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Animals and dietsThe animal experiments were conducted in accordance with the Guidelines for Animal Experiments and Act on Welfare and Management of Animals, Hokkaido University, and all experimental procedures were approved by the Animal Care and Use Committee of Hokkaido University. All animal experiments were carried out in accordance with ARRIVE guidelines. Twenty newborn female Holstein calves with an average birth weight of 37.1 ± 1.0 kg (mean ± standard error) were randomly allocated to either the control or treatment group at birth. All calves were housed individually in separate calf hutches containing sawdust bedding. Feeding and managing of animals until weaning at 50 d of age was performed as described previously17. After supplementing colostrum at birth, calves in both groups were fed 4 L of pasteurized whole milk (44.2% crude protein [CP] and 29.3% fat on a dry matter [DM] basis) as a transition milk during the first week since birth. From 8 days until weaning at 50 days of age, milk replacer (28.0% CP and 18.0% fat on a DM basis) was fed twice daily at 0830 and 1600 h. Water, calf starter (22.9% CP, 11.0% neutral detergent fiber [NDF], 5.6% acid detergent fiber [ADF], 6.2% crude ash, and 3.0% ether extract on a DM basis), and chopped Timothy hay (3.4% CP, 53.1% NDF, 34.2% ADF, 4.3% crude ash, and 1.7% ether extract on a DM basis) were provided for ad libitum intake from 3 days of age. In addition to voluntary intake of solid diets, the calves in the treatment group were orally administered with a mixture of ground Timothy hay and psyllium (4.4% CP, 78.6% NDF, 5.8% ADF, 3.9% crude ash, and 0.3% ether extract on a DM basis) from 3 days until weaning at 50 days of age. Timothy hay was ground for oral administration using a Wiley grinder (WM-3, Irie Shokai) with a 2-mm screen. To improve the handling of the treatment diet for oral administration, we incorporated psyllium, which is a dietary fiber that primarily improves gastrointestinal conditions in humans and can be incorporated in oral electrolyte solution supplemented to neonatal calves38. As a treatment diet, ground Timothy hay (50 g) and psyllium (6 g) were mixed with 200 mL of water. Owing to the adhesiveness of psyllium, the treatment diet formed a “hay ball” and showed slight stickiness, which facilitates swallowing by calves. At 3–7 days of age, one hay ball (50 g of fibrous diet) was orally administered after morning milk feeding. From 8 days of age to weaning, an additional hay ball was fed immediately after evening milk feeding (100 g fibrous diet per day).After weaning, animals in both dietary groups were merged into the same herd and managed on the same farm under identical conditions. From 9 months of age until calving, heifers were fed a ration containing Timothy hay, alfalfa hay, fescue hay, and concentrate. After calving, the cows were fed a diet for lactating cows, as described in Supplementary Table S8. Diets comprised a total mixed ration and were fed twice daily at 0900 and 1600 h. All animals had ad libitum access to water and mineral blocks throughout the experiment. Daily milk production for each cow was measured for the first 30 days of the lactation period and the average values for each dietary group on a weekly and monthly basis were calculated. Milk yield for four animals in each dietary group were not recorded due to health problems including mastitis and displaced abomasum symptoms after calving.In this study, all animals (n = 20) were maintained until 9 months of age, without severe problems. Owing to health problems, several animals were excluded from the experiment before parturition as follows: three animals (one in the control group and two in the treatment group) at 60 days before the expected calving date and one animal in the control group at 21 days before the expected calving date. One animal in the control group (15 days after calving) and two animals in the treatment group (calving day) were diagnosed with displaced abomasum symptoms and were excluded from further sampling. Owing to technical problems, samples were not collected from three animals aged 7 days in the treatment group and one animal aged 21 days in the control group. All other samples (n = 176) were obtained at the target sampling points.Sampling of rumen contentsRumen contents were collected orally using a stomach tube. The stomach tube and the sample collection flask were thoroughly cleaned using water between sample collections from individual animals; the first fraction of the sample was discarded to avoid contamination from the previous sample and saliva. All samples were collected at 4 h after morning feeding. Rumen contents were collected at 7, 21, 35, 49, and 56 days, and at 9 months of age, 60 and 21 days before the expected calving date, at calving day, and 21 days after calving. The pH was measured using a pH meter (pH meter F-51; Horiba, Kyoto, Japan) immediately after sampling. Samples were collected in a sterile 50 mL tube and immediately placed on ice, followed by storage at − 30 °C until use.Chemical analysisRumen contents (1.0 g) were centrifuged at 16,000×g at 4 °C for 5 min, and the supernatant was collected. The SCFA content was analyzed using a gas chromatograph (GC-14B; Shimadzu, Kyoto, Japan) as described previously39. In brief, the supernatant of the rumen contents was mixed with 25% meta-phosphoric acid at a 5:1 ratio, incubated overnight at 4 °C, and centrifuged at 10,000×g at 4 °C. The supernatant was then mixed with crotonic acid as an internal standard and injected into a gas chromatograph equipped with an ULBON HR-20 M fused silica capillary column (0.53 mm i.d. × 30 m length, 3.0 µm film; Shinwa, Kyoto, Japan) and a flame-ionization detector. d/l-lactic acid levels were measured using a commercial assay kit (Megazyme International Ireland, Wicklow, Ireland) according to the manufacturer’s instructions. NH3-N levels were measured via the phenol-hypochloride reaction method40 using a microplate reader at 660 nm (ARVO MX; Perkin Elmer, Yokohama, Japan).DNA extraction and rumen microbiota profiling via amplicon sequencingTotal DNA was extracted and purified using the repeated