Antechinus have recently been named, at least two of which are threatened. Important facets of the habitat use and extinction risk of one of these new species, the buff‐footed antechinus, A. mysticus, are not well understood. Previous research has suggested that the species utilizes a broad range of inter‐connected forest habitats in southeast Queensland (Qld), Australia. Based on this potentially connected habitat, we predicted that A. mysticus should have low population genetic structure, particularly in relation to its congener, the spatially restricted, high altitude, closed‐forest A. subtropicus. We genotyped nine microsatellite loci for six populations of A. mysticus, sampled throughout their known range in eastern Australia, and compared them with four proximate populations of A. subtropicus. Surprisingly, genetic structuring among southeast Qld populations of A. mysticus was moderate to high and similar to that between A. subtropicus populations. We postulate that all A. mysticus populations have declined recently (<100 generations), particularly the northernmost southeast Qld population, which may be at risk of extinction. Our results suggest that A. mysticus is limited to a more scattered and fragmented distribution than previously thought and may be in decline. The identification of population decline in this study and recently in other Antechinus suggests the extinction risk of many Australian mammals should be reassessed.Five new species within the Australian carnivorous marsupial genus  The range and historical connectivity of a species are often assumed on the basis of the habitat types in which they have been found. In closely related species, which are believed to occupy largely separate habitats across a similar range, a comparative analysis of population genetics allows the formulation and testing of assumptions about differential habitat use, past connectivity, and biogeography Avise, . UnderstAntechinus are one of only a few mammal genera worldwide which exhibit semelparous reproduction and, as a consequence, have been widely studied as a model in breeding biology  but occupy largely separate habitats  results support this contention, with A. mysticus showing very little diversity in southeast Qld, comprising just two haplotypes, only one base pair different (0.2%), across its range  over only half the geographic distance  and lower altitude open forest (A. mysticus) of southeast Qld  separating southeast Qld from Eungella or from other localities in mid‐east Qld   , while the fourth site, D'Aguilar, was located 75–124 km SLD south of these sites , which was designed for A. flavipes , 1 μl of GSLIZ600 sequencing size standard (Applied Biosystems), and 1 μl of a 1/3 dilution of each PCR product.Nine microsatellite loci (see Table 2.3A), private (unique) alleles (uA), rare alleles , observed (Ho), and expected (He) heterozygosity per population were estimated using GenAlEx 6.502  and estimated in Fstat 2.9.3.2 . The total number of alleles  for each locus‐population combination were carried out in Genepop 4.2 Rousset,  using 1,2.4FST  was analyzed using Mantel tests  was corrected for multiple tests using the BY False Discovery Rate method, which accounts for Type I error with less loss of power than the Bonferroni adjustment , which allowed us to compare with Mantel tests, and more accurately calculates isolation by distance and the influence of geographic variables on genetic structure  reduction in population size was tested using BOTTLENECK 1.2.02  was estimated for all loci in each population using Arlequin 3.5.2.2  within each species, Bayesian clustering of individuals without prior assignment to population was performed using the software package STRUCTURE 2.3.4  and 75 A. subtropicus  individuals were screened for nine microsatellite loci. One locus, Aa7Q, failed to amplify for all A. mysticus, presumably due to a mutation in the primer binding region  significantly departed from HWE at four of the six sites. This suggests that null alleles are present or the locus is under selection , six of the nine loci significantly departed from HWE. Patterns of genetic diversity and structure were initially tested using the seven loci successfully genotyped for both A. mysticus and A. subtropicus. However, a comparison of results for A. subtropicus showed that there was little difference between the seven and nine loci datasets, and therefore, the final analysis included seven loci for A. mysticus and nine for A. subtropicus.In total, 101 A. mysticus (4.8%) and A. subtropicus (4.0%). As there was no consistent pattern of linkage across sites or loci (data not shown), it was deemed unlikely that these low rates of linkage disequilibrium were due to physical linkage . An approximately equal number of alleles were observed for A. mysticus (214) and A. subtropicus (215), and the range of alleles per locus was the same for both species (2–9). The average A per site was also similar for both species , ranging from 3.4 (Cooloola) to 6.3 (D'Aguilar) for A. mysticus and 4.8 (Wrattens) to 6.8 (D'Aguilar) for A. subtropicus. AR and He were significantly lower at Cooloola than at any other A. mysticus population (p < 0.05), except Eungella where there was not a significant difference in He between populations  and Woondum (5.94) sites  in comparison to D'Aguilar  . Private alleles were low at all sites for both species , which was also the only A. subtropicus site with an M‐ratio substantially below the critical value Garza and Williamson  with evidence of significant inbreeding  than the geographically more isolated Eungella site (FST = 0.136–0.339) (Table FST = 0.053–0.113) . If the Eungella site was excluded, the total genetic variation between sites was slightly lower . However, if the Cooloola site was excluded, total variation among sites was reduced by ~36% . When Eungella and Cooloola were both excluded, total genetic variation among the four southeast Qld A. mysticus sites was 8.4% (p < 0.001). Mantel tests did not indicate a significant relationship between genetic and geographic distance when all sites were included , or when the strongly genetically differentiated Eungella  or Cooloola samples were individually excluded , or when both these sites were excluded together .AMOVA showed 18.5% of total genetic variation was partitioned among the six p = 0.025) , and the model containing this factor explained 47.1% of observed variation  as an explanatory variable p = 0.05  .Bayesian clustering analysis revealed a pattern of genetic structure concordant with the o et al.  method iAppendix a. GraphiAppendix a. Howeve3.3.2FST = 0.062–0.096) than Woondum and Wrattens were from each other (FST = 0.044) (Table FST = 0.09–0.134)  sites, Conondale was more divergent from the other two sites (4) Table . The morA. subtropicus sites (p < 0.001) and pairwise FST estimates showed a significant difference between all A. subtropicus sites (pairwise FST = 0.044–0.134) . Stepwise selection revealed that no geographic factors significantly (p < 0.05) explained genetic variation.AMOVA revealed 8.93% of total genetic variation partitioned among the four K = 2), one of which largely corresponded to D'Aguilar  were revealed in this study. As heterozygosity and allele frequency distribution strongly influence FST results, comparisons of FST values between taxa need to be made with caution  were excluded, genetic variation was significant and comparable between populations of both Antechinus species in southeast Qld. The microsatellite differentiation among these populations in both species was larger than that recorded between populations of the broad‐ranging A. flavipes flavipes in southeast Australia  were low to moderate for all populations of both species except A. mysticus at Cooloola, which had much lower variation. These values were comparatively lower than previously reported for other Antechinus .In the present study, levels of genetic variation  from southeast Qld conspecifics and is only 76–155 km from other southeast Qld A. mysticus sites  and genetic (>2% at Cytb) isolation from known conspecifics  than the other A. mysticus populations. Taken together, this suggests that the population is both small and isolated, factors which can cause high differentiation in FST values  was an order of magnitude lower than at the other A. agilis populations which show similarly deep microsatellite, but low Cytb divergence . However, given the large degree of differentiation reported, it is also likely that A. mysticus utilizes a more restricted array of habitats than previously assumed.It was hypothesized that Antechinus flavipes is the only other Antechinus species that occurs throughout southeast Qld. This species is broadly distributed throughout the region but was not caught at any of our study sites. Rather, it is predominantly found in dry, open sclerophyll habitats  (data not shown). It therefore seems likely that the three southeast Qld Antechinus species are largely partitioned into separate habitats: A. subtropicus in closed, high altitude, habitats; A. mysticus in intermediately closed medium altitude habitats; A. flavipes in drier, more open, and low altitude habitats. This suggests that A. mysticus, like A. subtropicus, is likely isolated to islands of mesic habitat. If so, A. flavipes might be expected to show relatively less population structure than these species in southeast Qld. While this is yet to be tested in Queensland, A. flavipes in southeastern Australia is less structured than either of the species in the present study  and climate change may have caused population reductions in A. mysticus and will likely continue to negatively affect both species and many other mammals (Woinarski et al., A. flavipes (Mijangos, Pacioni, Spencer, Hillyer, & Craig, A. mysticus, which is possibly severe at Cooloola, suggests that A. mysticus is at a more immediate extinction risk than A. subtropicus. Therefore, we believe A. mysticus could warrant threatened species listing. We advocate regular monitoring of A. mysticus and prioritization of habitat protection for this species.As well as land clearing, threats such as introduced predators (feral cats, Antechinus have previously been considered less at risk of extinction than many Australian mammals in a similar size range. However, the present study and recent listing of four species of Antechinus as at threat of extinction suggest this is no longer the case. There is little information on the population structure and abundance of many of Australia's largely endemic marsupial mammals (Woinarski, Burbidge, & Harrison, Antechinus and the ongoing decline of Australian mammals more broadly, it is apparent that the extinction risk of many of these taxa not currently listed as at risk of extinction should now be reassessed.Australia has experienced an extraordinarily high rate of mammalian extinction in the last ~200 years (Woinarski et al., None declared.TYM, SJF, and AMB designed the study. TYM collected and analyzed the data. DT constructed the maps and provided the vegetation data. TYM wrote the manuscript with assistance from all authors. Click here for additional data file.


