Genomic Signatures of Decline and Recovery in an Endangered Bat

Genomic Signatures of Decline and Recovery in an Endangered Bat | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genomic Signatures of Decline and Recovery in an Endangered Bat Marxe Altman-Orbach, Giorgia G. Auteri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8734754/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Tremendous population declines may result in the loss of genetic diversity, which can in turn lead to reduced phenotypic health, reduced adaptive potential, and further population declines. In other words, a severe demographic decline may ultimately lead to an extinction vortex. Therefore, understanding genetic diversity is vital for assessing the health of species that have undergone declines, and can even reflect the effectiveness of management actions. We investigated signatures of genetic bottlenecks in a species that has been the subject of focused management efforts since its 1976 listing as endangered in the US, the gray bat ( Myotis grisescens) . We used whole genome sequencing to calculate genetic diversity metrics, including inbreeding coefficients, Tajima’s D, and heterozygosity, and to infer effective population size and structuring. We found a loss of rare alleles and heterozygosity excess, in line with strong declines in relative abundance of the census population. However, we did not detect inbreeding, erasure of population substructure, or low effective population size. Despite an apparent bottleneck, the gray bat appears to have avoided detrimental genetic consequences associated with population declines, likely due to the conservation of connected populations of adequate effective size. Our findings highlight the importance and effectiveness of timely conservation interventions for preserving the genetic health of species. Strong declines in relative abundance (i.e., based on percentage of the initial population) do not necessarily lead to inbreeding when there is still adequate absolute abundance (i.e., number of individuals). Genetic diversity recovery gray bat (Myotis grisescens) admixture effective population Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The past century has seen an unprecedented and poorly addressed increase in human-caused extinction rates of species, representing a global biodiversity crisis (Scott et al. 2024 ). Extinction risk increases following large declines in census population sizes (i.e. demographic bottlenecks), which can lead to the loss of genetic diversity and subsequent loss of adaptive potential (Tanaka 1998 ). Ultimately, these effects can culminate in an extinction vortex and the loss of a species (Brook et al. 2002 , Tanaka 2018). Thus, genetic diversity, which allows populations to adapt to new stressors, is vital to track and to manage in vulnerable populations. Certain scenarios and species characteristics should theoretically preserve genetic diversity in populations experiencing demographic bottlenecks. For instance, mild to moderate population declines with rapid subsequent recovery are less likely to result in genetic bottlenecks than severe declines with slow recovery (Sonsthagen et al. 2017 , Lucena-Perez et al. 2021 , Olazcuaga et al. 2023 ). Life history traits of species can also influence the extent of genetic diversity loss. A large initial effective population size can buffer the loss of rare alleles. Additionally, high dispersal ability can contribute to the maintenance of rare alleles and facilitate gene flow (Frankham 1996 , Hedrick 2005 ). However, even being abundant, widespread, and mobile may not be enough to save a species from disastrous genetic decline in the face of external threats, such as habitat loss and fragmentation. For instance, European Nightjars ( Caprimulgus europaeus L.) suffered genetic diversity loss due to alteration of woodland habitat, despite previously being widespread (Day et al. 2025 ). Similarly, Passenger Pigeons ( Ectopistes migratorius L.) exhibited low heterozygosity despite massive population sizes, prior to intensive hunting pressure driving them to extinction (Hung et al. 2014 ). Many species do not recover genetic diversity even after demographic recovery (e.g., Bortoluzzi et al. 2019, Bozzuto et al. 2019 , Robinson et al. 2019 , etc.) For example, Northern elephant seals ( Mirounga angustirostris Gill) and Spotted Kiwis ( Apteryx owenii Gould) regained population numbers quickly post-bottleneck, but with reduced fitness (Hoelzel et al. 2024 ), high homozygosity (Ramstad et al. 2013 ), and continued reliance on conservation interventions. Though some species suffer lasting negative genetic repercussions after bottlenecks, population declines are not self-fulfilling prophecies. For instance, immigrants from large, genetically healthy source populations can introduce valuable genetic diversity to populations suffering inbreeding depression (e.g., Åkesson et al. 2016 ), and small populations can create favorable conditions for purging deleterious alleles (e.g., Robinson et al. 2019 , Ochoa and Gibbs 2021 ). Conservation action aims to protect the viability of species through actions such as safeguarding habitat, maintaining dispersal corridors, and sustaining critical population sizes. However, populations of concern often remain genetically vulnerable to collapse, even when protection criteria are met (Tallmon et al. 2004 , Bouzat 2010 ). Thus, it is important to assess genetic diversity when making conservation decisions. The mechanisms that maintain viability are not always well understood and can vary across contexts and taxa (de Kort et al. 2021 , Allen et al. 2022 , Hoffman et al. 2024 ). It can also be informative to identify populations that have resisted the loss of genetic diversity through demographic bottlenecks, and their associated life history traits. The gray bat ( Myotis grisescens Howell) was among the first species listed under the US Endangered Species Act, following estimated population declines of up to 80% between the mid 19th and mid 20th centuries (Brady et al. 1982 ). Large, concerted efforts to protect habitat (e.g., gating of sensitive hibernation sites) led to massive demographic recovery since the 1970s, such that the gray bat may now be an example of conservation success (Martin 2007 , Adams et al. 2024 ), although it is still on the endangered species list. The gray bat’s relatively low reproductive output (i.e., only one offspring yearly starting at age two) could decrease genetic diversity and increase inbreeding, although the species’ promiscuousness and dispersal ability may have the opposite effect. Gray bats, and temperate bat species in general, are also relatively long lived (14–17 years; Harvey 1992 ). However, the influence of longevity on susceptibility to genetic bottlenecks remain unclear (Bradke et al. 2021 , Clark et al. 2024 , Gargiulo et al. 2025 ). The few previously conducted studies of gray bat genetics have found high gene flow between two subpopulations east and west of the Mississippi River Alluvial Plain, and variable but mostly healthy levels of heterozygosity (Lindsay et al. 2015 , Nagel et al. 2024 ). However, no studies have examined gray bat populations north of the Mississippi River Alluvial Plain. Further, the single study assessing genetic bottlenecks in this species used microsatellite-based tests (Lindsay et al. 2015 ), which can have variable results due to smaller sample sizes (of the genome) and can be sensitive to underlying assumptions (Peery et al. 2012 ). No study has examined large portions of the gray bat genome through whole genome sequencing (WGS). We used gray bats to evaluate the genetic effects of a moderate demographic bottleneck with subsequent recovery on a long-lived species. We conducted non-lethal sampling of wild-caught individuals north of the Mississippi River Alluvial Plain. We analyzed genetic diversity and looked for signals of a genetic bottleneck using a thorough suite of tests and statistics. Specifically, we asked whether gray bats show genetic signals of population expansion, contraction, or stability following their demographic bottleneck. We also inferred demographic features, including effective population size and population structure, to ask whether genetic diversity levels in gray bats could be connected to robust reproductive populations or high admixture. Materials and Methods Study Site and Sample Collection We caught gray bats at a privately owned cave in Pike County, Missouri on 20-Oct-2022, in compliance with all animal welfare, state, and federal permits (Missouri State University IACUC #2022-14, MDC Wildlife Collector #60085, and US FWS T&E Recovery #ES04397C, respectively). During this time of the year, gray bats immigrate from bachelor and maternity colonies to hibernation sites, where they mate promiscuously prior to entering hibernation. The cave we sampled is used by gray bats as a minor hibernation site for some individuals during winter and as a temporary stop for other individuals during migration to other hibernation sites. High connectivity and gene flow have been observed across the gray bat range, likely due to dispersal propensity (Lindsay et al., 2015, Holliday et al. 2023, Nagel et al. 2024). Pike County is west-central within the gray bat range (Fig. 1) but likely represents both subpopulations identified by Lindsay et al. (2015). We obtained tissue samples from 23 adult gray bats using methods originally described by Worthington-Wilmer and Barratt (1996). A sterile, circular biopsy punch (2-mm diameter; Integra, Princeton, NJ, USA) was used to take two tissue punches from the flight membranes on each wing. Samples were immediately stored in microcentrifuge tubes with ethanol (97% concentration) then taken to the lab, where they were kept in a freezer at -20°C until extraction. For extractions, we used a DNeasy Blood and Tissue Kit (Qiagen, Germantown, MD, USA) to extract DNA at Missouri State University, following the standard kit protocols with some modifications. Specifically, to facilitate complete lysis of samples we used additional proteinase K (30–40 uL total per sample) and extended incubation time (24–72 hours) as needed, and to maximize DNA yields we used pre-chilled ethanol (2°C). Final DNA was eluted in water (ddH2O prewarmed to 70°C) and concentrated with a vacufuge. Sequencing, Filtering, and Alignment Extracted DNA was sent to The Center for Applied Genomics (Toronto, Canada) for sequencing in 151 base, paired-end fragments on 3 10B lanes of an Illumina NovaSeq X. To assist with low input DNA levels, PCR was used in library preparation. We performed all bioinformatic processes on a Linux workstation at Missouri State University. Adapters were removed from samples with TrimGalore! (V. 0.6.10; Krueger et al. 2015) and cutadapt (V. 5.2; Martin 2011). The quality of entire sequences was assessed with FastQC (V. 0.12.0; Andrews 2010) and the quality of bases was filtered to a Phred33 score of 20 using SAMtools (V.1.21; Danecek et al. 2021). There was no species-specific reference genome available for the gray bat. Therefore, we aligned our sequences against a reference genome for the cave bat ( M. velifer [J. A. Allen]; BioProject PRJNA103554; Vazquez et al. 2024) using BWA-mem (V.0.7.19; Li and Durban 2009, 2010). Gray bats are closely related to cave bats (Jones et al. 2002, Agnarsson et al. 2011) and our sequences mapped well to the reference genome. We removed optical duplicates from the resulting alignments with Picard MarkDuplicates (V.2.0.1; Broad Institute 2019). Indels in the sequences were realigned using GATK IndelRealigner (V.4.3.0.0; Van der Auwera and O'Connor 2020). Finally, we filtered sequences to remove the sex chromosomes and extrachromosomal scaffolds, so that only autosomes were considered for metrics of genetic diversity. Genetic Diversity To estimate measures of heterozygosity and homozygosity, we first used the empirical Bayesian approach implemented in ANGSD (V. 0.940; Korneliussen et al. 2014) to obtain a genotype likelihood model. We retained data for all 23 individuals, filtered for sites represented in at least 15 sequences with a minimum depth of 5x and a maximum depth of 60x.We obtained a folded site frequency spectrum from the autosome-only genotype likelihood model with ANGSD realsfs, then used ANGSD thetastat with 50 Kb windows to calculate the number of polymorphic sites (Watterson’s theta, θW), the mean number of pairwise differences (nucleotide diversity, π), and genome-wide Tajima’s D. Tajima’s D compares the relationship of θW and π, with a model population at constant size experiencing only neutral mutation. We calculated individual global and local mean rates of genomic heterozygosity (H), as well as low and high confidence intervals with ROHan (Renaud et al. 2019) using standard parameters in 1-Mb windows. We used VCFtools (V 0.1.18; Danecek et al. 2011) with the option “--het” to calculate individual expected and observed heterozygosity (H e and H o ). We calculated individual and per-site inbreeding coefficients (F IS and F S , respectively) using PCAngsd (Skotte et al. 2013). Inbreeding coefficients compare observed heterozygosity and homozygosity to that expected based on Hardy-Weinberg expected (HWE) proportions. A value of 0 indicates no deviation from HWE, whereas a negative value indicates an excess of heterozygosity, and a positive value indicates an excess of homozygosity. Population structure and effective population We used multiple methods to infer ancestry clusters and admixture among individuals. First, we used an iterative approach with PCAngsd (Skotte et al. 2013) to infer a covariance matrix, estimate relatedness, and estimate the number of ancestral populations. We then subset samples to 10 Mb from each chromosome, and used this subset data in NGSadmix (V.32; Meisner and Albrechtsen 2018, 2019) to infer ancestry among individuals. We ran NGSAdmix with 20 replications each of assumed ancestral populations of K = 1–8, using random seeds. To select the best value of K, we calculated ΔK with the NGSAdmix results using the Evanno method (Evanno et al. 2005) and evaluated PCAngsd’s automatic K detection. We then assigned individuals to clusters based on Q scores ≥ 0.9. We built structure plots using mean Q values in R. To estimate the strength of structuring between clusters, we used VCFtools to calculate the Weir Cockerham fixation index (F ST ). To estimate the number of reproducing individuals, we calculated recent effective population size (N e ) with linkage disequilibrium in GONE (Santiago et al. 2020), and we estimated long-term effective population size (N e.LT ) using the equation θW = 4 N e.LT µ (Watterson 1975). For the N e.LT equation, we first obtained the mean θW including runs of homozygosity of all individuals with ROHan. We used a mutation rate (µ) of 2.2x10 -9 , based on the average mutation rate of mammals and gray bat life history (Decher and Choate 1995, Kumar et al. 2002). To estimate recent N e , we considered non-indel sites with a maf of 0.1 and that were sequenced in 90% of samples to create a population ped file. We further filtered the minimum mean and individual depths to 7 and the maximum depths to 60. We ran GONE on a random sample of 20% of sites from the filtered set using standard parameters and used the mean of 40 runs to make N e estimates. Results Sequencing and Alignment Our overall alignment rate of gray bat sequences was 90.54% (SD ± 0.25%), which we found acceptable. After filtering, the mean depth of coverage was 19.65X ± 2.55 (SD). Coverage was relatively uniform among individuals, and so all 23 individuals sampled were included in all downstream analyses. After filtering and subsetting to autosomes, we considered 1,849,248,362 high quality sites. In the site allele frequency file, which used more aggressive filters including the exclusion of sex chromosomes and extra-chromosomal scaffolds, we considered 14,005,840 total base pairs. In the .ped