape mole-rat Georychus capensis (Pallas, 1778) occupies mesic areas of the South Africa50. In addition, we studied the social coruro Spalacopus cyanus (Molina, 1782) (Octodontidae) occupying various habitats in Chile51; and the solitary Upper Galilee Mountains blind mole rat Nannospalax galili (Nevo, Ivanitskaya & Beiles 2001) (Spalacidae) from Israel52. Further information about the species including number of individuals used in the study, their physiology and ecology is shown in Table 1.
All experiments were done on captive animals. Georychus capensis, C. hottentotus, and B. suillus, were captured about four months before the experiment, and kept in the animal facility at the University of Pretoria, South Africa (temperature: 23 °C; humidity: 40–60%, photoperiod: 12L:12D). The animals were housed in plastic boxes with wood shavings used as a bedding. Cryptomys hottentotus and G. capensis were fed with sweet potatoes; B. suillus with sweet potatoes, carrots, and fresh grass. Fukomys anselli, F. “Nsanje”, N. galili, and S. cyanus were kept for at least three years in captivity (or born in captivity) before the experiment in the animal facility at the University of South Bohemia in České Budějovice, Czech Republic (temperature: African mole-rats 25 °C, N. galili and S. cyanus 23 °C; humidity: 40–50%, photoperiod: 12L:12D). The animals were kept in terraria with peat as a substrate and fed with carrots, potatoes, sweet potatoes, beetroot, apple, and rodent dry food mix ad libitum.
Experimental design
We measured Tb and Ts in all species at six Tas (10, 15, 20, 25, 30 and 35 °C). Each individual of all species was measured only once in each Ta. Measurements were conducted in temperature controlled experimental rooms in České Budějovice and Pretoria. Each animal was tested on two experimental days.
The animals were placed in the experimental room individually in plastic buckets with wood shavings as bedding. On the first day, the experimental procedure started at Ta 25 °C. They spent 60 min of initial habituation in the first Ta after which Tb and Ts were measured as described in the following paragraphs. The Ta was then increased to 30 °C and 35 °C, respectively. After the experimental room reached the focal Ta, the animals were left minimally 30 min in each Ta to acclimate, and the measurements were repeated. Considering their relatively small body size, tested animals were very likely in thermal equilibrium after this period because mammals of a comparable body mass are thermally equilibrated after similar period of acclimation53,54,55,56. On the second day, the procedure was repeated with the initial Ta 20 °C and decreasing to 15 °C and 10 °C, respectively. The time span between the measurements of the same individual in different Ta was at least 150 min. Between experimental days, the animals were kept at 25 °C in the experimental room (individuals of social species were housed together with their family members).
Body temperature measurements
We used two sets of equipment to measure animal Tb and Ts. In B. suillus, G. capensis, and C. hottentotus, Tb was measured by intraperitoneally injected PIT tags ( More

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Sample collection
Algae were sampled from August 27th to September 21st (2017) from seven populations also collected for Bonthond et al. [28], including three native populations; Akkeshi (Japan), Soukanzan (Japan), Rongcheng (China); and four non-native populations; Pleudihen-sur-Rance (France), Nordstrand (Germany), Cape Charles Beach (Viriginia) and Tomales Bay (California, Fig. 1, Table S1). Individuals fixed to hard substratum (see [30]) were sampled at least a meter apart from one another and stored in separate plastic bags. As A. vermiculophyllum has a complex, haplodiplontic life-cycle only diploids were included in the experiment. Life-cycle stages were identified in the field with a dissecting microscope or post-hoc by microsatellite genotyping [31]. After transport in coolers and storage at 4 °C in the lab, bags with algae were shipped to Germany, arriving within 4–6 days after collection. In the climate room (15 °C), individuals were transferred to separate transparent aquaria with transparent lids, containing 1.75 L artificial seawater (ASW) prepared from tap water and 24 gL−1 artificial sea salt without CaCO3 (high CaCO3 concentrations increase disease risk, Weinberger data unpublished) and exposed to 12 h of light per day (86.0 µmol m−2s−1 at the water surface). Aquaria were moderately aerated with aeration stones. Per population, four diploid individuals were acclimated over 31–32 days to climate room conditions prior to starting the experiment. Water was exchanged weekly with new ASW enriched with 2 mL Provasoli-Enrichment Solution (PES; [32]). At the start of the experiment, wet weight was recorded and individuals were divided into two parts of ~10 g each and placed into two plastic tanks with 1.75 L water and 2 mL PES (Fig. 1).
Fig. 1: Schematic overview of the sampling design and experimental process.

Algae were collected from native populations Rongcheng (ron), Soukanzan (sou) and Akkeshi (akk) and non-native populations Tomales Bay (tmb), Cape Charles Beach (ccb), Pleudihen-sur-Rance (fdm) and Nordstrand (nor). In the climate room algae were acclimated for 5 weeks and divided into two thalli. One of the thalli was treated for three days with an antibiotic mixture after which both groups were monitored for six weeks, during which the treated algae received inoculum with each water change. Microbiota samples were taken in the field (tfield), directly after disturbance (t0) and after 1, 2, 4 and 6 weeks (t1, t2, t4 and t6).

