Ecology

Presence of mucin stabilizes survival of both the bacterium and the phage during 16 weeks of co-cultureAn overview of our main experimental setup is shown in Fig. 1. To avoid population bottlenecks, our sampling was based on the weekly collecting 20% of the cultures and replacing with the same volume of fresh medium. Long term co-existence of both F. columnare B245 and its phage V156 was observed in all treatments. In lake water with (LW + M) or without mucin (LW), the closest approximations of natural conditions for F. columnare, the phage titers remained similar until week 9, after which LW + M showed a significant decline in phage numbers compared to LW (LM, t1,46 = −2.737, P = 0.0088) with roughly a ten-fold difference at week 16 (Fig. 2a, Supplementary Fig. 1a). Bacterial population densities in these treatments were opposite and more dramatic, with an average of 45-fold higher numbers in LW + M than in LM across all time points after an initial spike at week 1 (LM, t1,77 = 4.836, P  More

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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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Geography and climatology of study areaThe area of study, Ghana, is on the coastal edge of tropical West African, bounded in latitude 4.5° N and 11.5° N and longitude 3.5° W and 1.5° E, and characterized by a tropical monsoon climate system23,24. Figure 1 shows map of the study area indicating the selected twenty two (22) sunshine measurement stations distributed across the four main climatological zones and Table 1 summarizes the geographical positions of selected stations.Figure 1Adapted from Asilevi27.Map of the study area showing all twenty two (22) synoptic stations distributed in four main climatological zones countrywide.Full size imageTable 1 Geographical position and elevation for study sites.Full size tableAtmospheric clarity over the area is closely connected to cloud amount distribution and rainfall activities, largely determined by the oscillatory migration of the Inter-Tropical Discontinuity (ITD), accounting for the West African Monsoon (WAM)25,26.Owing to the highly variable spatiotemporal distribution of cloud amount vis-à-vis rainfall activities, resulting in contrasting climatic conditions in different parts of the region, the country is partitioned by the Ghana Meteorological Agency (GMet) into four main agro-ecological zones namely, the Savannah, Transition, Forest and Coastal zones as shown in Fig. 123. As a result, the region experiences an estimated Global solar radiation (GSR) intensity peaks in April–May and then in October–November, with the highest monthly average of 22 MJm−2 day−1 over the savannah climatic zone and the lowest monthly average of 13 MJm−2 day−1 over the forest climatic zone27.Research datasetsGround-based measurement dataDaily sunshine duration measurement datasets (n) spanning 1983–2018 where derived for estimating Global solar radiation (GSR). The measurements were taken by the Campbell-Stokes sunshine recorder, mounted at the 22 stations shown in Fig. 1, under unshaded conditions to ensure optimum sunlight exposure. The device concentrates sunlight onto a thin strip of sunshine card, which causes a burnt line representing the total period in hours during which sunshine intensity exceeds 120.0 Wm−2 according to World Meteorological Organization (WMO) recommendations27. The as-received daily records were quality control checked by ensuring 0 ≤ n ≤ N, where N is the astronomical day length representing the possible maximum duration of sunshine in hours determined by Eq. 1 from the latitude (ϕ) of the site of interest and the solar declination (δ) computed by Eq. 227:$$ {text{N}} = frac{2}{15}cos^{ – 1} left[ { – tan phi tan {updelta }} right] $$
(1)
$$ {updelta } = 23.45sin left[ {360^{{text{o}}} times frac{{284 + {text{J}}}}{365}} right] $$
(2)
