Diverse but declining? Population genetic structure and genetic diversity of Nathusius’ pipistrelle along the Dutch coastline during the autumn migration period
Jaap
van
Schaik
1
Emailjaapvanschaik@gmail.com
S.
Schuler
1
K.
Stienstra
2
R.
Janssen
3
D.
Dekeukeleire
4
A
J.
P.C.
Boshamer
1✉
B.
Noort
2
J.
Steenbergen
2
S.
Lagerveld
2
C.
A.
Noort
1
1
A
Applied Zoology and Nature Conservation, Zoological Institute and Museum
University of Greifswald
Greifswald
Germany
2
Wageningen Marine Research
Den Helder
The Netherlands
3
Bionet Natuuronderzoek
Stein
The Netherlands
4
Research Institute for Nature and Forest (INBO)
Brussels
Belgium
5
A
A
A
0009-0004-5380-937X
J. van Schaik1§, S. Schuler1, K. Stienstra2, R. Janssen3, D. Dekeukeleire4, J.P.C. Boshamer5, B. Noort2, J. Steenbergen2, S. Lagerveld2
1 Applied Zoology and Nature Conservation, Zoological Institute and Museum, University of Greifswald, Greifswald, Germany
2 Wageningen Marine Research, Den Helder, The Netherlands
3 Bionet Natuuronderzoek, Stein, The Netherlands
4 Research Institute for Nature and Forest (INBO), Brussels, Belgium
5 Independent
§ Correspondence to: Jaap van Schaik, email: jaapvanschaik@gmail.com
ORCID:
J. van Schaik: 0000-0003-4825-7676
R. Janssen: 0009-0004-5380-937X
D. Dekeukeleire: 0000-0003-0664-8396
C.A. Noort: 0000-0002-2007-8839
S. Lagerveld: 0000-0003-1291-4021
Acknowledgements
This work was funded by a grant from Rijkswaterstaat (Grant Nr. 31170252) on behalf of the Ministry of Economic Affairs and Climate (EZK), under the umbrella of the Dutch Offshore Wind Ecological Programme (WOZEP). We would like to thank the following people for assistance with sampling: Frans Bosch, Adri Clements, Anne-Jifke Haarsma, Natasja Groenink, Wieneke Huls, Margaret Konings, Kris Lammers, Marije Langstraat, Harold van Lodewegen, Janmartin Rahder, Olga Stoker, Aleyna Urun, Tamara Vallina and Chris van der Vliet. Access to field sites was provided by Landschap Noord Holland (Roelf Hovinga, Chris van der Vliet, Tim Zutt), Staatsbosbeheer (Leon Kelder), NTKC (Mark Roest), and Waterschap Hollandse Delta (Peter Eilander, Esmée van der Pluijm - Schutgens). We would like to thank Ina Römer for assistance in the lab, and Gerald Kerth for access to laboratory facilities. We thank Henri Zomer and Marije Wassink for commenting on previous versions of this manuscript.
Abstract
Migratory bats are experiencing substantial increases in mortality risk from wind energy developments, but data on their migratory behavior and population dynamics are often lacking. Here, we develop a novel microsatellite panel for one such migratory bat species, the Nathusius’ pipistrelle (Pipistrellus nathusii), and apply it to 448 samples collected at stopover sites along the Dutch coast during autumn migration over four consecutive years. With this dataset, we assessed whether the population is genetically sub-structured, characterize its current genetic diversity, and evaluate whether mothers guide their offspring during migration. We found that the population is panmictic and diverse, with an effective population size estimate that cannot be distinguished from infinite. However, we also observed a consistent decline in allelic richness across the sampling period, as well as a heterozygote excess in individuals sampled as juveniles, both suggesting an ongoing population decline. We did not find any parent-offspring pairs in our dataset, which included 30 box captures where adult female and juvenile bats were found roosting together, suggesting that juvenile bats do not follow their mothers during their first migration. Our findings provide an initial characterization and baseline measure of genetic diversity for the Nathusius’ pipistrelle that can be used as a reference for subsequent studies and systematic efforts to monitor the genetic diversity of the species. Given that monitoring population trends of migratory bat species with traditional methods remains challenging, such tracking of genetic diversity may offer a valuable proxy by which to observe substantial population declines if they occur.
Keywords:
bat conservation
Chiroptera
Pipistrellus nathusii
wind energy
microsatellite
genetic monitoring
A
Introduction
In order to protect threatened species, it is pivotal to identify and preserve their habitats (Hoffmann et al. 2008). For migratory species, this encompasses both their summer and winter habitats, as well as the migratory routes between them (e.g. CMS 1979). For many migratory birds and bats, the rapid expansion of wind farms, especially along coastlines, at sea, and in narrow landscape corridors are making these migratory pathways increasingly more difficult to navigate. Indeed, it is estimated that hundreds of thousands of bats are killed annually by wind turbines (Hayes 2013; Voigt et al. 2015; Măntoiu et al. 2020), with the highest fatality rates occurring in migratory species during the migratory period (Kunz et al. 2007; Rydell et al. 2010). As such, there is an urgent need to assess the extent to which these developments threaten the viability of the affected populations (Frick et al. 2017), and to characterize the migratory behavior of these species in order to establish evidence-based action plans (Voigt 2020).
