Philippa Kaur

Quality control of the full-length transcriptomes
The FL transcriptomes for R. a. hainanus, R. a. himalayanus and Myotis ricketti were constructed based on sequencing data of three separated libraries on the PacBio Sequel platform. Specifically, a total of 3,444,947 subreads with 6,448,987,299 nucleotides, 3,255,638 subreads with 6,504,282,447 nucleotides and 3,403,451 subreads with 7,190,237,257 nucleotides were generated for R. a. hainanus, R. a. himalayanus and Myotis ricketti respectively. After quality control, we obtained 137,159 circular consensus sequencing (CCS) reads for R. a. hainanus, 137,160 CCS reads for R. a. himalayanus and 152,251 CCS reads for Myotis ricketti. With the standard IsoSeq. 3 classification and clustering pipeline, we identified 111,806 FLNC for R. a. hainanus, 105,713 FLNC for R. a. himalayanus and 122,222 FLNC for Myotis ricketti. After isoform-level polishing, 10384, 9984 and 10932 high quality isoforms were retained in R. a. hainanus, R. a. himalayanus and Myotis ricketti respectively. After removing redundancy with CD-HIT-EST and filtering isoforms shorter than 200 bp, the final FL transcriptomes for R. a. hainanus, R. a. himalayanus and Myotis ricketti (FL-CF-Rhai, FL-CF-Rhim and FL-FM-Myo, respectively) contain 10103, 9676 and 10504 FL isoforms with an average length of 2251, 2370 and 2530 bp, respectively (Table 2). Finally, the FL transcriptome from both CF and FM bats (FL-CF-FM) contains 26,342 transcripts with an average length of 2,405 bp (Table 2). BUSCO analysis revealed that a total of 2,354 (57.4%) BUSCOs were included in FL-CF-FM. We also found 39.9%, 38.1% and 41.9% BUSCOs in FL-CF-Rhai, FL-CF-Rhim and FL-FM-Myo, respectively (Table 4). Given the highly specialized function of the cochlea, we should not expect a high level of BUSCO value in FL transcriptome of cochlea. A recent single cell RNA-seq study has identified a similar number of genes expressed in the murine cochlea (a total of 12,944)30.
Table 4 Completeness of each of the four FL transcriptomes assessed by benchmarking universal single-copy ortholog (BUSCO) analysis.
Full size table

Quality control of annotation
Four FL transcriptomes (FL-CF-Rhai, FL-CF-Rhim, FL-FM-Myo, and FL-CF-FM) were functionally annotated by performing DIAMOND and BLASTx searches against the Nr and UniProt databases separately. For FL-CF-FM, 24,793 and 24,198 transcripts were annotated by Nr database and UniProt database, respectively (Table 3). After combining the annotation results from the two databases, a total of 24,833 transcripts were annotated in at least one database. We obtained similar annotation results for FL-CF-Rhai, FL-CF-Rhim and FL-FM-Myo (Table 3). Transcripts without annotations might be novel isoforms of echolocating animals or due to the lack of representative sequences for cochlea in public databases. More

These methods are an expanded version of those in our related work, Boakes et al.15.
The database was compiled over the period 2005–2008. Data collection equates to around 1500 person-days and data were gathered by a team of 21 people. Between them, team members were fluent in English, French, German, Mandarin, Russian, Spanish and Swedish. These languages were extremely helpful in transcribing museum specimen labels and in translating publications. However, the majority of publications were in English and we acknowledge that the database will be biased toward records published in English-language publications.
Our study focuses on the 130 galliform species that occur within the Palaearctic and Indo-Malay biogeographic realms22 (see Online-only Table 1). We have additionally included records of the Imperial Pheasant (Lophura imperialis) although it is now recognised that this is a hybrid and not a species. The geographic range of two of the species in the database, the Red Grouse (Lagopus lagopus) and the Rock Ptarmigan (Lagopus muta), extends to North America. North American data was often included in the information which museums sent us and in these instances we entered those records into the database since we thought they might be of use to researchers studying these species. However, it should be noted that we did not search exhaustively for records of these species in North America, we have merely included those that we came across.
We attempted to gather all species distribution data that could be accessed from five different sources; museum collections, literature records, banding (ringing) data, ornithological atlases and birdwatchers’ trip report websites. For each data source, exhaustive and systematic search strategies were adopted.
Museum collections
Using web-based searches and Roselaar23, 377 natural history collections were identified. We found contact details for 338 of these collections and requested by email or letter a list of the Galliformes in their holdings along with collection localities and dates. Non-respondents were recontacted. 135 museums were able to share data with us (see Online-only Table 2). Museum records were obtained through publicly available online databases e.g. ORNIS, electronic or paper catalogues sent to us by the museums or by visiting the museums and transcribing data directly from specimens or card catalogues. Almost half of the museums we contacted did not respond despite at least one follow-up enquiry, and there was substantial variation in the amount and format of data contributed by those that did reply. Altogether, over 50% of the records came from just six museums (Natural History Museum, London; Zoological Institute of the Russian Academy of Sciences, St Petersburg; Zoological Museum of Lomonosov Moscow State University; Field Museum of Natural History, Chicago; American Museum of Natural History, New York; National Museum of Natural History, Leiden), a single museum (the Natural History Museum, London) contributing nearly 20% of the museum records that could be georeferenced and dated15. Following databasing and/or georeferencing, records were returned to larger collections and to those who had requested the data.
Literature
Data from the literature were added to those previously collected by McGowan24. Entire series of key English-language international and regional ornithological journals such as Ibis, Bird Conservation International, Journal of the Bombay Natural History Society, and Kukila were scanned for relevant information, availability allowing. We began at the library of the Zoological Society of London and followed up missing journal issues at the BirdLife International library, Cambridge UK; the British Library, London, UK; the Edward Grey Institute, University of Oxford, UK. Relevant Chinese literature was also scanned. Additionally, data were obtained from regional reports, personal diaries, letters, newsletters etc stored in the archives of BirdLife International, Cambridge, UK; the World Pheasant Association, Newcastle, UK; the Edward Grey Institute, University of Oxford, UK. Several of the species/regional experts we consulted also contributed their personal records which were recorded in the database as ‘personal communications’. As far as it were possible, records were classed as primary or secondary data within the ‘dynamicProperties’ field of GalliForm14. It is important to note that some primary records or museum specimens will be duplicated within the database in the secondary data.
Banding records
Eighty-three ornithological banding groups were identified using web-based searches and were contacted via email. Thirty of these groups replied and only seven were able to provide us with data (see Table 1). The majority of galliform species tend not to be banded due to their large body sizes and spurs. Additionally, many of the banding groups kept their records on paper and were not able to send them to us. Nevertheless, we were able to access and georeference 15,152 banding records.
Table 1 The ringing groups that shared data with GalliForm.
Full size table

