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SamplesThe intestine and muscle samples from 22 wild boars were collected between September 4 and March 2, 2020, in Namie town in Fukushima prefecture. Furthermore, control intestine samples were collected from three wild boars in Hyogo prefecture. Each location is depicted in Fig. 1. In each case, after the licensed hunters slaughtered the wild boar to be exterminated, only the tissue was transferred to the study.Measurement of radioactivityRadioactivity in the muscle samples was determined by gamma-ray spectrometry using high-purity germanium (HPGe) detectors (Ortec Co., Oak Ridge, TN, USA), as described in our previous report3. Gamma rays from 137Cs were observed.Exposure dose estimationIn order to estimate internal and external dose rates of the wild boars according to the ICRP publication 10826, we supposed the shapes of wild boars as prolate spheroids whose long axis was to be their body lengths. The short axis was given from their weight assuming their specific gravities were the same
as water. The dose rates were calculated from the contribution of 137Cs, not including
natural radionuclides. The energy deposition to the spheroids by beta and gamma rays from radionuclides were calculated by the numerical simulation with the use of the Particle and Heavy Ion Transport code System (PHITS)27. For the sake of simplicity, we supposed the spheroids consisted of only muscle, which would give overestimated values because muscle contains more radio cesium than other organs. The external exposure dose was calculated from the air dose rates which were observed from the monitoring post near the boars captured place. The average values of the air dose rates were obtained from fitting observed data of two years with decay curve. The background due to the natural radionuclides was estimated to be 0.05 µGy/h which was observed before the Fukushima Daiichi accident, and was removed before the fittings. The half-lives of the air dose rates were 2000–3000 days depending on the environment. Assuming the external exposure dose was ascribed to the 137Cs included in the surface of the ground. The amount of the 137Cs was calculated so as to reproduce the observed air does rates. Since the maximum range of the beta ray from 137Cs is a few millimeters, almost all of the beta ray from inside the body should be absorbed in the boar’s body, but the beta ray from outside the body would stop in its fur. The beta rays contribute 100% to internal exposure dose but 0% to external one. Since the linear attenuation coefficient for gamma rays from 137Cs is 0.084 cm−1 = (12 cm)−1, some of the gamma rays cannot stop in the body depending on the size of the body. The numerical simulation suggested that 65–90 percent of the gamma rays from 137Cs inside the body would go out, and 40–65 percent of the gamma rays from 137Cs outside would go through the body.Pathological analysisA piece of the small intestine was fixed in 10% neutral formalin at 4 °C for 24–48 h. Then, paraffin blocks were prepared for pathomorphological examination using hematoxylin and eosin (HE) staining.Gene expression analysisTotal RNA was extracted from the whole tissue of the intestine using TRIzol Reagent (Life Technologies, Inc., Frederic, MD, USA) according to the manufacturer’s instructions. RNA concentration was measured using a NanoDrop spectrophotometer (Thermo Scientific, Wilmington, DE, USA), and cDNA was synthesized using random primers and SuperScript II (Life Technologies, Inc.). Real-time PCR for IFN-γ, TLR3, and CyclinG1 was performed using Brilliant SYBR Green QPCR Master Mix III (Stratagene, La Jolla, CA, USA) with an AriaMx system (Agilent Technologies, Santa Clara, CA, USA). Primer sequences were designed using Primer-BLAST with sequences obtained from GenBank as described in the previous report4. Amplification conditions were 95 °C for 3 min, 40 cycles at 95 °C for 5 s, and 60 °C for 20 s. Fluorescence signals measured during the amplification were analyzed. Ribosomal RNA primers were used as an internal control, and all data were normalized to constitutive rRNA values. Quantitative differences between the groups were analyzed using the AriaMx software (Agilent Technologies).Statistical analysisAll data are presented as mean ± standard error (SE) for each treatment group. Differences in mRNA expression among the groups were determined using the unpaired t-test with Welch’s correction. (Prism: GraphPad Software Inc., La Jolla, CA, USA). Differences were considered to be statistically significant at a P value of  More

Sites overview and characteristicsThis study focused on three regions located in subantarctic, arctic, and subarctic latitudes. The respective latitudinal and longitudinal ranges covered in this study were: 54.95 to 52.08 °S, and 72.03 to 67.34 °W in Patagonia; 67.44 to 67.54 °N, and 86.59 to 86.71 °E in Siberia; 63.21 to 68.63 °N, and −150.79 to −145.98 °W in Alaska (Figs. 1 and 2). The exact coordinates for each sample were included in the submitted dataset. The field campaigns were conducted in 2016, during the summer for each respective region: January-February in Chilean Patagonia, June-July in Alaska and July-August in Siberia.Fig. 1Location of the three areas included in this study (panel a). The permafrost state and the number of sites and samples per region is indicated for each area. General views of 5 sites are provided as examples (b–f). Panel B provides a large view of the ecosystem surrounding the wetland ALP2 (Alaska, exact location indicated by the white circle). Lake PCL1 (panel c) is representative of the lakes on Navarino island (Chilean Patagonia). The glacial lake SIL2 is shown in panel d. At site SIP5, the hollow at first plan is surrounded by palsa (hummock, second plan), characterized by dark organic matter and lichen vegetation (panel e). The PPP3 peatland shown in panel f is dominated by Sphagnum magellanicum, like most peatlands in the area.Full size imageFig. 