Contrasts in riverscape patterns of intraspecific genetic variation in a diverse Neotropical fish community of high conservation value.

Spatial patterns of genetic variation compared across species provide information about the predictability of genetic diversity in natural populations, and areas requiring conservation measures. Due to their remarkable fish diversity, rivers in Neotropical regions are ideal systems to confront theory with observations and would benefit greatly from such approaches given their increasing vulnerability to anthropogenic pressures. We used SNP data from 18 fish species with contrasting life-history traits, co-sampled across 12 sites in the Maroni- a major river system from the Guiana Shield -, to compare patterns of intraspecific genetic variation and identify their underlying drivers. Analyses of covariance revealed a decrease in genetic diversity as distance from the river outlet increased for 5 of the 18 species, illustrating a pattern commonly observed in riverscapes for species with low-to-medium dispersal abilities. However, the mean within-site genetic diversity was lowest in the two easternmost tributaries of the Upper Maroni and around an urbanized location downstream, indicating the need to address the potential influence of local pressures in these areas, such as gold mining or fishing. Finally, the relative influence of isolation by stream distance, isolation by discontinuous river flow, and isolation by spatial heterogeneity in effective size on pairwise genetic differentiation varied across species. Species with similar dispersal and reproductive guilds did not necessarily display shared patterns of population structure. Increasing the knowledge of specific life history traits and ecological requirements of fish species in these remote areas should help further understand factors that influence their current patterns of genetic variation.

First report of Powdery Mildew Caused by Golovinomyces ambrosiae on Industrial Hemp in New Jersey.

In December 2019, New Jersey became one of the first states to have its industrial hemp (Cannabis sativa L.) plan approved by the U.S. Department of Agriculture (USDA) following enactment of the 2018 Farm Bill that authorized the production of hemp. Following this approval, hemp was legally grown for the first time in 2020. During the growing seasons of 2020 and 2021, powdery mildew-like symptoms were observed during the summer months (Jun to Aug) in greenhouse hemp research and fall months (Aug to Oct) in field production plots on Rutgers Agricultural Experiment Station farms in southern and northern New Jersey. Symptoms were observed on leaves and stems of hemp cultivars 'CB Genius', 'Cherry Wine' and 'Bay Mist'. Symptoms initially appeared as small white patches of mycelia and conidia on the adaxial surface of leaves that gradually spread to entire leaves and stems. Leaf discoloration (e.g., chlorosis) and premature leaf drop were observed. More severe symptoms and damage were observed in the greenhouse than outdoor cultivation. A voucher specimen was deposited in the U.S National Fungus Collections, USDA-ARS, Beltsville, MD (accession number 929187). Morphological examination of the white colonies from the cultivar 'Baymist' was carried out using light microscopy and further characterized by sequencing. This isolate was labelled PMH2. Hyphae were septate, conidiophores were hyaline, unbranched, measuring 130 to 240 µm in length and produced 1 to 4 conidia in chains. Conidia were hyaline, ellipsoid to ovoid in shape and measured 20 to 36 ×10 to 18 µm (n=30). Oil-like drops were present within conidia, although no distinct fibrosin bodies were observed. Chasmothecia were not observed. Morphological observations were consistent with those of Golovinomyces spp. as described by Braun and Cook (2012). Morphological observations (conidiophore and conidial measurements) were also similar to the description of G. ambrosiae on Hemp, as described in Wiseman et al, 2021. Sequencing of internal transcribed spacer (ITS), large ribosomal subunit (28S), intergenic spacer (IGS), beta- tubulin (TUB2) and chitin synthase 1(CHS1) region, were carried out with the primer sets ITS5/ITS4, LSU1/LSU2, IGS-12a/NS1R, TubF1/TubR1 and gCS1a1/gCS1b respectively, as shown by Qiu et al. (2020). Maximum-likelihood phylogenetic analysis confirmed the grouping of the PMH2 isolate within the G. ambrosiae accessions. Each individual gene alignment was treated as a separate partition. Sequences were not concatenated for maximum -likelihood phylogenetic analysis. Sequence data were deposited in GenBank under the accessions OK626453 (ITS), OK626454 (28S), OL456201 (IGS), OL415512 (TUB2) and OL415513(CHS1). To fulfill Koch's postulates, two mature, potted plants of C. sativa cv. 'Alpha Explorer' were inoculated by gently pressing symptomatic hemp leaves onto their leaf surface. They were incubated in an indoor grow room at 23°C and relative humidity of 50%. Non-inoculated healthy plants of C. sativa cv. 'Alpha Explorer' served as control. Inoculated plants developed powdery mildew symptoms within 10 to 12 days, while all control plants were asymptomatic. The powdery mildew on inoculated plants was found to be morphologically similar to the original. G. ambrosiae has been reported on C.sativa in Oregon (Wiseman et al. 2021) and G. ambrosiae (as G. spadiceus) has been reported on Cannabis in Kentucky (Szarka et al. 2019), Ohio (Farinas and Hand 2020) and New York (Weldon et al. 2020). This is the first known report of Golovinomyces ambrosiae causing powdery mildew on hemp in New Jersey. With the recent opening ( Dec15, 2021) of cultivation licensing and retailing of recreational marijuana, the acreage of Hemp production in New Jersey is expected to significantly increase, particularly for greenhouse production. It is important to document the species to develop management strategies to control this disease.

