Journal of Heredity, 2023, XX, 1–6
https://doi.org/10.1093/jhered/esad056
Advance access publication 4 October 2023
Genome Resources
Received July 17, 2023;  Accepted September 24, 2023
Genome Resources
Reference genome for the Northern bat (Eptesicus 
nilssonii), a most northern bat species
Veronika N. Laine1,*, , Arto T. Pulliainen2,  and Thomas M. Lilley1,
1BatLab Finland, Zoology unit, Finnish Museum of Natural History, University of Helsinki, Helsinki, Finland, 
2Institute of Biomedicine, University of Turku, Turku, Finland
*Corresponding author: Veronika N. Laine, BatLab Finland, Zoology unit, Finnish Museum of Natural History, University of Helsinki, Helsinki, Finland. Postal 
address: Finnish Museum of Natural History, Pohjoinen Rautatiekatu 13, 00100 Helsinki  Email: veronika.laine@helsinki.fi
Corresponding Editor:  Alexander Suh
Abstract 
The northern bat (Eptesicus nilssonii) is the most northern bat species in the world. Its distribution covers whole Eurasia, and the species is thus 
well adapted to different habitat types. However, recent population declines have been reported and rapid conservation efforts are needed. Here 
we present a high-quality de novo genome assembly of a female northern bat from Finland (BLF_Eptnil_asm_v1.0). The assembly was generated 
using a combination of Pacbio and Omni-C technologies. The primary assembly comprises 726 scaffolds spanning 2.0 Gb, represented by a 
scaffold N50 of 102 Mb, a contig N50 of 66.2 Mb, and a BUSCO completeness score of 93.73%. Annotation of the assembly identified 20,250 
genes. This genome will be an important resource for the conservation and evolutionary genomic studies especially in understanding how rapid 
environmental changes affect northern species.
Key words: adaptation, Chiroptera, climate change, conservation, northern hemisphere
Introduction
The northern bat belongs to the global and speciose genus 
of serotine bats, Eptesicus (family Vespertilionidae, subfamily 
Vespertilioninae). It exhibits a wide trans-continental distri-
bution across Eurasia, running pretty much continuous across 
Siberia from Hokkaido Island to Fennoscandia (Suominen 
et al. 2020) (Fig. 1). Because of its broad distribution range 
and several isolated relict populations, which have arisen as 
a consequence of distribution range shifts caused by the last 
ice age, the possibility of differentiation across populations 
is apparent, even to the degree of some isolated populations 
forming distinct subspecies. With a distribution that extends 
well over the Arctic circle (Siivonen and Wermundsen 2008; 
Kotila et al. 2023) to the north, the species appears to be well 
adapted to northerly latitudes short active seasons (Kotila 
et al. 2023; Suominen et al. 2023), short nights (Vasko et 
al. 2020) and a long season of inactivity during the winter 
(Blomberg et al. 2021).
However, due to the extreme latitudinal distribution of the 
species, it is particularly vulnerable to the effects of climate 
change. Estimates suggest that the Arctic and Sub-Arctic are 
experiencing the most rapid effects of climate change, which 
along with urbanization and modification of natural habitats 
puts wildlife under immeasurable pressure (Rantanen et al. 
2022). Furthermore, the northern bat population in southern 
Sweden has already seen decline of c. 50%, which has been 
partially attributed to competition from bat species increasing 
their distribution range to the north as a consequence of cli-
mate change (Rydell et al. 2020). The production of a high-
quality reference genome allows the investigation of the 
effects of environmental change on the northern bat to as-
sist in better population viability assessments and planning 
of conservation measures via population genetic approaches.