bead-beating plus column method41. Rumen contents (0.25 g) were homogenized using sterile glass beads (0.4 g; 0.3 g of 0.1 mm and 0.1 g of 0.5 mm) and cell lysis buffer (1 mL; 500 mM NaCl, 50 mM Tris–HCl [pH 8.0], 50 mM ethylenediaminetetraacetic acid (EDTA), and 4% sodium dodecyl sulfate). The lysates were then incubated at 70 °C for 15 min, and the supernatant was collected for further processing. Bead-beating and incubation steps were repeated once, and all supernatants were combined. Total DNA was precipitated using 10 M ammonium acetate and isopropanol, followed by purification using the QIAamp Fast DNA Stool Mini Kit (Qiagen, Hilden, Germany). The DNA concentration was quantified using a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA) and adjusted with Tris–EDTA buffer to the appropriate concentration.For a comprehensive analysis of rumen bacterial communities, the MiSeq sequencing platform (Illumina, San Diego, CA, USA) was used. Total DNA obtained from the rumen contents was diluted to a final concentration of 5 ng/μL and subjected to PCR amplification of the V3-V4 regions of the 16S rRNA gene using the primer sets S-D-Bact-0341-b-S-17 (5′-CCTACGGGNGGCWGCAG-3′) and S-D-Bact-0785-a-A-21 (5′-GACTACHVGGGTATCTAATCC-3′)42. The PCR mixture consisted of 12.5 μL of 2× KAPA HiFi HotStart Ready Mix (Roche Sequencing, Basel, Switzerland), 0.1 μM of each primer, and 2.5 μL of DNA (5 ng/μL). PCR amplification was performed according to the following program described previously9: initial denaturation at 95 °C for 3 min; 25 cycles at 95 °C for 30 s, 55 °C for 30 s, and 72 °C for 30 s; and a final extension step at 72 °C for 5 min. Amplicons were purified using AMPure XP beads (Beckman-Coulter, Brea, CA, USA) and subjected to sequencing on the Illumina MiSeq platform (Illumina) using the MiSeq Reagent Kit v3 (2 × 300 paired-end). Data obtained from amplicon sequencing using the MiSeq platform were analyzed using QIIME2 version 2019.443. Paired reads were filtered, dereplicated, merged, and chimera-filtered using the q2-dada2 plugin44 to generate ASVs. Taxonomic classification of the ASVs was performed at the phylum, class, order, family, and genus levels using the SILVA 132 99% operational taxonomic units, full length, seven level taxonomy classifier (silva-132-99-nb-classifier.qza). Sequenced data were processed further and analyzed using R software version 3.6.245. ASV and taxonomy tables generated using QIIME2 were imported into R and merged with the sample metadata using the Phyloseq Bioconductor packages46. ASVs identified as Archaea, chloroplasts, and mitochondria were excluded. All samples were rarefied to a sampling depth of 16,805 reads, which was the smallest number of reads observed per sample in the filtered ASV table. Alpha diversity indices including Chao1, ACE, Shannon, and Simpson indices were calculated using the phyloseq function “estimate_richness”. PCoA was performed to determine differences in the microbial community structure based on the Bray–Curtis dissimilarity matrices at the genus level using the Phyloseq package. Venn diagrams were generated using ASVs showing mean relative sequence abundances of  > 0.1% in either the control or the treatment groups at each sampling point. The relative abundance of each bacterial taxon was calculated by dividing the number of reads assigned to each taxon by the total number of reads. Taxa with an average relative abundance  > 0.1% in  > 50% of samples in either the control or treatment group during at least one sampling point were used for the analysis. Hierarchical cluster analysis of bacterial genera determined via amplicon sequencing at 21 days after calving and the weekly and monthly average milk yield for the first 30 days of lactation period was performed using the distances calculated from Spearman’s correlation and average linkage clustering.Quantification of target bacterial species/groups using real-time PCRThe relative abundance of known ruminal bacterial species and groups, including the total bacteria, F. succinogenes, R. flavefaciens, Ruminococcus albus, Butyrivibrio spp., Prevotella spp., Selenomonas ruminantium, Megasphaera elsdenii, Treponema spp., Streptococcus bovis, Anaerovibrio lipolytica, and Ruminobacter amylophilus, was quantified using real-time PCR. Amplification was performed using a Light Cycler 480 system (Roche Applied Science, Mannheim, Germany) with a KAPA SYBR Fast qPCR Kit (Roche Sequencing, Basel, Switzerland) and the respective primer sets (Supplementary Table S9). The standards used for the real-time PCR were prepared as described previously47. Briefly, plasmid DNA containing the respective target bacterial 16S rRNA gene sequence was obtained by PCR cloning using the species/genus-specific or bacterial universal primer sets. The concentration of the plasmid was determined with a spectrometer. Copy number of each standard plasmid was calculated using the molecular weight of nucleic acid and the length (base pair) of the cloned standard plasmid. Ten-fold dilution series ranging from 1 to 108 copies were prepared for each target and run along with the samples. The respective genes were quantified using standard curves obtained from the amplification profile of the dilution series of the plasmid DNA standard (Supplementary Table S9). The PCR cycling conditions and reaction mixture were the same as those reported previously48. The relative abundance of each bacterial target was expressed as the proportion (%) of the abundance of the 16S rRNA genes of each bacterial target relative to that of the total bacteria.Statistical analysisAll data were sorted based on animal age into two sets, from 7 to 56 days of age and from 9 months of age to 21 days after calving, and analyzed separately. Data on fermentation parameters and bacterial abundance quantified via real-time PCR were analyzed using a repeated measures model using GraphPad Prism software version 9.1 (GraphPad Software, San Diego, CA, USA) with the fixed effects of dietary group, age, and diet × age interaction, and the random effect of animals within the groups. The Greenhouse–Geisser correction was used where sphericity was violated. If the P-value for the treatment effect was  More