Deep Learning to Distinguish ABCA4-Related Stargardt Disease from PRPH2-Related Pseudo-Stargardt Pattern Dystrophy.

(1) Background: Recessive Stargardt disease (STGD1) and multifocal pattern dystrophy simulating Stargardt disease ("pseudo-Stargardt pattern dystrophy", PSPD) share phenotypic similitudes, leading to a difficult clinical diagnosis. Our aim was to assess whether a deep learning classifier pretrained on fundus autofluorescence (FAF) images can assist in distinguishing ABCA4-related STGD1 from the PRPH2/RDS-related PSPD and to compare the performance with that of retinal specialists. (2) Methods: We trained a convolutional neural network (CNN) using 729 FAF images from normal patients or patients with inherited retinal diseases (IRDs). Transfer learning was then used to update the weights of a ResNet50V2 used to classify the 370 FAF images into STGD1 and PSPD. Retina specialists evaluated the same dataset. The performance of the CNN and that of retina specialists were compared in terms of accuracy, sensitivity, and precision. (3) Results: The CNN accuracy on the test dataset of 111 images was 0.882. The AUROC was 0.890, the precision was 0.883 and the sensitivity was 0.883. The accuracy for retina experts averaged 0.816, whereas for retina fellows it averaged 0.724. (4) Conclusions: This proof-of-concept study demonstrates that, even with small databases, a pretrained CNN is able to distinguish between STGD1 and PSPD with good accuracy.

Numerical uncertainty in analytical pipelines lead to impactful variability in brain networks.

The analysis of brain-imaging data requires complex processing pipelines to support findings on brain function or pathologies. Recent work has shown that variability in analytical decisions, small amounts of noise, or computational environments can lead to substantial differences in the results, endangering the trust in conclusions. We explored the instability of results by instrumenting a structural connectome estimation pipeline with Monte Carlo Arithmetic to introduce random noise throughout. We evaluated the reliability of the connectomes, the robustness of their features, and the eventual impact on analysis. The stability of results was found to range from perfectly stable (i.e. all digits of data significant) to highly unstable (i.e. 0 - 1 significant digits). This paper highlights the potential of leveraging induced variance in estimates of brain connectivity to reduce the bias in networks without compromising reliability, alongside increasing the robustness and potential upper-bound of their applications in the classification of individual differences. We demonstrate that stability evaluations are necessary for understanding error inherent to brain imaging experiments, and how numerical analysis can be applied to typical analytical workflows both in brain imaging and other domains of computational sciences, as the techniques used were data and context agnostic and globally relevant. Overall, while the extreme variability in results due to analytical instabilities could severely hamper our understanding of brain organization, it also affords us the opportunity to increase the robustness of findings.

Genetic diversity in a long-lived mammal is explained by the past's demographic shadow and current connectivity.

