Big mountains but small barriers: Population genetic structure of the Chinese wood frog (Rana chensinensis) in the Tsinling and Daba Mountain region of northern China

<p>Abstract</p> <p>Background</p> <p>Amphibians in general are poor dispersers and highly philopatric, and landscape features often have important impacts on their population genetic structure and dispersal patterns. Numerous studies have suggested that genetic differentiation among amphibian populations are particularly pronounced for populations separated by mountain ridges. The Tsinling Mountain range of northern China is a major mountain chain that forms the boundary between the Oriental and Palearctic zoogeographic realms. We studied the population structure of the Chinese wood frog (<it>Rana chensinensis</it>) to test whether the Tsinling Mountains and the nearby Daba Mountains impose major barriers to gene flow.</p> <p>Results</p> <p>Using 13 polymorphic microsatellite DNA loci, 523 individuals from 12 breeding sites with geographical distances ranging from 2.6 to 422.8 kilometers were examined. Substantial genetic diversity was detected at all sites with an average of 25.5 alleles per locus and an expected heterozygosity ranging from 0.504 to 0.855, and two peripheral populations revealed significantly lower genetic diversity than the central populations. In addition, the genetic differentiation among the central populations was statistically significant, with pairwise <it>F</it>_{<it>ST </it>}values ranging from 0.0175 to 0.1625 with an average of 0.0878. Furthermore, hierarchical AMOVA analysis attributed most genetic variation to the within-population component, and the between-population variation can largely be explained by isolation-by-distance. None of the putative barriers detected from genetic data coincided with the location of the Tsinling Mountains.</p> <p>Conclusion</p> <p>The Tsinling and Daba Mountains revealed no significant impact on the population genetic structure of <it>R. chensinensis</it>. High population connectivity and extensive juvenile dispersal may account for the significant, but moderate differentiation between populations. Chinese wood frogs are able to use streams as breeding sites at high elevations, which may significantly contribute to the diminishing barrier effect of mountain ridges. Additionally, a significant decrease in genetic diversity in the peripheral populations supports Mayr's central-peripheral population hypothesis.</p

Genome-Wide Identification and Evaluation of New Reference Genes for Gene Expression Analysis Under Temperature and Salinity Stresses in Ciona savignyi

Rapid adaptation/accommodation to changing environments largely contributes to maximal survival of invaders during biological invasions, usually leading to success in crossing multiple barriers and finally in varied environments in recipient habitats. Gene expression is one of the most important and rapid ways during responses to environmental stresses. Selection of proper reference genes is the crucial prerequisite for gene expression analysis using the common approach, real-time quantitative PCR (RT-qPCR). Here we identified eight candidate novel reference genes from the RNA-Seq data in an invasive model ascidian Ciona savignyi under temperature and salinity stresses. Subsequently, the expression stability of these eight novel reference genes, as well as other six traditionally used reference genes, was evaluated using RT-qPCR and comprehensive tool RefFinder. Under the temperature stress, two traditional reference genes, ribosomal proteins S15 and L17 (RPS15, RPL17), and one novel gene Ras homolog A (RhoA), were recommended as the top three stable genes, which can be used to normalize target genes with a high and moderate expression level, respectively. Under the salinity stress, transmembrane 9 superfamily member (TMN), MOB kinase activator 1A-like gene (MOB) and ubiquitin-conjugating enzyme (UBQ2) were suggested as the top three stable genes. On the other hand, several commonly used reference genes such as α-tubulin (TubA), β-tubulin (TubB) and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) showed unstable expressions, thus these genes should not be used as internal controls for gene expression analysis. We also tested the expression level of an important stress response gene, large proline-rich protein bag6-like gene (BAG) using different reference genes. As expected, we observed different results and conclusions when using different normalization methods, thus suggesting the importance of selection of proper reference genes and associated normalization methods. Our results provide a valuable reference gene resource for the normalization of gene expression in the study of environmental adaptation/accommodation during biological invasions using C. savignyi as a model

Influence of Artifact Removal on Rare Species Recovery in Natural Complex Communities Using High-Throughput Sequencing

Large-scale high-throughput sequencing techniques are rapidly becoming popular methods to profile complex communities and have generated deep insights into community biodiversity. However, several technical problems, especially sequencing artifacts such as nucleotide calling errors, could artificially inflate biodiversity estimates. Sequence filtering for artifact removal is a conventional method for deleting error-prone sequences from high-throughput sequencing data. As rare species represented by low-abundance sequences in datasets may be sensitive to artifact removal process, the influence of artifact removal on rare species recovery has not been well evaluated in natural complex communities. Here we employed both internal (reliable operational taxonomic units selected from communities themselves) and external (indicator species spiked into communities) references to evaluate the influence of artifact removal on rare species recovery using 454 pyrosequencing of complex plankton communities collected from both freshwater and marine habitats. Multiple analyses revealed three clear patterns: 1) rare species were eliminated during sequence filtering process at all tested filtering stringencies, 2) more rare taxa were eliminated as filtering stringencies increased, and 3) elimination of rare species intensified as biomass of a species in a community was reduced. Our results suggest that cautions be applied when processing high-throughput sequencing data, especially for rare taxa detection for conservation of species at risk and for rapid response programs targeting non-indigenous species. Establishment of both internal and external references proposed here provides a practical strategy to evaluate artifact removal process