Pteronotus parnellii species complex has been previously made according to mitochondrial DNA, and especially morphology and acoustics, in order to separate them into two species. In these studies, either sample sizes were too low when genetic and acoustic or morphological data were gathered on the same individuals, or genetic and other data were collected on different individuals. In this study, we intensively sampled bats in 4 caves and combined all approaches in order to analyse genetic, morphologic, and acoustic divergence between these lineages that live in the same caves in French Guiana.The distinction between lineages of neotropical bats from the P. alitonus and P. rubiginosus were revealed, in accordance with previous findings. No spatial genetic structure between caves was detected for both species. Hybridization appeared to be quite limited (0.1–4%) using microsatellite markers whereas introgression was more common (7.5%) and asymmetric for mitochondrial DNA (mtDNA).A multiplex of 20 polymorphic microsatellite markers was developed using the 454-pyrosequencing technique to investigate for the first time the extent of reproductive isolation between the two lineages and the population genetic structure within lineages. We genotyped 748 individuals sampled between 2010 and 2015 at the 20 nuclear microsatellite loci and sequenced a portion of the cytochrome c oxydase I gene in a subset of these. Two distinct, non-overlapping haplogroups corresponding to cryptic species Pteronotus parnelli, but deserve more in-depth studies to understand the evolutionary processes underlying asymmetric mtDNA introgression in this group of cryptic species.The extremely low rate of hybridization could be explained by differences in life cycle phenology between species as well as morphological and acoustical distinction between sexes in one or the other species. Taken together, these results add to our growing understanding of the nature of species boundaries in The online version of this article (10.1186/s12862-018-1289-8) contains supplementary material, which is available to authorized users. Semnopithecus entellus).Much natural diversity is morphologically hidden . The detDrosophila paulistorum for which semi-species are morphologically similar, but have different courtship song patterns [Although these cryptic species can usually be identified due to mitochondrial DNA (mtDNA) differences, reproductive isolation is only scarcely investigated. There is indeed an implicit assumption that the molecular genetic divergence among these cryptic species can be taken as a surrogate for reproductive isolation –14. Whilpatterns .Pipistrellus pipistrellus) had [P. pipistrellus and the soprano pipistrelle (P. pygmaeus). Despite being morphologically similar marked divergence in ecological requirements has been evidenced  also comprises a particularly remarkable example of hidden diversity. Currently, nine cryptic species widespread in Middle America and the Caribbean [P. parnellii complex. Phylogenetic analyses of mitochondrial genomes suggest that a split occurred in their maternal lineages ∼1.1–2.8 millions years ago, while the entire complex would have shared a common ancestor ∼2.5–6.1 millions years ago [Cryptic species are fairly common in echolocating bats . One of lus) had  in fact ed (e.g. ). The nearibbean –27 have ears ago .Pteronotus sp3 and sp4 has been proposed (sensu [b (Cyt b), or the y-linked Dby genes [P. rubiginosus corresponding to P. sp4 [P. sp3 has been named P. Alitonus [P. rubiginosus uses a peak echolocation frequency of 53 kHz, P. Alitonus 59 kHz. It is evident that we still know very little about these newly discovered groups in terms of their preferred habitats, prey and roosting places, their breeding ecology or their population structure and potential for interbreeding - i.e., not enough information has been gathered as yet to formally describe them as new species.In French Guiana, the presence of two sympatric groups referred to as d sensu , 27) usi usiPteroby genes , 25, 28 by genes , 28, andby genes . RecentlAlitonus : P. rubiPteronotus sp. roosting in four caves in French Guiana during the period 2010 to 2015. We combined different approaches in order to investigate genetic, morphological and acoustic divergence between the two groups. For the first time we tested whether the identified groups in the caves interbreed using autosomal microsatellite markers and searching for hybrids in the sampled populations. Lastly, we explored the spatial genetic structure of Pteronotus sp., notably whether caves affect the genetic structure of the population of Pteronotus sp. For the DNA studies we used the 5′ half of the mitochondrial CO1 and developed a panel of 20 specific microsatellites in one multiplex using the 454-pyrosequencing technique. The study’s aim was to make comprehensive genetic comparisons that would reveal relationships between the two groups and to understand their microevolutionary history - which might include hybridization and introgression patterns.In this study we have investigated colonies of Pteronotus individuals sampled in the present study display two highly supported groups, corresponding to P. alitonus (hereafter Pteronotus B) and P. rubiginosus (hereafter Pteronotus A) previously evidenced by [The phylogenetic reconstruction  but much larger between the groups (5.72%).We built a multiplex of 20 microsatellite markers  and four others appeared to be in Hardy-Weinberg disequilibrium for at least one of the two identified species . We did not find any loci in linkage disequilibrium. Consequently, we ran STRUCTURE using 15 loci to redefine the two clusters, and considered these 15 markers in subsequent analyses. The reduction of the number of markers did not change the assignation of individuals to cluster A and B. Over these 15 microsatellite markers, the two species presented similar mean number of alleles per locus: 6.71 for Pteronotus A and 7.82 for Pteronotus B.One locus showed significant signs of null allele frequency  and structure between caves .The two q-value reached by simulated parents in each cluster. For each genotyped individual, either the mean q-value (conservative approach) or the lower bound (relaxed approach) of the credibility interval returned by STRUCTURE was compared to those thresholds to determine whether the individual was hybrid. The simulation procedure led us to consider 0.90 and 0.76 as values for TP1 and TP2. Accordingly, one putative hybrid was detected with the “conservative” approach while 30 putative hybrids were detected with the “relaxed” approach. A further simulation study revealed that the probability to identify parental individuals as hybrids, either with the conservative or relaxed approach, was null with these thresholds used in a configuration where the number of hybrids is lower than the number of parental forms. No hybrid was detected using the computer program NEWHYBRIDS.Two thresholds, TP1 and TP2, were determined to identify hybrids based on the 15 microsatellite markers. They corresponded to the lowest n = 80), all 42 individuals assigned to Pteronotus B with nuclear DNA had Pteronotus B mitochondrial sequences. In contrast, 6 individuals out of the 38 assigned to Pteronotus A with nuclear DNA were found to have Pteronotus B mitochondrial sequences  had a mean forearm length of 64.37  vs 62.06  for Pteronotus B (n = 300) .Only 23 of the 30 individuals identified as putative nuclear hybrids with the relaxed approach have morphometric data. Excluding these 23 individuals to be conservative as well as the 6 mtDNA hybrids (see above), p = 2.7 × 10− 4). The post-hoc analysis revealed that the effect of sex was significantly different between the two groups , p = 1.5 × 10− 2) and was only found significant in group B .The global comparison between sexes showed non-homogeneous forearm length in at least one group , Due to the absence of F1 and to a small number of morphological data for putative hybrids  microphone recorded individuals, 146 (of which 15 are hybrids) were precisely measured for the Frequency of Maximal Energy (FME) with BatSound (FME dataset). Excluding the 15 hybrid individuals of the FME dataset and the four outliers from group B , Pteronotus A (n = 60) emitted at a call frequency situated around 53 kHz  while Pteronotus B (n = 67) emitted at a call frequency situated around 59 kHz  .Among the 262 (73 p = 2.4 × 10− 3). The post-hoc analysis revealed that the effect of sex was significantly different between the two groups , p = 1.4 × 10− 3) and was only found significant in group A .The global comparison between sexes showed non-homogeneous echolocation frequency in at least one group , Again, due to the absence of F1 and to a small number of acoustic data for putative hybrids  were found to be pregnant in July with one in MA and one in PA, and one lactating female was found in October (over 24). No juveniles were found during the different field periods. Concerning Pteronotus B, pregnant (27 over 74) and lactating (15 over 74) females were found in July and post-lactating (33 over 103) females between late August and October in the MA and PA caves  than the largest within group divergence – a typical value for interspecific differences in mammals [Fst value of 0.139 between P. alitonus and P. rubiginosus indicates a strong population genetic differentiation in the 15 microsatellite markers used.In this study, molecular, acoustic, and morphological differentiation has been analyzed in ngs see , 27, 28), 28Ptero mammals , 30. We P. alitonus and P. rubiginosus appear to have an overlapping reproductive season. However the very low proportions of pregnant and lactating P. rubiginosus females compared to P. alitonus, together with the absence of juveniles, suggest a small shift in the reproductive season between the two species. Alternatively, we cannot exclude the possibility that P. rubiginosus uses breeding sites other than caves , potentially reducing the competition with P. alitonus for food resources. The identification of a colony with pregnant  and lactating  females of P. rubiginosus in the roof of a village house in June 2017 may confort this second hypothesis. It was also suggested that P. alitonus prefers to forage in more dense forest than P. rubiginosus [iginosus . Such spP. rubiginosus is slightly larger than P. alitonus, as previously reported by de Thoisy et al. [P. alitonus. Sexual dimorphism had already been demonstrated by Clare et al. [P. alitonus may vary in sexual selection intensity over its distribution area – as it was observed for Rhinolophus ferrumequinum y et al.  showed ty et al. , 32. Fure et al.  in skullequinum , and the other around 59 kHz . Jiang et al. [Rhinolophus mehelyi and R. euryale [As previously reported for French Guiana and in the Central Amazon , 29, 31,g et al.  and Lin g et al.  suggeste euryale . We thus euryale , a neutrP. rubiginosus - which have longer forearms - use lower peak frequency of echolocation calls (53 kHz) than P. alitonus (59 kHz), in accordance with the mass-signal frequency allometry  are