Ornithological atlases
We digitised location data from 20 ornithological atlases (see Table 2). Data from several other atlases were not used since the range of dates for the records was wider than 20 years.
Table 2 The atlases that were digitised to be included in GalliForm.
Full size table

Trip report website data
We used the two trip report websites that were popular with birders during the data recording period (2005–2008), www.travellingbirder.com and www.birdtours.co.uk. At that time, eBird (probably the most relevant current online source today) did not cover the majority of the countries within our study region, and our intention with the deposition of this dataset is to focus on pre-eBird data that are more difficult and time consuming to access. We extracted data from all trip reports of birdwatching visits to European, Asian and North African countries. Care was taken to enter reports that featured on both websites once only.
Criteria for data inclusion
To be included in the database, records had to meet the following criteria:
1.
The record identified the species of the bird concerned.

2.
The record contained either a verbal description of the locality at which the bird concerned was observed or the co-ordinates at which the bird was observed.

Records of captive birds were excluded. Records relating to non-native occurrences were included but were flagged in the ‘establishmentMeans’ field as “introduced”.
Data entry
GalliForm14 was originally compiled in the programme Microsoft Access 2003. To maximise uniformity in data entry, all data recorders were given thorough and consistent training and each was provided with a set of database guidelines. An Access Database form was created to standardise data entry and to enable multiple members of the team to collect data simultaneously.
Each entry in GalliForm14 corresponds to a single record of a single species recorded in a specific location. The data fields of GalliForm14 are described in Online-only Table 3. The taxonomy used has been updated to be consistent with the BirdLife International 2019 taxonomy (datazone.birdlife.org). All information was entered exactly as it was described in the data source, with as much information extracted as possible. Multiple records from different sources which recorded the same information were still included in the interest of completeness. The only exception to this is the trip report data in which we did not enter identical records which occurred on both the Travelling Birder and Bird Tours websites.
The source of the data, i.e. literature, museum, atlas, ringing or website trip report is recorded in the ‘dynamicProperties’ field under the code “dataSource”. For literature data, (where known) the nature of the record, i.e. primary or secondary, is recorded under the code “datatype”.
Taxonomy has of course changed considerably over time. To allow for this we recorded the taxonomy as it was described in the data source in the ‘originalNameUsage’ field. The current taxonomy was then selected from a look-up table. If at the time of data entry, the data compiler was unsure which species the synonym referred to, the species was tagged as “unknown” and the species was designated at a later date following further research on the synonym.
Identical localities can also be described in multiple ways. We recorded the locality as it was given in the data source in the ‘verbatimLocality’ field. If the ‘verbatimLocality’ clearly tallied with a locality already within the database, the record was linked to that locality in order to increase georeferencing efficiency.
It was rare for a source to record absence of evidence, i.e. a survey for a species at a particular locality which failed to find that species. However, in the few cases where we did come across such records, the locality and date of the survey were recorded and “absent” was recorded in the ‘occurrenceStatus’ field.
Each record refers to an independent observation. For museum and ringing records, this means a single individual. For literature, atlas or trip report records this may refer to a group of birds observed in one particular locality, on one particular day. If given, the number of total individuals is recorded in the ‘individualCount’ field. The number of males and females is recorded in the ‘sex’ field and the number of juveniles and adults in the ‘lifeStage’ field. If the ‘lifeStage’ field is blank, it is reasonable to assume the individual(s) is an adult.
Occasionally, additional information about the observation might be included in the data source, for example the habitat the bird was observed in or whether the bird was common or rare in that locality. These data are recorded in the ‘habitat’ and ‘organismQuantity’ fields, respectively. Any additional information which did not fit within the structure of the database was recorded in the ‘occurrenceRemarks’ field, along with any notes found on museum labels.
For the purposes of data deposition, the database was converted to a tab-delimited CSV file with all fields following Darwin Core format. A full summary of these fields is given in Online-only Table 3.
Georeferencing
Locality descriptions were converted to geographic co-ordinates using a wide range of atlases and gazetteers, co-ordinates generally only being assigned if accurate to one degree (although in the majority of cases the locations were accurate to within 30 minutes, Table 3). We would initially search for a locality within the gazetteers available to us at the time. If the locality was not listed within those gazetteers we would search for the locality using atlases. Since this fieldwork was conducted, MaNIS standards have become widely used for studies of this kind, but these weren’t fully developed at the time of data collection25. Named places, e.g. towns or counties, were georeferenced using their geographic centre and georeferencing uncertainty measured from the centre to the edge of the named place. Often localities were given simply as the name of a river, mountain or Protected Area. In these instances we used the midpoint of the river between source and mouth (uncertainty measured as distance from midpoint to source/mouth), the summit of the mountain (uncertainty measured as distance from summit to approximate mountain foot) and the rough centre of the Protected Area (uncertainty measured as distance from centre to Protected Area edge). If a particular locality description matched two or more places their midpoint was taken (uncertainty measured as distance from midpoint to place). Offsets from localities (e.g. “50 km N of Kuala Lumpur”; “8 miles along the road from Sheffield to Chesterfield”) were measured using a digital atlas (uncertainty was approximated at the georeferencer’s discretion in these instances, usually between 3 and 10 arc-minutes, depending on the vagueness of the offset.) For georeferencing done ‘in house’, the gazeteer/atlas used was recorded.
Table 3 Georeference and date completeness of the records.
Full size table