Within-species genetic diversity is crucial for the persistence and integrity of populations and ecosystems. Conservation actions require an understanding of factors influencing genetic diversity, especially in the context of global change. Both population size and connectivity are factors greatly influencing genetic diversity; the relative importance of these factors can, however, change through time. Hence, quantifying the degree to which population size or genetic connectivity are shaping genetic diversity, and at which ecological time scale (past or present), is challenging, yet essential for the development of efficient conservation strategies. In this study, we estimated the genetic diversity of 42 colonies of Rhinolophus hipposideros, a long-lived mammal vulnerable to global change, sampling locations spanning its continental northern range. Here, we present an integrative approach that disentangles and quantifies the contribution of different connectivity measures in addition to contemporary colony size and historic bottlenecks in shaping genetic diversity. In our study, the best model explained 64% of the variation in genetic diversity. It included historic bottlenecks, contemporary colony size, connectivity and a negative interaction between the latter two. Contemporary connectivity explained most genetic diversity when considering a 65 km radius around the focal colonies, emphasizing the large geographic scale at which the positive impact of connectivity on genetic diversity is most profound and hence, the minimum scale at which conservation should be planned. Our results highlight that the relative importance of the two main factors shaping genetic diversity varies through time, emphasizing the relevance of disentangling them to ensure appropriate conservation strategies.

Deep learning-based classification of retinal atrophy using fundus autofluorescence imaging.

PURPOSE: To automatically classify retinal atrophy according to its etiology, using fundus autofluorescence (FAF) images, using a deep learning model. METHODS: In this study, FAF images of patients with advanced dry age-related macular degeneration (AMD), also called geographic atrophy (GA), and genetically confirmed inherited retinal diseases (IRDs) in late atrophic stages [Stargardt disease (STGD1) and Pseudo-Stargardt Pattern Dystrophy (PSPD)] were included. The FAF images were used to train a multi-layer deep convolutional neural network (CNN) to differentiate on FAF between atrophy in the context of AMD (GA) and atrophy secondary to IRDs. Three-hundred fourteen FAF images were included, of which 110 images were of GA eyes and 204 were eyes with genetically confirmed STGD1 or PSPD. In the first approach, the CNN was trained and validated with 251 FAF images. Established augmentation techniques were used and an Adam optimizer was used for training. For the subsequent testing, the built classifiers were then tested with 63 untrained FAF images. The visualization method was integrated gradient visualization. In the second approach, 10-fold cross-validation was used to determine the model's performance. RESULTS: In the first approach, the best performance of the model was obtained using 10 epochs, with an accuracy of 0.92 and an area under the curve for Receiver Operating Characteristic (AUC-ROC) of 0.981. Mean accuracy was 87.30 ± 2.96. In the second approach, a mean accuracy of 0.79 ± 0.06 was obtained. CONCLUSION: This study describes the use of a deep learning-based algorithm to automatically classify atrophy on FAF imaging according to its etiology. Accurate differential diagnosis between GA and late-onset IRDs masquerading as GA on FAF can be performed with good accuracy and AUC-ROC values.

Complementarity and discriminatory power of genotype and otolith shape in describing the fine-scale population structure of an exploited fish, the common sole of the Eastern English Channel.

Marine organisms show population structure at a relatively fine spatial scale, even in open habitats. The tools commonly used to assess subtle patterns of connectivity have diverse levels of resolution and can complement each other to inform on population structure. We assessed and compared the discriminatory power of genetic markers and otolith shape to reveal the population structure on evolutionary and ecological time scales of the common sole (Solea solea), living in the Eastern English Channel (EEC) stock off France and the UK. First, we genotyped fish with Single Nucleotide Polymorphisms to assess population structure at an evolutionary scale. Then, we tested for spatial segregation of the subunits using otolith shape as an integrative tracer of life history. Finally, a supervised machine learning framework was applied to genotypes and otolith phenotypes to probabilistically assign adults to subunits and assess the discriminatory power of each approach. Low but significant genetic differentiation was found among subunits. Moreover, otolith shape appeared to vary spatially, suggesting spatial population structure at fine spatial scale. However, results of the supervised discriminant analyses failed to discriminate among subunits, especially for otolith shape. We suggest that the degree of population segregation may not be strong enough to allow for robust fish assignments. Finally, this study revealed a weak yet existing metapopulation structure of common sole at the fine spatial scale of the EEC based on genotypes and otolith shape, with one subunit being more isolated. Our study argues for the use of complementary tracers to investigate marine population structure.