associated with assortative mating, and ultimately reproductive isolation and speciation, regardless of external barriers to gene flow [Furthermore, ry e.g. , 42). Ho. HoP. ru species , 28; howt et al.  have obs mehelyi ). Such dene flow .P. alitonus to P. rubiginosus . Our proportion of mtDNA hybrids in P. rubiginosus (0.158) is higher than that previously reported by Clare et al. [P. alitonus and 26 P. rubiginosus), but similarly, this hybrid recovered the mitochondrial DNA of P. alitonus (giving a proportion of hybrids of 0.04). Similar results have been reported in bats between the two sibling bat species Myotis myotis and Myotis blythii [Rhinolophus sinicus septentrionalis and R. s. sinicus [P. alitonus and P. rubiginosus.We found evidence of limited hybridization between the two species using the 15 microsatellite markers: only 1 individual (conservative approach) and 30 individuals (relaxed approach) out of 748 showed signs of hybridization. On the long term however, such events leave traces that are best observed in the mitochondrial genome: 6 individuals out of 80 contain COI sequence of the other species. The most striking finding is that in all cases mtDNA introgression has occurred asymmetrically, from e et al.  who iden blythii , and Rhi sinicus . Many di sinicus . DiffereBased on our analysis of genetic data at the scale of the colony, sex-biased dispersal is an unlikely explanation for the observed asymmetry since there is no evidence that males and females - of both species - disperse at different distances.P. rubiginosus males and P. alitonus females but not the opposite . Such asymmetry in mating between the sexes has been observed in insects [P. alitonus and P. rubiginosus, both in size and regarding the presence or absence of sexual dimorphism  and  insects  proposedsee also , 27, 28)P. alitonus has some selective advantage that promotes its introgression in an alternative environment  but at different frequencies, can however give some support to this hypothesis.Finally, the asymmetry of introgression could also arise if the mtDNA of lations,  or in a lations, . The mitlations, ). An inclations, –59) but FST = 0.13 versus FST = 0.002, respectively), suggesting the existence of high gene flow between caves for the two species. Thus the cave does not appear to be a structuring unit as the populations of P. alitonus and P. rubiginosus seem to form two unique populations covering distances up to 80 km (from the Nouragues region to the Kaw Mountains). One possible explanation for this lack of genetic structure is that the distance between caves is lower than the dispersal distance of both species. Thus, the structuring unit may be an ensemble of caves rather than each cave.For the first time, these two cryptic species have been studied using autosomal microsatellites. These new markers also allowed us to shed light on the genetic patterning of the populations. In our dataset, the level of genetic differentiation is an order of magnitude higher between species than within species  as they left the caves for foraging. We placed them individually in cotton bags until sampling. Adults were distinguished from juveniles by trans-illumination of the cartilaginous epiphyseal plates in the phalanges [Bats were caught in a Two-Bank HarpTrap  mitochondrial marker (500 bp) for 80 individuals randomly chosen using the primers Bat-COI-01-F (5’-TGAGCAGGAATAGTAGGCAC-3′) and Bat-COI-03-R (5’-CGGCAGGGTCAAAGAATGTG-3′). Sequencing reactions were performed with the corresponding amplifying primers using a BigDye Terminator Cycle Sequencing Kit v.2.0  and run on an ABI 3730 automated sequencer .Pteronotus gymnonotus and P. personatus were used as outgroups.In order to identify the species to which the sampled specimens belonged, we built a tree based on the COI sequences of these 80 individuals and added 28 samples obtained in previous studies , 25, 28 gappyout option. Phylogenetic reconstruction was achieved using the IQTree Maximum Likelihood method [COI sequences for the 108 specimens (80 + 28) were aligned using MACSE  with defd method  after audist.dna function of the “ape” package v.5 [The genetic distance between COI sequences was also computed for all pairs of sequences in order to compare within and between COI divergence. This was done in R language  using thkage v.5  with thePteronotus, along with the detection of hybrids, using the Bayesian software STRUCTURE 2.3.4 [K, the number of clusters, ranging from one to four, twenty independent runs being carried out for each value of K. The outputs from the 20 runs corresponding to the optimal value of K were then compiled using CLUMPP [q-values), and their 90% credibility interval, to the different clusters identified.The group identification of all individuals was also carried out using the microsatellites we developed specifically for RE 2.3.4 , 71. We RE 2.3.4  implemenRE 2.3.4 . For thig CLUMPP  to accesq-values in each cluster and used them to simulate individuals belonging to different hybrid classes  using the function hybridize from the package adegenet [q-values for the K clusters identified in the real population. Based on these q-values, we defined two thresholds . These thresholds were then used to categorize the sampled individuals either as a parent or a hybrid either on the basis on their mean q-value  or the lower bound of their 90% credibility interval . We ran additional simulations in order to assess whether hybrids detected with the relaxed approach may be misclassified parental forms. To do so, we simulated again hybrid classes  using the function hybridize and analysed the simulated individuals with STRUCTURE. We repeated the simulations thirty times and counted the number of parental forms detected as hybrids with the methods previously described. For this additional study, we simulated a few hybrids  to consider a scenario consistent with the proportion of hybrids detected in the sampled population. We used the relaxed approach to select unambiguously parental groups for the morphologic and acoustic analyses. The detection of hybrids was also performed using the computer program NEWHYBRIDS 1.1 [For the detection of hybrids, we selected the 80 individuals presenting the highest adegenet  implemenadegenet . These iRIDS 1.1  This anaP-values were adjusted using a Bonferroni correction. All subsequent analyses were performed without the loci for which we detected significant signs of null allele frequencies or deviance from Hardy-Weinberg equilibrium.Once the group of each individual was determined, microsatellite loci were examined for null (non-amplifying) alleles in each species using the program Micro-checker v.2.2.3 . The sigFST estimates for each group were assessed using FSTAT 2.9.3.2 [FST between each pair of caves. Exact G-tests of population differentiation [FST because it is considered to be the most powerful statistic available for this test [Allelic richness and Weir and Cockerham’s  2.9.3.2 . In ordentiation  were perhis test . A thoust-tests comparing males and females were performed in each group considered independently and the hypothesis that sexual dimorphism is identical in both groups was tested by comparing models Sex*Group and Sex+Group. Recorded body weights were not analysed because of the high variability according to time (before or after being fed) and period of capture (parturition state). Statistical analyses were performed with R version 3.3.3, and significant data were determined as having p-values less than 0.05.Only adults were measured  linked to a Pettersson D240X ultrasonic detector  . These outliers were removed from the analysis.Echolocation calls were recorded in 2015 from 262 Pteronotus identified, we analysed the reproductive status for each individual as well as their age (juvenile or adult) during the different sampling periods, without any statistical test due to the low number of individuals sampled for each period.In order to have an inkling as to the reproductive periods of the two groups of Additional file 1:Table S1. Table. List of external COI sequences used in this study. The sequence in bold corresponds to type-locality of P. rubiginosus, caught in MatoGrosso, Brazil compared to P. sp4 in De Thoisy et al. (2014). Table S2. Characteristics of microsatellite markers. Figure S3. Graphical representation of assignation probabilities for Pteronotus A and Pteronotus B sampled. Each individual (x axis) is represented by a vertical bar divided in two parts according to its assignation probability (y axis) in each of the two clusters. Figure S4. Graphical representation of the difference between the Conservative and the Relaxed approach for the detection of hybrids. Table S5. Pairwise Fst values between caves for each species and both sexes. Table S6. Life cycle of Pteronotus A and B. PRE = Pregnant, LAC = Lactating females, PLAC = Post-lactating females, NS = No status . n(A): number of Pteronotus A ; n(B): number of Pteronotus B ; n: total number of Pteronotus. (DOCX 319 kb)Additional file 2:Table S7. Data used in the study, including sex, forearm length, FME, genotype at 20 microsatellite markers for 748 Pteronotus sp. Data in bold are individuals identified as hybrids using microsatellite (N = 30) or mitochondrial data (N = 6). (XLSX 253 kb)


Thymus praecox. Here, we studied the environmental preferences, morphological and functional trait variation, and genetic variation using microsatellite markers of T. praecox. Results revealed significant, mainly site‐dependent environmental, phenotypic, and genetic differentiation. In general, individuals in badlands are shorter in height and have lower patch sizes (length × width), relative growth rates, and smaller stomata. The PCA additionally unveiled slightly increased leaf robustness, trichome density, decreased stomatal conductance, fewer females, and earlier phenology in badlands. We interpret differentiation patterns as adaptive responses to light, temperature, drought, and nutrient stress conditions supported by reviewed literature. Genetic differentiation was strongest between local badlands and grassland sites, and clearly weaker among locations and between sites  as indicated by GST, AMOVA, PCoA, and population structure. Our study supports the importance of small‐scale microhabitat conditions as a driver of microevolutionary processes, and the population's need for sufficient phenotypic variation and genetic resources to deal with environmental changes. We demonstrated that badlands are an appropriate model system for testing plant response to extreme habitats and that more research is needed on these fascinating landscapes.Environmental heterogeneity among sites can generate phenotypic and genetic variation facilitating differentiation and microevolution of plant populations. Badlands are desert‐like, predominantly vegetation‐poor habitats often embedded in (semi‐)dry grasslands. The desert‐like conditions of badlands demand extreme adaptation of plants, that is, phenotypic modifications in short‐term and/or natural adaptation in long‐term. However, detailed knowledge