2Maps of sampling sites in Patagonia, Alaska and Siberia, indicating the ecosystem type (lake, wetland, soil). The tables show the complete- (in white) and the partial- (in grey) characterization sites. The exact coordinates of each sample are provided in the data record (See data records section).Full size imageFor every site included in the present study, a set of nine qualitative environmental and/or ecological site-scale descriptors was selected and adapted from ENVO Environment Ontology40, which included for example permafrost state, biome, environmental feature and vegetation type (Table 1, Fig. 3). Permafrost state was obtained from the NSIDC permafrost map41. The biome, large-scale descriptor based on climate and vegetation criteria, was derived from Olson et al.42. Temperate forest, boreal forest, and tundra biomes were included. The environmental features that were representative for the three regions were considered: lakes, wetlands, broadleaf/coniferous/mixed forest soils, grassland, tundra, and palsa. All the metadata was included in the submitted dataset. Table 2 summarizes the main types of sampled ecosystems and their main characteristics in the three regions, while Supplementary Table S1 provides the details of each sampling site.Table 1 Overview of the dataset contained in Mimarks sheet.Full size tableFig. 3Description of the qualitative environmental/ecological descriptors used to describe every sample, derived from ENVO Environment Ontology40.Full size imageTable 2 Main types of sampled ecosystems in the three studied regions.Full size tableIn Alaska, the studied area ranged from the Alaska Range and Fairbanks area (interior, continental climate, 63–65°N, discontinuous permafrost) up to Toolik Field Station (North Slope, arctic climate, 66–69°N, continuous permafrost; Fig. 2). The physiochemistry and CH4 emissions of lakes ALL1 (Killarney lake), ALL2 (Otto lake), ALL3 (Nutella lake), and ALL4 (Goldstream lake) were previously characterized35. A number of heterogeneous soil and wetland samples were collected around the studied Alaskan lakes and/or from monitored sites, as detailed in Supplementary Table S1. In the Alaska Range and Fairbanks area, soils were mostly covered by mixed or taiga forests, alpine tundra, and bogs or fens wetlands. In the norther Brooks Ranges mountain system, the landscape was piedmont hills with a predominant soil of porous organic peat underlain by silt and glacial till, all in a permafrost state, characterized mainly by Sphagnum and Eriophorum vegetation, as well as dwarf shrubs.In Siberia, the studied area was located in the discontinuous permafrost region surrounding Igarka, on the eastern bank of the Yenisei River (Fig. 2). This region was mainly covered by forest, dominated by larch (Larix Siberica), birch (Betula Pendula), and Siberian pine (Pinus Siberica), and palsa landscapes (frozen peat mounts), the latter being dominated by moss, lichens, Labrador tea and dwarf birch. In degraded areas, thermokarst bogs were dominated by Sphagnum spp. and Eriophorum spp. Land cover was an indicator of permafrost status, since forested areas reflected a deep permafrost table ( >2 m) associated with Pleistocene permafrost, while palsa-dominated landscapes were indicative of the presence of near-surface ( More

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Each of the 23 key variables can be used for analysis. To validate the dataset, we used five plant-related variables (diversity of order, family, genus, species and species endemic to China) to demonstrate the process of using the dataset for analysis as follows:(1) For the four variables of plant taxa “order”, “family”, “genus” and “species”, the similarity and difference in spatial distribution pattern of diversity of different taxa in the Qinling-Daba Mountains climate transition zone were analyzed. The spatial distribution pattern of the diversity of the four taxa is shown in Fig. 3, which is increasingly lower from south (low latitude) to north (high latitude). This result is consistent with the classical latitudinal gradient model of plant diversity. The boundary between higher diversity in the south and lower diversity in the north is roughly located in the area of Funiu Mountains in the eastern Qinling-Daba Mountains, Taibai Mountains in the central Qinling-Daba Mountains and Baishui River in the western Qinling-Daba Mountains. However, with the reduction in taxon scale, the spatial distribution pattern of diversity tends to be complex. Orders (Fig. 3a) and families (Fig. 3b) can be divided by lines, while genera (Fig. 3c) need thicker lines, and species (Fig. 3d) can only be divided by polygons. Figure 3 shows that the taxonomic groups of families are more clearly divided, while species can only be divided by staggered bands. Therefore, when dividing the north–south boundary, the family taxon scale is appropriate, whereas