Methods
Biological materials
A female northern bat was sampled in February 2013 from 
Lieto, Finland (60.57 N, 22.43 E). An active bat was found 
inside a house in the middle of the hibernation season. The 
unusually behaving bat subsequently died after it had been 
rescued. From this sample a cell clone was derived from pri-
mary cells isolated from bat kidney tissue and immortalized 
via the transfer and stable production of the Simian virus 
40 Large T antigen (SV40LT). Kidneys were cut in small 
pieces (ca. 8 mm3) and incubated in 0.05% Trypsin-EDTA 
solution (Gibco #25300-054) for 15 h at 4 °C, followed by 
1 h at 37 °C in gentle rotation. Tissue pieces and detached 
cells were pelleted by centrifugation (300 × g, 10 min, room 
temperature), washed once with calcium- and magnesium-
free phosphate buffered saline (PBS, Lonza #BE17-516F), 
and  re-suspended in Dulbecco’s modified Eagle medium 
(DMEM, Lonza #12-709F) supplemented with 10% heat-
inactivated fetal bovine serum (iFBS, Gibco #10270-106), 
© The American Genetic Association. 2023.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), 
which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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100 U/mL penicillin and 100 µg/mL streptomycin antibac-
terial mixture (PEN/STREP, Biochrom #A2213) and 2.5 
µg/mL of anti-fungal amphotericin B (Sigma #2942). The 
mixtures of tissue pieces and cells were incubated at 37 
°C in humidified atmosphere with 5% CO2 in six-well cell 
culture plate format for up to 3 weeks. The medium was 
exchanged in every 3 to 4 days. The cells were detached by 
trypsinization,  re-cultured in DMEM/iFBS/PEN/STREP, and 
transfected (Fugene 6, Promega #E269A) with pBABE-puro 
SV40 LT plasmid (Addgene #13970, selection with 4 µg/mL 
puromycin A111380-03), allowing ectopic expression of the 
SV40LT. The SV40LT-immortalized cells were serially diluted 
and cultured in 96-well cell culture plate format in DMEM/
iFBS/PEN/STREP, allowing isolation of clonal cell lines 
originating from single transfected cells (Supplementary Fig. 
1). The clonal cell lines were expanded and routinely cultured 
in DMEM/iFBS/PEN/STREP at 37 °C in humidified atmos-
phere with 5% CO2. To prepare the sample for sequencing, 
25,000,000 cells of the clonal isolate 8+ (Supplementary Fig. 
1) were collected by trypsinization, washed twice with PBS 
and then frozen for storage at −80 °C. The high molecular 
weight DNA was extracted from the cell culture with Qiagen 
DNeasy Blood & Tissue Kit. DNA samples were quantified 
using Qubit 2.0 Fluorometer (Life Technologies, Carlsbad, 
CA, USA).
DNA sequencing and genome assembly
Pacbio library preparation and sequencing
The PacBio SMRTbell library (~20 kb) for PacBio Sequel was 
constructed using SMRTbell Express Template Prep Kit 2.0 
(PacBio, Menlo Park, CA, United States of America) using the 
manufacturer recommended protocol. The library was bound 
to polymerase using the Sequel II Binding Kit 2.0 (PacBio) and 
loaded onto PacBio Sequel II. Sequencing was performed on 
PacBio Sequel II 8M SMRT cells generating 281.2 gigabases 
of data.
Dovetail Omni-C library preparation and 
sequencing
For each Dovetail Omni-C library, chromatin was fixed in place 
with formaldehyde in the nucleus and then extracted. Fixed 
chromatin was digested with DNAse I, chromatin ends were 
repaired and ligated to a biotinylated bridge adapter followed 
by proximity ligation of adapter containing ends. After prox-
imity ligation, crosslinks were reversed, and the DNA purified. 
Purified DNA was treated to remove biotin that was not internal 
to ligated fragments. Sequencing libraries were generated using 
NEBNext Ultra enzymes and Illumina-compatible adapters. 
Biotin-containing fragments were isolated using streptavidin 
beads before PCR enrichment of each library. The library was 
sequenced as 150 bp paired-end on an Illumina HiSeqX plat-
form to produce an approximately 30× sequence coverage.