is missing about both plant phenotypic and genetic differentiation in this unique and widely occurring habitat type. The present study focused on the largest known badlands systems in Central Europe located in the “Drei Gleichen” region, a designated nature conservation area in Central Germany. Locations were suitable for this study in terms of having co‐occurring badlands and (semi‐)dry grassland habitats (sites) occupied by the pioneer plant  Thymus praecox between local badlands and surrounding grassland sites.Badlands, that is, desert‐like and widely vegetation‐poor habitats, demand extreme adaptation of plants. Focussing on the largest known badlands systems in Central Europe, we investigated small‐scale phenotypic and genetic differentiation in  Wind and water erosion are responsible for almost no organic soil layer leading to sparse vegetation cover  region between Gotha and Arnstadt . These badlands are embedded in semi‐natural grassland landscapes and have been formed through anthropogenic land use by sheep‐ and goat‐grazing presumably since medieval ages Klug, . The spahttps://en.climate-data.org, accessed 29 May 2019, 1982–2012) and thus more prone to badlands formations.Badlands habitats are rare in Central Europe. Particular geological, geomorphological, climatic, and anthropogenic factors are necessary to produce and preserve badlands formations. The soil types in the Drei Gleichen region originated in the Upper Triassic epoch and consist mostly of red‐brown siliceous clay stones and gray‐green to dark gray calcareous marlstones susceptible to erosion Klug, . On stee2 concentration, light intensity, and water supply  differentiation caused by habitat differences was found, for example, between ~100 km distant On the one hand, phenotypic modifications allow for (reversible) short‐term responses increasing individual's fitness in changing environments . On the other hand, genetic variation promotes long‐term adaptation and evolutionary persistence. When selective pressure acts consistently on specific traits favorable in the specific environment, environmental heterogeneity can lead to the formation of locally differentiated, environmentally adapted genotypes  is a pioneer plant species in badlands and surrounding (semi‐)dry grasslands. The economically important genus Thymus is known for its genetic compatibility, and thus sympatrically occurring species can cross with each other  is high in terms of morphological traits and chemical composition in secondary metabolites  traits, and genetic diversity using microsatellite markers, at sites of different study locations.Considering the noticeable environmental differences between badlands and surrounding (semi‐)dry grasslands, we hypothesize that microhabitat differences led to the formation of site‐specific phenotypes with corresponding genetic differentiation in 22.1Thymus praecox Opiz (Lamiaceae) is a perennial dwarf shrub growing up to 15 cm .We focused our study on four locations in the Drei Gleichen region between Gotha and Arnstadt, which has been a designated protected area for several decades  and categorized each location into (a) badlands with a vegetation cover equal or less than 15% and (b) grasslands with a vegetation cover equal or higher than 35% . If available, we sampled 20 individuals per site. We only collected individuals that were clearly separated from each other reducing the risk of sampling clonal ramets. The sample size of individuals at badlands in Mühlburg (Mu) and Wachsenburg (Wa) is lower because only a few individuals of T. praecox occupied these sites  at a maximum, yielding to six vegetation records in each location and 25 records in total. We calculated species richness, used the extended Braun–Blanquet scale , sex , and number of flowers per individual (mean number of flowers averaged from three inflorescences). We evaluated phenology as a ratio of fruiting to flowering individuals [%] and sex as the ratio of female to hermaphroditic individuals [%].We analyzed the following traits in situ: plant height (PH) as the shortest distance between ground and the highest flower inflorescence [cm], patch size as a product of maximum length and width [cm2] using the R package “LeafTraits” vers. 1.0 . We computed specific leaf areas (SLAs) as a ratio of mean leaf area [mm2] and mean leaf dry weight [mg], and leaf dry matter contents (LDMCs) as a ratio of mean leaf dry weight [mg] and mean leaf fresh weight [g] of each individual , b = 20%–60% (mean 40%), and c ≥ 60% (mean 80%). Furthermore, we prepared stomatal imprints from abaxial leaf surfaces of two fresh leaves per individual to measure guard cell length and width [µm] using a 400‐fold magnification , and to estimate stomatal density [mm−2] using a 200‐fold magnification . Measurements were conducted with an Olympus CH40 microscope. We replicated all stomatal measurements in three randomly chosen areas per leaf and recorded them with the program AxioVision vers. 4.8 . Then, we calculated stomatal pore surface (SPS) by guard cell length [µm] × guard cell width [µm] × π × 4–1 representing the surface area of a widely opened stomatal pore  and probably also patch size in 2.5T. praecox populations , 2.5 mM MgCl2, 0.2 mM dNTPs, 0.5 µM M13(‐21) tailed forward primer, 1 µM reverse primer, 1 µM M13(‐21) primer, PVP in locus‐specific concentrations , 0.025 µg/µl BSA, 5% DMSO, and 0.0125 U/µl Taq polymerase (Fermentas). We added PVP to prevent inhibitory effects of polyphenols and used DMSO and BSA to improve the amplification of GC‐rich DNA sequences  and checked the quality electrophoretically. We applied six variable microsatellite loci to infer genetic diversity and differentiation of n × touchdown cycles with previous denaturation at 94°C for 30 s, annealing at touchdown specific ta starting at tmax to a final temperature tmin with a reduction of 1°C in each cycle, and elongation at 72°C for 30 s; 20 × main amplification cycles of denaturation at 94°C for 30 s, annealing at locus‐specific ta for 90 s, and elongation at 72°C for 35 s; 10 × M13‐specific cycles of denaturation at 94°C for 30 s, annealing at 53°C for 45 s, and elongation at 72°C for 60 s; final elongation at 72°C for 10 min; and sample storage at 4°C. We combined products of different labels  for fragment length analyses using a LI‐COR Long Readir 4200 . Fragments were analyzed visually using an internal size standard.Then, we applied locus‐specific touchdown programs  to increase PCR specificity , for badlands and grassland sites (B and G), and for local badlands and grassland sites . We excluded wL, wK, wF, and wN because these indicator values showed almost no differences among location, between sites and between local sites (data not shown).We statistically evaluated data with R vers. 3.5.2 , we used Q–Q plots to verify/falsify normal distribution. Second, we tested differences between the two groups with unpaired, two‐sided Student's t test  by t.test or with Mann–Whitney U test (W) by wilcox.test if data were non‐normally distributed. For data sets with more than two groups, we applied analyses of variance  by aov or Kruskal–Wallis tests  by kruskal.test if data were non‐normally distributed. For ANOVA, we checked with the Fligner–Killeen test by fligner.test whether variances in each of the groups were homogeneous. We evaluated statistical differences at group levels with Tukey's “honest significant difference” method using the function TukeyHSD if ANOVA was applied, or pairwise Wilcoxon rank‐sum test with Holm correction using the function pairwise.wilcox.test if the Kruskal–Wallis test was applied. Phenology and floral phenotype are nominal data, and thus, we performed Pearson's chi‐squared test with Yates' continuity correction using the function cisq.test to examine differences among locations, between sites, and between local sites.First, we examined normal distribution between groups of environmental factors and phenotypic traits by a combined approach consisting of Shapiro–Wilk tests called with the function shapiro.test and Since standard statistical tests do not allow statements on whether environmental and phenotypic differences are strongest among locations, between sites, or between local sites (site × location), we carried out multiple linear regression models (LMs) implemented in the function lm. We log‐transformed response variables for normality and treated Ellenberg indicator values as numeric. We also executed multiple generalized linear models (GLMs) for count data with Poisson error structure, and proportion and binomial data with binomial error structure by the function glm Crawley, .p > .1) was excluded until the final minimal adequate model was reached  correlations. We excluded slope exposure because local sites were always situated on the same slope exhibiting no differences in exposure  with means of phenotypic traits based on local site data applying the function decorana implemented in the R package “vegan”  and local sites . Moreover, genetic diversity indices only slightly differed among loci  and flowers were frequently visited by pollinators possibly allowing sufficient mating processes. Furthermore, genetic diversity was not reduced in badlands sites (see Table K (K = number of genetic clusters). We chose a range of K from one to eight, and the optimal K was determined by STRUCTURE HARVESTER  correlation tests with the function cor.test between genetic diversity indices and sample size.In the third part of the analysis, we used the R package “polysat” vers. 1.7‐4 ))2 − 1]/[(1/(1 − He(pop2)))2 − 1] was calculated for each group combination, and ln RH estimates were standardized to zero mean and standard deviation of one  in slope, slope exposure, altitude, and soil depth  significant differences , phenology , SLA, and PCI (see Table The PCA of mean phenotypic traits indicated badlands (B) and grassland (G) sites as separate groups Figure . The fir3.2T. praecox differed only slightly among locations, sites, and local sites, but values were in general relatively high  and He , but not PAp  and uh .Genetic diversity indices of gh Table . Among lp < .001), and 99% and 1% variation between sites . PCoAs indicated a complete intermixture of individuals among locations and between sites  and locations (GST = 0.015), and highest genetic differentiation between local sites (GST = 0.035). We found weak, but significant, isolation by distance among local sites , and individuals are composed of different amounts of each genetic cluster  and genetic differentiation in In general, we observed congruent environmental and phenotypic patterns exhibiting the strongest differentiation between sites and local sites. Despite the strongest site differentiation, many plant phenotypic traits also differed among locations presumably caused by local environmental effects . For example, shallow, south‐exposed locations compared with flat, west‐exposed ones have a tenser hydrological balance that may influence phenotypic trait expression . However, badlands are notably different from surrounding grasslands due to lower vegetation