Database utilizedThe database comprises information obtained with permission from the Catalogue of Endangered Languages that is hosted on the Endangered Languages Project platform (https://www.endangeredlanguages.com/). The Endangered Languages Project was first developed and launched by Google, and is currently overseen by First People’s Cultural Council and the Institute for Language Information and Technology at Eastern Michigan University. Information about the languages in this project is provided by the Catalogue, which is produced by the University of Hawai’i at Mānoa and Eastern Michigan University, with funding provided by the U.S. National Science Foundation (Grants #1058096 and #1057725) and the Luce Foundation. The project is supported by a team of global experts comprising its Governance Council and Advisory Committee.In general, the Catalogue aims to present all languages that communities and scholars have pointed out to be at some level of risk as well as languages that have become dormant. In addition to being the largest database of endangered languages globally, the Catalogue is updated periodically based on feedback gathered from language communities and scholars worldwide. The data therefore represents what was most accurately known about the state of each language’s vitality at its point of utilization. At the time of usage, there were 3423 languages represented in the Catalogue that were determined to be at various levels of risk. Assessment of each language’s risk level is carried out using the Language Endangerment Index, which was developed for the Catalogue’s purposes. The Index is used to assess the level of endangerment of any given language based on whether there is intergenerational transmission of the language (whether the language is being passed on to younger generations), its absolute number of speakers, speaker number trends (whether numbers are stable, increasing, or decreasing), and domains of language use (whether the language is used in a wide number of domains or limited ones). The levels of endangerment that the Index generates include ‘safe’, ‘vulnerable’, ‘threatened’, ‘endangered’, ‘severely endangered’, and ‘critically endangered’. Languages for which it remains unclear if the language has gone extinct or whose last fluent speaker is reported to have died in recent times are referred to as ‘dormant’. Given that the focus of the Catalogue is languages that are at some level of threat, safe languages are excluded in general. Where locality information is available, each language is also accompanied with its latitudinal and longitudinal coordinates.Steps taken to prepare the data for network analysisThe data obtained from the Catalogue was further organized and cleaned up for analysis.