Assembly and scaffolding
For the Pacbio assembly, Wtdbg2 (version 2.5) (Ruan and Li 
2020) was run with the following parameters: --genome_size 
2.0g --read_type sq --min_read_len 20000 --min_aln_len 
8192 using the Pacbio CLR reads. Blobtools (version 1.1.1) 
(Laetsch and Blaxter 2017) was used to identify potential 
contamination in the assembly based on BLAST (version 2.9) 
(Altschul et al. 1990) results of the assembly against the nt 
database. A fraction of the scaffolds was identified as con-
taminant and were removed from the assembly. The filtered 
assembly (filtered.asm.cns.fa) was then used as an input to 
purge_dups (version 1.1.2) (Guan et al. 2020) and potential 
haplotypic duplications were removed from the assembly, 
resulting in the final purged.fa assembly.
Fig. 1. Distribution range of E. nilssonii. Map adapted from www.iucn.org.
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The input de novo assembly and Dovetail OmniC library 
reads were used as input data for HiRise, a software pipe-
line designed specifically for using proximity ligation data to 
scaffold genome assemblies (Putnam et al. 2016). Dovetail 
OmniC library sequences were aligned to the draft input 
assembly using bwa (0.7.17) (Li 2013) (https://github.com/
lh3/bwa) only using reads with MQ > 50. The separations 
of Dovetail OmniC read pairs mapped within draft scaffolds 
were analyzed by HiRise to produce a likelihood model for 
genomic distance between read pairs, and the model was used 
to identify and break putative misjoins, to score prospective 
joins, and make joins above a threshold. Due to possibility of 
remnants of plasmid containing SV40LT remaining in the ref-
erence genome, the sequence of SV40LT (NC_001669.1) was 
searched with Blast from the final assembly.
RNA sequencing
RNA was extracted from the cultured cells with QIAGEN 
RNeasy Plus Kit and a standard RNA library was prepared 
with rRNA-depletion with QIAGEN FastSelect HMR kit. 
The libraries were sequenced at an Illumina NovaSeq plat-
form (Illumina, CA) targeting approximately 20 million 150 
bp paired end reads.
Annotation
Repeat families found in the genome assemblies of Eptesicus 
nilssonii were identified de novo and classified using the soft-
ware package RepeatModeler (version 2.0.1) (Flynn et al. 
2020). RepeatModeler depends on the programs RECON 
(version 1.08) (Bao and Eddy 2002) and RepeatScout 
(version 1.0.6) (Price et al. 2005) for the de novo identifi-
cation of repeats within the genome. The custom repeat li-
brary obtained from RepeatModeler were used to discover, 
identify and mask the repeats in the assembly file using 
RepeatMasker (Version 4.1.0) (Smit et al. 2021). Coding 
sequences from Eptesicus fuscus (GCF_000308155.1), 
Myotis myotis (GCF_014108235.1) and Pipistrellus kuhlii 
(GCF_014108245.1) were used to train the initial ab in-
itio model for E. nilssonii using the AUGUSTUS software 
(version 2.5.5) (Stanke et al. 2008). Six rounds of predic-
tion optimization were done with the software package 
provided by AUGUSTUS. The same coding sequences were 
also used to train a separate ab initio model for E. nilssonii 
using SNAP (version 2006-07-28) (Korf 2004). RNAseq 
reads were mapped onto the genome using the STAR aligner 
software (version 2.7) (Dobin et al. 2013) and intron hints 
generated with the bam2hints tools within the AUGUSTUS 
software. MAKER (v3.01.03) (Cantarel et al. 2008), SNAP 
and AUGUSTUS (with intron-exon boundary hints provided 
from RNAseq) were then used to predict for genes in the 
repeat-masked reference genome. To help guide the predic-
tion process, Swiss-Prot peptide sequences from the UniProt 
database were downloaded and used in conjunction with the 
protein sequences from E. fuscus, M. myotis and P. kuhlii to 
generate peptide evidence in the Maker pipeline. Only genes 
that were predicted by both SNAP and AUGUSTUS software’s 
were retained in the final gene sets. To help assess the quality 
of the gene prediction, Annotation Edit Distance (AED) 
scores were generated for each of the predicted genes as part 
of the MAKER pipeline. Genes were further characterized for 
their putative function by performing a BLAST search of the 
peptide sequences against the UniProt database. tRNA were 
predicted using the software tRNAscan-SE (version 2.05) 
(Chan et al. 2021).