Full size image

Experimental setup
To rigorously disturb the microbial community, one of each of the pairs of aquaria containing the same algal individual was treated with a combination of antibiotics, aiming to increase the effectivity (10 mgL−1 ampicillin, 10 mgL−1 streptomycin, 10 mgL−1 chloramphenicol) and the other (control) remained untreated. All experimental work was conducted with disposable gloves and sterilized equipment, to minimize contamination. After three days, the water was removed from all tanks (treated and control) and the wet weight was recorded for all algae. All individuals were rinsed with one 1.75 L volume ASW and re-incubated in 1.75 L ASW. Subsequently, both groups received new ASW with 2 mL PES weekly and individuals treated with antibiotics received also 2 mL inoculum. The inoculum was prepared from individuals of all 7 populations, following the procedure to remove epibiota as described in Bonthond et al. [28]. Briefly, apical fragments of 1 g were separated from the thallus and transferred to 50 mL tubes containing 15 ± 1 glass beads (3 mm) and 15 mL ASW and vortexed for 6 min to separate epibiota from the algal tissue. In total, 8 samples were prepared from one individual per population. The resulting suspensions were pooled and mixed with glycerol (20% final glycerol concentration), aliquoted in 50 mL tubes and stored at −20 °C. For each water exchange, a new aliquot was defrosted at room temperature and added to the water of treated algae. Wet weight was recorded weekly with water exchanges. Before weighing the individual on aluminum foil, it was dipped twice on a separate aluminum foil sheet, to reduce attached water in a systematic way. Endo- and epiphytic microbiota were sampled in the field (tfield, [28]), at the start of the experiment (t0), after one week (t1), two weeks (t2), four weeks (t4) and six weeks (t6, Fig. 1). To equalize acclimation times across populations the experiment was stacked into five groups (Table S2). At each sampling moment, 0.5 or 1 g of tissue was separated from all individuals with sterilized forceps and epibiota were extracted similarly to the preparation of the inoculum. The resulting suspension was filtered through 0.2 µm pore size PCTA filters. Both the filters and the remaining tissue were preserved at −20 °C.
DNA extraction and amplicon sequencing
Tissue samples were defrosted, rinsed with absolute ethanol and DNA free water to remove hydro- and moderately lipophilic cells and molecules from the surface and cut to fragments with sterilized scissors. DNA was then extracted from these fragments (endobiota) and from preserved filters (epibiota) using the ZYMO Fecal/soil microbe kit (D6102; ZYMO-Research, Irvine, CA, USA), following the manufacturer’s protocol. Although this method to separate endo- and epibiota was shown to resolve distinct communities [28], tightly attached epiphytic cells may not be completely removed from the surface and detectable in endophytic samples as well. Two 16S-V4 amplicon libraries, over which the samples were divided in a balanced manner, were prepared as in Bonthond et al. [28], following the two-step PCR strategy from Gohl et al. [33], using the same set of 16S-V4 target primers and indexing primers. The libraries were sequenced on the Illumina MiSeq platform (2×300 PE) at the Max-Planck-Institute for Evolutionary Biology (Plön, Germany), including four negative DNA extraction controls and four negative and positive PCR controls (mock communities; ZYMO-D6311). The fastq files were de-multiplexed (0 mismatches). Relevant field samples from Bonthond et al. [28] were combined with the new dataset and assembled, quality filtered and classified altogether with Mothur v1.43.0 [34] using the SILVA-alignment release 132 [35]. Sequences were clustered within 3% dissimilarity into OTUs using the opticlust algorithm. Mitochondrial, chloroplast, eukaryotic and unclassified sequences were removed. To prepare the community matrix we discarded singleton OTUs (in the full dataset), samples with More

Department of Geography, Texas A&M University, College Station, TX, USA
J. Loisel

Department of Geography, University of Exeter, Exeter, UK
A. V. Gallego-Sala, M. J. Amesbury, D. J. Charman & T. P. Roland

Ecosystems and Environment Research Programme, University of Helsinki, Helsinki, Finland
M. J. Amesbury, A. Korhola, M. Väliranta, S. Juutinen, K. Minkkinen & S. Piilo

Department of Geography and Geotop Research Center, University of Quebec at Montreal, Montreal, Quebec, Canada
G. Magnan & M. Garneau

Magister of Environment and Soil Science Department, Tanjungpura University, Pontianak, Indonesia
G. Anshari

Department of Geography and Environment, University of Hawaii at Manoa, Honolulu, HI, USA
D. W. Beilman

Department of Ecology and Territory, Pontificial Xavierian University, Bogota, Colombia
J. C. Benavides

Organic Geochemistry Unit, School of Chemistry, and School of Earth Sciences, University of Bristol, Bristol, UK
J. Blewett & B. D. A. Naafs

Environmental Studies Program and Earth and Oceanographic Science Department, Bowdoin College, Brunswick, ME, USA
P. Camill