Deep Learning-Based Classification of Inherited Retinal Diseases Using Fundus Autofluorescence.

Background. In recent years, deep learning has been increasingly applied to a vast array of ophthalmological diseases. Inherited retinal diseases (IRD) are rare genetic conditions with a distinctive phenotype on fundus autofluorescence imaging (FAF). Our purpose was to automatically classify different IRDs by means of FAF images using a deep learning algorithm. Methods. In this study, FAF images of patients with retinitis pigmentosa (RP), Best disease (BD), Stargardt disease (STGD), as well as a healthy comparable group were used to train a multilayer deep convolutional neural network (CNN) to differentiate FAF images between each type of IRD and normal FAF. The CNN was trained and validated with 389 FAF images. Established augmentation techniques were used. An Adam optimizer was used for training. For subsequent testing, the built classifiers were then tested with 94 untrained FAF images. Results. For the inherited retinal disease classifiers, global accuracy was 0.95. The precision-recall area under the curve (PRC-AUC) averaged 0.988 for BD, 0.999 for RP, 0.996 for STGD, and 0.989 for healthy controls. Conclusions. This study describes the use of a deep learning-based algorithm to automatically detect and classify inherited retinal disease in FAF. Hereby, the created classifiers showed excellent results. With further developments, this model may be a diagnostic tool and may give relevant information for future therapeutic approaches.

Comparing perturbation models for evaluating stability of neuroimaging pipelines.

With an increase in awareness regarding a troubling lack of reproducibility in analytical software tools, the degree of validity in scientific derivatives and their downstream results has become unclear. The nature of reproducibility issues may vary across domains, tools, data sets, and computational infrastructures, but numerical instabilities are thought to be a core contributor. In neuroimaging, unexpected deviations have been observed when varying operating systems, software implementations, or adding negligible quantities of noise. In the field of numerical analysis, these issues have recently been explored through Monte Carlo Arithmetic, a method involving the instrumentation of floating-point operations with probabilistic noise injections at a target precision. Exploring multiple simulations in this context allows the characterization of the result space for a given tool or operation. In this article, we compare various perturbation models to introduce instabilities within a typical neuroimaging pipeline, including (i) targeted noise, (ii) Monte Carlo Arithmetic, and (iii) operating system variation, to identify the significance and quality of their impact on the resulting derivatives. We demonstrate that even low-order models in neuroimaging such as the structural connectome estimation pipeline evaluated here are sensitive to numerical instabilities, suggesting that stability is a relevant axis upon which tools are compared, alongside more traditional criteria such as biological feasibility, computational efficiency, or, when possible, accuracy. Heterogeneity was observed across participants which clearly illustrates a strong interaction between the tool and data set being processed, requiring that the stability of a given tool be evaluated with respect to a given cohort. We identify use cases for each perturbation method tested, including quality assurance, pipeline error detection, and local sensitivity analysis, and make recommendations for the evaluation of stability in a practical and analytically focused setting. Identifying how these relationships and recommendations scale to higher order computational tools, distinct data sets, and their implication on biological feasibility remain exciting avenues for future work.

Drivers of longitudinal telomere dynamics in a long-lived bat species, Myotis myotis.

Age-related telomere shortening is considered a hallmark of the ageing process. However, a recent cross-sectional ageing study of relative telomere length (rTL) in bats failed to detect a relationship between rTL and age in the long-lived genus Myotis (M. myotis and M. bechsteinii), suggesting some other factors are responsible for driving telomere dynamics in these species. Here, we test if longitudinal rTL data show signatures of age-associated telomere attrition in M. myotis and differentiate which intrinsic or extrinsic factors are likely to drive telomere length dynamics. Using quantitative polymerase chain reaction, rTL was measured in 504 samples from a marked population, from Brittany, France, captured between 2013 and 2016. These represent 174 individuals with an age range of 0 to 7+ years. We find no significant relationship between rTL and age (p = .762), but demonstrate that within-individual rTL is highly variable from year to year. To investigate the heritability of rTL, a population pedigree (n = 1744) was constructed from genotype data generated from a 16-microsatellite multiplex, designed from an initial, low-coverage, Illumina genome for M. myotis. Heritability was estimated in a Bayesian, mixed model framework, and showed that little of the observed variance in rTL is heritable (h2  = 0.01-0.06). Rather, correlations of first differences, correlating yearly changes in telomere length and weather variables, demonstrate that, during the spring transition, average temperature, minimum temperature, rainfall and windspeed correlate with changes in longitudinal telomere dynamics. As such, rTL may represent a useful biomarker to quantify the physiological impact of various environmental stressors in bats.