file, we considered 8,529,672 SNPs. Genetic Diversity We calculated multiple metrics of genetic diversity and heterozygosity within the gray bat genome. The autosome-only genome-wide mean θW per site was 0.0542 ± 0.007, with all individuals considered. The mean π per site was 0.067 (SE ± 1e-4). The mean Tajima’s D was 0.52 (SE ± 0.002; Fig. 2, Table 1), indicating a scarcity of rare alleles. Across the genome, Tajima’s D was normally distributed. Of all sites, 1.17% had Tajima’s D values higher than 2, and no sites had values lower than -2. The mean global genomic rate of heterozygosity (H) of the 23 individuals was 0.00322 (95% CI 0.00301–0.00343; Table 1). Observed heterozygosity was greater than expected heterozygosity (mean H o = 0.411, mean H e = 0.376). Indeed, all inbreeding coefficients reflected an excess of heterozygosity based on HWE proportions. At an individual level, mean F IS was -0.23, and at a per-site level mean F s was -0.18 (Table 1, Fig. 2). Population structure and effective population Both methods we used to infer population structure in gray bats indicated multiple populations with high gene flow. Evanno’s Delta K resulted in a best fit of 3 population clusters (Table 2), although with the iterative approach implemented in NGSAdmix, we detected just 2 population clusters (Table 1). With K = 2 populations, 65% of individuals were assigned to a single cluster (11 in cluster A; 4 in cluster B; Fig 3a) and structure was extremely weak (weighted F ST = 6.69e-05). With K = 3 populations, structure was weak, only 30% of individuals were assigned to a single cluster, and no individuals were assigned to the third cluster (weighted F ST = 0.00255; 4 in cluster A; 3 in cluster B; 0 in cluster C; Fig. 3b). Individuals assigned to unique clusters when K = 3 were consistently assigned to the same cluster when K = 2. Thus, we decided that 2 subpopulations (with high admixture) is most likely. Using Watterson’s theta, we estimated an N e.LT of 70,738 (95% CI = 68,153–75,000) reproductive individuals (Table 1), which is about half of the minimum recorded census population size from the mid 20 th century, and about one tenth of the maximum historical estimates of census population in Missouri (Brady et al. 1982, Martin 2007, Elliott 2008). Estimates of N e over the past 150 generations show a 94% population decline, with strong declines (≥1% of population loss per generation) starting 108 generations ago and continuing for 37 consecutive generations before hitting the nadir (71 generations/142 years ago, assuming a generation time of 2 years). The 150-generation maximum N e of 218,297 dropped to a minimum of 13,002. N e remained at the nadir for 44 generations. Strong increases (≥1% of population per generation) in N e began 35 generations ago (70 years) and continued until 21 generations ago. For the past 20 generations, contemporary N e (N e.Cont ) has remained plateaued at 53,643–59,487 (mean = 57,147), 26.18% of the previous maximum. Discussion Despite successful recovery of gray bat population numbers, our analyses show that the species’ demographic bottleneck resulted in a loss of rare alleles, heterozygosity excess, and a notable decrease in effective population size. In other words, demographic decline led to a genetic bottleneck characterized by population contraction (Maruyama and Fuerst 1985 , Nei 2005 ). Our estimates of effective population size reaffirm population demographics inferred through census-based methods (e.g., Elliott 2008), with an effective population decline of 94%. However, N e never dropped below critical thresholds for genetic drift (Shaffer and Samson 1985 , Frankham et al. 1996). Near the central area of the gray bat range, we detected multiple subpopulations with high admixture between them. Following a reduction in census size, early conservation actions by managers (e.g., installing bat gates on hibernation sites) and life history traits of the species (e.g., high dispersal, promiscuous mating, and high starting population size) may have helped the gray bat recover demographically and reduced their risk of inbreeding depression, despite a bottleneck. Our study builds upon previous genetic research on gray bat population structuring and demographic trajectories by Nagel et al. ( 2024 ) and Lindsay et al. ( 2015 ). Our inference of two subpopulations with high gene flow supports the previously suggested east-west divide (Lindsay et al. 2015 Nagel et al. 2024 ). Our negative F IS indicates that outbreeding, accompanied by population structure and admixture occurs in this centrally located population. This specifically highlights that central populations might be important conservation priorities because they i) can harbor individuals with genetic makeups of both western and eastern populations, and ii) are likely important for immigration and gene-flow across the range. We were also able to more clearly identify population trajectories, especially recent ones, compared to previous studies. Like Nagel et al. ( 2024 ), we found evidence of large historical effective population sizes (N e.LT ). Our estimate of 70,738 (95% CI = 68,153–75,000) falls well within the range of that suggested by Nagel et al. 2024 (95% CI 41,400–243,122), and helps to narrow the estimate. Additionally, we estimated N e.Cont for gray bats for the first time (57,147, with most recent estimates within 20 generations before present) and N e.Bot at the extreme of the bottleneck (just 13,001.9, 62 generations ago). These recent estimates reveal the substantial decline experienced by the species, followed by partial recovery to 26.18% of their historical numbers (in contrast to the overall signal of expansion found by Nagel et al. 2024 , and lack of detected bottleneck in Lindsay et al 2015 ). Our estimates of recent N e mirror observations of changes in the census population size over time, and indicate that a WGS approach may be especially useful when estimating recent demographic histories for species that, in contrast, have not been well monitored. Similarly, recent work highlights the use of effective population size estimates for differentiating between naturally low populations and those in decline (Liu et al. 2026 ). On a hopeful note for gray bat recovery, we found recent increases in effective population and no evidence of homozygosity excess associated with inbreeding depression. Signals of population change We asked whether gray bats show genetic signs of population decline, expansion, or stability following their demographic bottleneck. Gray bat census populations have been markedly unstable over the past ~ 200 years (e.g., Brady et al. 1982 , Martin 2007 , Elliott 2008). After severe declines, the initiation of management actions, such as cave gating, resulted in population expansion (Adams et al. 2024 ). Historical maximum population sizes, and thus the magnitude of gray bat declines, have been inferred through the sizes of guano piles and stains left by roosting bats on cave ceilings. Our study represents the first genetic inference of gray bat effective population size changes over recent time. Our results support the timelines of previous inferences of population crashes, followed by recovery (Fig. 4 ), with a severe decline occurring due to cave disturbance around the time of the American Civil War, and recovery beginning as caves became protected habitat under the Endangered Species Act. While both estimates of census population sizes (e.g., from guano piles, Kunz et al. 2009 ) and effective population size are subject to assumptions and prone to some uncertainty, the similarity of the two estimated histories reflect the same story: one in which gray bats experienced a recent precipitous decline due to anthropogenic factors, and partially recovered following conservation intervention. As expected from a genetic bottleneck, we found an underrepresentation of rare alleles in modern gray bat populations (based on Tajima’s D, F statistics; Table 1 ; Fig. 2 ). This loss of rare alleles is characteristic of recent population contraction from a moderate demographic bottleneck (Gattepaille et al. 2013 ). Gray bats have a high overall rate of global genomic heterozygosity (Table 1 ) compared to other species (e.g., Prasad et al. 2021), in line with other myotine bats (e.g., Lilley et al. 2020a , b ; Vannatta and Carver 2024 ). Previous studies have found that heterozygosity is positively correlated with fitness, even when a bottleneck has occurred (e.g., Price and Hadfield 2014 ). This component of genetic health has likely supported gray bats as their populations recover demographically and genetically. The gray bat is only one of several North American Myotis species to have undergone population decline. The Indiana bat ( M. Sodalis Miller and Allen), like the gray bat, was an early conservation target after undergoing declines related to habitat disturbance. Unlike the gray bat (Bernard et al. 2017 ), Indiana bats also experienced subsequent, albeit moderate, declines in the early 2000s due to the disease white-nose syndrome (Cheng et al. 2021 ). Recently, Kwait et al. ( 2025 ) conducted a genomic assessment of the species and did not detect a bottleneck associated with white-nose syndrome, nor with historic declines. However, a third species, the little brown bat ( M. Lucifugus [le Conte]), did show signs of increased genetic drift and reduced genetic diversity due to recent and severe population declines related to white-nose syndrome (Auteri and Knowles 2020 ). The amount of time from, and severity of, initial decline likely influenced the ability to detect reductions in genetic diversity in each of these species, with the little brown bat’s decline being the most recent and most severe, while the Indiana bat’s decline was the least severe, though it occurred around the same time as the gray bat’s. Different methods of evaluating genetic consequences of decline may also lead to different conclusions, highlighting the need for careful consideration of how sampling and analytic methodologies align with study objectives. Maintenance of genetic diversity through demographic history, admixture, and life-history traits We questioned whether admixture and effective population size were explanatory mechanisms of genetic diversity levels in gray bats. Although we only looked at individuals from a single cave, we found evidence for very weak genetic substructure in the population, with high admixture (K = 2, weighted F ST = 6.69e-05; Fig. 3 ). Weak structure is understandable, since gray bats regularly travel far distances seasonally (up to 437 km; Tuttle 1976a , Holliday et al. 2023 ) and males often visit multiple caves to mate promiscuously during the reproductive period (Tuttle 1976b ). Additionally, previous habitat losses of up to 50% (Elliott 2008) may have resulted in admixture as gray bats joined new reproductive colonies. Admixture at this single site may also be a result of decreased boundaries for gene flow north of the Mississippi River Alluvial Plain. Regardless, conservation action on both sides of the gray bat range appears to have maintained population connectivity and gene flow between subpopulations. Healthy gene flow and admixture between large subpopulations may have buffered genetic drift and protected against the loss of heterozygosity (Jangjoo et al. 2016 , Ceballos et al. 2018). While strong decreases in census population result in detrimental genetic consequences for affected species, increased extinction threat due to inbreeding depression is most likely to occur when the absolute population size drops below a certain threshold. Because gray bats form dense colonies in limited habitat (95% reproduce and overwinter in only 15 locations), they are extremely vulnerable to habitat disturbance – the loss of even a single cave can lead to substantial population decline. However, they also had historically large census and effective population sizes. This likely meant that, even after population losses of ~ 95% (large declines relative to base population), the absolute number of individuals remained substantial. At the nadir of their bottleneck (N e.Bot = ~ 13,000), gray bats still maintained large effective population sizes compared to many species experiencing bottlenecks associated with substantial inbreeding depression (e.g., N e = 64–133, Robinson et al. 2016 ; or N e = 100, Hoelzel et al. 2024 ). Theory predicts that populations are most likely to suffer inbreeding depression below very low census population sizes (i.e., < 1000; Frankham et al. 2014 ), which the gray bat never crossed (Brady et al. 1982 , Shaffer and Samson 1985 , Gomulkiewicz and Holt 1995 ). Thus, we suggest distinguishing between “relative demographic bottlenecks”, in which a population experiences a large drop relative to their original population size (e.g., 90%), and “absolute demographic bottlenecks”, in which a population drops to few individuals (e.g., < 1000). In populations that experience strong absolute demographic bottlenecks, heightened loss of genetic diversity is more likely to lead to inbreeding depression. Species with slow life histories (i.e., long-lived) may respond to demographic bottlenecks differently than shorter-lived species. However, the direction of, and mechanisms underlying, this effect are unclear. Some evidence points to long-lived species being resistant to genetic bottlenecks (e.g., Hailer et al. 2006 ), while other studies indicate that these species do not show genetic diversity loss readily in tests (especially m-ratio) and may thus be vulnerable to extinction debt (Peery et al. 2012 , Bradke et al. 2021 , Clark et al. 2024 , Gargiulo et al. 2025 ). However, most studies on longevity and bottlenecks have used microsatellite tests. Next generation genomic studies on bottlenecks, such as this one, may be especially helpful for understanding the relationship between longevity and the genetic consequences of declines. Anecdotally, the three such studies of Myotis species (Auteri and Knowles 2020 , Gignoux-Wolfsohn et al. 2021 , Kwait et al. 2025 ) suggest a negative relationship between longevity and resilience to genetic diversity loss. Little brown bats have the longest lifespan of the three species, followed by gray bats, and Indiana bats (Griffin and Hitchcock 1965 , Thomson 1982 , Decher and Choate 1995 ). Correspondingly, little brown bats have clear signatures of genetic diversity loss following declines (Auteri & Knowles 2020 , Gignoux-Wolfsohn et al. 2021 ), gray bats have intermediate signatures (this study, Lindsay et al. 2015 ), and Indiana bats have weak signatures (Kwait et al. 2025 ). Although this comparison is informal because of differences between the species, scenarios, and studies, the Myotis genus may be a useful study system for understanding the links between longevity and susceptibility to genetic diversity loss. The genus is speciose (100 + species) with substantial variation in longevity (5 to 40 + years; Huang et al. 2020 ), and with many species experiencing population declines. Conclusions As humans continue to affect biodiversity across the globe, it is important to recognize that species can recover from severe declines. However, successful conservation, including recovery of genetic diversity, likely hinges on timely and effective intervention. For instance, assisted immigration of bighorn sheep ( Ovis canadensis Shaw) implemented soon after a demographic bottleneck led to rapid improvements in genetic diversity and survival rates (Poirier et al. 2018). Similarly, the rapid recovery in gray bat effective population — following restriction of human disturbance at critical hibernation sites across the range — shows that timely and well-planned conservation action (taken after a relative bottleneck, but before numbers cross critical thresholds), can be extremely successful. Conversely, if declines are allowed to progress and reach an extreme absolute bottleneck, wherein lower critical thresholds are crossed, populations