1.

Identifier code
Where available, the ISO 639-3 code for each language was utilized as its unique identifier. Otherwise, its LINGUIST List local use code was utilized. These are temporary codes that are not in the current version of the ISO 639-3 Standard for languages. For languages with neither, unique 3-letter codes were constructed.

2.

Endangerment level
Each language’s endangerment level appeared together with a level of certainty score in the same cell in the original data file. Both pieces of information were split into separate columns and only endangerment levels were utilized.
For languages where different data were available in the Catalogue depending on resource utilized, the data was listed in additional columns. The endangerment level data points utilized in these cases were the ones with the most complete and updated information. If there was no data available regarding endangerment level, this information was also reflected.

3.

Coordinates
Where exact coordinates were not available, coordinates were approximated using Google maps based on the location description provided in the Catalogue source (e.g., the Tel Aviv district), attained from other sources such as Glottolog, UNESCO Atlas of the World’s Languages in Danger, or approximated from maps provided in other sources. ‘NA’ was indicated in the field for coordinates if none could be found.
Coordinates found to be inaccurate were rejected, for example in the instance that coordinates provided indicate a different location than the country the language is supposedly found in. The above steps were then taken to populate the coordinates field.
In instances where a language appears in more than one country, these are listed in separate rows as separate entries. Where there are two sets of coordinates for a country, the set that best corresponds with the written description in the Catalogue source, has greater detail, or is more recent is chosen. Where there are more than two sets of coordinates, a middle point is chosen as being representative of the language’s location, by plotting all coordinates on MapCustomizer (www.mapcustomizer.com).

4.

Language family
On the Catalogue, the information regarding language family may be multi-tiered. For example, Laghuu falls under the Lolo-Burmese branch of the Sino-Tibetan family. For this study, the broader family is utilized—in the case of Laghuu the label ‘Sino-Tibetan’ is used.
Mixed languages, pidgins, and creoles have all been categorized as ‘contact languages’.
Language isolates are listed as ‘isolates’.

5.

Region

The Catalogue groups ‘Mexico, Central America, Caribbean’ together under region. Central America and Caribbean are listed as separate regions in this study, with Mexico falling under Central America.Network constructionA spatial network of endangered languages was constructed from the database. Each node represented an endangered language, and edges or links depicted the distance between the locations of the languages as specified in the database. A distance matrix containing the distances between all endangered languages was computed by using functions from the ‘geosphere’ R package. Specifically, Haversine distances were computed for each pair of longitude and latitude points in the dataset. The radius of the earth used in the Haversine distance calculation is 6,378,137 m (for more details see: https://www.rdocumentation.org/packages/geosphere/versions/1.5-14/topics/distHaversine). Haversine distance refers to the shortest distance between two points on a spherical earth, also referred to as the “great-circle-distance”29.Sensitivity analyses of edge thresholdsThe distance matrix is a fully connected network with weighted, undirected links. We set out to capture the strongest or “closest” spatial relationships among the endangered languages, therefore an edge threshold was applied to the distance matrix such that only the edges in the xth lowest percentile were retained in the spatial network. Such an approach allows for the analysis of the most meaningful (i.e., the physically closest) spatial relations in the dataset and how they relate to language endangerment status. The edges were then transformed into unweighted connections to create a simple unweighted, undirected graph for analysis. In order to determine the value of x (i.e., the percentile at which the edge threshold is to be applied), we constructed 10 spatial networks that retained edges with distances below the 1st, 2nd, 3rd… 10th percentile (in increments of 1%) of all distances in the matrix. Additional information of the distances depicted by the edges in each of the 10 networks is provided in Supplementary Information.These 10 networks were then analyzed for their macro- and meso-scale network properties. A summary of macro and meso-scale network measures used in this analysis and their definitions is provided in Table 1, which depicts the 10 networks showing similar patterns in their network structures.Table 1 An overview of macro- and meso-level network measures of spatial networks with different thresholds.Full size tableResultsAs expected, network density and average degree of the networks, which serve as indicators of the number of edges relative to the number of nodes in the network, increased as the edge threshold used to connect nodes became more liberal. The relatively high values of C (i.e., high levels of local clustering among nodes) and low values of ASPL (i.e., relatively short paths despite large size of network) suggested the presence of small world structure30. The community detection analysis using the Louvain method31 indicated strong evidence of community structure in the networks—suggesting the presence of clusters of endangered languages.The point at which the vast majority of nodes was located within the largest connected component of the network occurred at the 5% edge threshold. Because the 5% network was not too fragmented, we report the analyses conducted on the largest connected component of the 5% network in the following subsections. Please see Supplementary Information for additional details behind the rationale for selecting the 5% network for further analyses. The smaller connected components were excluded. Note however that our results are robust across spatial networks of various edge thresholds (due to lack of space, please see Supplementary Information for a complete summary of all reported analyses conducted on all 10 spatial networks).Macro-level analysis: assortative mixing of endangerment statusesMethodTo investigate the macro-level structure of the spatial network of endangered languages, we computed the assortativity coefficient of the spatial network. Specifically, we wanted to know if the endangerment statuses of the languages tended to cluster at the global level of the entire network. If the assortativity coefficient is positive, the languages in the network would tend to be connected to languages of similar levels of endangerment. If the assortativity coefficient is negative, the languages in the network would tend to be connected to languages of dissimilar levels of endangerment.ResultsThere is a significant positive correlation (Spearman’s rank correlation) between the endangerment status of connected pairs of endangered languages in the network, r = 0.20, p  More

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