Genetic drift during the spread phase of a biological invasion.

Recent theoretical and experimental models have revealed the role played by evolution during species spread, and in particular have questioned the influence of genetic drift at range edges. By investigating the spread of an aquatic invader in patchy habitats, we quantified genetic drift and explored its consequences for genetic diversity and fitness. We examined the interplay of gene flow and genetic drift in 36 populations of the red swamp crayfish, Procambarus clarkii, in a relatively recently invaded wetland area (30 years, Brière, northwest France). Despite the small spatial scale of our study (15 km2 ), populations were highly structured according to the strong barrier of land surfaces and revealed a clear pattern of colonization through watercourses. Isolated populations exhibited small effective sizes and low dispersal rates that depended on water connectivity, suggesting that genetic drift dominated in the evolution of allele frequencies in these populations. We also observed a significant decrease in the genetic diversity of isolated populations over only a 2-year period, but failed to demonstrate an associated fitness cost using fluctuating asymmetry. This study documents the possible strong influence of genetic drift during the spread of a species, and such findings provide critical insights into the current context of profound rearrangements in species distributions due to global change.

Automated quantification of choroidal neovascularization on Optical Coherence Tomography Angiography images.

OBJECTIVES: To report the design of an automated quantification algorithm for choroidal neovascularization (CNV) in the context of neovascular age-related macular degeneration (AMD), based on Optical Coherence Tomography Angiography (OCTA) images. MATERIAL AND METHODS: In this study, 54 patients (mean age 75.80 ± 14.29 years) with neovascular AMD (type 1 and type 2 CNV) were included retrospectively and separated into two groups (Group 1-24 images; Group 2-30 images), according to the lesion topology. All patients underwent a 3 × 3 mm OCTA examination (AngioVue, Optovue, Freemont, California). The proposed algorithm is based on segmentation and enhancement methods including Frangi filter, Gabor wavelets and Fuzzy-C-Means Classification. Our results were compared to the manual quantifications given by the embedded quantification software "AngioAnalytics". RESULTS: Automated CNV segmentation and quantification of three neovascular AMD biomarkers: the total vascular area (TVA), the total area (TA) and the vascular density (VD) were possible in all cases. Automated versus manual quantification comparison revealed a statistically significant difference for TVA and VD measurements for both groups (p = 0.00036 for Group 1 TVA, p < 0.0001 for Group 1 VD and Group 2 TVA and VD). The difference in TA measurements was not significant in Group 2 (p = 0.143). Bland-Altman analysis revealed low inter-method bias for TA measurements and higher bias for TVA and VD. CONCLUSION: This paper presents a method for segmenting and quantifying CNV that constitutes a valid option for clinicians. Complementary validations have to be carried out to compare our method's accuracy to "AngioAnalytics".

Longitudinal comparative transcriptomics reveals unique mechanisms underlying extended healthspan in bats.

Bats are the longest-lived mammals, given their body size. However, the underlying molecular mechanisms of their extended healthspans are poorly understood. To address this question we carried out an eight-year longitudinal study of ageing in long-lived bats (Myotis myotis). We deep-sequenced ~1.7 trillion base pairs of RNA from 150 blood samples collected from known aged bats to ascertain the age-related transcriptomic shifts and potential microRNA-directed regulation that occurred. We also compared ageing transcriptomic profiles between bats and other mammals by analysis of 298 longitudinal RNA sequencing datasets. Bats did not show the same transcriptomic changes with age as commonly observed in humans and other mammals, but rather exhibited a unique, age-related gene expression pattern associated with DNA repair, autophagy, immunity and tumour suppression that may drive their extended healthspans. We show that bats have naturally evolved transcriptomic signatures that are known to extend lifespan in model organisms, and identify novel genes not yet implicated in healthy ageing. We further show that bats' longevity profiles are partially regulated by microRNA, thus providing novel regulatory targets and pathways for future ageing intervention studies. These results further disentangle the ageing process by highlighting which ageing pathways contribute most to healthy ageing in mammals.