In Europe Nathusius’ pipistrelle (Pipistrellus nathusii) is one of the species with the highest observed number of fatalities at wind turbines (Dürr 2023). Female maternity colonies of this small (6-10g) bat species are found throughout Central and Northeastern Europe (Russ 2022). Here, females give birth, often to two offspring (Vierhaus 2004). While some populations at the southern edge of the range are presumed to be sedentary, most populations migrate in a southwesterly direction in autumn, sometimes in excess of 2000km (Alcalde et al. 2021; Vasenkov et al. 2022). During summer, males establish and defend mating roosts along the migratory routes (Gerell-Lundberg & Gerell 1994). In autumn, females and juveniles of both sexes join males at these mating roosts to form temporary harems of up to a dozen individuals for one or more days (Gerell-Lundberg & Gerell 1994). In winter, individuals hibernate solitarily or in small groups in minimally insulated roosts (e.g. crevices in buildings, stacks of firewood; Gebhard 1997; Russ 2022).
Over the past decade, acoustic monitoring, analysis of wind farm fatalities, and telemetry studies have improved our understanding of the migratory behavior and routes used by Nathusius’ pipistrelle. A diversity of migratory pathways has been identified or suggested across Europe (e.g. Russ 2022), notably including several that cross both the Baltic and North seas, where wind energy activities are expanding rapidly. Rydell and colleagues (2014) found that the species migrates across a broad front, with activity concentrating along coastlines and large rivers, but also over open sea throughout the Baltic and southeastern North Sea. Likewise, an increasing number of records from the United Kingdom (NNPP 2022) suggests that a proportion of the population crosses the southern North Sea, as additionally supported by offshore acoustic monitoring (Brabant et al. 2019, Lagerveld et al. 2021, 2023). An analysis of wind turbine fatalities in Germany found that juveniles and female bats may be killed at a higher rate, but did not find evidence that migratory individuals were at higher risk than those from local populations (Kruszynski et al. 2022). Despite these advances, the overall population size and population dynamics remain poorly characterized as traditional census methods are inefficient due to the dispersed nature of the population (Frick et al. 2017), and the ability to generate population trends from acoustic and tracking data remains elusive. As a result, effect assessments that aim to understand the potential risks posed by new wind farms cannot be adequately carried out.
The application of population genetic approaches can help characterize population structure, trends and movement dynamics. At the most basic level, population genetic analyses can establish whether the sampled individuals belong to a large panmictic population, or whether the population is composed of genetically differentiated sub-populations. Moreover, genetic diversity metrics such as heterozygosity, allelic richness, and effective population size estimates can help infer population trends (Schwarz et al. 2007; Willi et al. 2022). In this context, repeated sampling of the population can be an especially powerful tool to reveal population declines through losses in genetic diversity (Hoban et al. 2014) and increased genetic drift. Such metrics can subsequently be incorporated into population viability analyses to inform conservation measures (Frankham et al. 2014). Finally, kinship analyses can help elucidate the social structure of a species, and can be used to assess the degree to which individuals captured together are genetically related (e.g. Stumpf et al. 2017). To date, the population genetic structure of Nathusius’ pipistrelle has not been investigated, and no marker panels for estimating and tracking temporal patterns of genetic diversity exist.
Here, we present a novel panel of 21 microsatellite loci for Nathusius’ pipistrelle, and use it to investigate the population genetic structure and relatedness of 448 individuals sampled at nine coastal locations in the Netherlands between 2020 and 2023 during the autumn migration period. Specifically, we first investigated the genetic sub-structuring of the collected samples using both Bayesian (Structure) and k-means clustering approaches. We predicted that since individuals are mating in the sampled stopover habitats, that the population will be unstructured, although cryptic population structuring could still exist through assortative mating in sympatry. Subsequently, we characterize the allelic richness, heterozygosity, FIS and effective population size estimates of the sampled population, and compare estimates across years and between bat sex and age classes. Tracking such diversity metrices over time will help evaluate the extent of the presumed ongoing population decline, although we did not expect to observe a statistically significant decline within the four year period sampled here. Finally, we investigated the pairwise relatedness of all samples and performed parentage analysis to search for parent-offspring pairs across the entire dataset. We hypothesized that since adult females and juveniles (sensu young-of-the-year) were often recorded to roost together at the sampled stopover sites, juveniles might follow their mothers during their first migration, as has been suggested at a more local scale (< 50km) between summer habitats and hibernacula in Natterer’s bats (Stumpf et al. 2017). In this case, we would expect to recover mother-offspring pairs originating from samples taken concurrently, especially amongst adult-juvenile pairs sampled simultaneously from the same bat box. Moreover, if the two offspring raised by a mother during a summer migrated together, we would expect that pairs of juveniles sampled together would be related at the half- or full-sib level.