Mitochondrial genome assembly
The raw Pacbio subreads were aligned M. myotis mitochondria 
(NC_029346.1) with minimap2 (version 2.24) (Li 2018). 
The aligned reads were extracted with Samtools sort and 
transformed from bam-format to fastq-format with Samtools 
fastq (version 1.16.1) (Danecek et al. (2021). The mitochon-
drial genome was assembled with Canu (version 2.1.1.) 
(Koren et al. 2017) (genomeSize=20k, corOutCoverage=999) 
and visual inspection and consensus sequence of the assembled 
contigs was made with Geneious (version 11.0.3.) (https://
www.geneious.com). Annotation was done with MitoZ (ver-
sion 3.4) (Meng et al. 2019) using clade Chordata.
Genome quality assessment
BUSCO (version 4.0.5) (Manni et al. 2021) was used to eval-
uate genome quality and completeness with the eukaryotes 
database (eukaryota_odb10) that contains 255 genes and 70 
taxa. See Table 1 for a list of software used in this study.
Results
Nuclear assembly
We generated a de novo nuclear genome assembly of the 
northern bat (BLF_Eptnil_asm_v1.0) using 12 million PacBio 
CLR reads and 160 million read pairs of OmniC data. The 
Pacbio data yielded ~141 fold coverage (N50 read length 
31,406 bp; minimum read length 50 bp; mean read length 
22,041.9 bp; maximum read length 27,6123 bp) based on 
the final assembled genome size of 2.0 Gb. Assembly statistics 
are reported in tabular form in Table 2. The final assembly 
consists of 726 scaffolds spanning 2.0 Gb with contig N50 of 
66.2 Mb, scaffold N50 of 142.1 Mb, largest contig of 202.7 
Mb, largest scaffold of 239.8 Mb and number of gaps is 166. 
The Omni-C contact map suggests that the primary assembly 
is highly contiguous (Fig. 2). Gene annotation predicted total 
of 20,250 genes. RepeatMasker masked 36.41% of the ge-
nome of which class I TEs repeats were 18.17% and class II 
TEs repeats 2.98%. The assembly has a BUSCO completeness 
score of 93.73% using the eukaryota gene set.
Mitochondrial assembly
The mitochondrial genome assembled with Canu resulted in 
five contigs which were aligned with Geneious and the final 
consensus had size of 17,011 bp. The base composition of the 
final assembly version is A = 32.1%, C = 23.9%, G = 14.9%, 
T = 29.1%, and consists of 22 transfer RNAs and 13 protein 
coding genes.
Discussion
Here, we present high-quality reference genome for E. 
nilssonii. It adds to the growing number of bat genomes 
available for research (Jebb et al. 2020). The production of 
this reference genome can assist in understanding the fate of 
the species by allowing in-depth analysis of population con-
nectivity and structure, the number of ancestral populations 
and possible population differentiation that could have led 
to potentially beneficial local adaptations. Furthermore, 
the production of various genomes assists in illuminating 
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the spectacular adaptive radiation and systematics of bats 
(Jebb et al. 2020), with further potential of uncovering 
mechanisms that allow bats to harbor a variety of zoonotic 
pathogens without apparent harm (Jakava-Viljanen et al. 
2010; O’Shea et al. 2014; Veikkolainen et al. 2014; Kivistö 
et al. 2019).
Eptesicus nilssonii has a remarkable distribution range 
spanning the entire Palearctic boreal zone (Suominen et al. 
2020). However, the species also has little possibility adjust 
its distribution range to the north in response to the current 
climate change. With E. nilssonii being the northernmost bat 
species in the world, we can also expect the reference genome 
to provide additional and complimentary insights into genetic 
components of cold adaptation (Yurchenko et al. 2018).
We also broaden the potential use of cell lines to the as-
sist in the production of high-quality reference genomes. 