where J represents the number for the Julian day of the year (first January is 1 and second January is 2).NASA-POWER Global solar radiation (GSR) reanalysis dataThe satellite-based Global solar radiation (GSR) dataset for specific longitudes and latitudes of all 22 stations, assessed in the study, were retrieved from the National Aeronautics and Space Administration-Prediction of Worldwide Energy Resources (NASA-POWER) reanalysis repository based on the Modern Era Retrospective-Analysis for Research and Applications (MERRA-2) assimilation model products, developed from Surface Radiation Budget, and spanning equal study period (1983–2018). The datasets are accessible on a daily and monthly temporal resolution scales at 0.5° × 0.5° spatial coverage via a user friendly web-based mapping portal: https://power.larc.nasa.gov/data-access-viewer/17. The advantage of the NASA-POWER reanalysis GSR, is the wide spatial coverage, and thus can be used to develop a high spatial resolution of solar radiation across the study area.The POWER Project analyzes, synthesizes and makes available surface radiation related parameters on a global scale, primarily from the World Climate Research Programme (WCRP), Global Energy and Water cycle Experiment (GEWEX), Surface Radiation Budget (SRB) project (Version 2.9), the Clouds and the Earth’s Radiant Energy System (CERES), FLASHFlux (Fast Longwave and Shortwave Radiative Fluxes from CERES and MODIS), and the Global Modeling and Assimilation Office (GMAO)17. Table 2 shows the source satellites and the corresponding temporal coverage used in the development of NASA-POWER GSR products.Table 2 Satellites providing the NASA-POWER GSR datasets20.Full size tableThe monthly average NASA-POWER all-sky shortwave surface radiation reanalysis products are statistically validated, showing reasonable biases of − 6.6–13%, against a global network of surface radiation measurement metadata in an integrated database from the Baseline Surface Radiation Network (BSRN) of the World Radiation Monitoring Center (WRMC)20,22. The datasets are widely used in renewable energy application16,22, agricultural modelling of crop yields28, crop simulation exercises29, and plant disease modelling30.Furthermore, in order to assess the suitability of the NASA-POWER surface solar radiation products for the study area, a synthetic sunshine duration based Global solar radiation (GSR) is developed from the Angstrom-Prescott sunshine duration model by Eq. 3 for comparisons27.$$ {text{GSR}} = left[ {{text{a}} + {text{b}}frac{{text{n}}}{{text{N}}}} right]{text{H}}_{{text{o}}} $$
(3)
were Ho (kWhm−2 day−1) is the daily extraterrestrial solar radiation on an horizontal surface, n is the daily sunshine duration measurements obtained from the Ghana Meteorological Agency (GMet), and N is the maximum possible daily sunshine duration or the day length in hours determined by Eq. 1. Generalized regression constants a = 0.25 and b = 0.5 for the study area were determined by Asilevi27 from experimental radiometric data based on correlation regression analysis between atmospheric clarity index (GSR/Ho) and atmospheric cloudlessness index (n/N), for estimating solar radiation over the study area, and compared with other satellite data retrieved from the National Renewable Energy Laboratory (NREL) and the German Aerospace Centre (DLR)27. Ho was calculated from astronomical parameters by Eq. 4:$$ {text{H}}_{0} = frac{{24{ } cdot { }60}}{pi } cdot {text{G}}_{{{text{sc}}}} cdot {text{d}}_{{text{r}}} left[ {omega_{{text{s}}} sin varphi sin delta + cos varphi cos delta sin omega_{{text{s}}} } right] $$
(4)