Material and Methods
Bat Capture and DNA Extraction
A
A total of 448 Pipistrellus nathusii samples were collected (Table 1). Samples were collected in nine forest patches distributed across three Dutch coastal provinces (Friesland, North Holland and South Holland) between August and October over the course of four years (2020–2023). Two different capture methods were used: box captures and mist-netting. For the first, artificial bat boxes (Model types: Schwegler 2FN and 1FF, Vivara PL 01, and ‘Boshamer’ type 1 and 2) were checked for the presence of P. nathusii during the day. If present, the bats were removed from the box, processed, and returned to the box within 60 min of capture. This yielded 211 samples, including 110 samples from 30 boxes where adult female and juvenile bats were caught together from the same box. For the second method, mistnets (Ecotone, Gdynia, Poland and Solida Safety Line, Helmstedt, Germany) and three-bank harptraps (Faunatec Austbat, Victoria, Australia) were placed at night. Here, bats were removed from the net or harptrap, processed, and released within 30 min of capture. For all bats, processing involved measurement of forearm length, weight, and determining the sex and age. Age was scored as either adult or juvenile (sensu Young-of-the-year; scoring as in van Schaik et al. 2015). Full capture information for each individual can be found in the Table S5. All capture and sampling were performed under license (Capture and handling: permit no. 2018–057682; DNA sampling: AVD248002016459 / VZZ-18-005 and AVD24800202114476 / VZZ-2021-001).A 3-mm wing-punch was taken for genotyping (Wilmer & Barratt 1996). Wing-punch samples were preserved in 70% ethanol prior to DNA extraction. Total genomic DNA was extracted following a salting-out extraction method using 4M ammonium acetate precipitation method. Extractions were eluted into 70µl of Low TE-Buffer, and stored at -20°C prior to genotyping.
|
Province
|
Location
|
Lat/Long
|
2020
|
2021
|
2022
|
2023
|
Total
|
|---|---|---|---|---|---|---|---|
|
Friesland
|
Zurich
|
N 53.10 E 5.39
|
4
|
4
|
|||
|
Kornwerderzand
|
N 53.07 E 5.34
|
15
|
42
|
26
|
83
|
||
|
North Holland
|
Noorderhaven
|
N 52.88 E 4.76
|
12
|
24
|
20
|
14
|
70
|
|
Callantsoog
|
N 52.84 E 4.71
|
13
|
19
|
25
|
2
|
59
|
|
|
Wildrijk
|
N 52.79 E 4.70
|
4
|
43
|
42
|
89
|
||
|
Ananasbos
|
N 52.80 E 4.73
|
34
|
34
|
||||
|
‘t Zand
|
N 52.85 E 4.77
|
6
|
6
|
||||
|
South Holland
|
Hoek van Holland
|
N 51.99 E 4.12
|
30
|
6
|
36
|
||
|
Goeree
|
N 51.66 E 4.26
|
67
|
67
|
||||
|
Total
|
65
|
111
|
129
|
143
|
448
|
Microsatellite development and genotyping
A microsatellite library was generated using next-generation sequencing (MiSeq Nano v2) based on a pooled sample containing equal DNA concentrations from eight samples (1 µg total weight; performed by Genoscreen, Lille, France). This resulted in 45,900 merged reads, which were screened for the presence of microsatellite repeat motifs, resulting in 169 potential primer pairs (QDD v3; Meglécz et al. 2010). Subsequently, 50 primer pairs were selected and ordered as unlabeled primers based on expected product length, compatibility of the annealing temperatures of the forward and reverse primer, and the number of observed repeats in the microsatellite motif.
A
The ordered primers were first evaluated for successful amplification on a pooled sample of six individuals. The 5µl PCR reactions contained 1 µl of DNA, 2.5µl Type-IT PCR-mix (Qiagen), and 1.5µl of both primers diluted to 0.2µM. PCR reactions consisted of an initial denaturation at 95º C for 5min, 35 cycles of denaturation at 95º C for 30s, primer annealing at 58º C for 90s, and elongation at 72º C for 60s, and a final elongation of 60min at 60º C. Products were evaluated on a 1.5% Agarose-gel using GelRed® (Biotium Inc.) staining. Those that amplified successfully were subsequently run for four individual samples (2 male, 2 female) using the same procedure to evaluate potential diversity, secondary amplifications and consistency of amplification. Thirty primers were subsequently ordered with a fluorescent label for further testing.Fluorescent primers were evaluated using the same PCR conditions and same four individuals. In this round, 1 µl of product was combined with 9µl of Formamide and 0.2µl of GeneScan LIZ500 size standard (Applied Biosystems) and visualized using an ABI3130 (Applied Biosystems). In addition to the 30 primer pairs described above, 60 primer pairs for microsatellite loci developed for other bat species that were available as fluorescently labeled primers in our lab were similarly evaluated (Myotis bechsteinii: van Schaik et al. 2018; M. nattereri and M. daubentonii: Stumpf et al. 2017; Other: Castella & Ruedi 2000, Miller-Butterworth et al. 2002, O’Donnell et al. 2016). In total, 25 microsatellite loci (21 newly developed loci, 4 previously established for other species) were selected for analysis and divided into four multiplexes (Table 2). PCR conditions and fragment analysis for multiplex amplification were identical to the conditions described above for primer testing. Scoring of microsatellite loci was performed in Genemapper (v5; Applied Biosystems).
Statistical analysis
All statistical analyses were performed in R (v4.3.2; R Core Team 2023) unless otherwise noted.
After initial scoring, raw fragment sizes and allele calls were plotted to check for scoring mistakes and allele call consistency. Markers were evaluated for deviation from Hardy-Weinberg equilibrium (implemented in Pegas v1.3; Paradis 2010), and pairwise index of association (r̄d using the pair.ia function in poppr v2.9.6; Kamvar et al. 2014) was calculated to evaluate linkage disequilibrium (Agapow & Burt 2001). For both tests, the P-value was adjusted for multiple testing using the Benjamini-Hochberg method (Benjamini & Hochberg 1995). The frequency of null alleles was assessed using the Brookfield method (Brookfield 1996), implemented in PopGenReport (v3.1; Adamack & Gruber 2014). To confirm that the microsatellite panel was appropriate to distinguish individual genotypes, we calculated the probability of identity between siblings (PIDsibs; Waits et al. 2001). To check for recaptures, we searched for duplicate genotypes using the mlg.id function in poppr (Kamvar et al. 2014). For recovered duplicates, the second sampling of an individual was removed from the dataset prior to further analysis.