may experience reductions in genetic diversity from which recovery is either impossible or requires prolonged, even indefinite conservation intervention. Declarations Acknowledgements We thank Victor Piñeiro, Jeanette Bailey, and Aleana Savage, for assistance in the field, Vona Kuczynsa for assistance with field site selection, Lynn Robbins for use of field equipment, the private property owners for access to the site, and Manny Vazquez for providing a reference genome. Deb Finn, Sean Maher, Rebecca Taylor, Shelly Colatskie, Kendra Edge, Carly Trujillo, and Joey Curti provided valuable information and feedback on this project. Funding: Funding was provided by Missouri State University. Conflicts of interest: The authors declare that they have no conflicts of interest. Ethics approval: All research was done in appliance with appropriate IACUC and permitting guidelines (Missouri State University IACUC #2022-14, MDC Wildlife Collector #60085, and US FWS T&E Recovery #ES04397C) Consent to participate: Not applicable Consent for publication: Not applicable Availability of data and material: Raw sequence data is available on GenBank (SRA Accession: PRJNA1363576) Code availability: code is available on github https://github.com/marxeao/GrayBatPopGen. We highlight that demographic bottlenecks do not always lead to genetic collapse. We show the other side of the coin – conservation intervention, and not just destruction, can leave a genetic mark. Author Contributions: GA and MA-O both conceived the project. MA-O performed data analysis, writing of the original draft, revision of subsequent drafts, and data visualization. GA performed fieldwork, in-house lab work, review and editing of the manuscript, and supervision of the project. References Adams, A. M., Trujillo, L. A., Campbell, C. J., Akre, K. L., Arroyo-Cabrales, J., Burns, L., Coleman, J. T. H., Dixon, R. D., Francis, C. M., Gamba-Rios, M., Kuczynska, V., McIntire, A., Medellín, R. A., Morris, K. M., Ortega, J., Reichard, J. D., Reichert, B., Segers, J. L., Whitby, M. D., & Frick, W. F. (2024) The state of the bats in North America. Ann N York Acad Sci 1541:115–128. https://doi.org/10.1111/nyas.15225 Åkesson, M., Liberg, O., Sand, H., Wabakken, P., Bensch, S. and Flagstad, Ø. (2016) Genetic rescue in a severely inbred wolf population. Mol Ecol 25:4745–4756. https://doi.org/10.1111/mec.13797 Agnarsson I, Zambrana-Torrelio CM, Flores-Saldana NP, May-Collado LJ (2011) A time-calibrated species-level phylogeny of bats (Chiroptera, Mammalia). PLoS Curr 3:RRN1212. https://doi.org/10.1371/currents.rrn1212 Allen AM, Jongejans E, Pol M van de, Ens BJ, Frauendorf M, Sluijs M van der, Kroon H de (2022) The demographic causes of population change vary across four decades in a long-lived shorebird. Ecology 103(4):e3615. https://doi.org/10.1002/ecy.3615 Andrews, S (2010) Fastqc: A quality control tool for high throughput sequence data [Online]. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed 1 March 2023. Auteri GG, Knowles LL (2020) Decimated little brown bats show potential for adaptive change. Sci Rep 10(1):3023. https://doi.org/10.1038/s41598-020-59797-4 Bernard RF, Willcox EV, Parise KL, Foster JT, McCracken GF (2017) White-nose syndrome fungus, Pseudogymnoascus destructans, on bats captured emerging from caves during winter in the southeastern United States. BMC Zoöl 2(1):12. https://doi.org/10.1186/s40850-017-0021-2 Bortoluzzi C, Bosse M, Derks MFL, Crooijmans RPMA, Groenen MAM, Megens H (2020) The type of bottleneck matters: Insights into the deleterious variation landscape of small managed populations. Evol Appl 13(2):330–341. https://doi.org/10.1111/eva.12872 Bouzat JL (2010) Conservation genetics of population bottlenecks: the role of chance, selection, and history. Conserv Genet 11(2):463–478. https://doi.org/10.1007/s10592-010-0049-0 Bozzuto C, Biebach I, Muff S, Ives AR, Keller LF (2019) Inbreeding reduces long-term growth of Alpine ibex populations. Nat Ecol Evol 3(9):1359–1364. https://doi.org/10.1038/s41559-019-0968-1 Brady J, Kunz T, Tuttle MD, Wilson M (1982) Gray bat recovery plan. U.S. Fish and Wildlife Service, Washington, D.C. 143 pp. Bradke DR, Altobelli JT, Russell AL, Jaeger CP, Moore JA (2021) Low Bottleneck Detection in Long-Lived Species Despite Lost Genetic Diversity: A Case Study of Tuatara and Eastern Massasauga Rattlesnakes. J Hered 112(4):346–356. https://doi.org/10.1093/jhered/esab025 Broad Institute (2019) Picard tools. Available online at https://broadinstitute.github.io/picard/. Accessed 9 October 2024. Brook BW, Tonkyn DW, O’Grady JJ, Frankham R (2002) Contribution of Inbreeding to Extinction Risk in Threatened Species. Conserv Ecol 6(1). https://doi.org/10.5751/es-00387-060116 Cheng TL, Reichard JD, Coleman JTH, Weller TJ, Thogmartin WE, Reichert BE, Bennett AB, Broders HG, Campbell J, Etchison K, Feller DJ, Geboy R, Hemberger T, Herzog C, Hicks AC, Houghton S, Humber J, Kath JA, King RA, Loeb SC, Massé A, Morris KM, Niederriter H, Nordquist G, Perry RW, Reynolds RJ, Sasse DB, Scafini MR, Stark RC, Stihler CW, Thomas SC, Turner GG, Webb S, Westrich BJ, Frick WF (2021) The scope and severity of white‐nose syndrome on hibernating bats in North America. Conserv Biol 35(5):1586–1597. https://doi.org/10.1111/cobi.13739 Clark MI, Fitzpatrick SW, Bradburd GS (2024) Pitfalls and windfalls of detecting demographic declines using population genetics in long‐lived species. Evol Appl 17(7):e13754. https://doi.org/10.1111/eva.13754 Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G, Durbin R, Group 1000 Genomes Project Analysis (2011) The variant call format and VCFtools. Bioinformatics 27(15):2156–2158. https://doi.org/10.1093/bioinformatics/btr330 Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H (2021) Twelve years of SAMtools and BCFtools. GigaScience 10(2):giab008. https://doi.org/10.1093/gigascience/giab008 Day G, Bolderstone T, Conway GJ, Cross T, Davis T, Dolan M, Greening M, Neale C, Nicholson I, Nicholson K, Ward A, Ward N, Fox G, Harney E, Hipperson H, Maher K, Thompson J, Tucker R, Waters D, Durrant KL, Burke T, Slate J, Arnold KE (2025) The Genomic Signature of Demographic Decline in a Long‐Distance Migrant in a Range‐Extreme Population. Mol Ecol 34(13):e17805. https://doi.org/10.1111/mec.17805 Decher J, Choate JR (1995) Myotis grisescens. Mamm Species (510):1–7. https://doi.org/10.2307/3504084 de Kort H, Prunier JG, Ducatez S, Honnay O, Baguette M, Stevens VM, Blanchet S (2021) Life history, climate and biogeography interactively affect worldwide genetic diversity of plant and animal populations. Nat Commun 12(1):516. https://doi.org/10.1038/s41467-021-20958-2 Elliott WR (2007) Gray and Indiana Bat Population Trends in Missouri. In: 2007 National Cave and Karst Management Symposium. pp 46–61 Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software structure: a simulation study. Mol Ecol 14(8):2611–2620. https://doi.org/10.1111/j.1365-294x.2005.02553.x Frankham R (1996) Relationship of Genetic Variation to Population Size in Wildlife. Conserv Biol 10(6):1500–1508. https://doi.org/10.1046/j.1523-1739.1996.10061500.x Frankham R, Bradshaw CJA, Brook BW (2014) Genetics in conservation management: Revised recommendations for the 50/500 rules, Red List criteria and population viability analyses. Biol Conserv 170:56–63. https://doi.org/10.1016/j.biocon.2013.12.036 Gargiulo R, Budde KB, Heuertz M (2025) Mind the lag: understanding genetic extinction debt for conservation. Trends Ecol Evol 40(3):228–237. https://doi.org/10.1016/j.tree.2024.10.008 Gattepaille LM, Jakobsson M, Blum MG (2013) Inferring population size changes with sequence and SNP data: lessons from human bottlenecks. Heredity 110(5):409–419. https://doi.org/10.1038/hdy.2012.120 Gignoux‐Wolfsohn SA, Pinsky ML, Kerwin K, Herzog C, Hall M, Bennett AB, Fefferman NH, Maslo B (2021) Genomic signatures of selection in bats surviving white‐nose syndrome. Mol Ecol 30(22):5643–5657. https://doi.org/10.1111/mec.15813 Gomulkiewicz R, Holt RD (1995) WHEN DOES EVOLUTION BY NATURAL SELECTION PREVENT EXTINCTION? Evolution 49(1):201–207. https://doi.org/10.1111/j.1558-5646.1995.tb05971.x Griffin DR, Hitchcock HB (1965) Probable 24-Year Longevity Records for Myotis lucifugus. J Mammal 46(2):332–332. https://doi.org/10.2307/1377857 Hailer F, Helander B, Folkestad AO, Ganusevich SA, Garstad S, Hauff P, Koren C, Nygrd T, Volke V, Vil C, Ellegren H (2006) Bottlenecked but long-lived: high genetic diversity retained in white-tailed eagles upon recovery from population decline. Biol Lett 2(2):316–319. https://doi.org/10.1098/rsbl.2006.0453 Harvey, MJ (1992) Bats of the Eastern United States . Arkansas Game & Fish Commission. 47 pp. Hedrick P (2005) ‘Genetic restoration:’ a more comprehensive perspective than ‘genetic rescue.’ Trends Ecol Evol 20(3):109. https://doi.org/10.1016/j.tree.2005.01.006 Hoelzel AR, Gkafas GA, Kang H, Sarigol F, Boeuf BL, Costa DP, Beltran RS, Reiter J, Robinson PW, McInerney N, Seim I, Sun S, Fan G, Li S (2024) Genomics of post-bottleneck recovery in the northern elephant seal. Nat Ecol Evol 8(4):686–694. https://doi.org/10.1038/s41559-024-02337-4 Hoffman JI, Vendrami DLJ, Hench K, Chen RS, Stoffel MA, Kardos M, Amos W, Kalinowski J, Rickert D, Köhrer K, Wachtmeister T, Goebel ME, Bonin CA, Gulland FMD, Dasmahapatra KK (2024) Genomic and fitness consequences of a near-extinction event in the northern elephant seal. Nat Ecol Evol 8(12):2309–2324. https://doi.org/10.1038/s41559-024-02533-2 Holliday C, Wisby JP, Roby PL, Samoray ST, Vannatta JM (2023) Modeling migration and movement of gray bats. J Wildl Manag 87(3). https://doi.org/10.1002/jwmg.22364 Huang Z, Whelan CV, Dechmann D, Teeling EC (2020) Genetic variation between long-lived versus short-lived bats illuminates the molecular signatures of longevity. Aging (Albany NY) 12(16):15962–15977. https://doi.org/10.18632/aging.103725 Hung C-M, Shaner P-JL, Zink RM, Liu W-C, Chu T-C, Huang W-S, Li S-H (2014) Drastic population fluctuations explain the rapid extinction of the passenger pigeon. Proc Natl Acad Sci 111(29):10636–10641. https://doi.org/10.1073/pnas.1401526111 Jangjoo M, Matter SF, Roland J, Keyghobadi N (2016) Connectivity rescues genetic diversity after a demographic bottleneck in a butterfly population network. Proc Natl Acad Sci 113(39):10914–10919. https://doi.org/10.1073/pnas.1600865113 Jones KE, Purvis A, MacLarnon A, Bininda‐Emonds ORP, Simmons NB (2002) A phylogenetic supertree of the bats (Mammalia: Chiroptera). Biol Rev 77(2):223–259. https://doi.org/10.1017/s1464793101005899 Korneliussen TS, Albrechtsen A, Nielsen R (2014) ANGSD: Analysis of Next Generation Sequencing Data. BMC Bioinform 15(1):356. https://doi.org/10.1186/s12859-014-0356-4 Krueger F, James F, Ewels P, Afyounian E, Weinstein M, Schuster-Boeckler B, Hulselmans G, Sclamons (2015) TrimGalore: A wrapper around Cutadapt and FastQC to consistently apply adapter and quality trimming to FastQ files, with extra functionality for RRBS data. Available online at https://github.com/FelixKrueger/TrimGalore?tab=readme-ov-file. Accessed 2 February 2023. https://doi.org/10.5281/zenodo.7598955 Kumar S, Subramanian S (2002) Mutation rates in mammalian genomes. Proc Natl Acad Sci 99(2):803–808. https://doi.org/10.1073/pnas.022629899 Kunz T H, Betke M, Hristov NI, Vonhof MJ (2009) Methods for assessing colony size, population size, and relative abundance of bats. In: Kunz TH, Parsons S (eds) Ecological and Behavioral Methods for the Study of Bats. Johns Hopkins University Press, Baltimore, MD, pp. 133–157 Kwait R, Eskew EA, Gignoux‐Wolfsohn S, Pinsky ML, Vonhof M, Hines B, Maslo B (2025) Low‐Coverage Whole‐Genome Analysis of Population Structure, Bottlenecks, and Selection in Indiana Bats Before and After White‐Nose Syndrome. Mol Ecol :e70172. https://doi.org/10.1111/mec.70172 Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25(14):1754–1760. https://doi.org/10.1093/bioinformatics/btp324 Li H, Durbin R (2010) Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26(5):589–595. https://doi.org/10.1093/bioinformatics/btp698 Lilley TM, Sävilammi T, Ossa G, Blomberg AS, Vasemägi A, Yung V, Vendrami DLJ, Johnson JS (2020a) Population Connectivity Predicts Vulnerability to White-Nose Syndrome in the Chilean Myotis (Myotis chiloensis) - A Genomics Approach. G3: Genes, Genomes, Genet 10(6):2117–2126. https://doi.org/10.1534/g3.119.401009 Lilley TM, Wilson IW, Field KA, Reeder DM, Vodzak ME, Turner GG, Kurta A, Blomberg AS, Hoff S, Herzog CJ, Sewall BJ, Paterson S (2020b) Genome-Wide Changes in Genetic Diversity in a Population of Myotis lucifugus Affected by White-Nose Syndrome. G3: Genes, Genomes, Genet 10(6):2007–2020. https://doi.org/10.1534/g3.119.400966 Lindsay DL, Guan X, Farrington HL, Blake MD, Barker ND, Carr MR, P EN, Lance RF (2015) Genetic Structure Among Hibernacula of the Endangered Gray Bat (Myotis grisescens). Acta Chiropterologica 17(2):293–306. https://doi.org/10.3161/15081109acc2015.17.2.005 Liu Y, Craig SBB, Bechsgaard JS, Dicks LV, Bruun HH, Høye TT, Bilde T, Thomsen PF (2026) Recent Demographic History Inferred From Allele Frequency Spectra: Evaluation by Simulations and Insights From Pool Sequencing of Wild Bees. Mol Ecol 35(1):e70169. https://doi.org/10.1111/mec.70169 Lucena‐Perez M, Kleinman‐Ruiz D, Marmesat E, Saveljev AP, Schmidt K, Godoy JA (2021) Bottleneck‐associated changes in the genomic landscape of genetic diversity in wild lynx populations. Evol Appl 14(11):2664–2679. https://doi.org/10.1111/eva.13302 Martin CO (2007) Assessment of the Population Status of the Gray Bat (Myotis grisescens); Status Review, DoD Initiatives, and Results of a Multi-Agency Effort to Survey Wintering Populations at Major Hibernacula, 2005-2007. https://doi.org/10.21236/ada473199 Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetJ 17(1):10–12. https://doi.org/10.14806/ej.17.1.200 Maruyama T, Fuerst PA (1985) Population bottlenecks and nonequilibrium models in population genetics. Ii. Number of alleles in a small population that was formed by a recent bottleneck. Genetics 111(3):675–689. https://doi.org/10.1093/genetics/111.3.675 Meisner J, Albrechtsen A (2018) Inferring Population Structure and Admixture Proportions in Low-Depth NGS Data. Genetics 210(2):719–731. https://doi.org/10.1534/genetics.118.301336 Meisner J, Albrechtsen A (2019) Testing for Hardy–Weinberg equilibrium in structured populations using genotype or low‐depth next generation sequencing data. Mol Ecol Resour 19(5):1144–1152. https://doi.org/10.1111/1755-0998.13019 Nagel JJ, Nelson DM, Gugger PF (2024) Population Genetic Structure and Effective Size of Two Endangered Cave Bat Species. Acta Chiropterologica 25(2):203–211. https://doi.org/10.3161/15081109acc2023.25.2.001 Nei M (2005) Bottlenecks, Genetic Polymorphism and SpeciationThis article is dedicated to the memory of Takeo Maruyama. Genetics 170(1):1–4. https://doi.org/10.1093/genetics/170.1.1 Ochoa A, Gibbs HL (2021) Genomic signatures of inbreeding and mutation load in a threatened rattlesnake. Mol Ecol 30(21):5454–5469. https://doi.org/10.1111/mec.16147 Olazcuaga L, Lincke B, DeLacey S, Durkee LF, Melbourne BA, Hufbauer RA (2023) Population demographic history and evolutionary rescue: Influence of a bottleneck event. Evol Appl 16(8):1483–1495. https://doi.org/10.1111/eva.13581 Peery MZ, Kirby R, Reid BN, Stoelting R, Doucet‐bëer E, Robinson S, Vásquez‐carrillo C, Pauli JN, Palsbøll PJ (2012) Reliability of genetic bottleneck tests for detecting recent population declines. Mol Ecol 21(14):3403–3418. https://doi.org/10.1111/j.1365-294x.2012.05635.x Poirier M, Coltman DW, Pelletier F, Jorgenson J, Festa‐Bianchet M (2019) Genetic decline, restoration and rescue of an isolated ungulate population. Evol Appl 12(7):1318–1328. https://doi.org/10.1111/eva.12706 Prasad A, Lorenzen ED, Westbury MV (2022) Evaluating the role of reference‐genome phylogenetic distance on evolutionary inference. Mol Ecol Resour 22(1):45–55. https://doi.org/10.1111/1755-0998.13457 Price MR, Hadfield MG (2014) Population Genetics and the Effects of a Severe Bottleneck in an Ex Situ Population of Critically Endangered Hawaiian Tree Snails. PLoS ONE 9(12):e114377. https://doi.org/10.1371/journal.pone.0114377 Ramstad KM, Colbourne RM, Robertson HA, Allendorf FW, Daugherty CH (2013) Genetic consequences of a century of protection: serial founder events and survival of the little spotted kiwi ( Apteryx owenii ). Proc R Soc B: Biol Sci 280(1762):20130576. https://doi.org/10.1098/rspb.2013.0576 Renaud G, Hanghøj K, Korneliussen TS, Willerslev E, Orlando L (2019) Joint Estimates of Heterozygosity and Runs of Homozygosity for Modern and Ancient Samples. Genetics 212(3):587–614. https://doi.org/10.1534/genetics.119.302057 Robinson JA, Räikkönen J, Vucetich LM, Vucetich JA, Peterson RO, Lohmueller KE, Wayne RK (2019) Genomic signatures of extensive inbreeding in Isle Royale wolves, a population on the threshold of extinction. Sci Adv 5(5):eaau0757. https://doi.org/10.1126/sciadv.aau0757 Robinson JA, Vecchyo DO-D, Fan Z, Kim BY, vonHoldt BM, Marsden CD, Lohmueller KE, Wayne RK (2016) Genomic Flatlining in the Endangered Island Fox. Curr Biol 26(9):1183–1189. https://doi.org/10.1016/j.cub.2016.02.062 Santiago E, Novo I, Pardiñas AF, Saura M, Wang J, Caballero A (2020) Recent Demographic History Inferred by High-Resolution Analysis of Linkage Disequilibrium. Mol Biol Evol 37(12):3642–3653. https://doi.org/10.1093/molbev/msaa169 Scott M, Parker G, Juntti M, Castellino J, Forero O, Mell I, Jerome G, Amati M, Hernandez C, Buntine C, Dodd A, Hamiduddin I, Brien CO, Lucocq H, Lennon M, Blyth R, Parker G, Scott M (2024) The Biodiversity Crisis – Planning for Nature Recovery? Plan Theory Pr 25(1):103–140. https://doi.org/10.1080/14649357.2024.2322879 Shaffer ML, Samson FB (1985) Population Size and Extinction: A Note on Determining Critical Population Sizes. Am Nat 125(1):144–152. https://doi.org/10.1086/284332 Skotte L, Korneliussen TS, Albrechtsen A (2013) Estimating Individual Admixture Proportions from Next Generation Sequencing Data. Genetics 195(3):693–702. https://doi.org/10.1534/genetics.113.154138 Sonsthagen SA, Wilson RE, Underwood JG (2017) Genetic implications of bottleneck effects of differing severities on genetic diversity in naturally recovering populations: An example from Hawaiian coot and Hawaiian gallinule. Ecol Evol 7(23):9925. https://doi.org/10.1002/ece3.3530 Tallmon DA, Luikart G, Waples RS (2004) The alluring simplicity and complex reality of genetic rescue. Trends Ecol Evol 19(9):489–496. https://doi.org/10.1016/j.tree.2004.07.003 Tanaka Y (2000) Extinction of populations by inbreeding depression under stochastic environments. Popul Ecol 42(1):55–62. https://doi.org/10.1007/s101440050009 Tanaka Y (1998) Theoretical aspects of extinction by inbreeding depression. Res Popul Ecol 40(3):279–286. https://doi.org/10.1007/bf02763459 Thomson CE (1982) Myotis sodalis. Mamm Species 163 : 1–5. https://doi.org/10.2307/3504013 Tuttle MD (1976a) Population ecology of the gray bat ( Myotis grisescens) : Philopatry, timing and patterns of movement, weight loss during migration, and seasonal adaptive strategies. Occasional Papers to the Museum of Natural History 54:1–38. The University of Kansas. Lawrence, Kansas. Tuttle MD (1976b) Population Ecology of the Gray Bat (Myotis Grisescens): Factors Influencing Growth and Survival of Newly Volant Young. Ecology 57(3):587–595. https://doi.org/10.2307/1936443 US Fish and Wildlife Service (USFWS) (2025) Gray bat ( Myotis grisescens ). Available online at https://www.fws.gov/species/gray-bat-myotis-grisescens. Accessed 15 November 2025. Van der Auwera GA, O'Connor BD (2020) Genomics in the Cloud: Using Docker, GATK, and WDL in Terra. O'Reilly Media, Sebastopol, CA, USA. 467 pp. Vannatta JM, Carver BD (2024) Range-wide population genetic structure and genetic diversity of Southeastern Myotis (Myotis austroriparius). Mammal Res 69(4):577–595. https://doi.org/10.1007/s13364-024-00759-w Vazquez JM, Lauterbur ME, Mottaghinia S, Gaucherand L, Maesen S, Singer M, Villa S, Bucci M, Fraser D, Gray-Sandoval G, Haidar ZR, Han M, Kohler W, Lama TM, Corf AL, Loyer C, McMillan D, Li S, Lo J, Rey C, Capel SL, Slocum K, Sui M, Thomas W, Tyburec JD, Brem R, Miller R, Buchalski M, Vazquez-Medina JP, Pfeffer S, Etienne L, Enard D, Sudmant PH (2025) Extensive longevity and DNA virus-driven adaptation in nearctic Myotis bats. bioRxiv :2024.10.10.617725. https://doi.org/10.1101/2024.10.10.617725 Watterson GA (1975) On the number of segregating sites in genetical models without recombination. Theor Popul Biol 7(2):256–276. https://doi.org/10.1016/0040-5809(75)90020-9 Worthington-Wilmer J, Barratt E (1996) A non-lethal method of tissue sampling for genetic studies of chiropterans. Bat Research News 37(1):1-3. Tables Table 1. Main statistics calculated and brief interpretations. Tajima’s D F ST F IS F S H H e H o Subpopulations N e.LT N e.Bot N e.Cont Value 0.52 0.000067–0.0026 -0.23 -0.18 0.00322 0.376 0.411 2–3 70,738 13,001.9 56,050.9 Variation 0.002 SE n/a -0.24 – -0.22 (95% CI) -0.18 – -0.18 (95% CI) 0.00301–0.00343 (95% CI) 0.376–0.376 (range) 0.405–0.416 (range) n/a 68,153–75,000 (95% CI) n/a 53,643.2–58,253.3 Interpretation Excess of common alleles Weak population structure Heterozygosity excess Heterozygosity excess High heterozygosity High genomic heterozygosity High heterozygosity/Heterozygosity excess Population structure and admixture occur Large effective base population Strong reduction, but still many individuals at minimum Large increase from N e.Bot , but lower than N e.LT Table 2. ΔK calculated using the Evanno et al. (2005) method for different assumed numbers of population clusters (K). Mean best likelihood and standard deviation is presented as well. K 1 2 3 4 5 6 7 8 ΔK a a 5.90 1.14 0.75 0.65 0.72 0.68 Likelihood (x10 6 ) -4.62 -4.44 -4.30 -4.18 -4.06 -3.94 -3.83 -3.72 St dev (x10 3 ) 0 0.17 7.77 7.78 7.74 6.12 9.69 5.55 a Evanno’s method cannot calculate ΔK for 1 or 2 population clusters. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8734754","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597343421,"identity":"c464ee01-ff8c-4fa2-ba3f-13d7b8bfebfb","order_by":0,"name":"Marxe Altman-Orbach","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+0lEQVRIiWNgGAWjYHAC9t9/GGwY2CAcCZgoM149EjwMaQgtPBDVBLUcRvAIatFtP3zAQOLP+Wg+/uMPH/PusbC3Zz9/8AFDhXViAw4tZmfSEhIM227ntknkGBvzPJNI7OFJZjZgOJOOW8uBHIMDiQ0gLTxs0jwHJBJ4GJLZJBjbDuPWcv6NYcOBP+dy2/iPPwNpsefhf8z+g/EfHi03cowZG9gO5LYxJJiBtDD2SCSzMTA24NPyLI2ZsS0Z7BfDOQeAfrnx2Fgi4Vi6MW6HJR9jZvhjlzu///jDB28O1Nmz9yc+/PChxloWlxYcIIE05aNgFIyCUTAK0AAAfxtWABXcyG0AAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0008-7398-8044","institution":"Missouri State University","correspondingAuthor":true,"prefix":"","firstName":"Marxe","middleName":"","lastName":"Altman-Orbach","suffix":""},{"id":597343422,"identity":"8c26ec97-016a-41d2-8928-4e239f606f48","order_by":1,"name":"Giorgia G. Auteri","email":"","orcid":"https://orcid.org/0000-0002-5579-8078","institution":"Bat Conservation International","correspondingAuthor":false,"prefix":"","firstName":"Giorgia","middleName":"G.","lastName":"Auteri","suffix":""}],"badges":[],"createdAt":"2026-01-29 19:30:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8734754/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8734754/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103851265,"identity":"56f796fd-14bd-482a-8d87-8e2090394b43","added_by":"auto","created_at":"2026-03-03 16:48:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":500628,"visible":true,"origin":"","legend":"\u003cp\u003eApproximation of the gray bat range, in yellow, within the eastern United States, adapted from the US Fish and Wildlife Service occurrence map (USFWS 2025). The two halves of the range are separated by the Mississippi River Alluvial Plain, shown in gray. The red X shows Pike County’s location within Missouri.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8734754/v1/72342d6c79478f1fedeb7d38.png"},{"id":103851263,"identity":"3b5cd862-a5f2-4288-8e0c-5e6a510505d1","added_by":"auto","created_at":"2026-03-03 16:48:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":97193,"visible":true,"origin":"","legend":"\u003cp\u003eInbreeding statistics from 23 individuals. a) Tajima’s D score calculated in 50,000 base pair windows across the genome, with the frequency at which each score occurred. b) Per-site inbreeding coefficients across the gray bat genome. c) Individuals’ inbreeding coefficients.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8734754/v1/c979561439b414cf6de9d188.png"},{"id":103851264,"identity":"e0ff1ee7-8211-4d19-8de4-4a88b4f41c36","added_by":"auto","created_at":"2026-03-03 16:48:20","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":121567,"visible":true,"origin":"","legend":"\u003cp\u003eAssignment of bats to subpopulations by Q score. Each bar represents a single individual. Individuals were assigned to clusters, represented by distinct colors, when Q \u0026gt; 0.9. a: cluster assignment when there were 2 assumed populations. b: cluster assignments when there were 3 assumed populations.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8734754/v1/1571c62aeb7b8f4cdffd5e92.png"},{"id":103851266,"identity":"60c11d34-fbcc-47de-8fd3-4afef102eb64","added_by":"auto","created_at":"2026-03-03 16:48:20","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":24566,"visible":true,"origin":"","legend":"\u003cp\u003eEffective population size of gray bats over the past 200 generations, with more recent time on the left. N\u003csub\u003ee\u003c/sub\u003e was calculated in GONE.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8734754/v1/ed7d91716ce68db3600d7372.jpeg"},{"id":109067582,"identity":"9744f2b1-6109-41c9-99f5-0c9624e5e6d9","added_by":"auto","created_at":"2026-05-12 09:56:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1172163,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8734754/v1/d3a2f13c-fc9c-4e7e-9600-278d3724865f.pdf"}],"financialInterests":"","formattedTitle":"Genomic Signatures of Decline and Recovery in an Endangered Bat","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe past century has seen an unprecedented and poorly addressed increase in human-caused extinction rates of species, representing a global biodiversity crisis (Scott et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Extinction risk increases following large declines in census population sizes (i.e. demographic bottlenecks), which can lead to the loss of genetic diversity and subsequent loss of adaptive potential (Tanaka \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Ultimately, these effects can culminate in an extinction vortex and the loss of a species (Brook et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Tanaka 2018). Thus, genetic diversity, which allows populations to adapt to new stressors, is vital to track and to manage in vulnerable populations.\u003c/p\u003e \u003cp\u003eCertain scenarios and species characteristics should theoretically preserve genetic diversity in populations experiencing demographic bottlenecks. For instance, mild to moderate population declines with rapid subsequent recovery are less likely to result in genetic bottlenecks than severe declines with slow recovery (Sonsthagen et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Lucena-Perez et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Olazcuaga et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Life history traits of species can also influence the extent of genetic diversity loss. A large initial effective population size can buffer the loss of rare alleles. Additionally, high dispersal ability can contribute to the maintenance of rare alleles and facilitate gene flow (Frankham \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1996\u003c/span\u003e, Hedrick \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). However, even being abundant, widespread, and mobile may not be enough to save a species from disastrous genetic decline in the face of external threats, such as habitat loss and fragmentation. For instance, European Nightjars (\u003cem\u003eCaprimulgus europaeus\u003c/em\u003e L.) suffered genetic diversity loss due to alteration of woodland habitat, despite previously being widespread (Day et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Similarly, Passenger Pigeons (\u003cem\u003eEctopistes migratorius\u003c/em\u003e L.) exhibited low heterozygosity despite massive population sizes, prior to intensive hunting pressure driving them to extinction (Hung et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Many species do not recover genetic diversity even after demographic recovery (e.g., Bortoluzzi et al. 2019, Bozzuto et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Robinson et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, etc.) For example, Northern elephant seals (\u003cem\u003eMirounga angustirostris\u003c/em\u003e Gill) and Spotted Kiwis (\u003cem\u003eApteryx owenii\u003c/em\u003e Gould) regained population numbers quickly post-bottleneck, but with reduced fitness (Hoelzel et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), high homozygosity (Ramstad et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and continued reliance on conservation interventions.\u003c/p\u003e \u003cp\u003eThough some species suffer lasting negative genetic repercussions after bottlenecks, population declines are not self-fulfilling prophecies. For instance, immigrants from large, genetically healthy source populations can introduce valuable genetic diversity to populations suffering inbreeding depression (e.g., \u0026Aring;kesson et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and small populations can create favorable conditions for purging deleterious alleles (e.g., Robinson et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Ochoa and Gibbs \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Conservation action aims to protect the viability of species through actions such as safeguarding habitat, maintaining dispersal corridors, and sustaining critical population sizes. However, populations of concern often remain genetically vulnerable to collapse, even when protection criteria are met (Tallmon et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Bouzat \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Thus, it is important to assess genetic diversity when making conservation decisions. The mechanisms that maintain viability are not always well understood and can vary across contexts and taxa (de Kort et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Allen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Hoffman et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It can also be informative to identify populations that have resisted the loss of genetic diversity through demographic bottlenecks, and their associated life history traits.