When possible, localities we could not georeference ourselves were sent to regional experts.
92% of our localities are georeferenced to an accuracy of 30 minutes, corresponding to 82% of occurrence records (see Table 3).
We had less success at georeferencing museum records than literature records15, due in part to difficulties in reading hand-writing on specimen labels. Older records were also harder to georeference, presumably due to changes in place names over time, and to some early ornithologists failing to document the collection locality. As might be expected, localities from countries that do not use the Roman alphabet were also harder to georeference.
Some records were excluded from the database based on their locality: records which we thought were trading localities, notably Malacca in Malaysia and Leadenhall Market in the UK; records from captive specimens, e.g. zoological gardens.
Dating
49% of records are dated to within an accuracy of one year. Where possible, we assigned date ranges to undated records. For example, if the name of the collector was given on a museum specimen and we knew when that collector was active in that region, we assigned a date range covering that period. There remain undated records which could perhaps be dated in this way. Undated literature records were designated as occurring before their publication date. We were able to date 89% of records to within 10 years. More

Affiliations

School of Geosciences, University of Edinburgh, Edinburgh, UK
Andrew T. Nottingham & Patrick Meir

Smithsonian Tropical Research Institute, Panama City, Panama
Andrew T. Nottingham, Esther Velasquez & Benjamin L. Turner

Research School of Biology, Australian National University, Canberra, Australian Capital Territory, Australia
Patrick Meir

Authors
Andrew T. Nottingham

Patrick Meir

Esther Velasquez

Benjamin L. Turner

Corresponding author
Correspondence to Andrew T. Nottingham. More

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Main features of mycorrhizal genomes
We compared 62 draft genomes from mycorrhizal fungi, including 29 newly released genomes, and predicted 9344–31,291 protein-coding genes per species (see “Methods”, Supplementary Information and Supplementary Data 1). This set includes new genomes from the early diverging fungal clades in the Russulales, Thelephorales, Phallomycetidae, and Cantharellales (Basidiomycota), and Helotiales and Pezizales (Ascomycota). We combined these mycorrhizal fungal genomes with 73 fungal genomes from wood decayers, soil/litter saprotrophs, and root endophytes (Fig. 1 and Supplementary Data 2). There was little variation in the completeness of the gene repertoires, based on Benchmarking Universal Single-Copy Orthologs (BUSCO) analysis (coefficient of variation, c.v. = 7.98), despite variation in assembly contiguity (Fig. 1). Genome size varied greatly within each phylum, with genomes of mycorrhizal fungi being larger than those of saprotrophic species (Figs. 1 and 2, and Supplementary Data 2; P  More

Chemical properties of submicrometer aerosols
Figure 1a presents the time series of the mass concentrations of OC and water-soluble OC (WSOC) in the submicrometer aerosol particles during the cruise. Overall, the patterns of the temporal OC and WSOC variations were similar to each other (r = 0.632, p  More

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