Genomes, such as the one presented here provide researchers 
and conservation scientists tools to access the most advanced 
downstream bioinformatic methods to better safeguard 
global biodiversity.
Supplementary Material
Supplementary material is available at Journal of Heredity 
online.
Supplementary Fig. 1. Morphology of the clonal SV40LT-
immortalized kidney cells of the reference northern bat. Phase 
contrast microscope images at 40× of all the isolated clonal 
cell lines. The clonal isolate 8+ was used for the sequencing.
Acknowledgments
No ethics permits were needed in the process. We thank 
Dovetail Genomics for the sequencing, help and support.
Funding
This work was supported by Emil Aaltonen foundation; 
Academy of Finland (AP, TML grant # 329250); Dovetail tree 
of life grant award.
Conflict of interest statement. None declared.
Table 1. Assembly pipeline and software usage.
Method Software Version
K-mer counting Meryl 1.4
De novo assembly (contigs) Wtdbg2 2.5
Contamination screening
  Alignment Blast 2.9
  Screening Blobtools 1.1.1
Low-coverage, duplicated contigs removal purge_dups 1.1.2
Scaffolding
  Omni-C scaffolding HiRise n/a
  Omni-C alignment to draft bwa 0.7.17
  Omni-C Contact map generation Juicer 1.5
Annotation
  Repeat family identification RepeatModeler 2.0.1
RECON 1.0.8
RepeatScout 1.0.6
  Repeat masking RepeatMasker 4.1.0
  Gene prediction optimisation AUGUSTUS 2.5.5
SNAP 2006-07-28
  RNA read alignment Star 2.7
  Intron hints bam2hints (AUGUSTUS) 2.5.5
  Annotation MAKER v3.01.03
AUGUSTUS 2.5.5
SNAP 2006-07-28
  Gene function prediction Blast 2.9
  tRNA prediction tRNAscan-SE 2.05
Mitogenome assembly
  Read alignment Minimap2 2.24
  Read extraction Samtools 1.16.1
  Assembly Canu 2.1.1.
  Visual inspection and consensus Geneious 11.0.3
  Annotation MitoZ 3.4
Genome quality assessment
  General metrics QUAST 5.2.0
  Genome quality and completeness BUSCO 4.0.5
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Table 2. Summary statistics of the sequencing datasets used and the assembly.
Bioprojects NCBI BioProject PRJNA984811
NCBI BioSample SAMN35778752
Genome sequence NCBI Genome accessions JAULJE000000000
Sequencing data PacBio CLR reads Run1 Run2
SRR24953704 SRR24953705
Omni-C Illumina reads Run1
SRR24955932
RNAseq Run1
SRR24955933
Genome assembly quality metrics Assembly identifier BLF_Eptnil_asm_v1.0
Pacbio read coverage 141x
Number of contigs 902
Contig N50 (bp) 5,96,08,874
Longest contigs 10,32,11,782
Number of scaffolds 726
Scaffold N50 (bp) 10,23,62,837
Largest scaffold 13,43,36,775
Gaps 166
Size of final assembly (bp) 2,00,10,80,703
BUSCO C S D F M
Completeness (eukaryota), N = 255 93.73% 92.94% 2 8 8
Organelle Complete mitochondrial sequence OR162376
Fig. 2. Omni-C contact density map.
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Data Availability
Data generated for this study are available under NCBI 
BioProject PRJNA984811. Raw sequencing data for reference 
sample (NCBI BioSample SAMN35778752) are deposited in 
the NCBI Short Read Archive (SRA) under SRR24953704 and 
SRR24953705 for PacBio sequencing data, SRR24955932 
for Omni-C Illumina Short read sequencing data and for 
RNAseq SRR24955933. This Whole Genome Shotgun project 
has been deposited at DDBJ/ENA/GenBankunder the acces-
sion JAULJE000000000. The version described in this paperis 
version JAULJE010000000. The GenBank organelle genome 
assembly for the mitochondrial genome is OR162376.