\u003c/p\u003e \u003cp\u003eThe gray bat (\u003cem\u003eMyotis grisescens\u003c/em\u003e Howell) was among the first species listed under the US Endangered Species Act, following estimated population declines of up to 80% between the mid 19th and mid 20th centuries (Brady et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). Large, concerted efforts to protect habitat (e.g., gating of sensitive hibernation sites) led to massive demographic recovery since the 1970s, such that the gray bat may now be an example of conservation success (Martin \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Adams et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), although it is still on the endangered species list. The gray bat\u0026rsquo;s relatively low reproductive output (i.e., only one offspring yearly starting at age two) could decrease genetic diversity and increase inbreeding, although the species\u0026rsquo; promiscuousness and dispersal ability may have the opposite effect. Gray bats, and temperate bat species in general, are also relatively long lived (14\u0026ndash;17 years; Harvey \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). However, the influence of longevity on susceptibility to genetic bottlenecks remain unclear (Bradke et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Clark et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Gargiulo et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The few previously conducted studies of gray bat genetics have found high gene flow between two subpopulations east and west of the Mississippi River Alluvial Plain, and variable but mostly healthy levels of heterozygosity (Lindsay et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Nagel et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, no studies have examined gray bat populations north of the Mississippi River Alluvial Plain. Further, the single study assessing genetic bottlenecks in this species used microsatellite-based tests (Lindsay et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which can have variable results due to smaller sample sizes (of the genome) and can be sensitive to underlying assumptions (Peery et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). No study has examined large portions of the gray bat genome through whole genome sequencing (WGS).\u003c/p\u003e \u003cp\u003eWe used gray bats to evaluate the genetic effects of a moderate demographic bottleneck with subsequent recovery on a long-lived species. We conducted non-lethal sampling of wild-caught individuals north of the Mississippi River Alluvial Plain. We analyzed genetic diversity and looked for signals of a genetic bottleneck using a thorough suite of tests and statistics. Specifically, we asked whether gray bats show genetic signals of population expansion, contraction, or stability following their demographic bottleneck. We also inferred demographic features, including effective population size and population structure, to ask whether genetic diversity levels in gray bats could be connected to robust reproductive populations or high admixture.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Site and Sample Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe caught\u003cem\u003e\u0026nbsp;\u003c/em\u003egray bats at a privately owned cave in Pike County, Missouri on 20-Oct-2022, in compliance with all animal welfare, state, and federal permits (Missouri State University IACUC #2022-14, MDC Wildlife Collector #60085, and US FWS T\u0026amp;E Recovery #ES04397C, respectively). During this time of the year, gray bats immigrate from bachelor and maternity colonies to hibernation sites, where they mate promiscuously prior to entering hibernation. The cave we sampled is used by gray bats as a minor hibernation site for some individuals during winter and as a temporary stop for other individuals during migration to other hibernation sites. High connectivity and gene flow have been observed across the gray bat range, likely due to dispersal propensity (Lindsay et al., 2015, Holliday et al. 2023, Nagel et al. 2024). Pike County is west-central within the gray bat range (Fig. 1) but likely represents both subpopulations identified by Lindsay et al. (2015).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe obtained tissue samples from 23 adult gray bats using methods originally described by Worthington-Wilmer and Barratt (1996). A sterile, circular biopsy punch (2-mm diameter; Integra, Princeton, NJ, USA) was used to take two tissue punches from the flight membranes on each wing. Samples were immediately stored in microcentrifuge tubes with ethanol (97% concentration) then taken to the lab, where they were kept in a freezer at -20\u0026deg;C until extraction. For extractions, we used a DNeasy Blood and Tissue Kit (Qiagen, Germantown, MD, USA) to extract DNA at Missouri State University, following the standard kit protocols with some modifications. Specifically, to facilitate complete lysis of samples we used additional proteinase K (30\u0026ndash;40 uL total per sample) and extended incubation time (24\u0026ndash;72 hours) as needed, and to maximize DNA yields we used pre-chilled ethanol (2\u0026deg;C). Final DNA was eluted in water (ddH2O prewarmed to 70\u0026deg;C) and concentrated with a vacufuge.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequencing, Filtering, and Alignment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExtracted DNA was sent to The Center for Applied Genomics (Toronto, Canada) for sequencing in 151 base, paired-end fragments on 3 10B lanes of an Illumina NovaSeq X. To assist with low input DNA levels, PCR was used in library preparation. We performed all bioinformatic processes on a Linux workstation at Missouri State University. Adapters were removed from samples with TrimGalore! (V. 0.6.10; Krueger et al. 2015) and cutadapt (V. 5.2; Martin 2011). The quality of entire sequences was assessed with FastQC (V. 0.12.0; Andrews 2010) and the quality of bases was filtered to a Phred33 score of 20 using SAMtools (V.1.21; Danecek et al. 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThere was no species-specific reference genome available for the gray bat. Therefore, we aligned our sequences against a reference genome for the cave bat\u003cem\u003e\u0026nbsp;\u003c/em\u003e(\u003cem\u003eM. velifer\u0026nbsp;\u003c/em\u003e[J. A. Allen]; BioProject PRJNA103554; Vazquez et al. 2024) using BWA-mem (V.0.7.19; Li and Durban 2009, 2010). Gray bats are closely related to cave bats (Jones et al. 2002, Agnarsson et al. 2011) and our sequences mapped well to the reference genome.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe removed optical duplicates from the resulting alignments with Picard MarkDuplicates (V.2.0.1; Broad Institute 2019). Indels in the sequences were realigned using GATK IndelRealigner (V.4.3.0.0; Van der Auwera and O\u0026apos;Connor 2020). Finally, we filtered sequences to remove the sex chromosomes and extrachromosomal scaffolds, so that only autosomes were considered for metrics of genetic diversity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic Diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo estimate measures of heterozygosity and homozygosity, we first used the empirical Bayesian approach implemented in ANGSD (V. 0.940; Korneliussen et al. 2014) to obtain a genotype likelihood model. We retained data for all 23 individuals, filtered for sites represented in at least 15 sequences with a minimum depth of 5x and a maximum depth of 60x.We obtained a folded site frequency spectrum from the autosome-only genotype likelihood model with ANGSD realsfs, then used ANGSD thetastat with 50 Kb windows to calculate the number of polymorphic sites (Watterson\u0026rsquo;s theta, \u0026theta;W), the mean number of pairwise differences (nucleotide diversity, \u0026pi;), and genome-wide Tajima\u0026rsquo;s D. Tajima\u0026rsquo;s D compares the relationship of \u0026theta;W and \u0026pi;, with a model population at constant size experiencing only neutral mutation. We calculated individual global and local mean rates of genomic heterozygosity (H), as well as low and high confidence intervals with ROHan (Renaud et al. 2019) using standard parameters in 1-Mb windows.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe used VCFtools (V 0.1.18; Danecek et al. 2011) with the option \u0026ldquo;--het\u0026rdquo; to calculate individual expected and observed heterozygosity (H\u003csub\u003ee\u003c/sub\u003e and H\u003csub\u003eo\u003c/sub\u003e). We calculated individual and per-site inbreeding coefficients (F\u003csub\u003eIS\u003c/sub\u003e and F\u003csub\u003eS\u003c/sub\u003e, respectively) using PCAngsd (Skotte et al. 2013). Inbreeding coefficients compare observed heterozygosity and homozygosity to that expected based on Hardy-Weinberg expected (HWE) proportions. A value of 0 indicates no deviation from HWE, whereas a negative value indicates an excess of heterozygosity, and a positive value indicates an excess of homozygosity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation structure and effective population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used multiple methods to infer ancestry clusters and admixture among individuals. First, we used an iterative approach with PCAngsd (Skotte et al. 2013) to infer a covariance matrix, estimate relatedness, and estimate the number of ancestral populations. We then subset samples to 10 Mb from each chromosome, and used this subset data in NGSadmix (V.32; Meisner and Albrechtsen 2018, 2019) to infer ancestry among individuals. We ran NGSAdmix with 20 replications each of assumed ancestral populations of K = 1\u0026ndash;8, using random seeds. To select the best value of K, we calculated \u0026Delta;K with the NGSAdmix results using the Evanno method (Evanno et al. 2005) and evaluated PCAngsd\u0026rsquo;s automatic K detection. We then assigned individuals to clusters based on Q scores \u0026ge; 0.9. We built structure plots using mean Q values in R. To estimate the strength of structuring between clusters, we used VCFtools to calculate the Weir Cockerham fixation index (F\u003csub\u003eST\u003c/sub\u003e).\u003c/p\u003e\n\u003cp\u003eTo estimate the number of reproducing individuals, we calculated recent effective population size (N\u003csub\u003ee\u003c/sub\u003e) with linkage disequilibrium in GONE (Santiago et al. 2020), and we estimated long-term effective population size (N\u003csub\u003ee.LT\u003c/sub\u003e) using the equation \u0026theta;W = 4 N\u003csub\u003ee.LT\u0026nbsp;\u003c/sub\u003e\u0026micro; (Watterson 1975). For the N\u003csub\u003ee.LT\u003c/sub\u003e equation, we first obtained the mean \u0026theta;W including runs of homozygosity of all individuals with ROHan. We used a mutation rate (\u0026micro;) of 2.2x10\u003csup\u003e-9\u003c/sup\u003e, based on the average mutation rate of mammals and gray bat life history (Decher and Choate 1995, Kumar et al. 2002). To estimate recent N\u003csub\u003ee\u003c/sub\u003e, we considered non-indel sites with a maf of 0.1 and that were sequenced in 90% of samples to create a population ped file. We further filtered the minimum mean and individual depths to 7 and the maximum depths to 60. We ran GONE on a random sample of 20% of sites from the filtered set using standard parameters and used the mean of 40 runs to make N\u003csub\u003ee\u0026nbsp;\u003c/sub\u003eestimates.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSequencing and Alignment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur overall alignment rate of gray bat sequences was 90.54% (SD \u0026plusmn; 0.25%), which we found acceptable. After filtering, the mean depth of coverage was 19.65X \u0026plusmn; 2.55 (SD). Coverage was relatively uniform among individuals, and so all 23 individuals sampled were included in all downstream analyses. After filtering and subsetting to autosomes, we considered 1,849,248,362 high quality sites. In the site allele frequency file, which used more aggressive filters including the exclusion of sex chromosomes and extra-chromosomal scaffolds, we considered 14,005,840 total base pairs. In the .ped file, we considered 8,529,672 SNPs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic Diversity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe calculated multiple metrics of genetic diversity and heterozygosity within the gray bat genome. The autosome-only genome-wide mean \u0026theta;W per site was 0.0542 \u0026plusmn; 0.007, with all individuals considered. The mean \u0026pi; per site was 0.067 (SE \u0026plusmn; 1e-4). The mean Tajima\u0026rsquo;s D was 0.52 (SE \u0026plusmn; 0.002; Fig. 2, Table 1), indicating a scarcity of rare alleles. Across the genome, Tajima\u0026rsquo;s D was normally distributed. Of all sites, 1.17% had Tajima\u0026rsquo;s D values higher than 2, and no sites had values lower than -2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe mean global genomic rate of heterozygosity (H) of the 23 individuals was 0.00322 (95% CI 0.00301\u0026ndash;0.00343; Table 1). Observed heterozygosity was greater than expected heterozygosity (mean H\u003csub\u003eo\u0026nbsp;\u003c/sub\u003e= 0.411, mean H\u003csub\u003ee\u0026nbsp;\u003c/sub\u003e= 0.376). Indeed, all inbreeding coefficients reflected an excess of heterozygosity based on HWE proportions. At an individual level, mean F\u003csub\u003eIS\u003c/sub\u003e was -0.23, and at a per-site level mean F\u003csub\u003es\u003c/sub\u003e was -0.18 (Table 1, Fig. 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePopulation structure and effective population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth methods we used to infer population structure in gray bats indicated multiple populations with high gene flow. Evanno\u0026rsquo;s Delta K resulted in a best fit of 3 population clusters (Table 2), although with the iterative approach implemented in NGSAdmix, we detected just 2 population clusters (Table 1). With K = 2 populations, 65% of individuals were assigned to a single cluster (11 in cluster A; 4 in cluster B; Fig 3a) and structure was extremely weak (weighted F\u003csub\u003eST\u0026nbsp;\u003c/sub\u003e= 6.69e-05). With K = 3 populations, structure was weak, only 30% of individuals were assigned to a single cluster, and no individuals were assigned to the third cluster (weighted F\u003csub\u003eST\u0026nbsp;\u003c/sub\u003e= 0.00255; 4 in cluster A; 3 in cluster B; 0 in cluster C; Fig. 3b). Individuals assigned to unique clusters when K = 3 were consistently assigned to the same cluster when K = 2. Thus, we decided that 2 subpopulations (with high admixture) is most likely.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUsing Watterson\u0026rsquo;s theta, we estimated an N\u003csub\u003ee.LT\u003c/sub\u003e of 70,738 (95% CI = 68,153\u0026ndash;75,000) reproductive individuals (Table 1), which is about half of the minimum recorded census population size from the mid 20\u003csup\u003eth\u003c/sup\u003e century, and about one tenth of the maximum historical estimates of census population in Missouri (Brady et al. 1982, Martin 2007, Elliott 2008). Estimates of N\u003csub\u003ee\u003c/sub\u003e over the past 150 generations show a 94% population decline, with strong declines (\u0026ge;1% of population loss per generation) starting 108 generations ago and continuing for 37 consecutive generations before hitting the nadir (71 generations/142 years ago, assuming a generation time of 2 years). The 150-generation maximum N\u003csub\u003ee\u0026nbsp;\u003c/sub\u003eof 218,297 dropped to a minimum of 13,002. N\u003csub\u003ee\u0026nbsp;\u003c/sub\u003eremained at the nadir for 44 generations. Strong increases (\u0026ge;1% of population per generation) in N\u003csub\u003ee\u003c/sub\u003e began 35 generations ago (70 years) and continued until 21 generations ago. For the past 20 generations, contemporary N\u003csub\u003ee\u0026nbsp;\u003c/sub\u003e(N\u003csub\u003ee.Cont\u003c/sub\u003e) has remained plateaued at 53,643\u0026ndash;59,487 (mean = 57,147), 26.18% of the previous maximum.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite successful recovery of gray bat population numbers, our analyses show that the species\u0026rsquo; demographic bottleneck resulted in a loss of rare alleles, heterozygosity excess, and a notable decrease in effective population size. In other words, demographic decline led to a genetic bottleneck characterized by population contraction (Maruyama and Fuerst \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1985\u003c/span\u003e, Nei \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Our estimates of effective population size reaffirm population demographics inferred through census-based methods (e.g., Elliott 2008), with an effective population decline of 94%. However, N\u003csub\u003ee\u003c/sub\u003e never dropped below critical thresholds for genetic drift (Shaffer and Samson \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1985\u003c/span\u003e, Frankham et al. 1996). Near the central area of the gray bat range, we detected multiple subpopulations with high admixture between them. Following a reduction in census size, early conservation actions by managers (e.g., installing bat gates on hibernation sites) and life history traits of the species (e.g., high dispersal, promiscuous mating, and high starting population size) may have helped the gray bat recover demographically and reduced their risk of inbreeding depression, despite a bottleneck.