Department of Geology, Chulalongkorn University, Bangkok, Thailand
S. Chawchai

Department of Geography, University of California, Los Angeles, Los Angeles, CA, USA
A. Hedgpeth

Max Planck Institute for Meteorology, Hamburg, Germany
T. Kleinen & V. Brovkin

Faculty of Engineering, Chemical and Environmental Engineering, University of Nottingham, Nottingham, UK
D. Large

Centro de Investigación GAIA Antártica, University of Magallanes, Punta Arenas, Chile
C. A. Mansilla

Climate and Environmental Physics, Physics Institute and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
J. Müller & F. Joos

Consortium Érudit, Université de Montréal, Montreal, Quebec, Canada
S. van Bellen

Department of Ecology and Conservation Biology, Texas A&M University, College Station, TX, USA
J. B. West

Department of Earth and Environmental Sciences, Lehigh University, Bethlehem, PA, USA
Z. Yu

Institute for Peat and Mire Research, School of Geographical Sciences, Northeast Normal University, Changchun, China
Z. Yu

Department of Environmental Studies, Mount Holyoke College, South Hadley, MA, USA
J. L. Bubier

Department of Geography, McGill University, Montreal, Quebec, Canada
T. Moore

Department of Physical Geography, Stockholm University, Stockholm, Sweden
A. B. K. Sannel

School of Geography, Geology and the Environment, University of Leicester, Leicester, UK
S. Page

Department of Earth and Environmental Sciences, KU Leuven, Leuven, Belgium
M. Bechtold & W. Swinnen

School of Geography & Sustainable Development, University of St Andrews, St Andrews, UK
L. E. S. Cole

Department of Earth, Ocean & Atmospheric Science, Florida State University, Tallahassee, FL, USA
J. P. Chanton

Department of Bioscience, Aarhus University, Roskilde, Denmark
T. R. Christensen

Department of Earth Sciences, University of Toronto, Toronto, Ontario, Canada
M. A. Davies & S. A. Finkelstein

Instituto Franco-Argentino para el Estudio del Clima y sus Impactos, Buenos Aires, Argentina
F. De Vleeschouwer

Institute for the Study of Earth, Oceans, and Space, University of New Hampshire, Durham, NH, USA
S. Frolking & C. Treat

Department of Geobotany and Plant Ecology, University of Lodz, Lodz, Poland
M. Gałka

Laboratoire d’Ecologie Fonctionnelle et Environnement, UMR 5245, CNRS-UPS-INPT, Toulouse, France
L. Gandois

Cranfield Soil and Agrifood Institute, Cranfield University, Cranfield, UK
N. Girkin

Department of Renewable Resources, University of Alberta, Edmonton, Alberta, Canada
L. I. Harris

Stockholm Environment Institute, University of York, York, UK
A. Heinemeyer

Max Planck Institute for Biogeochemistry, Jena, Germany
A. M. Hoyt

Lawrence Berkeley National Laboratory, Berkeley, CA, USA
A. M. Hoyt

Florence Bascom Geoscience Center, United States Geological Survey, Reston, VA, USA
M. C. Jones

Department of Marine and Coastal Environmental Science, Texas A&M University at Galveston, Galveston, TX, USA
K. Kaiser

Department of Biology, University of Victoria, Victoria, British Columbia, Canada
T. Lacourse

Faculty of Geographical and Geological Sciences, Climate Change Ecology Research Unit, Adam Mickiewicz University, Poznań, Poland
M. Lamentowicz

Natural Resources Institute Finland (Luke), Helsinki, Finland
T. Larmola

Agroscope, Zurich, Switzerland
J. Leifeld

Institute for Atmospheric and Earth System Research, University of Helsinki, Helsinki, Finland
A. Lohila

Finnish Meteorological Institute, Climate System Research, Helsinki, Finland
A. Lohila

Department of Geography, Royal Holloway, University of London, Egham, UK
A. M. Milner

Department of Forest Sciences, University of Helsinki, Helsinki, Finland
K. Minkkinen

School of Earth and Environmental Sciences, University of Queensland, Brisbane, Queensland, Australia
P. Moss

Lamont-Doherty Earth Observatory, Palisades, NY, USA
J. Nichols

National Park Service, Washington DC, WA, USA
J. O’Donnell

Department of Environment & Geography, University of York, York, UK
R. Payne

Department of Chemistry, and Department of Geological and Environmental Science, Hope College, Holland, MI, USA
M. Philben

Department of Geography and Environmental Science, University of Reading, Reading, UK
A. Quillet

Department of Applied Earth Sciences, Uva Wellassa University, Badulla, Sri Lanka
A. S. Ratnayake

School of Biosciences, University of Nottingham, Nottingham, UK
S. Sjögersten

Département de Géographie, Université de Montréal, Montréal, Québec, Canada
O. Sonnentag & J. Talbot

Geography, School of Natural and Built Environment, Queen’s University Belfast, Belfast, UK
G. T. Swindles

Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA, USA
A. C. Valach

Department of Environment and Sustainability, Grenfell Campus, Memorial University, Corner Brook, Newfoundland, Canada
J. Wu More