References
Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local align-
ment search tool. J Mol Biol. 1990:215:403–10.
Bao Z, Eddy SR. Automated de novo identification of repeat sequence 
families in sequenced genomes. Genome Res. 2002:12:1269–1276.
Blomberg AS, Vasko V, Meierhofer MB, Feva T, Lilley TM. Winter activ-
ity of boreal bats. Mamm Biol. 2021:101:609–618. doi: 10.1007/
s42991-021-00111-8
Cantarel BL, Korf, I, Robb, SM, Parra G, Ross E, Moore B, Holt C, 
Sánchez Alvarado A, Yandell M. MAKER: an easy-to-use annota-
tion pipeline designed for emerging model organism genomes. Ge-
nome Res. 2008:18:188–96.
Chan PP, Lin BY, Mak AJ, Lowe TM. tRNAscan-SE 2.0: improved de-
tection and functional classification of transfer RNA genes. Nucleic 
Acids Res. 2021:49:9077–9096.
Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, 
Whitwham A, Keane T, McCarthy SA, Davies RM, et al. Twelve 
years of SAMtools and BCFtools. Gigascience. 2021:10:giab008.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut 
P, Chaisson M, Gingeras TR. STAR: ultrafast universal RNA-seq 
aligner. Bioinformatics. 2013:29:15–21.
Flynn JM, Hubley R, Goubert C, Rosen J, Clark AG, Feschotte C, Smit 
AF. RepeatModeler2 for automated genomic discovery of transpos-
able element families. PNAS. 2020:117:9451–9457.
Guan D, McCarthy SA, Wood J, Howe K, Wang Y, Durbin R. 
Identifying and removing haplotypic duplication in primary ge-
nome assemblies. Bioinformatics. 2020:36:2896–2898.
Jakava-Viljanen M, Lilley T, Kyheroinen E-M, Huovilainen A. First en-
counter of European bat lyssavirus type 2 (EBLV-2) in a bat in Fin-
land. Epidemiol Infect. 2010:138:1581–1585.
Jebb D, Huang Z, Pippel M, Hughes GM, Lavrichenko K, Devanna 
P, Winkler S, Jermiin LS, Skirmuntt EC, Katzourakis A, et al. Six 
reference-quality genomes reveal evolution of bat adaptations. Na-
ture. 2020:583:578–584.
Kivistö I, Tidenberg E-M, Lilley T, Suominen K, Forbes KM, Vapalahti 
O, Huovilainen A, Sironen T. First report of Coronaviruses in north-
ern European bats. Vector-Borne Zoonotic Dis. 2019:20:155–158. 
doi: 10.1089/vbz.2018.2367
Kotila M, Suominen KM, Vasko VV, Blomberg AS, Lehikoinen A, 
Andersson T, Aspi J, Cederberg, T, Hänninen J, Inkinen J, et 
al. Large-scale long-term passive-acoustic monitoring reveals 
spatio-temporal activity patterns of boreal bats. Ecography. 
2023:2023:e06617. doi: 10.1111/ecog.06617
Koren S, Walenz BP, Berlin K, Miller JR, Phillippy AM. Canu: scalable 
and accurate long-read assembly via adaptive k-mer weighting and 
repeat separation. Genome Res. 2017:27:722–736. doi: 10.1101/
gr.215087.116
Korf I. Gene finding in novel genomes. BMC Bioinform. 2004:5:59.
Laetsch DR, Blaxter ML. BlobTools: interrogation of genome 
assemblies. F1000Research. 2017:6:1287.
Li H. Aligning sequence reads, clone sequences and assembly contigs 
with BWA-MEM. arXiv preprint arXiv. 2013:1303.3997. doi: 
10.48550/arXiv.1303.3997
Li H. Minimap2: pairwise alignment for nucleotide sequences. Bioin-
formatics. 2018:34:3094–3100.
Manni M, Berkeley MR, Seppey M, Simão FA, Zdobnov EM. BUSCO 
update: novel and streamlined workflows along with broader and 
deeper phylogenetic coverage for scoring of eukaryotic, prokary-
otic, and viral genomes. Mol Biol Evol. 2021:38:4647–4654.