\u003c/p\u003e \u003cp\u003eOur study builds upon previous genetic research on gray bat population structuring and demographic trajectories by Nagel et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Lindsay et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Our inference of two subpopulations with high gene flow supports the previously suggested east-west divide (Lindsay et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e Nagel et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our negative F\u003csub\u003eIS\u003c/sub\u003e indicates that outbreeding, accompanied by population structure and admixture occurs in this centrally located population. This specifically highlights that central populations might be important conservation priorities because they i) can harbor individuals with genetic makeups of both western and eastern populations, and ii) are likely important for immigration and gene-flow across the range.\u003c/p\u003e \u003cp\u003eWe were also able to more clearly identify population trajectories, especially recent ones, compared to previous studies. Like Nagel et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), we found evidence of large historical effective population sizes (N\u003csub\u003ee.LT\u003c/sub\u003e). Our estimate of 70,738 (95% CI\u0026thinsp;=\u0026thinsp;68,153\u0026ndash;75,000) falls well within the range of that suggested by Nagel et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e (95% CI 41,400\u0026ndash;243,122), and helps to narrow the estimate. Additionally, we estimated N\u003csub\u003ee.Cont\u003c/sub\u003e for gray bats for the first time (57,147, with most recent estimates within 20 generations before present) and N\u003csub\u003ee.Bot\u003c/sub\u003e at the extreme of the bottleneck (just 13,001.9, 62 generations ago). These recent estimates reveal the substantial decline experienced by the species, followed by partial recovery to 26.18% of their historical numbers (in contrast to the overall signal of expansion found by Nagel et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, and lack of detected bottleneck in Lindsay et al \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Our estimates of recent N\u003csub\u003ee\u003c/sub\u003e mirror observations of changes in the census population size over time, and indicate that a WGS approach may be especially useful when estimating recent demographic histories for species that, in contrast, have not been well monitored. Similarly, recent work highlights the use of effective population size estimates for differentiating between naturally low populations and those in decline (Liu et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). On a hopeful note for gray bat recovery, we found recent increases in effective population and no evidence of homozygosity excess associated with inbreeding depression.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSignals of population change\u003c/h2\u003e \u003cp\u003eWe asked whether gray bats show genetic signs of population decline, expansion, or stability following their demographic bottleneck.\u003c/p\u003e \u003cp\u003eGray bat census populations have been markedly unstable over the past ~\u0026thinsp;200 years (e.g., Brady et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1982\u003c/span\u003e, Martin \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2007\u003c/span\u003e, Elliott 2008). After severe declines, the initiation of management actions, such as cave gating, resulted in population expansion (Adams et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Historical maximum population sizes, and thus the magnitude of gray bat declines, have been inferred through the sizes of guano piles and stains left by roosting bats on cave ceilings. Our study represents the first genetic inference of gray bat effective population size changes over recent time. Our results support the timelines of previous inferences of population crashes, followed by recovery (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), with a severe decline occurring due to cave disturbance around the time of the American Civil War, and recovery beginning as caves became protected habitat under the Endangered Species Act. While both estimates of census population sizes (e.g., from guano piles, Kunz et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and effective population size are subject to assumptions and prone to some uncertainty, the similarity of the two estimated histories reflect the same story: one in which gray bats experienced a recent precipitous decline due to anthropogenic factors, and partially recovered following conservation intervention.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs expected from a genetic bottleneck, we found an underrepresentation of rare alleles in modern gray bat populations (based on Tajima\u0026rsquo;s D, F statistics; Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This loss of rare alleles is characteristic of recent population contraction from a moderate demographic bottleneck (Gattepaille et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Gray bats have a high overall rate of global genomic heterozygosity (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) compared to other species (e.g., Prasad et al. 2021), in line with other myotine bats (e.g., Lilley et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003eb\u003c/span\u003e; Vannatta and Carver \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Previous studies have found that heterozygosity is positively correlated with fitness, even when a bottleneck has occurred (e.g., Price and Hadfield \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This component of genetic health has likely supported gray bats as their populations recover demographically and genetically.\u003c/p\u003e \u003cp\u003eThe gray bat is only one of several North American \u003cem\u003eMyotis\u003c/em\u003e species to have undergone population decline. The Indiana bat (\u003cem\u003eM. Sodalis\u003c/em\u003e Miller and Allen), like the gray bat, was an early conservation target after undergoing declines related to habitat disturbance. Unlike the gray bat (Bernard et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), Indiana bats also experienced subsequent, albeit moderate, declines in the early 2000s due to the disease white-nose syndrome (Cheng et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Recently, Kwait et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) conducted a genomic assessment of the species and did not detect a bottleneck associated with white-nose syndrome, nor with historic declines. However, a third species, the little brown bat (\u003cem\u003eM. Lucifugus\u003c/em\u003e [le Conte]), did show signs of increased genetic drift and reduced genetic diversity due to recent and severe population declines related to white-nose syndrome (Auteri and Knowles \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The amount of time from, and severity of, initial decline likely influenced the ability to detect reductions in genetic diversity in each of these species, with the little brown bat\u0026rsquo;s decline being the most recent and most severe, while the Indiana bat\u0026rsquo;s decline was the least severe, though it occurred around the same time as the gray bat\u0026rsquo;s. Different methods of evaluating genetic consequences of decline may also lead to different conclusions, highlighting the need for careful consideration of how sampling and analytic methodologies align with study objectives.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMaintenance of genetic diversity through demographic history, admixture, and life-history traits\u003c/h2\u003e \u003cp\u003eWe questioned whether admixture and effective population size were explanatory mechanisms of genetic diversity levels in gray bats.\u003c/p\u003e \u003cp\u003eAlthough we only looked at individuals from a single cave, we found evidence for very weak genetic substructure in the population, with high admixture (K\u0026thinsp;=\u0026thinsp;2, weighted F\u003csub\u003eST\u003c/sub\u003e = 6.69e-05; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Weak structure is understandable, since gray bats regularly travel far distances seasonally (up to 437 km; Tuttle \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e1976a\u003c/span\u003e, Holliday et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and males often visit multiple caves to mate promiscuously during the reproductive period (Tuttle \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e1976b\u003c/span\u003e). Additionally, previous habitat losses of up to 50% (Elliott 2008) may have resulted in admixture as gray bats joined new reproductive colonies. Admixture at this single site may also be a result of decreased boundaries for gene flow north of the Mississippi River Alluvial Plain. Regardless, conservation action on both sides of the gray bat range appears to have maintained population connectivity and gene flow between subpopulations. Healthy gene flow and admixture between large subpopulations may have buffered genetic drift and protected against the loss of heterozygosity (Jangjoo et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Ceballos et al. 2018).\u003c/p\u003e \u003cp\u003eWhile strong decreases in census population result in detrimental genetic consequences for affected species, increased extinction threat due to inbreeding depression is most likely to occur when the absolute population size drops below a certain threshold. Because gray bats form dense colonies in limited habitat (95% reproduce and overwinter in only 15 locations), they are extremely vulnerable to habitat disturbance \u0026ndash; the loss of even a single cave can lead to substantial population decline. However, they also had historically large census and effective population sizes. This likely meant that, even after population losses of ~\u0026thinsp;95% (large declines relative to base population), the absolute number of individuals remained substantial. At the nadir of their bottleneck (N\u003csub\u003ee.Bot\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;~\u0026thinsp;13,000), gray bats still maintained large effective population sizes compared to many species experiencing bottlenecks associated with substantial inbreeding depression (e.g., N\u003csub\u003ee\u003c/sub\u003e = 64\u0026ndash;133, Robinson et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; or N\u003csub\u003ee\u003c/sub\u003e = 100, Hoelzel et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Theory predicts that populations are most likely to suffer inbreeding depression below very low census population sizes (i.e., \u0026lt;\u0026thinsp;1000; Frankham et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), which the gray bat never crossed (Brady et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1982\u003c/span\u003e, Shaffer and Samson \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e1985\u003c/span\u003e, Gomulkiewicz and Holt \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Thus, we suggest distinguishing between \u0026ldquo;relative demographic bottlenecks\u0026rdquo;, in which a population experiences a large drop relative to their original population size (e.g., 90%), and \u0026ldquo;absolute demographic bottlenecks\u0026rdquo;, in which a population drops to few individuals (e.g., \u0026lt;\u0026thinsp;1000). In populations that experience strong absolute demographic bottlenecks, heightened loss of genetic diversity is more likely to lead to inbreeding depression.\u003c/p\u003e \u003cp\u003eSpecies with slow life histories (i.e., long-lived) may respond to demographic bottlenecks differently than shorter-lived species. However, the direction of, and mechanisms underlying, this effect are unclear. Some evidence points to long-lived species being resistant to genetic bottlenecks (e.g., Hailer et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), while other studies indicate that these species do not show genetic diversity loss readily in tests (especially m-ratio) and may thus be vulnerable to extinction debt (Peery et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Bradke et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Clark et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Gargiulo et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, most studies on longevity and bottlenecks have used microsatellite tests. Next generation genomic studies on bottlenecks, such as this one, may be especially helpful for understanding the relationship between longevity and the genetic consequences of declines. Anecdotally, the three such studies of \u003cem\u003eMyotis\u003c/em\u003e species (Auteri and Knowles \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Gignoux-Wolfsohn et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Kwait et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) suggest a negative relationship between longevity and resilience to genetic diversity loss. Little brown bats have the longest lifespan of the three species, followed by gray bats, and Indiana bats (Griffin and Hitchcock \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1965\u003c/span\u003e, Thomson \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e1982\u003c/span\u003e, Decher and Choate \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Correspondingly, little brown bats have clear signatures of genetic diversity loss following declines (Auteri \u0026amp; Knowles \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Gignoux-Wolfsohn et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), gray bats have intermediate signatures (this study, Lindsay et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and Indiana bats have weak signatures (Kwait et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Although this comparison is informal because of differences between the species, scenarios, and studies, the \u003cem\u003eMyotis\u003c/em\u003e genus may be a useful study system for understanding the links between longevity and susceptibility to genetic diversity loss. The genus is speciose (100\u0026thinsp;+\u0026thinsp;species) with substantial variation in longevity (5 to 40\u0026thinsp;+\u0026thinsp;years; Huang et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and with many species experiencing population declines.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAs humans continue to affect biodiversity across the globe, it is important to recognize that species can recover from severe declines. However, successful conservation, including recovery of genetic diversity, likely hinges on timely and effective intervention. For instance, assisted immigration of bighorn sheep (\u003cem\u003eOvis canadensis\u003c/em\u003e Shaw) implemented soon after a demographic bottleneck led to rapid improvements in genetic diversity and survival rates (Poirier et al. 2018). Similarly, the rapid recovery in gray bat effective population \u0026mdash; following restriction of human disturbance at critical hibernation sites across the range \u0026mdash; shows that timely and well-planned conservation action (taken after a relative bottleneck, but before numbers cross critical thresholds), can be extremely successful. Conversely, if declines are allowed to progress and reach an extreme absolute bottleneck, wherein lower critical thresholds are crossed, populations may experience reductions in genetic diversity from which recovery is either impossible or requires prolonged, even indefinite conservation intervention.