cover and species diversity, higher light availability, steeper slopes, shallower, drier and less calcareous soils, and slightly higher temperatures (wT). Badlands with steeper hill slopes are mostly situated above grasslands, which explains significant higher altitudes in statistical analyses , and weakly to lower soil humidity (PCA). Increased light intensity is able to reduce plant elongation and growth, and thus plant height  relates to photosynthesis and relative growth rate , and in PCA, less stomatal conductance (PCI) of individuals growing in badlands sites associated with higher light intensity, sparser vegetation cover, and lower soil moisture. Stomatal density increase and stomatal size decrease with increasing light intensity resulting in a bigger membrane surface‐to‐volume ratio and allowing a faster conductance regulation . An explanation might be the deficient soil moisture and higher temperature (wT) reducing the investment of individuals in reproduction. Studies on T. praecox were generally low, but statistically significantly different among locations, between sites, and between local sites , and badlands may have been formed since the medieval ages by sheep‐ and goat‐grazing Bricks, . SelectiT. praecox individuals between local sites dry grasslands in the Drei Gleichen region of Thuringia . Despite being only a few meters (1–20 m) apart, we observed significant phenotypic  differentiation between sites and genetic differences between local sites. Extreme light, temperature, drought, and nutrient stress in badlands compared with grasslands led to environmental heterogeneity among  sites, promoting the formation of phenotypically and genetically differentiated T. praecox individuals. The close proximity of individuals at both sites prevents stronger differentiation by allowing some genetic exchange. The low number of applied microsatellite markers may limit the strength of the observed genetic differentiation patterns in this study, and significantly more markers should be involved in future work.Our findings indicate small‐scale differentiation in In general, recorded phenotypic traits showed morphological and performance‐linked differences, evidently related to environmental differences than explained by location‐specific conditions alone. Common‐garden and transplant experiments should be the next step to test phenotypic trait stability, fitness differences, adaptation through natural selection, and thus ecotype formation.We demonstrated that desert‐like, dry, and nutrient‐poor badlands and their surrounding (semi‐)dry grasslands are a suitable model habitat for studying phenotypic and genetic differentiation in plants. The worldwide occurrence and the few studies about badlands (e.g., Guàrdia et al., None declared.K.K. and K.P. together developed the project concept. K.P. obtained approvals from lower nature conservation authorities. K.K. recorded environmental and phenotypic data and took phenotypic and genetic samples during fieldwork. In the laboratory, K.K. analyzed samples and gathered data except stomatal trait data recorded by K.P. Furthermore, K.K. and S.T. statistically analyzed environmental, phenotypic, and genetic data. The manuscript was written with contributions from all authors. Click here for additional data file.


Zostera marina, at five sites around the Isles of Scilly Special Area of Conservation, UK, in 2010 and compared this to 23 years of annual ecological monitoring (1996–2018). We found low diversity and long-term declines in abundance in this relatively pristine but isolated location. Inclusion of the snapshot of genotypic, but less-so genetic, diversity improved prediction of abundance trajectories; however, this was spatial scale-dependent. Selection of the appropriate level of genetic organization and spatial scale for monitoring is, therefore, important to identify drivers of eco-evolutionary dynamics. This has implications for the use of population genetic information in conservation, management, and spatial planning.In isolated or declining populations, viability may be compromised further by loss of genetic diversity. Therefore, it is important to understand the relationship between long-term ecological trajectories and population genetic structure. However, opportunities to combine these types of data are rare, especially in natural systems. Using an existing panel of 15 microsatellites, we estimated allelic diversity in seagrass,  However, empirical evidence across a range of major marine and terrestrial vegetation systems has resulted in equivocal support for these predictions, limiting the contribution of genetic information to conservation policy and management. In particular, the relationship between population distribution and genetic diversity has been investigated in temperate4 and tropical forests6, as well as seagrass ecosystems8 and coral reefs9, with mixed results.Established population genetic theory predicts that increased fragmentation, as well as reduced local population size, is associated with a range of negative genetic consequences. Smaller and more isolated local populations are expected to lead to lower effective population size, increased inbreeding, as well as less allelic diversity10. This should be based on knowledge of the processes and mechanisms driving eco-evolutionary dynamics governing abundance and distribution. We set out to investigate this issue using the case study of a fragmented population of seagrass, subject to long-term ecological monitoring that allows us to quantify population dynamics over appropriate temporal and spatial scales11.It has become clear that it is necessary to identify the appropriate temporal and spatial scales to measure both genetic diversity and ecological indicators13, covering approximately 0.3 to 0.6 million km2 and are the structural basis of key coastal marine ecosystems14. They are terrestrial-derived, marine angiosperms that rank amongst the most productive providers of ecosystem services, encompassing supporting, provisioning, regulating, and cultural services16. Specifically, seagrass meadows support high biodiversity18 and contribute to blue carbon sequestration, nutrient cycling, erosion protection, raw materials, recreation, and nursery functions for many marine organisms21.Seagrasses are globally distributed along coastlines22 and have been undergoing large-scale declines over recent decades25. These global declines are due to a variety of factors, related either directly  or indirectly  to anthropogenic activities26.However, seagrass meadows are suffering extreme levels of damage in some parts of the world27. These local and regional effects determine the ability of seagrass populations to survive in the face of future environmental changes29. Relatedly, isolation from surrounding populations can have profound effects on genetic variability and resulting population viability30. Thus understanding the genetic population structure  is critical to understanding the extent to which genetic drift, dispersal, and selection contribute to long-term population decline with subsequent negative effects on ecosystem services33.Declines in seagrass populations potentially result in increased fragmentation within meadows, as well as increased isolation between meadows35. However, average single locus genetic diversity  may not be associated with population dynamics if neutral markers are used36. It is hypothesized that multi-locus genotypic diversity may provide a more reliable indicator of relevant genetic diversity as this estimates the number of genetically unique individuals, which presumably may vary in ecologically important ways37. As well as being associated with increased productivity38 and recovery34, higher genotypic diversity has been shown to increase diversity of associated communities36.In natural monocultures, such as many seagrass ecosystems, genetic diversity may be considered similarly to species diversity, as part of the on-going diversity-stability debate39 or, at best, over a couple of years40. While these short-term studies are valuable to test mechanistic hypotheses, natural and anthropogenic drivers may be chronic in nature, applying sustained forcing over multiple generations. It has been found that most species are not driven to extinction before genetic factors impact on them41. However, the relationship between genetic diversity and long-term population trajectories is less well understood.Many studies into the role of genetic diversity on population fate have focused on snapshot indicators of population state; e.g., resilience. Metrics are typically recorded within a season44) and none, to our knowledge, combine ecological monitoring of seagrass distribution with data on population genetic structure. Conversely, there are numerous published studies of seagrass population genetics, but these are not formally linked to contemporary dynamics48.Two sources of information are needed to tackle the challenges presented above: long-term ecological monitoring and population genetic sampling. Studies combining both these types of information are rare in any natural system. In the case of seagrasses, there are relatively few long-term studies  that include appropriate temporal resolution to investigate population dynamics  and an analysis of population genetic structure from sampling conducted in 2010. Our specific aims were to:Z. marina population genetic variation at five long-term monitoring survey sites across the Isles of Scilly.Quantify Test the hypothesis that allelic diversity is likely to be low, and inbreeding high, at this relatively isolated location.Identify the spatial scale of long-term monitoring and level of genetic organization that best informs seagrass abundance trajectories.The focus of our study is the seagrass meadows around the Isles of Scilly; an archipelago situated approximately 25 miles off the southwest tip of the UK. The Isles of Scilly includes the largest continuous expanse of eelgrass, Our goal was to assess allelic diversity in fragmented seagrass meadows subject to long-term ecological monitoring. To accomplish this, we calculated key population genetic metrics, partitioned genetic variation over spatial scales, assessed spatial independence of our five survey sites, and then present this in the context of seagrass abundance trajectories using 23 years of annual monitoring data.Z. marina leaf samples were genotyped at 15 microsatellite loci that are highly polymorphic at other northern European sites51. Around the Isles of Scilly, seven loci  were found to be monomorphic. These seven loci were removed from all subsequent analyses, leaving a total of eight informative markers .A total of 96 Amongst the remaining loci, null allele frequencies were very low. In individual samples where >10% null alleles were found; these were not consistent across loci and populations. Therefore, no further loci were discounted due to presence of null alleles. However, two loci (ZMF02381 and CL380Contig1) showed evidence of linkage to other loci (linkage disequilibrium) and were excluded