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extraction and sequencing\nTissue samples (n = 144) of morphologically identified bats of R. acuminatus (n = 10), R. affinis (n = 57), R. malayanus (n = 14), and R. shameli (n = 63) were analysed for this study. These bats were captured with mist nets and harp-traps during several field surveys in Cambodia (2010), Laos (2007) and Vietnam (2011) to promote bat conservation. In 2007, the Muséum national d’Histoire naturelle (MNHN, Paris, France) was mandated by UNESCO and the World Heritage House of Luang Prabang to conduct a mammal survey in northern Laos. In 2010, the MNHN was mandated by UNESCO and the National Authority of Preah Vihear to conduct a mammal survey in northern Cambodia. Field surveys in Vietnam were authorized by the Forest Protection Department of the Ministry of Agriculture and Rural Development, the local manager boards of different protected areas (see online supplementary Table S1), and the Institute of Ecology and Biological Resources (IEBR, Hanoi, Vietnam). The animals were handled according to guidelines and recommendations of the American Society of Mammologists27. They were measured, photographed and identified by the authors (AH, GS and VTT). Tissue samples were taken from the chest muscles of voucher specimens or from the patagium (biopsy punches; 2 mm diameter) of released bats. Samples were preserved in 95% ethanol.\nTotal DNA was extracted using QIAGEN DNeasy Tissue Kit (Qiagen, Germany) in accordance with the manufacturer’s instructions. The barcode fragment of the CO1 gene (657 bp) was amplified and sequenced using the primers UTyr and C1L70528. PCR amplifications of the CO1 gene were performed as previously published29. PCR products were purified using ExoSAP Kit (GE Healthcare, UK) and sequenced using the Sanger method on an ABI 3730 automatic sequencer at the Centre National de Séquençage (Genoscope) in Evry (France). Haplotypes were assembled with forward and reverse eletcropherograms using Sequencher 5.1 (Gene Codes Corporation, Ann Arbor, MI, USA). No gaps and stop codons were found in the CO1 sequences after translation into amino-acids. Sequences generated for this study were deposited in the GenBank database (accession numbers MW712891-MW713034) (see online supplementary Table S1).\n\nAnalyses of CO1 sequences\nOur sequences were aligned with 199 additional CO1 sequences downloaded from GenBank. Note that the CO1 sequences of seven bats found positive for viruses closely related to SARS-CoV-21,5,6 were assembled on Geneious Prime 2020.0.3 (Biomatters Ltd., Auckland, New Zealand) by mapping available SRA data to a CO1 reference. Sequences were aligned using AliView 1.2230. Our final CO1 alignments contain 37 sequences for R. acuminatus, 44 sequences for R. malayanus, 82 sequences for R. shameli, and 180 sequences for R. affinis. These four alignments were analysed in PopART 1.513 to construct haplotype networks using the median joining method with equal weights for all mutations. The 62 localities where bats were sampled are shown in the map of Fig. 2 and their geographic coordinates are detailed in online supplementary Table S1.\n\nPrediction of ecological niches\nFor bat SCoVrCs, the ecological niche was inferred using GPS data collected for viruses published during the last two decades. The list of the 19 available geographic records is provided in online supplementary Table S2. For bat SCoV2rCs, the ecological niche was initially predicted using the four geographic localities where viruses were previously detected1,5–7: two in Yunnan, one in northern Cambodia, and one in eastern Thailand (data set A). However, the use of only four records is questionable since Van Proosdij et al.22 have estimated that a minimum of 13 records is required to develop accurate distribution models for widespread taxa. For that reason, we used a genetic approach to increase the number of geographic records. Since the detection of identical CO1 sequences in different bat populations is indicative of recent dispersal events of females, we also selected the 17 geographic records where bats showed the same CO1 haplotypes than virus-positive bats (data set B: 21 points; see online supplementary Table S1).\nFor each of the three data sets (bat SCoVrCs; data sets A and B for bat SCoV2rCs), the 19 bioclimatic variables available in the WorldClim database31 were studied for an area corresponding to the minimum and maximum latitudes and longitudes of the selected points (19 points for bat SCoVrCs; 4 and 21 points, respectively for the SCoV2rCs data sets A and B) and the caret R package32 was used to determine the least correlated variables (|r|\u003c 0.7)33. For bat SCoVrCs, the following five predictor bioclimatic variables were retained: Bio3 (isothermality), Bio4 (temperature seasonality), Bio5 (maximum temperature of the warmest month), Bio15 (precipitation seasonality), and Bio18 (precipitation of the warmest quarter). For data set A, the following seven predictor bioclimatic variables were retained: Bio3, Bio7 (temperature annual range), Bio10 (mean temperature of the warmest quarter), Bio13 (precipitation of the wettest month), Bio14 (precipitation of driest month), Bio15, and Bio18. For data set B, the following seven predictor bioclimatic variables were selected: Bio2 (mean diurnal range), Bio3, Bio7, Bio10, Bio13, Bio15, Bio17 (precipitation of the driest quarter), and Bio18. Ecological niche modelling was performed with the MaxEnt algorithm using ENMTools in R34. The MaxEnt approach was chosen for its ability to work with presence-only data sets and to produce results with a low sample size35. The area under the curve (AUC) of the receiver operating characteristic plot was used as a first measure of model accuracy, a value of 0.5 indicating model accuracy not better than random, and a value of 1 indicating perfect model fit36,37. To test for sampling bias, the distribution model using all selected localities was tested against a null model developed by 1000 times drawing an equal number of random points from the entire study area37. The position of the AUC value was tested against the 95% confidence interval (CI) of the 1000 AUC values of the null-models. If the AUC value is ≥ 95% CI null-model’s AUCs, the model is considered performing significantly better than a random model37.\n\nEthical statement\nEthical review and approval were not available for our study because the field missions were carried out between 2004 and 2011, i.e., before the creation of the ethical committee at the Muséum national d’Histoire naturelle. However, the field studies were carried out in compliance with the ARRIVE guidelines."}