Genetic population structure was inferred using two approaches. First, population sub-structuring was evaluated using a Bayesian clustering approach, implemented in Structure (v2.3.4; Pritchard et al. 2000). We evaluated a range of potential subpopulations (K) from 1 to 4, with 5 iterations per K, where each run consisted of a 200,000 step burn-in and a run length of 500,000 steps. We used an admixture model, with uncorrelated allele frequencies between populations, without using location information as a prior, and an individual alpha for each population (initial alpha: 0.1). Support for the most likely number of clusters was estimated using the log-likelihood method, implemented in StructureSelector (Li & Liu 2018). Second, we performed K-means clustering (Jombart et al. 2010), as implemented in adegenet (v2.1.10; Jombart 2008). Here, BIC values were inspected for between 1 and 20 clusters with 100 PCs retained for initial inference. We subsequently used a discriminant analysis of principal components (DAPC) to visualize the inferred clusters at the best K value, using the optim.a.score function to determine the number of retained principal components for the visualization.
Genetic diversity metrics were generated for 1) the full dataset, 2) per sampling year, and 3) between sex and age classes (ie. adult male, adult female, juvenile male, juvenile female). For each dataset, the number of alleles per locus, the allelic richness, expected and observed heterozygosity, and Fis values were calculated using the hierfstat package (v0.5-11; Goudet et al. 2005). For the second and third datasets, the number of private alleles per class was calculated in PopGenReport (Adamack & Gruber 2014). Effective population size was estimated using NeEstimator v2 (Do et al. 2014) using the Linkage Disequilibrium method with a minimum allele frequency threshold of 0.01 and a 95% confidence interval calculated using the parametric method.
Pairwise relatedness was calculated in related (Pew et al. 2015) using the Wang relatedness estimator (Wang 2002). In the initial analysis, we calculated the pairwise relatedness of every pair of individuals in the dataset. Next, to test whether adult females and juveniles residing in the same box were more related than average, we then subset all adult female-offspring pairs caught together and compared their relatedness to that observed across the whole population. Finally, to evaluate whether juveniles potentially migrate together with their half- or full-sibling, we similarly subset the cases where two or more juveniles were found residing in the same box.
Parentage analysis was performed in Cervus (v3.0.7; Marshall et al. 1998). All adult males and females were considered as potential parents (82 males, 171 females) and all juveniles (n = 193) as potential offspring. We simulated 100,000 offspring, with a 1% genotyping error rate, and 99.5% of loci typed. The estimated proportion of candidate mothers and fathers sampled in the dataset was intentionally overestimated (15%) to ensure that even potential pairs with several mismatches would be reported. All pairs with a positive LOD score were evaluated and considered true parent-offspring pairs if they had 0 or 1 mismatches across all loci.
|
Locus
|
Forward / Reverse (5' − 3')
|
Source / Accession No.
|
Fl. label / Size range
|
Multiplex / PCR Conc.
|
Repeat Motif
|
No. Alleles
|
Ho
|
He
|
FIS
|
|---|---|---|---|---|---|---|---|---|---|
|
PiNa09
|
F: TGACATCATTACCCTGCCGG
|
this study
|
FAM
|
3
|
(AGAT)7
|
20
|
0.849
|
0.859
|
0.013
|
|
R: TCTCAGGATGCAGTTTGTGGA
|
PQ641316
|
110–170
|
0.15µM
|
||||||
|
PiNa12
|
F: ACCCACTAATCTATCTTACCCATCC
|
this study
|
VIC
|
1
|
(AGAT)13
|
10
|
0.813
|
0.768
|
-0.058
|
|
R: TCCCACTGCTGAAAGATGGA
|
PQ641317
|
90–120
|
0.1µM
|
||||||
|
PiNa15
|