the species scale is more appropriate when studying the north–south transition zone.Fig. 3Spatial distribution of diversity of orders, families, genera and species. (a) The blue dotted line is basically the dividing line of the order diversity of 50 species. The order diversity to the north of the blue dotted line is lower than 50 species, and the order diversity to the south of the blue dotted line is higher than 50 species. (b) The blue dotted line is basically the dividing line of the family diversity of 150 species. The family diversity to the north of the blue dotted line is lower than 150 species, and the family diversity to the south of the blue dotted line is higher than 150 species. (c) The thicker blue dotted line is basically the dividing line of genus diversity of 578–681 species. The genus diversity to the north of the blue dotted line is lower than 578 species, and the genus diversity to the south of the blue dotted line is higher than 681 species. (d) The blue area is basically the dividing line of species diversity of 1385–1618 species. The species diversity to the north of the blue dotted line is lower than 1385 species, and the species diversity to the south of the blue dotted line is higher than 1618 species.Full size imageThe dataset can also count the orders, families and genera that appear in 58 nature reserves, indicating that these orders, families and genera are widely distributed in this area, while the orders, families and genera that only appear in a single nature reserve indicate that these taxa are unique to this nature reserve in this area, reflecting their locality and uniqueness, which is helpful to understanding the specific distribution of plants in detail. The relevant statistics are as follows:
There are 28 orders present in every nature reserve:
Liliales, Dipsacales, Lamiales, Fabales, Ericales, Poales, Saxifragales, Malpighiales, Malvales, Asterales, Fagales, Gentianales, Geraniales, Ranunculales, Rosales, Solanales, Apiales, Cornales, Brassicales, Caryophyllales, Dioscoreales, Santalales, Myrtales, Asparagales, Celastrales, Sapindales, Alismatales, and Boraginales.The order that only appears in one nature reserve is Petrosaviales, which appears in the Dabashan Nature Reserve in Chongqing.
There are 51 families present in every nature reserve:
Liliaceae, Primulaceae, Plantaginaceae, Lamiaceae, Euphorbiaceae, Cannabaceae, Juncaceae, Fabaceae, Poaceae, Elaeagnaceae, Betulaceae, Apocynaceae, Violaceae, Malvaceae, Crassulaceae, Campanulaceae, Asteraceae, Orchidaceae, Polygonaceae, Orobanchaceae, Onagraceae, Gentianaceae, Geraniaceae, Ranunculaceae, Rubiaceae, Rosaceae, Caprifoliaceae, Thymelaeaceae, Apiaceae, Cyperaceae, Cornaceae, Paeoniaceae, Brassicaceae, Amaryllidaceae, Caryophyllaceae, Rhamnaceae, Santalaceae, Asparagaceae, Celastraceae, Sapindaceae, Adoxaceae, Araliaceae, Berberidaceae, Hydrangeaceae, Scrophulariaceae, Convolvulaceae, Urticaceae, Salicaceae, Papaveraceae, Iridaceae, and Boraginaceae.There are 15 families that only appear in one nature reserve, as shown in Table 2.Table 2 Endemic families of the nature reserves in the Qinling-Daba Mountains and surrounding areas.Full size table
There are 54 genera present in every nature reserve:
Patrinia, Polygonum, Sanicula, Plantago, Allium, Delphinium, Euphorbia, Juncus, Cynanchum, Trigonotis, Artemisia, Sorbus, Polygonatum, Scutellaria, Cirsium, Viburnum, Ajuga, Viola, Galium, Geranium, Salix, Epilobium, Gentiana, Ranunculus, Malus, Acer, Rubia, Rosa, Torilis, Lonicera, Adenophora, Philadelphus, Cornus, Paeonia, Rhamnus, Rumex, Carex, Thalictrum, Asparagus, Carpesium, Clematis, Potentilla, Euonymus, Eleutherococcus, Berberis, Spiraea, Rubus, Populus, Vicia, Silene, Iris, Poa, Aster, and Buddleja.There were 225 genera that only appeared in one nature reserve, as shown in Figshare file 269.(2) For the “species endemic to China” variable of plants, we can see from the diversity distribution pattern of species endemic to China in this region (Fig. 4) that the number of endemic species in the Qinling-Daba Mountains is higher than that of species outside of the region, which reflects the strong transition zone in the Qinling-Daba Mountains. The variables of species endemic to China obtained from the Qinling-Daba Mountains and their surroundings were clustered by the Bray–Curtis dissimilarity measure70 and Ward’s minimum variance (the clustering method recommended for plant cluster analysis). The clustering results are shown in Fig. 5a. At the same time, the clustering results are displayed in space. Figure 5b shows that category 3 extends from the east outside the Qinling-Daba Mountains to the Baishuijiang Nature Reserve inside the western Qinling-Daba Mountains, which is consistent with the fact that the Qinling-Daba Mountains are an important ecogeographical “corridor” connecting the east and the west.Fig. 4Spatial distribution of diversity of species endemic to China in the Qinling-Daba Mountains and adjacent areas.Full size imageFig. 5(a) Clustering results of Ward’s connection aggregation of species endemic to China in 58 nature reserves. (b) Spatial distribution of clustering results of species endemic to China; the larger the dot and the darker the color, the earlier it is merged into this category, and the smaller the dot and the lighter the color, the later it is merged into this category.Full size image More

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