Title: Ecological relevance of least cost path analysis: An easy implementation method for landscape urban planning.

Landscape connectivity promotes dispersal and other types of movement, including foraging activity; consequently, the inclusion of connectivity concept is a priority in conservation and landscape planning in response to fragmentation. Urban planners expect the scientific community to provide them with an easy, but scientifically rigorous, method to identify highly connecting contexts in landscapes. The least-cost paths (LCP) method is one of the simplest resistance-based models that could be a good candidate to spatially identify areas where movement is potentially favored in a given landscape. We tested the efficiency of LCP predictions to detect highly connecting landscape contexts facilitating individual movements compared to those performed in un-connecting landscape contexts. We used a landscape-level behavioral experiment based on a translocation protocol and individual repeated measures. In the city of Rennes (France), 30 male hedgehogs (Erinaceus europaeus) were translocated and radio-tracked in both highly connecting and un-connecting contexts, respectively, which were determined by the presence and absence of modelled LCPs. Individual movement patterns were compared between the two predicted contexts. Individuals travelled longer distances, moved faster, and were more active in the highly connecting contexts compared to the un-connecting contexts. Moreover, in highly connecting contexts, hedgehog movement followed LCP orientation, with individuals using more wooded habitats than other land cover class. By using a rigorous experimental design, this study validated the ecological relevance of LCP analysis to identify highly connecting areas, and could be easily implemented by urban landscape planners.

Exploring the causes of small effective population sizes in cyst nematodes using artificial Globodera pallida populations.

The effective size of a population is the size of an ideal population which would undergo genetic drift at the same rate as the real population. The balance between selection and genetic drift depends on the effective population size ( Ne), rather than the real numbers of individuals in the population ( N). The objectives of the present study were to estimate Ne in the potato cyst nematode Globodera pallida and to explore the causes of a low Ne/ N ratio in cyst nematodes using artificial populations. Using a temporal analysis of 24 independent populations, the median Ne was 58 individuals (min Ne = 25 and max Ne = 228). Ne is commonly lower than N but in the case of cyst nematodes, the Ne/ N ratio was extremely low. Using artificial populations showed that this low ratio did not result from migration, selection and overlapping generations, but could be explain by the fact that G. pallida populations deviate in structure from the assumptions of the ideal population by having unequal sex ratios, high levels of inbreeding and a high variance in family sizes. The consequences of a low Ne, resulting in a strong intensity of genetic drift, could be important for their control because G. pallida populations will have a low capacity to adapt to changing environments.

Coexistence of two sympatric cryptic bat species in French Guiana: insights from genetic, acoustic and ecological data.

BACKGROUND: The distinction between lineages of neotropical bats from the 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. RESULTS: 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 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). CONCLUSIONS: 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 Pteronotus parnelli, but deserve more in-depth studies to understand the evolutionary processes underlying asymmetric mtDNA introgression in this group of cryptic species.

Measuring Genetic Differentiation from Pool-seq Data.

The advent of high throughput sequencing and genotyping technologies enables the comparison of patterns of polymorphisms at a very large number of markers. While the characterization of genetic structure from individual sequencing data remains expensive for many nonmodel species, it has been shown that sequencing pools of individual DNAs (Pool-seq) represents an attractive and cost-effective alternative. However, analyzing sequence read counts from a DNA pool instead of individual genotypes raises statistical challenges in deriving correct estimates of genetic differentiation. In this article, we provide a method-of-moments estimator of [Formula: see text] for Pool-seq data, based on an analysis-of-variance framework. We show, by means of simulations, that this new estimator is unbiased and outperforms previously proposed estimators. We evaluate the robustness of our estimator to model misspecification, such as sequencing errors and uneven contributions of individual DNAs to the pools. Finally, by reanalyzing published Pool-seq data of different ecotypes of the prickly sculpin Cottus asper, we show how the use of an unbiased [Formula: see text] estimator may question the interpretation of population structure inferred from previous analyses.