F: ACCTCTAGTGCCTGAAAGACA
|
this study
|
FAM
|
1
|
(AC)3AT(AC)16
|
15
|
0.716
|
0.730
|
0.020
|
|
R: GTGAGAAGCCAAGTCCCACA
|
PQ641318
|
140–180
|
0.1µM
|
||||||
|
PiNa16
|
F: GGACAAGCCTTCAGCCAACT
|
this study
|
PET
|
2
|
(AAT)10
|
7
|
0.767
|
0.799
|
0.041
|
|
R: TTAGATGCAACCCAGGTGCC
|
PQ641319
|
160–210
|
0.22µM
|
||||||
|
PiNa19
|
F: CCCATATGACCCAATGGCCA
|
this study
|
FAM
|
2
|
(AC)10(CA)6CG(CA)5
|
13
|
0.815
|
0.802
|
-0.015
|
|
R: TGCCTCTGTTAGCCATATCTCAG
|
PQ641320
|
160–200
|
0.2µM
|
||||||
|
PiNa20
|
F: TCACAGATCTGATGAGCCAGT
|
this study
|
PET
|
4
|
(AT)23
|
22
|
0.874
|
0.886
|
0.015
|
|
R: CAGGGTTTCCAACATGTGACA
|
PQ641321
|
140–210
|
0.45µM
|
||||||
|
PiNa21
|
F: CCTCCTCTAGTCTTTGGAAGGG
|
this study
|
NED
|
1
|
(AC)20
|
22
|
0.879
|
0.866
|
-0.014
|
|
R: GCTGCAATCCCAGAACTCCT
|
PQ641322
|
160–210
|
0.2µM
|
||||||
|
PiNa24
|
F: TACGTTGCTGTTTAGAATGACTAGT
|
this study
|
VIC
|
2
|
(AC)11
|
7
|
0.605
|
0.604
|
-0.001
|
|
R: TTCATAAGGAAAGCAGGGCACT
|
PQ641323
|
180–200
|
0.22µM
|
||||||
|
PiNa25
|
F: GCCTCAAATATCACTAGTGCTGC
|
this study
|
FAM
|
3
|
(ACT)13
|
13
|
0.685
|
0.689
|
0.008
|
|
R: CACATATGCGGGTCCCAGAT
|
PQ641324
|
170–210
|
0.25µM
|
||||||
|
PiNa27
|
F: ACAGGGAACTCATAGTCTTGGC
|
this study
|
FAM
|
4
|
(AAAT)10
|
15
|
0.831
|
0.806
|
-0.030
|
|
R: GTTCCATGCCTGTGTCTGCT
|
PQ641325
|
180–220
|
0.05µM
|
||||||
|
PiNa32
|
F: GGTGCTGTGAATGAGAAGGC
|
this study
|
FAM
|
1
|
(AC)18
|
15
|
0.861
|
0.882
|
0.025
|
|
R: GACAGGTTGCAGTAGCTGGT
|
PQ641326
|
200–250
|
0.2µM
|
||||||
|
PiNa33
|
F: ACCCTTCAGAGCATAGTTAAGGC
|
this study
|
PET
|
2
|
(AC)4CC(AC)11
|
15
|
0.856
|
0.883
|
0.032
|
|
R: GAAAGCGACAGGAGAGGAGC
|
PQ641327
|
230–270
|
0.5µM
|
||||||
|
PiNa35
|
F: GCACCTTTGAGCAACTGGTG
|
this study
|
VIC
|
3
|
(AC)21
|
40
|
0.918
|
0.924
|
0.008
|
|
R: CACTCCCTGAATTCCAGCAGA
|
PQ641328
|
200–240
|
0.3µM
|
||||||
|
PiNa36
|
F: GTCTGGGCCTTTGGACTGAA
|
this study
|
PET
|
3
|
(AGAT)7
|
18
|
0.801
|
0.819
|
0.022
|
|
R: CCTCAGGGTTAGAGTGCTGT
|
PQ641329
|
230–290
|
0.25µM
|
||||||
|
PiNa38
|
F: ACCCAAGTAAGGAGCATGCA
|
this study
|
VIC
|
2
|
(AT)7
|
5
|
0.548
|
0.524
|
-0.044
|
|
R: CAAAGTCGTCTTATATGCCGGA
|
PQ641330
|
240–260
|
0.22µM
|
||||||
|
PiNa45
|
F: CCACCGGCTGATCTAATTAGCA
|
this study
|
VIC
|
1
|
(AC)20
|
15
|
0.845
|
0.844
|
0.000
|
|
R: TCAGGTTTACCAGAGCACGG
|
PQ641331
|
240–290
|
0.2µM
|
||||||
|
PiNa48
|
F: ATGTGACTAGGGCTGCTTGG
|
this study
|
FAM
|
2
|
(AC)17
|
17
|
0.884
|
0.879
|
-0.004
|
|
R: ATCACAACCACTGGAGCATCA
|
PQ641332
|
280–320
|
0.45µM
|
||||||
|
G6
|
F: GGCTTTTTGAAAAGACTGAGG
|
Castella & Ruedi 2000
|
PET
|
3
|
(GT)12
|
21
|
0.900
|
0.897
|
-0.002
|
|
R: ACATCAGCCAGTTCCTGTTC
|
AF203665
|
90–140
|
0.1µM
|
||||||
|
GTUN9
|
F: AATGAAGCAAAGAGAAACAATGG
|
O'Donnell et al. 2016
|
VIC
|
3
|
(AC)12
|
17
|
0.918
|
0.912
|
-0.005
|
|
R: GTTTC-TGGAAACTTGGAAATGTGACC
|
KT013260
|
120–170
|
0.05µM
|
||||||
|
GTVIA
|
F: ACAGCTGCCAGGAATCTGAC
|
van Schaik et al. 2018
|
NED
|
4
|
(CA)7CG(CA)10
|
15
|
0.852
|
0.858
|
0.009
|
|
R: TGACCCAGTCTCCTCCAAAG
|
MG321325
|
170–210
|
0.1µM
|
||||||
|
Mschreib3
|
F: AGCCAGGCACAGCTCAC
|
Miller-Butterworth et al. 2002
|
NED
|
4
|
(CA)19
|
34
|
0.920
|
0.917
|
-0.002
|
|
R: GTTTTC-TTTGGCATCTGAAGG
|
AY056590
|
240–300
|
0.25µM
|
Results
Marker characteristics
In total, 446 of the 448 samples were successfully amplified, with a maximum of one locus missing per individual (overall missing data: 0.05%). Four loci of the 25 loci that were included in the multiplexes could not be consistently scored or showed significant homozygote excess (Table S1), and were thus excluded from the analysis. None of the remaining 21 loci deviated significantly from Hardy-Weinberg equilibrium (Table S2), showed signs of null alleles (Figure S1), or were significantly linked (max r̄d = 0.035; Table S3). Summary statistics per locus are provided in Table 2.