from the analysis. Loci were retained in this analysis that showed no significant linkage disequilibrium , we estimated genotypic diversity from the number of multilocus genotypes (MLG) at each survey site and corresponding clonal diversity index, R. At four sites, there were 10–12 MLGs; however, at Old Grimsby Harbour there were only seven MLGs. Since we assessed a slightly reduced sample size at Old Grimsby Harbour (n = 16 rather than 20) and probable replicate samples were removed, we verified this result by comparing estimated multilocus genotypes (eMLG) at all sites based on the reduced sample size, with very similar results  were identified across the whole of the Isles of Scilly. At individual sampling sites, Ar ranged from 1.833 at Little Arthur and Old Grimsby Harbour to 2.167 at Broad Ledges Tresco. Allele sizes for all loci at each survey site are shown in Supplementary Table There was little evidence of inbreeding at each survey site, with Fis values ranging from −0.096 at Higher Town Bay to 0.044 at Broad Ledges Tresco, and 95% confidence intervals (c.i.s) spanning zero in all cases Table .Fixation indices comparing observed heterozygosity with that expected under Hardy-Weinberg equilibrium were: Fit = 0.166 , Fst = 0.014 , and Fis = 0.105 . Therefore, overall lower-than-expected heterozygosity (Ho / He = 1 - Fit = 0.834) is primarily underpinned by lower-than-expected heterozygosity associated with inbreeding (Ho / He = 1 - Fis = 0.895) rather than restricted gene flow between sampling sites (Ho / He = 1 - Fst = 0.986). This finding is corroborated by analysis of molecular variance (AMOVA), partitioning population genetic variation into three levels: between the five survey sites, between individuals within sites, and within individuals  and level of genetic organization (genetic vs. genotypic diversity) that best predict seagrass decline. With only a single snapshot of allelic diversity, this is intended as a guide for future efforts to link population genetics with long-term ecological monitoring, rather than to draw strong conclusions on causality.As part of a mixture model predicting shoot density and quadrat occupancy, we compared a global model including statistical interactions between allelic diversity (in 2010) and abundance trajectories (1996–2018), then compared this with nested models removing the statistical interactions. This was performed separately with either clonal richness or allele richness as the measure of allelic diversity.In the case of clonal richness, R, inclusion of even a single snapshot of diversity substantially improved model performance, both for predicting quadrat occupancy ΔAICc = 45.5) and within-quadrat shoot density (ΔAICc = 4.94). However, in the case of allele richness, Ar, inclusion of genetic information only provided a substantial improvement for quadrat occupancy prediction (ΔAICc = 6.73) but not for within-quadrat shoot density (ΔAICc = 1.32)  alleles per locus46. Therefore, we conclude that the Z. marina population in the Isles of Scilly shows unusually low allelic richness for this species. It is well known that Z. marina across the north Atlantic suffered drastic losses during the 1930s, due to ‘wasting disease’61. Also, eutrophication has resulted in further, substantial seagrass population declines62. These types of pressure create the potential for genetic bottlenecks63. While other sites across northern Europe may have retained relatively high allelic richness overall through mixing of locally surviving populations, it seems likely that the isolation of the Isles of Scilly has restricted population genetic recovery. The nearest Z. marina population to the Isles of Scilly is at Mounts Bay, Cornwall, approximately 50 km distant. While strong east-west currents could transport plant material over that distance, this represents a considerable geographic barrier.In our study, we found seven loci to be monomorphic. Following removal of a further two microsatellites due to linkage disequilibrium, we found an average of 2.0 alleles (maximum = 3) per polymorphic locus. This is compared to between two and eleven alleles per locus (mean = 4.3) in 64. Under extreme high levels of disturbance, the majority of genets can be removed from a location, leading to very low clonal richness65. Regular disturbance events have also been linked with low levels of clonal diversity in tropical seagrasses, whereas less frequent disturbance had no effect66, suggesting some threshold effect in a species’ ability to resist loss of genetic diversity . Old Grimsby Harbour is clearly impacted by damage to the seabed, which provides external forcing of the spatial pattern of seagrass – essentially large holes in previously more uniform vegetation59. In contrast, it is not immediately clear what the cause of decline is at Higher Town Bay and a much larger range of patch sizes and spatial complexity is observed there52, likely driven by endogenous dynamics67.In colonizing coral species, clonal richness declines with increasing physical disturbanceZ. marina populations, we found three out of five sites to be in Hardy-Weinberg equilibrium. This suggests relatively unrestricted gene flow within individual sites, consistent with primarily sexual reproduction through flowering, rather than clonal expansion via rhizomes. Similar findings of Hardy-Weinberg equilibrium in Z. marina populations have been reported across northern Europe46. As flowering plants, seagrasses can reproduce both sexually, through flowering, and asexually, via rhizome extension. Flowering was to be positively associated with seagrass cover the following year, at the same sampling sites as the current study43. Our results corroborate this finding and do not consistently indicate any disruption to the mating system in declining sites. More broadly, when genetic bottlenecks affect populations, higher than expected levels of heterozygosity are predicted69, in contrast to our findings. If lower than expected levels of allelic richness were the result of a genetic bottleneck driven by historical losses in the last century, the effects of that bottleneck are no longer evident in observed heterozygosity.While allelic richness was low compared to other 51. Comparing pairwise Fst differences between sites with geographic distances, we found no evidence of isolation by distance. Other studies have identified isolation by distance between Zostera marina populations over much larger geographic ranges, e.g., 150–5000 km70. Isolation by distance has been reported at the <50 km scale in Z. marina but this was only associated with intertidal populations71; whereas our populations were sub-tidal. More generally, isolation by distance studies carried out in several seagrass species, notably Posidonia oceanica, Z. marina, and Z. noltii have pointed to this effect occurring over the hundred to thousands of kilometres70.Across the whole of the Isles of Scilly, we found significantly less heterozygosity than predicted under Hardy-Weinberg equilibrium. This could be caused by reduced gene flow between sites but other measures of spatial population structure suggest this is not substantial. Nested analysis of molecular variance indicated only 1.5% of total variation attributable to between site differences. This is very similar to levels reported by in the Wadden SeaAs hypothesised, we did find that lower genotypic diversity is associated with both lower quadrat occupancy and lower within-quadrat shoot density. In addition, lower genotypic diversity was associated with long-term decline at both spatial scales, but substantially more pronounced at the quadrat-occupancy scale. It is important to re-emphasise the limitations of using a single snapshot of population genetics in this study. We are not able to quantify any temporal changes in allelic diversity and our study was not intended to test causal mechanisms or eco-evolutionary feedback processes. However, it is clear that inclusion of some population genetic information improved prediction of abundance trajectories. We hope this provides encouragement for future research in that we have shown ecological and genetic measures are not totally disconnected in this ecosystem.The value of our current findings is twofold. We show that identifying links between genetic and ecological signals is scale-dependent, providing guidance for future, evidence-based conservation studies. Additionally, we highlight the spatial and temporal scales at which eco-evolutionary dynamics occur, allowing manipulative experiments to test mechanistic hypotheses efficiently.72 and supported empirically across a number of species and ecosystems41. However, detailed inspection of individual case studies has led to equivocal and scale-dependent findings. Seagrasses often exist as natural monocultures and, therefore, make ideal model systems to test ecological and population genetic hypotheses. This model has proved informative in both population dynamic74 and population genetic studies66 but these are rarely linked. In this study, we estimated a suite of common genetic and genotypic diversity parameters amongst Zostera marina populations exhibiting a range of long-term population trajectories. Overall, we found evidence of low allelic diversity, as well as declining abundance over a decadal timescale. Integration of these data sources highlighted that genotypic, rather than genetic, diversity may be the appropriate level of genetic organization and that quadrat occupancy, rather than within-quadrat shoot density, may be the appropriate spatial scale to develop future research into causal links. This adds to the growing body of evidence that identifying genetic and ecological indicators underpinned by eco-evolutionary mechanisms is essential for marine spatial planning, conservation, and management. Our findings also help to address urgent calls to develop integrated ecological and genetic frameworks to inform evidence-based management of this globally important but widely threatened group75, as well as other vegetative ecosystems.The positive association between genetic diversity and population size has been predicted by theoryZostera marina , Higher Town Bay , Little Arthur , Old Grimsby Harbour , and West Broad Ledges  , and Zostera marina clones have been estimated at less than 2 m across76. This combination of random bearing and distance sampling where possible, with haphazard sampling > 2 m apart where not, is almost identical to that of 66.At each of the five long-term monitoring sites, we randomly placed 25 quadrats (0.25 m × 0.25 m) within 30 m of a central datum, using pre-assigned coordinates each yearAt each quadrat station, we collected approximately 2–3 cm of fresh tissue from a single leaf, which was transported to the laboratory for further analysis. Samples were then washed using freshwater and raked to remove epiphytic algae. Leaf samples were dried at −109 °C for 24 to 48 h using a SCANVAC CoolSafe 110–4 Pro and then stored frozen (−20 °C) until DNA was extracted from leaf tissue.Frozen samples were ground with a Precellys