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Victor Pi\u0026ntilde;eiro, Jeanette Bailey, and Aleana Savage, for assistance in the field, Vona Kuczynsa for assistance with field site selection, Lynn Robbins for use of field equipment, the private property owners for access to the site, and Manny Vazquez for providing a reference genome. Deb Finn, Sean Maher, Rebecca Taylor, Shelly Colatskie, Kendra Edge, Carly Trujillo, and Joey Curti provided valuable information and feedback on this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eFunding was provided by Missouri State University. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eAll research was done in appliance with appropriate IACUC and permitting guidelines\u0026nbsp;(Missouri State University IACUC #2022-14, MDC Wildlife Collector #60085, and US FWS T\u0026amp;E Recovery #ES04397C)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u0026nbsp;\u003c/strong\u003eRaw sequence data is available on GenBank (SRA\u0026nbsp;Accession:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ePRJNA1363576)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u003c/strong\u003e code is available on github https://github.com/marxeao/GrayBatPopGen.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWe highlight that demographic bottlenecks do not always lead to genetic collapse. We show the other side of the coin \u0026ndash; conservation intervention, and not just destruction, can leave a genetic mark.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eGA and MA-O both conceived the project. MA-O performed data analysis, writing of the original draft, revision of subsequent drafts, and data visualization. GA performed fieldwork, in-house lab work, review and editing of the manuscript, and supervision of the project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdams, A. M., Trujillo, L. A., Campbell, C. J., Akre, K. L., Arroyo-Cabrales, J., Burns, L., Coleman, J. T. H., Dixon, R. D., Francis, C. M., Gamba-Rios, M., Kuczynska, V., McIntire, A., Medell\u0026iacute;n, R. A., Morris, K. M., Ortega, J., Reichard, J. D., Reichert, B., Segers, J. L., Whitby, M. D., \u0026amp; Frick, W. F. (2024) The state of the bats in North America. Ann N York Acad Sci 1541:115\u0026ndash;128. https://doi.org/10.1111/nyas.15225\u003c/li\u003e\n\u003cli\u003e\u0026Aring;kesson, M., Liberg, O., Sand, H., Wabakken, P., Bensch, S. and Flagstad, \u0026Oslash;. (2016) Genetic rescue in a severely inbred wolf population. Mol Ecol\u003cem\u003e \u003c/em\u003e25:4745\u0026ndash;4756. https://doi.org/10.1111/mec.13797\u003c/li\u003e\n\u003cli\u003eAgnarsson I, Zambrana-Torrelio CM, Flores-Saldana NP, May-Collado LJ (2011) A time-calibrated species-level phylogeny of bats (Chiroptera, Mammalia). PLoS Curr 3:RRN1212. https://doi.org/10.1371/currents.rrn1212\u003c/li\u003e\n\u003cli\u003eAllen AM, Jongejans E, Pol M van de, Ens BJ, Frauendorf M, Sluijs M van der, Kroon H de (2022) The demographic causes of population change vary across four decades in a long-lived shorebird. Ecology 103(4):e3615. https://doi.org/10.1002/ecy.3615\u003c/li\u003e\n\u003cli\u003eAndrews, S (2010) Fastqc: A quality control tool for high throughput sequence data [Online]. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc/. Accessed 1 March 2023.\u003c/li\u003e\n\u003cli\u003eAuteri GG, Knowles LL (2020) Decimated little brown bats show potential for adaptive change. Sci Rep 10(1):3023. https://doi.org/10.1038/s41598-020-59797-4\u003c/li\u003e\n\u003cli\u003eBernard RF, Willcox EV, Parise KL, Foster JT, McCracken GF (2017) White-nose syndrome fungus, Pseudogymnoascus destructans, on bats captured emerging from caves during winter in the southeastern United States. BMC Zoöl 2(1):12. https://doi.org/10.1186/s40850-017-0021-2\u003c/li\u003e\n\u003cli\u003eBortoluzzi C, Bosse M, Derks MFL, Crooijmans RPMA, Groenen MAM, Megens H (2020) The type of bottleneck matters: Insights into the deleterious variation landscape of small managed populations. Evol Appl 13(2):330\u0026ndash;341. https://doi.org/10.1111/eva.12872\u003c/li\u003e\n\u003cli\u003eBouzat JL (2010) Conservation genetics of population bottlenecks: the role of chance, selection, and history. Conserv Genet 11(2):463\u0026ndash;478. https://doi.org/10.1007/s10592-010-0049-0\u003c/li\u003e\n\u003cli\u003eBozzuto C, Biebach I, Muff S, Ives AR, Keller LF (2019) Inbreeding reduces long-term growth of Alpine ibex populations. Nat Ecol Evol 3(9):1359\u0026ndash;1364. https://doi.org/10.1038/s41559-019-0968-1\u003c/li\u003e\n\u003cli\u003eBrady J, Kunz T, Tuttle MD, Wilson M (1982) Gray bat recovery plan. U.S. Fish and Wildlife Service, Washington, D.C. 143 pp.\u003c/li\u003e\n\u003cli\u003eBradke DR, Altobelli JT, Russell AL, Jaeger CP, Moore JA (2021) Low Bottleneck Detection in Long-Lived Species Despite Lost Genetic Diversity: A Case Study of Tuatara and Eastern Massasauga Rattlesnakes. J Hered 112(4):346\u0026ndash;356. https://doi.org/10.1093/jhered/esab025\u003c/li\u003e\n\u003cli\u003eBroad Institute (2019) Picard tools. Available online at https://broadinstitute.github.io/picard/. Accessed 9 October 2024. \u003c/li\u003e\n\u003cli\u003eBrook BW, Tonkyn DW, O\u0026rsquo;Grady JJ, Frankham R (2002) Contribution of Inbreeding to Extinction Risk in Threatened Species. Conserv Ecol 6(1). https://doi.org/10.5751/es-00387-060116\u003c/li\u003e\n\u003cli\u003eCheng TL, Reichard JD, Coleman JTH, Weller TJ, Thogmartin WE, Reichert BE, Bennett AB, Broders HG, Campbell J, Etchison K, Feller DJ, Geboy R, Hemberger T, Herzog C, Hicks AC, Houghton S, Humber J, Kath JA, King RA, Loeb SC, Mass\u0026eacute; A, Morris KM, Niederriter H, Nordquist G, Perry RW, Reynolds RJ, Sasse DB, Scafini MR, Stark RC, Stihler CW, Thomas SC, Turner GG, Webb S, Westrich BJ, Frick WF (2021) The scope and severity of white‐nose syndrome on hibernating bats in North America. Conserv Biol 35(5):1586\u0026ndash;1597. https://doi.org/10.1111/cobi.13739\u003c/li\u003e\n\u003cli\u003eClark MI, Fitzpatrick SW, Bradburd GS (2024) Pitfalls and windfalls of detecting demographic declines using population genetics in long‐lived species. Evol Appl 17(7):e13754. https://doi.org/10.1111/eva.13754\u003c/li\u003e\n\u003cli\u003eDanecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA, Handsaker RE, Lunter G, Marth GT, Sherry ST, McVean G, Durbin R, Group 1000 Genomes Project Analysis (2011) The variant call format and VCFtools. Bioinformatics 27(15):2156\u0026ndash;2158. https://doi.org/10.1093/bioinformatics/btr330\u003c/li\u003e\n\u003cli\u003eDanecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, Li H (2021) Twelve years of SAMtools and BCFtools. GigaScience 10(2):giab008. https://doi.org/10.1093/gigascience/giab008\u003c/li\u003e\n\u003cli\u003eDay G, Bolderstone T, Conway GJ, Cross T, Davis T, Dolan M, Greening M, Neale C, Nicholson I, Nicholson K, Ward A, Ward N, Fox G, Harney E, Hipperson H, Maher K, Thompson J, Tucker R, Waters D, Durrant KL, Burke T, Slate J, Arnold KE (2025) The Genomic Signature of Demographic Decline in a Long‐Distance Migrant in a Range‐Extreme Population. Mol Ecol 34(13):e17805. https://doi.org/10.1111/mec.17805\u003c/li\u003e\n\u003cli\u003eDecher J, Choate JR (1995) Myotis grisescens. Mamm Species (510):1\u0026ndash;7. https://doi.org/10.2307/3504084\u003c/li\u003e\n\u003cli\u003ede Kort H, Prunier JG, Ducatez S, Honnay O, Baguette M, Stevens VM, Blanchet S (2021) Life history, climate and biogeography interactively affect worldwide genetic diversity of plant and animal populations. Nat Commun 12(1):516. https://doi.org/10.1038/s41467-021-20958-2\u003c/li\u003e\n\u003cli\u003eElliott WR (2007) Gray and Indiana Bat Population Trends in Missouri. In: 2007 National Cave and Karst Management Symposium. pp 46\u0026ndash;61\u003c/li\u003e\n\u003cli\u003eEvanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software structure: a simulation study. Mol Ecol 14(8):2611\u0026ndash;2620. https://doi.org/10.1111/j.1365-294x.2005.02553.x\u003c/li\u003e\n\u003cli\u003eFrankham R (1996) Relationship of Genetic Variation to Population Size in Wildlife. Conserv Biol 10(6):1500\u0026ndash;1508. https://doi.org/10.1046/j.1523-1739.1996.10061500.x\u003c/li\u003e\n\u003cli\u003eFrankham R, Bradshaw CJA, Brook BW (2014) Genetics in conservation management: Revised recommendations for the 50/500 rules, Red List criteria and population viability analyses. Biol Conserv 170:56\u0026ndash;63. https://doi.org/10.1016/j.biocon.2013.12.036\u003c/li\u003e\n\u003cli\u003eGargiulo R, Budde KB, Heuertz M (2025) Mind the lag: understanding genetic extinction debt for conservation. Trends Ecol Evol 40(3):228\u0026ndash;237. https://doi.org/10.1016/j.tree.2024.10.008\u003c/li\u003e\n\u003cli\u003eGattepaille LM, Jakobsson M, Blum MG (2013) Inferring population size changes with sequence and SNP data: lessons from human bottlenecks. Heredity 110(5):409\u0026ndash;419. https://doi.org/10.1038/hdy.2012.120\u003c/li\u003e\n\u003cli\u003eGignoux‐Wolfsohn SA, Pinsky ML, Kerwin K, Herzog C, Hall M, Bennett AB, Fefferman NH, Maslo B (2021) Genomic signatures of selection in bats surviving white‐nose syndrome. Mol Ecol 30(22):5643\u0026ndash;5657. https://doi.org/10.1111/mec.15813\u003c/li\u003e\n\u003cli\u003eGomulkiewicz R, Holt RD (1995) WHEN DOES EVOLUTION BY NATURAL SELECTION PREVENT EXTINCTION? Evolution 49(1):201\u0026ndash;207. https://doi.org/10.1111/j.1558-5646.1995.tb05971.x\u003c/li\u003e\n\u003cli\u003eGriffin DR, Hitchcock HB (1965) Probable 24-Year Longevity Records for Myotis lucifugus. J Mammal 46(2):332\u0026ndash;332. https://doi.org/10.2307/1377857\u003c/li\u003e\n\u003cli\u003eHailer F, Helander B, Folkestad AO, Ganusevich SA, Garstad S, Hauff P, Koren C, Nygrd T, Volke V, Vil C, Ellegren H (2006) Bottlenecked but long-lived: high genetic diversity retained in white-tailed eagles upon recovery from population decline. Biol Lett 2(2):316\u0026ndash;319. https://doi.org/10.1098/rsbl.2006.0453\u003c/li\u003e\n\u003cli\u003eHarvey, MJ (1992) \u003cem\u003eBats of the Eastern United States\u003c/em\u003e. Arkansas Game \u0026amp; Fish Commission. 47 pp.\u003c/li\u003e\n\u003cli\u003eHedrick P (2005) \u0026lsquo;Genetic restoration:\u0026rsquo; a more comprehensive perspective than \u0026lsquo;genetic rescue.\u0026rsquo; Trends Ecol Evol 20(3):109. https://doi.org/10.1016/j.tree.2005.01.006\u003c/li\u003e\n\u003cli\u003eHoelzel AR, Gkafas GA, Kang H, Sarigol F, Boeuf BL, Costa DP, Beltran RS, Reiter J, Robinson PW, McInerney N, Seim I, Sun S, Fan G, Li S (2024) Genomics of post-bottleneck recovery in the northern elephant seal. Nat Ecol Evol 8(4):686\u0026ndash;694. https://doi.org/10.1038/s41559-024-02337-4\u003c/li\u003e\n\u003cli\u003eHoffman JI, Vendrami DLJ, Hench K, Chen RS, Stoffel MA, Kardos M, Amos W, Kalinowski J, Rickert D, K\u0026ouml;hrer K, Wachtmeister T, Goebel ME, Bonin CA, Gulland FMD, Dasmahapatra KK (2024) Genomic and fitness consequences of a near-extinction event in the northern elephant seal. Nat Ecol Evol 8(12):2309\u0026ndash;2324. https://doi.org/10.1038/s41559-024-02533-2\u003c/li\u003e\n\u003cli\u003eHolliday C, Wisby JP, Roby PL, Samoray ST, Vannatta JM (2023) Modeling migration and movement of gray bats. J Wildl Manag 87(3). https://doi.org/10.1002/jwmg.22364\u003c/li\u003e\n\u003cli\u003eHuang Z, Whelan CV, Dechmann D, Teeling EC (2020) Genetic variation between long-lived versus short-lived bats illuminates the molecular signatures of longevity. Aging (Albany NY) 12(16):15962\u0026ndash;15977. https://doi.org/10.18632/aging.103725\u003c/li\u003e\n\u003cli\u003eHung C-M, Shaner P-JL, Zink RM, Liu W-C, Chu T-C, Huang W-S, Li S-H (2014) Drastic population fluctuations explain the rapid extinction of the passenger pigeon. Proc Natl Acad Sci 111(29):10636\u0026ndash;10641. https://doi.org/10.1073/pnas.1401526111\u003c/li\u003e\n\u003cli\u003eJangjoo M, Matter SF, Roland J, Keyghobadi N (2016) Connectivity rescues genetic diversity after a demographic bottleneck in a butterfly population network. Proc Natl Acad Sci 113(39):10914\u0026ndash;10919. https://doi.org/10.1073/pnas.1600865113\u003c/li\u003e\n\u003cli\u003eJones KE, Purvis A, MacLarnon A, Bininda‐Emonds ORP, Simmons NB (2002) A phylogenetic supertree of the bats (Mammalia: Chiroptera). Biol Rev 77(2):223\u0026ndash;259. https://doi.org/10.1017/s1464793101005899\u003c/li\u003e\n\u003cli\u003eKorneliussen TS, Albrechtsen A, Nielsen R (2014) ANGSD: Analysis of Next Generation Sequencing Data. BMC Bioinform 15(1):356. https://doi.org/10.1186/s12859-014-0356-4\u003c/li\u003e\n\u003cli\u003eKrueger F, James F, Ewels P, Afyounian E, Weinstein M, Schuster-Boeckler B, Hulselmans G, Sclamons (2015) TrimGalore: A wrapper around Cutadapt and FastQC to consistently apply adapter and quality trimming to FastQ files, with extra functionality for RRBS data. Available online at https://github.com/FelixKrueger/TrimGalore?tab=readme-ov-file. Accessed 2 February 2023. https://doi.org/10.5281/zenodo.7598955\u003c/li\u003e\n\u003cli\u003eKumar S, Subramanian S (2002) Mutation rates in mammalian genomes. Proc Natl Acad Sci 99(2):803\u0026ndash;808. https://doi.org/10.1073/pnas.022629899\u003c/li\u003e\n\u003cli\u003eKunz T H, Betke M, Hristov NI, Vonhof MJ (2009) Methods for assessing colony size, population size, and relative abundance of bats. In: Kunz TH, Parsons S (eds) Ecological and Behavioral Methods for the Study of Bats. Johns Hopkins University Press, Baltimore, MD, pp. 133\u0026ndash;157\u003c/li\u003e\n\u003cli\u003eKwait R, Eskew EA, Gignoux‐Wolfsohn S, Pinsky ML, Vonhof M, Hines B, Maslo B (2025) Low‐Coverage Whole‐Genome Analysis of Population Structure, Bottlenecks, and Selection in Indiana Bats Before and After White‐Nose Syndrome. Mol Ecol :e70172. https://doi.org/10.1111/mec.70172\u003c/li\u003e\n\u003cli\u003eLi H, Durbin R (2009) Fast and accurate short read alignment with Burrows\u0026ndash;Wheeler transform. Bioinformatics 25(14):1754\u0026ndash;1760. https://doi.org/10.1093/bioinformatics/btp324\u003c/li\u003e\n\u003cli\u003eLi H, Durbin R (2010) Fast and accurate long-read alignment with Burrows\u0026ndash;Wheeler transform. Bioinformatics 26(5):589\u0026ndash;595. https://doi.org/10.1093/bioinformatics/btp698\u003c/li\u003e\n\u003cli\u003eLilley TM, S\u0026auml;vilammi T, Ossa G, Blomberg AS, Vasem\u0026auml;gi A, Yung V, Vendrami DLJ, Johnson JS (2020a) Population Connectivity Predicts Vulnerability to White-Nose Syndrome in the Chilean Myotis (Myotis chiloensis) - A Genomics Approach. G3: Genes, Genomes, Genet 10(6):2117\u0026ndash;2126. https://doi.org/10.1534/g3.119.401009\u003c/li\u003e\n\u003cli\u003eLilley TM, Wilson IW, Field KA, Reeder DM, Vodzak ME, Turner GG, Kurta A, Blomberg AS, Hoff S, Herzog CJ, Sewall BJ, Paterson S (2020b) Genome-Wide Changes in Genetic Diversity in a Population of Myotis lucifugus Affected by White-Nose Syndrome. G3: Genes, Genomes, Genet 10(6):2007\u0026ndash;2020. https://doi.org/10.1534/g3.119.400966\u003c/li\u003e\n\u003cli\u003eLindsay DL, Guan X, Farrington HL, Blake MD, Barker ND, Carr MR, P EN, Lance RF (2015) Genetic Structure Among Hibernacula of the Endangered Gray Bat (Myotis grisescens). Acta Chiropterologica 17(2):293\u0026ndash;306. https://doi.org/10.3161/15081109acc2015.17.2.005\u003c/li\u003e\n\u003cli\u003eLiu Y, Craig SBB, Bechsgaard JS, Dicks LV, Bruun HH, H\u0026oslash;ye TT, Bilde T, Thomsen PF (2026) Recent Demographic History Inferred From Allele Frequency Spectra: Evaluation by Simulations and Insights From Pool Sequencing of Wild Bees. Mol Ecol 35(1):e70169. https://doi.org/10.1111/mec.70169\u003c/li\u003e\n\u003cli\u003eLucena‐Perez M, Kleinman‐Ruiz D, Marmesat E, Saveljev AP, Schmidt K, Godoy JA (2021) Bottleneck‐associated changes in the genomic landscape of genetic diversity in wild lynx populations. Evol Appl 14(11):2664\u0026ndash;2679. https://doi.org/10.1111/eva.13302\u003c/li\u003e\n\u003cli\u003eMartin CO (2007) Assessment of the Population Status of the Gray Bat (Myotis grisescens); Status Review, DoD Initiatives, and Results of a Multi-Agency Effort to Survey Wintering Populations at Major Hibernacula, 2005-2007. https://doi.org/10.21236/ada473199\u003c/li\u003e\n\u003cli\u003eMartin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnetJ 17(1):10\u0026ndash;12. https://doi.org/10.14806/ej.17.1.200\u003c/li\u003e\n\u003cli\u003eMaruyama T, Fuerst PA (1985) Population bottlenecks and nonequilibrium models in population genetics. Ii. Number of alleles in a small population that was formed by a recent bottleneck. Genetics 111(3):675\u0026ndash;689. https://doi.org/10.1093/genetics/111.3.675\u003c/li\u003e\n\u003cli\u003eMeisner J, Albrechtsen A (2018) Inferring Population Structure and Admixture Proportions in Low-Depth NGS Data. Genetics 210(2):719\u0026ndash;731. https://doi.org/10.1534/genetics.118.301336 \u003c/li\u003e\n\u003cli\u003eMeisner J, Albrechtsen A (2019) Testing for Hardy\u0026ndash;Weinberg equilibrium in structured populations using genotype or low‐depth next generation sequencing data. Mol Ecol Resour 19(5):1144\u0026ndash;1152. https://doi.org/10.1111/1755-0998.13019\u003c/li\u003e\n\u003cli\u003eNagel JJ, Nelson DM, Gugger PF (2024) Population Genetic Structure and Effective Size of Two Endangered Cave Bat Species. Acta Chiropterologica 25(2):203\u0026ndash;211. https://doi.org/10.3161/15081109acc2023.25.2.001\u003c/li\u003e\n\u003cli\u003eNei M (2005) Bottlenecks, Genetic Polymorphism and SpeciationThis article is dedicated to the memory of Takeo Maruyama. Genetics 170(1):1\u0026ndash;4. https://doi.org/10.1093/genetics/170.1.1\u003c/li\u003e\n\u003cli\u003eOchoa A, Gibbs HL (2021) Genomic signatures of inbreeding and mutation load in a threatened rattlesnake. Mol Ecol 30(21):5454\u0026ndash;5469. https://doi.org/10.1111/mec.16147\u003c/li\u003e\n\u003cli\u003eOlazcuaga L, Lincke B, DeLacey S, Durkee LF, Melbourne BA, Hufbauer RA (2023) Population demographic history and evolutionary rescue: Influence of a bottleneck event. Evol Appl 16(8):1483\u0026ndash;1495. https://doi.org/10.1111/eva.13581\u003c/li\u003e\n\u003cli\u003ePeery MZ, Kirby R, Reid BN, Stoelting R, Doucet‐b\u0026euml;er E, Robinson S, V\u0026aacute;squez‐carrillo C, Pauli JN, Palsb\u0026oslash;ll PJ (2012) Reliability of genetic bottleneck tests for detecting recent population declines. Mol Ecol 21(14):3403\u0026ndash;3418. https://doi.org/10.1111/j.1365-294x.2012.05635.x\u003c/li\u003e\n\u003cli\u003ePoirier M, Coltman DW, Pelletier F, Jorgenson J, Festa‐Bianchet M (2019) Genetic decline, restoration and rescue of an isolated ungulate population. Evol Appl 12(7):1318\u0026ndash;1328. https://doi.org/10.1111/eva.12706\u003c/li\u003e\n\u003cli\u003ePrasad A, Lorenzen ED, Westbury MV (2022) Evaluating the role of reference‐genome phylogenetic distance on evolutionary inference. Mol Ecol Resour 22(1):45\u0026ndash;55. https://doi.org/10.1111/1755-0998.13457\u003c/li\u003e\n\u003cli\u003ePrice MR, Hadfield MG (2014) Population Genetics and the Effects of a Severe Bottleneck in an Ex Situ Population of Critically Endangered Hawaiian Tree Snails. PLoS ONE 9(12):e114377. https://doi.org/10.1371/journal.pone.0114377\u003c/li\u003e\n\u003cli\u003eRamstad KM, Colbourne RM, Robertson HA, Allendorf FW, Daugherty CH (2013) Genetic consequences of a century of protection: serial founder events and survival of the little spotted kiwi (\u003cem\u003eApteryx owenii\u003c/em\u003e). Proc R Soc B: Biol Sci 280(1762):20130576. https://doi.org/10.1098/rspb.2013.0576\u003c/li\u003e\n\u003cli\u003eRenaud G, Hangh\u0026oslash;j K, Korneliussen TS, Willerslev E, Orlando L (2019) Joint Estimates of Heterozygosity and Runs of Homozygosity for Modern and Ancient Samples. Genetics 212(3):587\u0026ndash;614. https://doi.org/10.1534/genetics.119.302057\u003c/li\u003e\n\u003cli\u003eRobinson JA, R\u0026auml;ikk\u0026ouml;nen J, Vucetich LM, Vucetich JA, Peterson RO, Lohmueller KE, Wayne RK (2019) Genomic signatures of extensive inbreeding in Isle Royale wolves, a population on the threshold of extinction. Sci Adv 5(5):eaau0757. https://doi.org/10.1126/sciadv.aau0757\u003c/li\u003e\n\u003cli\u003eRobinson JA, Vecchyo DO-D, Fan Z, Kim BY, vonHoldt BM, Marsden CD, Lohmueller KE, Wayne RK (2016) Genomic Flatlining in the Endangered Island Fox. Curr Biol 26(9):1183\u0026ndash;1189. https://doi.org/10.1016/j.cub.2016.02.062\u003c/li\u003e\n\u003cli\u003eSantiago E, Novo I, Pardi\u0026ntilde;as AF, Saura M, Wang J, Caballero A (2020) Recent Demographic History Inferred by High-Resolution Analysis of Linkage Disequilibrium. Mol Biol Evol 37(12):3642\u0026ndash;3653. https://doi.org/10.1093/molbev/msaa169\u003c/li\u003e\n\u003cli\u003eScott M, Parker G, Juntti M, Castellino J, Forero O, Mell I, Jerome G, Amati M, Hernandez C, Buntine C, Dodd A, Hamiduddin I, Brien CO, Lucocq H, Lennon M, Blyth R, Parker G, Scott M (2024) The Biodiversity Crisis \u0026ndash; Planning for Nature Recovery? Plan Theory Pr 25(1):103\u0026ndash;140. https://doi.org/10.1080/14649357.2024.2322879\u003c/li\u003e\n\u003cli\u003eShaffer ML, Samson FB (1985) Population Size and Extinction: A Note on Determining Critical Population Sizes. Am Nat 125(1):144\u0026ndash;152. https://doi.org/10.1086/284332\u003c/li\u003e\n\u003cli\u003eSkotte L, Korneliussen TS, Albrechtsen A (2013) Estimating Individual Admixture Proportions from Next Generation Sequencing Data. Genetics 195(3):693\u0026ndash;702. https://doi.org/10.1534/genetics.113.154138\u003c/li\u003e\n\u003cli\u003eSonsthagen SA, Wilson RE, Underwood JG (2017) Genetic implications of bottleneck effects of differing severities on genetic diversity in naturally recovering populations: An example from Hawaiian coot and Hawaiian gallinule. Ecol Evol 7(23):9925. https://doi.org/10.1002/ece3.3530\u003c/li\u003e\n\u003cli\u003eTallmon DA, Luikart G, Waples RS (2004) The alluring simplicity and complex reality of genetic rescue. Trends Ecol Evol 19(9):489\u0026ndash;496. https://doi.org/10.1016/j.tree.2004.07.003\u003c/li\u003e\n\u003cli\u003eTanaka Y (2000) Extinction of populations by inbreeding depression under stochastic environments. Popul Ecol 42(1):55\u0026ndash;62. https://doi.org/10.1007/s101440050009\u003c/li\u003e\n\u003cli\u003eTanaka Y (1998) Theoretical aspects of extinction by inbreeding depression. Res Popul Ecol 40(3):279\u0026ndash;286. https://doi.org/10.1007/bf02763459\u003c/li\u003e\n\u003cli\u003eThomson CE (1982) \u003cem\u003eMyotis sodalis. \u003c/em\u003eMamm Species\u003cem\u003e \u003c/em\u003e163\u003cem\u003e:\u003c/em\u003e1\u0026ndash;5. https://doi.org/10.2307/3504013 \u003c/li\u003e\n\u003cli\u003eTuttle MD (1976a) Population ecology of the gray bat (\u003cem\u003eMyotis grisescens)\u003c/em\u003e: Philopatry, timing and patterns of movement, weight loss during migration, and seasonal adaptive strategies. \u003cem\u003eOccasional Papers to the Museum of Natural History \u003c/em\u003e54:1\u0026ndash;38. The University of Kansas. Lawrence, Kansas.\u003c/li\u003e\n\u003cli\u003eTuttle MD (1976b) Population Ecology of the Gray Bat (Myotis Grisescens): Factors Influencing Growth and Survival of Newly Volant Young. Ecology 57(3):587\u0026ndash;595. https://doi.org/10.2307/1936443\u003c/li\u003e\n\u003cli\u003eUS Fish and Wildlife Service (USFWS) (2025) Gray bat (\u003cem\u003eMyotis grisescens\u003c/em\u003e). Available online at https://www.fws.gov/species/gray-bat-myotis-grisescens. Accessed 15 November 2025.\u003c/li\u003e\n\u003cli\u003eVan der Auwera GA, O\u0026apos;Connor BD (2020) Genomics in the Cloud: Using Docker, GATK, and WDL in Terra. O\u0026apos;Reilly Media, Sebastopol, CA, USA. 467 pp.\u003c/li\u003e\n\u003cli\u003eVannatta JM, Carver BD (2024) Range-wide population genetic structure and genetic diversity of Southeastern Myotis (Myotis austroriparius). Mammal Res 69(4):577\u0026ndash;595. https://doi.org/10.1007/s13364-024-00759-w\u003c/li\u003e\n\u003cli\u003eVazquez JM, Lauterbur ME, Mottaghinia S, Gaucherand L, Maesen S, Singer M, Villa S, Bucci M, Fraser D, Gray-Sandoval G, Haidar ZR, Han M, Kohler W, Lama TM, Corf AL, Loyer C, McMillan D, Li S, Lo J, Rey C, Capel SL, Slocum K, Sui M, Thomas W, Tyburec JD, Brem R, Miller R, Buchalski M, Vazquez-Medina JP, Pfeffer S, Etienne L, Enard D, Sudmant PH (2025) Extensive longevity and DNA virus-driven adaptation in nearctic Myotis bats. bioRxiv :2024.10.10.617725. https://doi.org/10.1101/2024.10.10.617725\u003c/li\u003e\n\u003cli\u003eWatterson GA (1975) On the number of segregating sites in genetical models without recombination. Theor Popul Biol 7(2):256\u0026ndash;276. https://doi.org/10.1016/0040-5809(75)90020-9 \u003c/li\u003e\n\u003cli\u003eWorthington-Wilmer J, Barratt E (1996) A non-lethal method of tissue sampling for genetic studies of chiropterans. Bat Research News 37(1):1-3. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1. Main statistics calculated and brief interpretations.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"864\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eTajima\u0026rsquo;s D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eF\u003csub\u003eS\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eH\u003csub\u003ee\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eH\u003csub\u003eo\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSubpopulations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eN\u003csub\u003ee.LT\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eN\u003csub\u003ee.Bot\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eN\u003csub\u003ee.Cont\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003e0.000067\u0026ndash;0.0026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.00322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u0026ndash;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e70,738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e13,001.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e56,050.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eVariation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.002 SE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-0.24 \u0026ndash; -0.22 (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e-0.18 \u0026ndash; -0.18 (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.00301\u0026ndash;0.00343 (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e0.376\u0026ndash;0.376\u003c/p\u003e\n \u003cp\u003e(range)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.405\u0026ndash;0.416 (range)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003e68,153\u0026ndash;75,000 (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003en/a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e53,643.2\u0026ndash;58,253.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003eInterpretation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003eExcess of common alleles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 62px;\"\u003e\n \u003cp\u003eWeak population structure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eHeterozygosity excess\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eHeterozygosity excess\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eHigh heterozygosity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eHigh genomic heterozygosity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eHigh heterozygosity/Heterozygosity excess\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003ePopulation structure and admixture occur\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 67px;\"\u003e\n \u003cp\u003eLarge effective base population\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003eStrong reduction, but still many individuals at minimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eLarge increase from N\u003csub\u003ee.Bot\u003c/sub\u003e, but lower than N\u003csub\u003ee.LT\u0026nbsp;\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2. \u0026Delta;K calculated using the Evanno et al. (2005) method for different assumed numbers of population clusters (K). Mean best likelihood and standard deviation is presented as well.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003eK\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026Delta;K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e5.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003eLikelihood (x10\u003csup\u003e6\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-4.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-4.44\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-4.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-4.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-3.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e-3.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 136px;\"\u003e\n \u003cp\u003eSt dev (x10\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e7.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e7.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e6.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e9.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 67px;\"\u003e\n \u003cp\u003e5.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eEvanno\u0026rsquo;s method cannot calculate \u0026Delta;K for 1 or 2 population clusters.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Genetic diversity, recovery, gray bat (Myotis grisescens), admixture, effective population","lastPublishedDoi":"10.21203/rs.3.rs-8734754/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8734754/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTremendous population declines may result in the loss of genetic diversity, which can in turn lead to reduced phenotypic health, reduced adaptive potential, and further population declines. In other words, a severe demographic decline may ultimately lead to an extinction vortex. Therefore, understanding genetic diversity is vital for assessing the health of species that have undergone declines, and can even reflect the effectiveness of management actions. We investigated signatures of genetic bottlenecks in a species that has been the subject of focused management efforts since its 1976 listing as endangered in the US, the gray bat (\u003cem\u003eMyotis grisescens)\u003c/em\u003e. We used whole genome sequencing to calculate genetic diversity metrics, including inbreeding coefficients, Tajima\u0026rsquo;s D, and heterozygosity, and to infer effective population size and structuring. We found a loss of rare alleles and heterozygosity excess, in line with strong declines in relative abundance of the census population. However, we did not detect inbreeding, erasure of population substructure, or low effective population size. Despite an apparent bottleneck, the gray bat appears to have avoided detrimental genetic consequences associated with population declines, likely due to the conservation of connected populations of adequate effective size. Our findings highlight the importance and effectiveness of timely conservation interventions for preserving the genetic health of species. Strong declines in relative abundance (i.e., based on percentage of the initial population) do not necessarily lead to inbreeding when there is still adequate absolute abundance (i.e., number of individuals).\u003c/p\u003e","manuscriptTitle":"Genomic Signatures of Decline and Recovery in an Endangered Bat","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-03 16:48:15","doi":"10.21203/rs.3.rs-8734754/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3689501c-9eda-455d-8ab1-8f5a7deee284","owner":[],"postedDate":"March 3rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Reject and invite resubmission","date":"2026-05-11T14:57:22+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T18:58:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-03 16:48:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8734754","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8734754","identity":"rs-8734754","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}