Growing old, yet staying young: The role of telomeres in bats' exceptional longevity.

Understanding aging is a grand challenge in biology. Exceptionally long-lived animals have mechanisms that underpin extreme longevity. Telomeres are protective nucleotide repeats on chromosome tips that shorten with cell division, potentially limiting life span. Bats are the longest-lived mammals for their size, but it is unknown whether their telomeres shorten. Using >60 years of cumulative mark-recapture field data, we show that telomeres shorten with age in Rhinolophus ferrumequinum and Miniopterus schreibersii, but not in the bat genus with greatest longevity, Myotis. As in humans, telomerase is not expressed in Myotis myotis blood or fibroblasts. Selection tests on telomere maintenance genes show that ATM and SETX, which repair and prevent DNA damage, potentially mediate telomere dynamics in Myotis bats. Twenty-one telomere maintenance genes are differentially expressed in Myotis, of which 14 are enriched for DNA repair, and 5 for alternative telomere-lengthening mechanisms. We demonstrate how telomeres, telomerase, and DNA repair genes have contributed to the evolution of exceptional longevity in Myotis bats, advancing our understanding of healthy aging.

Functional connectivity in replicated urban landscapes in the land snail (Cornu aspersum).

Urban areas are highly fragmented and thereby exert strong constraints on individual dispersal. Despite this, some species manage to persist in urban areas, such as the garden snail, Cornu aspersum, which is common in cityscapes despite its low mobility. Using landscape genetic approaches, we combined study area replication and multiscale analysis to determine how landscape composition, configuration and connectivity influence snail dispersal across urban areas. At the overall landscape scale, areas with a high percentage of roads decreased genetic differentiation between populations. At the population scale, genetic differentiation was positively linked with building surface, the proportion of borders where wooded patches and roads appeared side by side and the proportion of borders combining wooded patches and other impervious areas. Analyses based on pairwise genetic distances validated the isolation-by-distance and isolation-by-resistance models for this land snail, with an equal fit to least-cost paths and circuit-theory-based models. Each of the 12 landscapes analysed separately yielded specific relations to environmental features, whereas analyses integrating all replicates highlighted general common effects. Our results suggest that urban transport infrastructures facilitate passive snail dispersal. At a local scale, corresponding to active dispersal, unfavourable habitats (wooded and impervious areas) isolate populations. This work upholds the use of replicated landscapes to increase the generalizability of landscape genetics results and shows how multiscale analyses provide insight into scale-dependent processes.

Combining noninvasive genetics and a new mammalian sex-linked marker provides new tools to investigate population size, structure and individual behaviour: An application to bats.

Monitoring wild populations is crucial for their effective management. Noninvasive genetic methods provide robust data from individual free-ranging animals, which can be used in capture-mark-recapture (CMR) models to estimate demographic parameters without capturing or disturbing them. However, sex- and status-specific behaviour, which may lead to differences in detection probabilities, is rarely considered in monitoring. Here, we investigated population size, sex ratio, sex- and status-related behaviour in 19 Rhinolophus hipposideros maternity colonies (Northern France) with a noninvasive genetic CMR approach (using faeces) combined with parentage assignments. The use of the DDX3X/Y-Mam sexual marker designed in this study, which shows inter- and intrachromosomal length polymorphism across placental mammals, together with eight polymorphic microsatellite markers, produced high-quality genetic data with limited genotyping errors and allowed us to reliably distinguish different categories of individuals (males, reproductive and nonreproductive females) and to estimate population sizes. We showed that visual counts represent well-adult female numbers and that population composition in maternity colonies changes dynamically during the summer. Before parturition, colonies mainly harbour pregnant and nonpregnant females with a few visiting males, whereas after parturition, colonies are mainly composed of mothers and their offspring with a few visiting nonmothers and males. Our approach gives deeper insight into sex- and status-specific behaviour, a prerequisite for understanding population dynamics and developing effective monitoring and management strategies. Provided sufficient samples can be obtained, this approach can be readily applied to a wide range of species.