Ceramic Kit 1.4/2.8 mm. Genomic DNA was extracted using Qiagen DNeasy Plant Mini Kits following the manufacturer’s instructions. Purity and concentration of DNA was determined by the A260/280 nm absorbance using an Implen NanoDrop 2000 Spectrophotometer and stored at −20 °C.51. Microsatellite loci were amplified by polymerase chain reaction (PCR). Each forward primer was labelled with a fluorescent dye (FAM or HEX) to allow multiplexing, labelled and pooled as per51.We used an existing panel of 15 polymorphic microsatellite markersPCR amplification was accomplished according to the manufacturer’s protocols using Qiagen Type-it Microsatellite PCR kits. Reactions were performed in a reaction volume of 15 μl containing 7.5 μl of 2x Type-it Multiplex PCR Master Mix, 1.5 μl of 10x primer mix, approximately 4.2–5.9 μl RNase-free water, and approximately 0.1–1.8 μl of template DNA. PCR cycling conditions comprised an initial denaturation heat activation at 95 °C for 5 min, followed by 3-step cycling of 95 °C for 30 s (denaturation), 57–63 °C for 90 s , and 72 °C for 30 s (extension) for 28 cycles followed by a terminal extension phase at 60 °C for 30 min.PCR products were sent to the Institute of Biological, Environmental, and Rural Sciences (IBERS), Aberystwyth University, UK, to obtain read lengths.77. We visualized the scoring and binning of microsatellite alleles using the R package MsatAllele, version 1.078. This permitted the identification of monomorphic and polymorphic loci, as well as providing a further check of allele identifiability at the population level. Presence of null alleles was assessed using FreeNA software79.Microsatellite DNA fragment lengths were used as the basis of allele scoring. This was achieved using the R package Fragman, version 1.0.9, automatically with default settings and confirmed through manual inspection80. Pairwise comparisons between all loci  were made using likelihood ratio tests with Benjamini and Yekutieli false discovery rate correction81.To verify the independence of loci, we tested for linkage disequilibrium using GENEPOP, version 4.2SEX82. This identified 23 of our 96 samples as having an unacceptably low probability of being from independent reproductive events (P < 0.05), i.e., clonal replicates, and these were removed for subsequent analysis.We assessed the probability of two or more samples coming from independent reproductive events (rather than resampling of the same genet) by identifying potential resampled individuals using P83. We also calculated a common measure of genotypic diversity: clonal richness, R = (MLG − 1) / (N − 1), where N is the sample size84.We tested whether differing sample sizes between sites introduced bias by comparing the observed number of multilocus genotypes (MLG) at each survey site, with the number of expected multilocus genotypes (eMLG – the number of multilocus genotypes predicted using the lowest number of samples) at each site, using the R package Poppr, version 2.8.183.To evaluate microsatellite diversity in the Isles of Scilly eelgrass, we calculated the observed number of alleles , estimates of observed (Ho) and expected (He – assuming Hardy-Weinberg equilibrium) heterozygosity, and Wright’s fixation indices  in the five populations using the R package Poppr, version 2.8.1Z. marina around the Isles of Scilly, a hierarchical analysis of molecular variance (AMOVA) was performed using GenoDive, version 2.0b2385. Nested variance components were: between the five survey sites, between individuals within sites, and between alleles within individuals. Pairwise estimates of Fst were also calculated86, with 95% confidence intervals obtained through bootstrapping (1000 iterations).To assess the spatial population genetic structure of 87. Pairwise Euclidean distances were calculated using the R package Geosphere, version 1.5–788. We tested the statistical significance of correlation between genetic and geographic distances using a Mantel test with 10,000 permutations89.We explored the potential relationship between genetic and geographic distance by comparing the pairwise Fst differences with distances between sitesZ. marina abundance was estimated from shoot counts in replicate quadrats at each of five survey sites over 23 years. These zero-inflated data were partitioned into presence / absence within individual quadrats and counts in non-zero quadrats, then assessed simultaneously using a mixture model, implemented as a GLM statistical model90. Presence / absence was modelled as a binomial distribution; non-zero count data were modelled as a zero-truncated negative binomial distribution. Ecological monitoring year (1996–2018) and allelic diversity (either Ar or R) in 2010 were fitted as fixed effects, both as main effects and through their statistical interaction.We tested hypotheses on the relationship between genetic diversity and long-term population abundance trajectories using a Generalised Linear Modelling (GLM) framework. We assessed the effects of genetic diversity  and genotypic diversity  on population abundance using separate GLMs. 91.Where statistical analysis was performed using R, this was run in version 3.5.1Supplementary Information


Atherosclerosis is the main pathological basis of ischemic cardiovascular and cerebrovascular diseases and has attracted more attention in recent years. Multiple studies have demonstrated that the signal transducer and activator of transcription 3 (STAT3) plays essential roles in the process of atherosclerosis. Moreover, aberrant STAT3 activation has been shown to contribute to the occurrence and development of atherosclerosis. Therefore, the study of STAT3 inhibitors has gradually become a focal research topic. In this review, we describe the crucial roles of STAT3 in endothelial cell dysfunction, macrophage polarization, inflammation, and immunity during atherosclerosis. STAT3 in mitochondria is mentioned as well. Then, we present a summary and classification of STAT3 inhibitors, which could offer potential treatment strategies for atherosclerosis. Furthermore, we enumerate some of the problems that have interfered with the development of mature therapies utilizing STAT3 inhibitors to treat atherosclerosis. Finally, we propose ideas that may help to solve these problems to some extent. Collectively, this review may be useful for developing future STAT3 inhibitor therapies for atherosclerosis. Cardiovascular and cerebrovascular diseases, especially heart attack and stroke, significantly contribute to worldwide mortality STATs, consisting of seven members  In this review, by summarizing the current literature, we highlight the essential roles of STAT3 in atherosclerosis and present STAT3 inhibitors that may become potential treatment agents for atherosclerosis. First, we describe the general background of STAT3, including its structure, function, and regulation. Subsequently, we discuss the pathological roles of STAT3 in atherosclerosis from three independent but related biological processes, endothelial cell dysfunction, macrophage polarization, inflammation, and immunity. Moreover, we summarize the current inhibitors of STAT3 Table  and explMembers of the STAT protein family are localized in the cytoplasm, can translocate into the nucleus to bind DNA, and dually function in signal transduction and transcriptional regulation. These proteins have been shown to participate in diverse cellular processes, including stem cell maintenance, lipid metabolism, neuron function, carcinogenesis, inflammation, and immunity STATs range in size from 750 to 850 amino acid residues and contain the following 6 conserved domains: 1) a helical N-terminus domain (ND) that can promote binding with DNA and regulate translocation into the nucleus, 2) a coiled-coil domain (CCD) that provides the action site for transcription factors and regulatory proteins, 3) a central DNA-binding domain (DBD) that can determine the specific DNA sequence binding with STATs, 4) a linker domain (LD) that affects DNA binding stability, 5) an Src homology 2 (SH2) domain that recognizes phosphotyrosine residues and is closely related to STAT activation, and 6) a C-terminal transactivation domain (TAD) with a conserved tyrosine residue at position 705 (Tyr-705) and a serine phosphorylation site at 727 (Ser-727) , like other STAT family proteins Figure . Among tMoreover, STAT3 has been identified as having distinct isoforms, STAT3α, STAT3β, STAT3γ, and STAT3δ, which are considered determinants of its functional heterogeneity STAT3 functions as an essential signal transduction effector protein for cytokine- and hormone-induced pathways that control the development, proliferation or differentiation, and homeostasis of numerous cell types. Like other STATs, STAT3 is mostly activated by phosphorylation of its tyrosine and serine residues via signaling from upstream regulators However, like phosphorylated STATs, unphosphorylated STAT proteins can also translocate to and prominently exist in the nucleus in various types of mammalian cells in quiescence Thus, STAT3 functions in two distinct ways in cytokine-dependent transcription: by playing a role in the primary response through formation of p-STAT3 dimers and by playing a secondary role in the complete response through the action of increased amounts of unphosphorylated STAT3.2O2-induced cell apoptosis and death depend on JAK2 and STAT3 activation The IL-6 family of cytokines, including IL-6, leukemia inhibitory factor (LIF), oncostatin M (OSM), neuropoietin (NP), cardiotrophin-1 (CT-1), and ciliary neurotrophic factor (CNTF), are important regulators of JAK2/STAT3 signaling pathway activation in the development of atherosclerosis  in vivo and in vitroIL-10 is an immunomodulatory cytokine that has potent anti-inflammatory activity, and its signaling pathway has been well characterized in macrophages and T lymphocytes. IL-10 signaling is mediated via a transmembrane receptor, which consists of a ligand-binding IL-10 receptor1 (IL-10R1) chain and an accessory subunit, IL-10R2 2-4GAA) 3GAA is an optimal variant for STAT1, which prefers binding sites containing an intrasite spacer of three bases (CCG). On the other hand, STAT3, STAT4, and STAT5a/b prefer to bind at sites with 2- to 4-base spacers, and most high-equilibrium binding intensity interactions occur at sites with a 3-base spacer STAT3 can be phosphorylated and activated by different factors, among which the activation process through JAKs is the best studied. Various types of stimuli, including cytokines and growth factors, can initiate STAT3 activation. When receptors on the membrane bind with their ligands  and dimerize, pairs of JAKs are recruited to the receptor domains and initiate self-activation via either auto- or transphosphorylation Molecules that negatively regulate STAT3, including protein tyrosine phosphatases (PTPs), protein inhibitors of activated STAT (PIASs), and suppressor of cytokine signaling 3 (SOCS3), are critical to prevent its hyperphosphorylation. PTPs regulate the activation of STATs through direct and indirect dephosphorylation of p-STAT3. The