where Gsc is the Solar constant in MJm−2 min−1, dr is the relative Earth–Sun distance in meters (m), (omega_{s}) is the sunset hour angle (angular distance between the meridian of the observer and the meridian whose plane contains the sun), (delta) is the angle of declination in degrees (°) and (varphi) is the local latitude. A detailed presentation of the calculation was published in a previous work27.Statistical assessment analysisFor the purpose of assessing the NASA-POWER derived monthly mean GSR (GSRn) datasets in comparison with the estimated Global Solar Radiation (GSRe) datasets used in this paper, the following deviation and correlation methods in Eqs. 5–11, each showing a complimentary result were used: Standard deviation (({upsigma })), residual error (RE), Root mean square error (RMSE), Mean bias error (MBE), Mean percentage error (MPE), Pearson’s correlation coefficient (r), and Willmott index of agreement (d) for n observations31,32,33,34,35. GSRe, GSRn, and RE represent the estimated GSR, NASA-POWER GSR, and the residual error between GSRe and GSRn respectively. A positive RE indicates that sunshine-based estimated GSR is larger than the NASA-POWER reanalysis dataset, while a negative RE indicates that sunshine-based estimated GSR is smaller than the NASA-POWER reanalysis dataset. The arithmetic mean of any dataset is µ.The standard deviation (({upsigma })) was used to check the upper and lower limits of distribution around the mean deviations between GSRe and GSRn in order to ascertain violations between both datasets33. The RMSE is a standard statistical metric to quantify error margins in meteorology and climate research studies, and by definition is always positive, representing zero in the ideal case, plus a smaller value signifying a good marginal deviation31. The MBE is a good indicator for under-or overestimation in observations, with MBE values closest to zero being desirable. The MPE further indicates the percentage deviation between the GSRe and GSRn individual datasets35.$$ {upsigma } = sqrt {frac{1}{{{text{n}} – 1}}mathop sum limits_{{{text{i}} = 1}}^{{text{n}}} left( {{text{GSR}} – {upmu }} right)^{2} } $$
(5)
$$ {text{RE}} = {text{GSR}}_{{text{e}}} – {text{GSR}}_{{text{n}}} $$
(6)
$$ {text{RMSE}} = sqrt {frac{1}{{text{n}}}mathop sum limits_{{{text{i}} = 1}}^{{text{n}}} left( {{text{RE}}} right)^{2} } $$
(7)
$$ {text{MBE}} = frac{1}{{text{n}}}mathop sum limits_{{{text{i}} = 1}}^{{text{n}}} left( {{text{RE}}} right) $$
(8)
$$ {text{MPE}} = frac{1}{{text{n}}}mathop sum limits_{{{text{i}} = 1}}^{{text{n}}} left( {frac{{{text{RE}}}}{{{text{GSR}}_{{text{e}}} }} times 100{text{% }}} right) $$
(9)
$$ {text{r}} = frac{{mathop sum nolimits_{{{text{i}} = 1}}^{{text{n}}} left( {{text{GSR}}_{{text{e}}} – {upsigma }_{{text{e}}} } right)left( {{text{GSR}}_{{text{n}}} – {upsigma }_{{text{n}}} } right)}}{{left( {{text{n}} – 1} right){upsigma }_{{text{e}}} {upsigma }_{{text{n}}} }} $$
(10)
$$ {text{d}} = 1 – left[ {frac{{mathop sum nolimits_{{{text{i}} = 1}}^{{text{n}}} left( {{text{GSR}}_{{text{e}}} – {text{GSR}}_{{text{n}}} } right)^{2} }}{{mathop sum nolimits_{{{text{i}} = 1}}^{{text{n}}} left( {left| {{text{GSR}}_{{text{e}}} – {text{GSR}}_{{{text{nave}}}} left| + right|{text{GSR}}_{{text{n}}} – {text{GSR}}_{{{text{nave}}}} } right|} right)^{2} }}} right] $$
(11)
Further, as with other statistical studies in meteorology36, the Pearson’s correlation coefficient (r) was used to quantify the strength of correlation between GSRe and GSRn. Finally, the Willmott index of agreement (d) commonly used in meteorological literature computed from Eq. 7 is used to assess the degree of GSRe/GSRn agreement34. More

Soil sample collectionSoil samples were collected from three different crude oil reservoirs et al. Faisaliyyah, Al Sina’iyah, and Ghubairah located in Riyadh, Saudi Arabia. Briefly, 400 g of soil samples were collected at 0–10 cm depth, under aseptic conditions. Samples were sieved by 2.5 mm pore size sieves, homogenized, and stored at 4ºC until use.Sources of different hydrocarbonsDifferent samples of crude oil, kerosene, diesel, and used oil were collected in sterile flasks from the tankers of Saudi Aramco Company (Dammam, Saudi Arabia). Additionally, another flask was prepared by mixing 1% of each oil in MSM liquid media to make up the mixed oil. The oil samples were sterilized by Millex® Syringe