Meng G, Li Y, Yang C, Liu S. MitoZ: a toolkit for animal mitochondrial 
genome assembly, annotation and visualization. Nucleic Acids Res. 
2019:47:e63.
O’Shea TJ, Cryan PM, Cunningham AA, Fooks AR, Hayman DTS, Luis 
AD, Peel AJ, Plowright RK, Wood JLN. Bat flight and zoonotic 
viruses. Emerg Infect Dis. 2014:20:741–745.
Price AL, Jones NC, Pevzner PA. De novo identification of repeat 
families in large genomes. Bioinformatics. 2005:21:i351–i358.
Putnam NH, O’Connell BL, Stites JC, Rice BJ, Blanchette M, Calef R, 
Troll CJ, Fields A, Hartley PD, Sugnet CW, et al. Chromosome-scale 
shotgun assembly using an in vitro method for long-range linkage. 
Genome Res. 2016:26:342–350.
Rantanen M, Karpechko AY, Lipponen NK, Hyvärinen O, Ruosteenoja 
K, Vihma T, Laaksonen A. The Arctic has warmed nearly four 
times faster than the globe since 1979. Commun Earth Environ. 
2022:3:168.
Ruan J, Li H. Fast and accurate long-read assembly with wtdbg2. Nat 
Methods. 2020:17:155–158.
Rydell J, Elfström M, Eklöf J, Sánchez-Navarro S. Dramatic decline of 
northern bat Eptesicus nilssonii in Sweden over 30 years. R Soc 
Open Sci. 2020:7:191754.
Siivonen Y, Wermundsen T. Distribution and foraging habitats of bats 
in northern Finland: Myotis daubentonii occurs north of the Arctic 
Circle. Vespertilio. 2008:12:41–48.
Smit AF, Hubley R, Green P. RepeatMasker 2021 [accessed 18 July 
2021]. www.repeatmasker.org
Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and 
syntenically mapped cDNA alignments to improve de novo gene 
finding. Bioinformatics. 2008:24:637–644.
Suominen KM, Kotila M, Blomberg AS, Pihlström H, Ilyukha V, 
Lilley TM. Northern Bat Eptesicus nilssonii (Keyserling and 
Blasius, 1839). In: Hackländer K, Zachos FE, editors. Handbook 
of the mammals of Europe. Cham, Switzerland: Springer; 2020. 
p. 1–27.
Suominen KM, Vesterinen EJ, Kivistö I, Reiman M, Virtanen T, 
Meierhofer MB., Vasko V, Sironen T, Lilley TM. Environmental 
features around roost sites drive species-specific roost preferences 
for boreal bats. Glob Ecol Conserv. 2023:e02589. doi: 10.1016/j.
gecco.2023.e02589
Vasko V, Blomberg AS, Vesterinen EJ, Suominen KM, Ruokolainen 
L, Brommer JE, Norrdahl K, Niemelä P, Laine VN, Selonen V, et 
al. Within-season changes in habitat use of forest-dwelling boreal 
bats. Ecol Evol. 2020:10:4164–4174.
Veikkolainen V, Vesterinen EJ, Lilley TM, Pulliainen AT. Bats as reservoir 
hosts of human bacterial pathogen, Bartonella mayotimonensis. 
Emerg Infect Dis. 2014:20:960–967.
Yurchenko AA, Daetwyler HD, Yudin N, Schnabel RD, Vander Jagt 
CJ, Soloshenko V, Lhasaranov B, Popov R, Taylor JF, Larkin DM. 
Scans for signatures of selection in Russian cattle breed genomes 
reveal new candidate genes for environmental adaptation and ac-
climation. Sci Rep. 2018:8:12984.
D
ow
nloaded from
 https://academ
ic.oup.com
/jhered/advance-article/doi/10.1093/jhered/esad056/7288784 by Bibliothek am
 G
uisanplatz user on 05 January 2024