Recaptures
We recovered 8 duplicate genotypes (Table 3). Given the low probability of identity across all loci (PIDsibs = 3.09x10− 10), these were considered recaptures of the same individual. All recaptures occurred within the same sampling location, with 7 of 8 recaptures occurring in different years. Five of the 8 recaptured individuals were male, of which two were captured from the same bat box in consecutive years (individuals 4 and 5; Table 3). One adult female was recaptured within the same year, 49 days after initial capture (individuals 6, Table 3). No note was made of an existing hole or scar in the wing tissue, suggesting the wing-punch had fully healed by the time of the second sampling event.
|
Pair
|
Location
|
Capture method
|
Date
|
Age
|
Sex
|
|---|---|---|---|---|---|
|
1
|
Noorderhaven
|
Box
|
25.08.2020
|
Ad
|
Male
|
|
1
|
Noorderhaven
|
Box
|
23.09.2021
|
Ad
|
Male
|
|
2
|
Hoek van Holland
|
Box
|
25.08.2020
|
Ad
|
Male
|
|
2
|
Hoek van Holland
|
Net
|
22.09.2021
|
Ad
|
Male
|
|
3
|
Callantsoog
|
Box
|
13.09.2020
|
Ad
|
Male
|
|
3
|
Callantsoog
|
Box
|
17.09.2021
|
Ad
|
Male
|
|
4
|
Wildrijk
|
Box*
|
10.10.2021
|
Juv
|
Male
|
|
4
|
Wildrijk
|
Box*
|
25.09.2022
|
Ad
|
Male
|
|
5
|
Wildrijk
|
Box*
|
09.09.2021
|
Ad
|
Male
|
|
5
|
Wildrijk
|
Box*
|
31.08.2022
|
Ad
|
Male
|
|
6
|
Hoek van Holland
|
Box
|
25.08.2020
|
Ad
|
Female
|
|
6
|
Hoek van Holland
|
Net
|
13.10.2020
|
Ad
|
Female
|
|
7
|
Hoek van Holland
|
Box
|
15.09.2020
|
Ad
|
Female
|
|
7
|
Hoek van Holland
|
Box
|
15.10.2021
|
Ad
|
Female
|
|
8
|
Noorderhaven
|
Box
|
23.09.2021
|
Juv
|
Female
|
|
8
|
Noorderhaven
|
Net
|
02.09.2023
|
Ad
|
Female
|
* denotes that the individual was captured from the same bat box both times
Genetic diversity
The Structure analysis showed highest log-likelihood support for a single population (K = 1), with higher values of K only partitioning small fractions of individual ancestry into additional clusters (Figure S2). The k-means clustering analysis found highest support (lowest BIC value) for 3 clusters, however when visualized in a DAPC, the three clusters were not spatially segregated and formed a single cluster divided into equal thirds (Figure S2). Taken together, we therefore conclude that all samples likely belong to a single panmictic population.
Across the full dataset, we recovered a diverse (average alleles per locus = 16.95), and well-mixed (FIS = 0.001) population, that could not be distinguished from an infinitely large population (Ne = 198,229, range = 7721-∞; Table 4). When we subdivided the samples by sampling year, metrics were broadly similar over time, with a subtle consecutive decline in allelic richness over the four year sampling period (from 12.816 to 12.489; Table 4). When considered per bat sex and age class, we observed a notable heterozygote excess in both juvenile classes and homozygote excess in both adult classes, although all four were statistically insignificant as indicated by the inclusion of 0 in the 95% confidence intervals (Table 4).
|
Population
|
N
|
Na
|
K
|
Priv All
|
Ho
|
He
|
Fis±CI
|
Ne±CI
|
|---|---|---|---|---|---|---|---|---|
|
Full population
|
438
|
16.95
|
16.944
|
NA
|
0.816
|
0.818
|
0.001 (-0.009–0.011)
|
198229 (7721-∞)
|
|
Per Year
|
||||||||
|
2020
|
64
|
12.86
|
12.816
|
11
|
0.829
|
0.819
|
-0.013 (-0.033–0.007)
|
10350 (802-∞)
|
|
2021
|
106
|
13.86
|
12.605
|
17
|
0.813
|
0.819
|
0.007 (-0.009–0.021)
|
22881 (1898-∞)
|
|
2022
|
126
|
14.05
|
12.595
|
16
|
0.811
|
0.817
|
0.005 (-0.015–0.026)
|
∞ (2982-∞)
|
|
2023
|
142
|
14.24
|
12.489
|
12
|
0.816
|
0.818
|
0.001 (-0.012–0.018)
|
∞ (16385-∞)
|
|
Per Class
|
||||||||
|
Adult male
|
77
|
12.81
|
12.784
|
7
|
0.809
|
0.823
|
0.015 (-0.007–0.039)
|
∞ (3209-∞)
|
|
Adult female
|
168
|
14.81
|
13.035
|
20
|
0.807
|
0.816
|
0.012 (-0.005–0.027)
|
∞ (18863-∞)
|
|
Juvenile male
|
93
|
13.71
|
13.245
|
9
|
0.829
|
0.818
|
-0.015 (-0.033–0.007)
|
∞ (3829-∞)
|
|
Juvenile female
|
100
|
13.52
|
12.903
|
12
|
0.824
|
0.814
|
-0.014 (-0.032–0.008)
|
∞ (3800-∞)
|
Pairwise relatedness and parentage
A
Average pairwise relatedness across the whole dataset was − 0.002 (Fig. 1a). The maximum observed pairwise relatedness across all pairs of individuals was 0.43, suggesting no direct parent-offspring or full-sib pairs. This was confirmed by the parentage analysis, which did not recover a single parent-offspring pair with less than 2 mismatches across all loci (see Table S4 for all pairs with positive LOD-score).Similarly, no closely related pairs were observed when only considering the pairwise relatedness of adult females and juvenile bats that were captured together from the same box (n = 93; mean = -0.004; max = 0.179; Fig. 1b). When comparing juvenile-pairs recovered from the same box, most pairs appeared similarly unrelated (mean = -0.0103; Fig. 1c), however one pair was related at the half-sib level (relatedness: 0.2635; both juvenile females).