Filters (Merck Millipore co., Burlington, MA, United States) and stored at 4 °C for further usage.Isolation and identification of fungal speciesThe fungal species in the soil contaminated by crude oil were identified using the dilution method. Briefly, 10% of each soil sample was dissolved in distilled water and vortexed thoroughly. Then, 0.2 ml of each sample was cultured on a sterile PDA plate incubated at 28 °C for three days until the growth of different fungal colonies. Carefully, each colony was isolated, re-cultured on new PDA McCartney bottles of PDA slant, and incubated at 28 °C for three days. The fungi were identified microscopically using standard taxonomic keys based on typical mycelia growth and morphological characteristics provided in the mycological keys54. Besides, the taxonomy of the isolated yeast strains was confirmed by the API 20 C AUX kit (Biomerieux Corp., Marcy-l’Étoile, France) (data not shown). The morphology of pure cultures was tested and identified under a light microscope as described before55.The incidence of each strain was calculated as follows:$$ Incidence ;(% ) = frac{{{text{Number }};{text{of }};{text{samples }};{text{showed }};{text{microbial }};{text{growth}}}}{{{text{Total }};{text{samples}}}} times 100 $$Hydrocarbon tolerance testThe growth rate of isolated strains was tested in a liquid medium of MSM mixed with 1% of either crude oil, used oil, diesel, kerosene, or mixed oil. Furthermore, a control sample of MSM liquid medium without any of the oils tested and all culture media were autoclaved at 121 °C for 30 min. After cooling, 1 ml of each isolate was inoculated with one of the above mixtures and incubated at 25 °C on an orbital shaker. The growth rate was measured every three days for a month for each treatment versus the control. All experiments were performed in triplicates.Scanning electron microscopy (SEM)The morphology of different strains of the isolated fungi was tested by SEM, as previously described56, with some modifications. Briefly, 1 ml of each growing strain, in the liquid media, was centrifuged at the maximum speed (14,000 rpm) for 1 min, followed by fixation with 2.5% glutaraldehyde, and overnight incubation at 5 °C. Later, the sample was pelleted, washed with distilled water, then dehydrated with different ascending concentrations of ethanol (30, 50, 70, 90, 100 (v/v)) for 15 min at room temperature. Finally, samples were examined in the Prince Naif Research Centre (King Saud University, Riyadh, Saudi Arabia) by the JEOL JEM-2100 microscope (JEOL, Peabody, MA, United States), according to the manufacturer instructions.Crude oil degradation assayA modified version of the DCPIP assay57 was employed to assess the oil-degrading ability of the fungal isolates. For each strain, 100 ml of the autoclaved MSM was mixed with 1% (V/V) of one of the hydrocarbons (crude oil, used oil, diesel, kerosene, or mixed oil), 0.1% (v/v) of Tween 80, and 0.6 mg/mL of the redox indicator (DCPIP). Then, 1–2 ml of different fungi growing in liquid media (24–48 h) add to the Crude Oil Degradation media, prepared previously, and incubated for two weeks in a shaking incubator at 25 °C. All flasks were covered and protected from light, aeration, or temperature exchanges to reduce the effects of oil weathering (evaporation, photooxidation). The surfactant Tween 80 was used for bio-stimulation and acceleration of the biosurfactant production by increasing metabolism58. A non-inoculated Crude Oil Degradation media was used as the negative control. Afterward, the colorimetric analysis for the change in DCPIP color was estimated, spectrophotometrically, at 420 nm. All experiments were performed in triplicates.Preparation of cell-free supernatant (CFS)To prepare the Cell-Free Supernatant (CFS), all isolates were grown in MSM broth medium with 1% of either crude oil, used oil, diesel, kerosene, or mixed oil for 30 days in a shaking incubator at 25 °C. After incubation, the cells were removed by centrifugation at 10,000 rpm for 30 min at 4 °C. The supernatant (CFS) was collected and filter-sterilized with a 0.45 μm pore size sterile membrane. CFS was screened for the production of different biosurfactants. All the experiments were carried out in triplicates, and the average values were calculated.Drop-Collapse assayThe Drop-Collapse assay was performed as previously described9, with some modifications. 