indirect regulators, including CD45 and PTP-1B, can downregulate STAT3 activation via dephosphorylation of JAK2 Atherosclerosis is an intricate process involving multiple cell types, such as endothelial cells and macrophages. In Table Endothelial cells play central roles in the functions of the cardiovascular system 2O2 may cause OSI in endothelial cells through the IL-6/JAK2/STAT3 signaling pathway, the high activation of which allows STAT3 to maintain a phosphorylated state 2O2-simulated oxidative stimulation, the activation rate of SOCS3 slows down and the STAT3 phosphatase TC-PTP (TC45) mislocalizes to the cytoplasm, both of which weaken their inhibition of STAT3 phosphorylation Recently, an increasing number of studies have demonstrated the high activation of the JAK2/STAT3 signaling pathway in the OSI of various cell types , which suggests the essential role of this pathway in the modulation of oxidative stress responses Sustained STAT3 phosphorylation also leads to the abnormal expression of adhesion molecules There are 3 main adhesion molecules related to atherosclerosis: intercellular adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1), and platelet endothelial cell adhesion molecule-1 (PECAM-1) In the stimulation of ROS, the transcription rate of SOCS3 slows down, and STAT3 phosphatase TC-PTP (TC45) mislocalizes to the cytoplasm, both of which weaken their inhibition of STAT3 phosphorylation and promote the hyperphosphorylation of STAT3 One important manifestation of endothelial cell dysfunction is the abnormal expression of adhesion molecules, which can be caused by ROS through the IL-6/JAK2/STAT3 signaling pathway. This pathway can be highly activated by ROS, leading to the sustained phosphorylation of STAT3 and the further overrelease of ICAM-1 and VCAM-1. Excess adhesion molecules induce inflammatory responses and aggravate endothelial cell dysfunction, thus prompting the development of atherosclerosis Macrophages are now thought to be present in all stages of atherosclerosis, from the initiation and expansion of lesions to necrosis or rupture, and have become a clinical manifestation of atherosclerosis Macrophages can respond to various environmental stimuli by adopting one of several functional phenotypes, such as the classically activated macrophage phenotype (M1), the alternatively activated macrophage phenotype (M2), and the Mox macrophage phenotype Basic and clinical studies have shown that unchecked chronic inflammation is responsible for many deadly cardiovascular diseases, including atherosclerosis Human atherosclerotic plaques contain various inflammatory cell types, including monocytes, macrophages, and mast cells. Recruitment of these inflammatory cells by adhesion molecules (ICAM-1 and VCAM-1) and E-selectin in endothelial cells is the critical step for inducing the formation of atherosclerotic plaques TNF-α and IL-1β have been demonstrated to induce the massive IL-6 expression and aggravate vascular inflammation, thus promoting the formation of atherosclerotic plaques Excessive proliferation and migration of abnormal VSMCs are major causes of atherosclerosis development Accumulating studies have demonstrated that activated STAT3 participates in the proliferation of VSMCs. After inhibiting STAT3 in human aortic VSMCs cells, cell proliferation is obviously reduced, while activating STAT3 via LIF enhances cell proliferation  in vivoThe transcript levels of PDGF-BB in human atherosclerotic plaques are increased compared to those in normal arteries Phenotypic switching is also a pivotal step underlying many VSMC-related pathological conditions, especially atherosclerosis. Liao and colleagues have demonstrated that the JAK/STAT3 signaling pathway is a central regulator of the VSMC phenotypic switch + T cells, play a central role in the physiological process of atherosclerosis based on their vital roles in the cellular immune network. CD4+ T cells are divided into various cell types according to different effectors, including Th1, Th2, Th9, Th17, Th22, T follicular helper (Tfh), and regulatory T (Treg) cells Immune cells, especially CD42+ sustains the production of two cytokines (IL-21 and IL-4) known to be regulated by IL-6 in CD4+ cells + T cells in atherosclerosis.After the discovery of Th17 cells, several groups addressed their potential contribution to atherogenesis (-/-) mice Treg cells provide protection against autoimmunity and are regarded as promising targets of clinical therapies to treat various diseases caused by autoimmunity, including atherosclerosis + T cells into Th17 cells by the IL-6/JAK2/STAT3 pathway A comparison of DCs in different intima showed that the atherosclerotic intima contains significantly more DCs than the normal intima, suggesting that DCs play an active role in the initial stage of atherosclerosis 2+ homeostasis and affects the mitochondrial production of ATP and ROS, thus playing a crucial role in atherosclerosis.In addition to its well-established roles in the nucleus during the progression of atherosclerosis, STAT3 is also present in mitochondria and contributes to the regulation of the electron transport chain (ETC) activity. As a major source of cellular ROS, mitochondrial-derived reactive oxygen species (mtROS) are natural byproducts of the ETC In addition to STAT3, other STATs, especially STAT1 and STAT2, also play essential roles in atherosclerosis. STAT1 has been identified as a regulator of foam cell formation and atherosclerotic lesion development in an intraperitoneal inflamemation model and an atherosclerosis-susceptible bone marrow transplantation mouse model The above findings provide convincing evidence that STAT3 can be a novel therapeutic target for atherosclerosis. Therefore, intensive efforts have been devoted to developing STAT3 inhibitors. Currently, there are two strategies for inhibiting the STAT3 signaling pathway: indirect and direct STAT3 inhibitors. The former strategy can block molecules upstream of the STAT3 signaling pathway and indirectly inhibit the signal transduction functions of STAT3, for example, by inhibiting the function of JAKs, Src, Abl, and Lyn Since the substitution of valine with phenylalanine at amino acid 617 (V617F) within the JH2 'kinase-like' domain of JAK2 was demonstrated to result in an overactivation of JAK2, inhibitors targeting JAK2 specifically have become the focus of studies. Over the years, numerous JAK2 inhibitors have been designed, including ruxolitinib, tofacitinib, AG490, AZD1480, SB1578, and WP1066 There has been recent interest in the direct inhibition of STAT3, and this approach has mainly focused on targeting the SH2 domain Peptide and peptide-like STAT3 inhibitors, including PpYLKTK  STA-21 (deoxytetrangomycin) and its structurally optimized analog LLL12 can bind to the SH2 domain, blocking STAT3 dimerization and DNA binding Curcuma longa), has many pharmacological activities, including antioxidant, anticarcinogenic, antiobesity, antiarthritic, analgesic, hepatoprotective, antiangiogenesis, and anti-inflammatory properties Another group of inhibitors targeting the SH2 domain of STAT3 are derivatives from natural compounds. Curcumin, a naturally derived phytochemical from plants such as turmeric , a unique ammonia acid residue in the STAT3 DBD region Because DBDs are flat with similarities among different isoforms of the same transcription factor family, they are generally considered “undruggable”. Although substantial progress has been made regarding inhibitors targeting the DBD of STAT3, these inhibitors still face challenges similar to those related to the therapeutic utilization of the SH2 domain described above.Inhibitors targeting ND and TAD can mediate the binding of STAT3 dimers and regulate DNA transcription, which may be useful for treating atherosclerosis by suppressing endothelial cell dysfunction, inflammation, and abnormal immune cell differentiation. Researchers have synthesized a highly selective STAT3 ND inhibitor, ST3-Hel2A-2, that effectively activates the expression of the proapoptotic gene CHOP and thus induces apoptosis in tumor cells As we have illustrated above, individuals with abnormal STAT3 activity are prone to atherosclerosis. Thus, targeting the STAT3 pathway has become a popular therapeutic approach in the treatment of atherosclerosis. Currently, an increasing number of STAT3 inhibitors have been identified and shown effects on not only inflammatory or proliferative diseases but also on diseases caused by vascular cell function Inhibitors approved by the FDA, including ruxolitinib and tofacitinib, suggest that STAT3-inhibiting strategies may offer promising developments in clinical fields Previously, substantial evidence has provided support for the hypothesis that STAT3 is a prominent regulator of various cancers, ischemic injury, and obesity. However, the role of STAT3 in the regulation of atherosclerosis has not been clearly illustrated, and atherosclerosis is still a threat to humans. In this review, we provided a basic overview of STAT3 and showed its pathological roles in atherosclerosis. After summarizing previous studies, we illustrated how aberrant STAT3 activation contributes to endothelial cell dysfunction, macrophage polarization, inflammation, and immunity and may thus become an essential modulator during atherosclerosis. Therefore, inhibitors targeting the STAT3 signaling pathway are expected to be promising therapeutic methods for the treatment of atherosclerosis. We then summarized the current STAT3 inhibitors, which are indirect and direct inhibitors, among which we may find novel therapies for atherosclerosis.However, the road leading from STAT3 inhibitors to mature therapies for atherosclerosis remains long, and the therapeutic application of STAT3 inhibitors has some limitations. First, STAT3 inhibitors are toxic and potent. From a security standpoint, natural products, including curcumin and resveratrol, are good candidates for STAT3 inhibition because of their noticeable dose-limiting toxicity Moreover, current STAT3 inhibitors are not sensitive to the different isoforms of STAT3. Four isoforms of STAT3 have been identified, STAT3α, STAT3β, STAT3γ, and STAT3δ, which all have different functions. STAT3α and STAT3β have been verified to have contrasting effects during the process of atherosclerosis bcl-xL and VEGF, playing an important role in the maintenance of cardiac function Finally, it is important to consider any negative bodily effects when holistically inhibiting STAT3 activation, as STAT3 is found to have positive effects on some processes, including cardiac protection, wound healing, and angiogenesis The time to adopt treatment is also essential, as earlier treatment times are correlated with increased effectiveness. However, due to low penetrance in diseased regions, identifying the stage of atherosclerosis is difficult. Thus, the effectiveness of such therapies also depends on advances in diagnostic methods. After overcoming these challenges, the inhibition of STAT3 has the potential to be a valuable therapeutic strategy for atherosclerosis.