Figure 1 Histograms of observed pairwise relatedness for a) the entire dataset (n = 95,703), b) across all potential mother-offspring pairs that were sampled from within the same bat box (n = 93), and c) across pairs of juveniles sampled from within the same bat box (n = 43)
a) b)
c)
Discussion
Characterizing the migratory behavior, population dynamics and current genetic diversity of migratory species is urgently needed in the face of the existential risk posed by the rapid expansion of wind energy developments across the world. Here, we provide the first microsatellite marker panel for the Nathusius’ pipistrelle, a migratory bat species that is amongst the most frequently observed casualties at wind farms in Europe. By employing this panel to a four year dataset of over 400 individuals sampled along the Dutch coastline during the autumn migration period, we provide a first baseline estimate of current genetic diversity and address several unresolved questions regarding the population structure and migratory behavior of the species.
Population genetic structure
We find no evidence of population sub-structuring or deviation from Hardy-Weinberg equilibrium in our dataset, suggesting that all individuals that reside or migrate along the Dutch coastline belong to a single panmictic population. These observations are consistent with descriptions of long-distance male dispersal and establishment of mating territories along the migratory pathways (e.g. Pētersons 2004), which result in gene flow between populations from a wide summer catchment area. Based on ring recoveries and proposed migratory pathways (Russ 2022), this suggests that the entire Fennoscandian and Baltic region may effectively represent a single genetically unstructured population. Similar patterns of weak population structuring have been observed in two other European migratory bat species, Pipistrellus pygmaeus (Bryja et al. 2009) and Nyctalus noctula (Petit & Mayer 1999). However, in N. noctula, weak population structuring and limits to gene flow were observed in some populations, possibly caused by geographic barriers (Petit & Mayer 1999). Further sampling and analysis of Nathusius’ pipistrelle across its distribution range and along other migratory pathways are needed to evaluate whether similar patterns exist in this species.
Genetic diversity and trend
Overall, we observed a genetically diverse population, with an effective population size estimate in the hundreds of thousands that cannot be distinguished from a population of infinite size. Detecting population decline and genetic erosion in large populations is notoriously difficult, and simulations have shown that even substantial demographic declines may not be distinguished (Hoban et al. 2014). Nevertheless, the size and diversity of the genetic population being investigated here may in fact be favorable to detecting population declines, and we observed two indicators that may be indicative of such recent population decline.
First, we observed a consistent decline in allelic richness over the four sampled years. Microsatellite loci accrue variation (new alleles) over time through mutation, and as a result large populations may be highly polymorphic at such loci, with many of the alleles being present at very low frequencies. As such populations decline, these rare alleles may readily drift to extinction, resulting in a reduction in allelic richness, but little to no change in expected heterozygosity (Hoban et al. 2014). Such reductions in allelic richness have been observed empirically for abundant marine fish species (Pinsky & Palumbi 2014), where allelic richness was 12% lower on average in overfished populations. In fact, large populations may stand to lose disproportionately large amounts of allelic diversity, as illustrated by Allendorf et al. (2024), who calculated that a reduction of Baltic herring populations from 31 billion to 9 billion individuals could be expected to reduce the number of alleles in the population by approximately 70% if the population were to remain in drift-mutation equilibrium. While the Nathusius’ pipistrelle population present in Northern Europe almost certainly does not total in the billions, it has likely accrued a substantial amount of genetic diversity over the course of several centuries. Thus, while the difference in allelic richness observed here is still subtle (2.5% between the first and last year) and is not statistically significant, it nevertheless potentially represents a considerable decline in overall population size that deserves further investigation.
Second, we observed a marked trend towards heterozygote excess in both juvenile males and females. As above, during rapid population declines both allelic diversity and heterozygosity decline, but at different rates, with allelic diversity declining more rapidly (e.g. Hoban et al. 2014). This results in a transient period where the observed number of alleles is lower than the number of alleles expected under mutation-drift equilibrium (ie. a heterozygote excess; Cornuet & Luikart 1996). Thus, the heterozygote excess observed here, while again statistically insignificant, may point towards a recent population decline.