100 µl of crude oil was applied on glass slides, then 10 µl of each CFS was added to the center of the slide surface and incubated for a minute at room temperature. The slides were imaged by a light microscope using the 10X objective lenses. The spreading on the soil surface was scored by either « + » to indicate the level of positive spreading, biosurfactant production, or «—» for negative spreading. Biosurfactant production was considered positive at the drop diameter ≥ 0.5 mm, compared to the negative control (treated with distilled water).Oil spreading assayAn amount of 20 ml of water was added to the Petri plate (size of 100 mm) and mixed with 20 µl of crude oil or mixed oil, which created a thin layer on the water surface. Then, 10 µl of CFS was delivered onto the surface of the oil, and the clear zone surrounding the CFS drop was observed. The results were compared to the negative control (without CFS) and positive control of 1% SDS41. We have measured the clear zones diameter from images and calculate the actual values in regards to the diameter of the Petri dish (10 cm). The assay was performed in triplicates.Emulsification activity assayThe emulsification activity of each isolate was assessed by mixing equal volumes of MSM broth medium of each isolate with different oils in separate tubes. The samples were homogenized by vortex at high speed for two minutes at room temperature (25 °C) and allowed to settle for 24 h. The tests were performed in duplicate. Then, the emulsification index was calculated as follows59:$$ Emulsification; activity; left( % right) = frac{{{text{Height }};{text{of }};{text{emulsion }};{text{layer}}}}{{{text{Total }};{text{height}}}} times 100 $$Recovery of biosurfactantsThe recovery of biosurfactants from CFS was tested through different assays:Acid precipitation assay3 ml of each CFS was adjusted by 6 N HCl to pH 2 and incubated for 24 h at 4 °C. Later, equal volumes of chloroform/methanol mixture (2:1 v/v) were added to each tube, vortexed, and incubated overnight at room temperature. Afterward, the samples were centrifuged for 30 min at 10,000 rpm (4 °C), the precipitate (Light brown colored paste) was air-dried in a fume hood, and weighed53.Solvent extraction assayThe CFS containing biosurfactant was treated with a mixture of extraction solvents (equal volumes of methanol, chloroform, and acetone). Then, the new mixture was incubated in a shaking incubator at 200 rpm, 30 °C for 5 h. The precipitate was separated into two layers, in which the lower layer (White) was isolated, dried, weighed, and stored60.Ammonium sulfate precipitation assayThe CFS containing biosurfactant was precipitated with 40% (w/v) ammonium sulfate and incubated overnight at 4 °C. The samples were centrifuged at 10,000 rpm for 30 min (4 °C). The precipitate was collected and extracted with an amount of acetone equal to the volume of the supernatant. After centrifugation, the precipitate (Creamy-white) was isolated, air-dried in a fume hood, and weighed53.Zinc sulfate precipitation methodSimilarly, 40% (w/v) zinc sulfate was mixed with the CFS containing biosurfactant. Then, the mixture was incubated at 4 °C, overnight. The precipitate (Light Brown) was collected by centrifugation at 10,000 rpm for 30 min (4 °C), air-dried in a fume hood, and weighed53.Statistical analysisAll experiments were performed in triplicate, and the results were expressed as the mean values ± standard deviation (SD). One-way ANOVA and Dunnett’s tests were used to estimate the significance levels at P  More