Despite being categorized as a species where direct risk factors are likely impacting the population (e.g. Meinig et al. 2020), robust population trends using traditional survey methods are rare. A TRIM-analysis conducted on box survey data from North Rhine-Westphalia in Germany, suggests that the local population has strongly declined since 2000 (Meinig et al. 2020). In acoustic surveys at offshore sites in the North Sea, Lagerveld et al. (2023) found a significantly lower activity in 2020 than in the three years prior, but concluded that there was insufficient evidence to definitively establish a decline. Therefore, the genetic indications of a potential decline observed here are broadly concordant with those based on other survey methodologies, but a broad-scale and long-term monitoring that corroborates these results is needed.
Mother-offspring guidance
We found no evidence for mother-offspring pairs across a sample of thirty boxes where both adult females and juvenile bats were present together (n = 93 total potential pairs). Indeed, no parent-offspring pairs were recovered across the entire dataset, strongly suggesting that offspring do not migrate together with their mothers in this species. Baerwald & Barclay (2016) reached the same conclusion for two migratory bat species in North America (Lasiurus cinereus and Lasionycteris noctivagans), based on genetic samples taken from wind turbine fatalities. Instead, it appears plausible that Nathusius’ pipistrelles are born with a genetically pre-defined migratory vector, as observed in many migratory songbirds (Berthold 2001), and use the Earth’s magnetic field to navigate (Holland et al. 2006; Lindecke et al. 2021) towards their goal.
Nevertheless, juvenile bats may (additionally) use cues from other conspecifics during migration, such as their siblings or other colony members. We found one pair of juvenile females, out of 43 juvenile pairs that were caught simultaneously from the same bat box, related at the half-sib level. However, we cannot establish whether these individuals are maternally or paternally related, and thus cannot evaluate whether they may have migrated together. Moreover, while there is consensus that most mothers give birth to two offspring (reviewed in Vierhaus 2004), it is unclear whether these individuals are related at the full-sib or half-sib level, or a mix of both. Regardless, it does not appear to be a widespread phenomenon, as the vast majority of juveniles was not found with related conspecifics. The use of social cues from non-directly related conspecifics would be impossible to detect genetically. Acoustic surveys have shown a high concentration of Nathusius’ pipistrelle activity along coastlines during the migratory period (Ahlén et al. 2009, Ijäs et al. 2017), suggesting that juvenile bats could potentially locate and use cues from unrelated conspecifics during migration at such landscape features. However, Baerwald & Barclay (2016) found no evidence that wind turbine fatalities originated from the same areas, using stable isotope analysis.
Conservation Implications and Recommendations
Assessing the threat posed by anthropogenic change to environments requires a fundamental understanding of the biology and population dynamics of the affected species. The establishment of a microsatellite panel for the Nathusius’ pipistrelle has allowed us to contribute to several outstanding questions. For one, the lack of population sub-structuring in the individuals that migrate along the Dutch coastline means that the population can be managed as one entity. This is fortunate, as it means that methods such as acoustic monitoring and telemetry can be applied without caveats regarding the unknown population assignment of observed individuals.
Next, our findings, combined with those of previous studies, suggest that juvenile bats migrate according to an innate migratory vector, perhaps in combination with some social cues from unrelated conspecifics. The lack of mother-offspring guidance implies that juveniles will not immediately face higher mortality risk if their guide (mother) is killed along the migratory route. However, if migratory behavior is genetically pre-defined, juveniles may be highly susceptible to mortality at wind farms development placed along important landscape features (e.g. coastlines) in the vector direction that they instinctively follow.
Perhaps most importantly, our study highlights the feasibility of genetic methods as a monitoring tool, that may allow for inference regarding the population dynamics of a species that is otherwise very difficult to monitor accurately. Systematic genetic monitoring of trends in allelic richness and other diversity metrics, coupled with forward genetic simulations of varying scenarios of population size and decline, could provide indispensable insights into the trajectory of the population. Similarly, analysis of historical samples (ie. collections of carcasses from wind-farm surveys, museum specimens), could provide valuable context regarding how genetic diversity has already changed. While such genetic diversity monitoring will undoubtedly remain comparatively crude, if declines are strong enough, they will provide irrefutable proof of population decline that can contribute to evidence-based action plans that strive for adequate species protection (e.g. more conservative curtailment regimes during migration). We hope that this work can act as a baseline reference of genetic diversity in the Nathusius’ pipistrelle, and encourages further genetic monitoring of the species throughout its range.
A
Declarations
Statement of Animal Ethics
All capture and sampling were performed under license (Capture and handling: permit no. 2018–057682; DNA sampling: AVD248002016459 / VZZ-18-005 and AVD24800202114476 / VZZ-2021-001).
Competing Interests
The authors have no relevant financial or non-financial interests to disclose.
A
Author Contributions
The study was conceived by J.vS., R.J., D.D., J.S., and S.L.; Sample collection was coordinated by K.S., and performed by K.S., RJ, DD, J.P.C.B., B.N. and S.L.; Labwork and statistical analysis were performed by J.vS. and S.S.; Funding acquisition and project management were performed by J.vS., J.S. and S.L.; The first draft of the manuscript was written by J.vS., and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.
A
Data availability
Newly developed microsatellite loci were deposited in GenBank under Accession Nos. PQ641316- PQ641332. Complete sample information and genotypes are available in Supplementary Table S5.
Electronic Supplementary Material
Below is the link to the electronic supplementary material
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