Unreliable quantitation of species abundance based on high-throughput sequencing data of zooplankton communities

High-throughput sequencing (HTS) is rapidly becoming a popular and robust tool to characterize biodiversity of complex communities, especially for those dominated by microscopic species such as zooplankton. The popular use of HTS-based methods has prompted a possible method of inferring relative species abundance from sequencing data. However, these methods remain largely untested in many communities as to whether sequence data can reliably quantify relative species abundance. Here we tested the relationship between species abundance and sequence abundance in zooplankton using 2 methods: (1) spiking known amounts of indicator species into existing zooplankton communities, and (2) comparing results obtained from parallel replicates for the same natural zooplankton communities. Although we detected a general trend that low-abundance species usually corresponded to low-abundance sequence reads, further statistical analyses revealed that sequencing data could not reliably quantify relative species abundance, even for the same indicator species spiked into different zooplankton communities. The distribution of sequence reads statistically varied even between parallel replicates of the same natural zooplankton communities. Our study reveals that sequence abundance may generally qualitatively reflect species abundance as the general trend between these 2 variables exists; however, extra caution is required when using HTS-based approaches to make quantitative inferences regarding zooplankton communities

Isolation and Characterization of Novel Microsatellite Markers for Yellow Perch (Perca flavescens)

To perform whole genome scanning for complex trait analysis, we isolated and characterized a total of 21 novel genomic-SSRs and EST-SSRs for yellow perch (Perca flavescens), using the methods of construction of SSR-enrichment libraries and EST database mining of a related species P. fluviatilis. Of 16 genomic-SSR primer pairs examined, eight successfully amplified scorable products. The number of alleles at these informative loci varied from 3 – 14 with an average of 8.5 alleles per locus. When tested on wild perch from a population in Pennsylvania, observed and expected heterozygosities ranged from 0.07 – 0.81 and from 0.37 – 0.95, respectively. Of 2,226 EST sequences examined, only 110 (4.93%) contained microsatellites and for those, 13 markers were tested, 12 of which exhibited polymorphism. Compared with genomic-SSRs, EST-SSRs exhibited a lower level of genetic variability with the number of alleles of averaging only 2.6 alleles per locus. Cross-species utility indicated that three of the genomic-SSRs and eight of the EST-SSRs successfully cross-amplified in a related species, the walleye (Sander vitreus)

Distribution patterns of dinoflagellate communities along the Songhua River

Background Dinoflagellates have the potential to pose severe ecological and economic damages to aquatic ecosystems. It is therefore largely needed to understand the causes and consequences of distribution patterns of dinoflagellate communities in order to manage potential environmental problems. However, a majority of studies have focused on marine ecosystems, while the geographical distribution patterns of dinoflagellate communities and associated determinants in freshwater ecosystems remain unexplored, particularly in running water ecosystems such as rivers and streams. Methods Here we utilized multiple linear regression analysis and combined information on species composition recovered by high-throughput sequencing and spatial and environmental variables to analyze the distribution patterns of dinoflagellate communities along the Songhua River. Results After high-throughput sequencing, a total of 490 operational taxonomic units (OTUs) were assigned to dinoflagellates, covering seven orders, 13 families and 22 genera. Although the sample sites were grouped into three distinctive clusters with significant difference (p  0.05). Among all 24 environmental factors, two environmental variables, including NO3-N and total dissolved solids (TDS), were selected as the significantly influential factors (p < 0.05) on the distribution patterns of dinoflagellate communities based on forward selection. The redundancy analysis (RDA) model showed that only a small proportion of community variation (6.1%) could be explained by both environmental (NO3-N and TDS) and dispersal predictors (watercourse distance) along the River. Variance partitioning revealed a larger contribution of local environmental factors (5.85%) than dispersal (0.50%) to the total variation of dinoflagellate communities. Discussion Our findings indicated that in addition to the two quantifiable processes in this study (species sorting and dispersal), more unquantifiable stochastic processes such as temporal extinction and colonization events due to rainfall may be responsible for the observed geographical distribution of the dinoflagellate community along the Songhua River. Results obtained in this study suggested that deeper investigations covering different seasons are needed to understand the causes and consequences of geographical distribution patterns of dinoflagellate biodiversity in river ecosystems

CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases

CASM-AMFMNet: A Network based on Coordinate Attention Shuffle Mechanism and Asymmetric Multi-Scale Fusion Module for Classification of Grape Leaf Diseases

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases

Optimization and performance testing of a sequence processing pipeline applied to detection of nonindigenous species

Genetic taxonomic assignment can be more sensitive than morphological taxonomic assignment, particularly for small, cryptic or rare species. Sequence processing is essential to taxonomic assignment, but can also produce errors because optimal parameters are not known a priori. Here, we explored how sequence processing parameters influence taxonomic assignment of 18S sequences from bulk zooplankton samples produced by 454 pyrosequencing. We optimized a sequence processing pipeline for two common research goals, estimation of species richness and early detection of aquatic invasive species (AIS), and then tested most optimal models’ performances through simulations. We tested 1,050 parameter sets on 18S sequences from 20 AIS to determine optimal parameters for each research goal. We tested optimized pipelines’ performances (detectability and sensitivity) by computationally inoculating sequences of 20 AIS into ten bulk zooplankton samples from ports across Canada. We found that optimal parameter selection generally depends on the research goal. However, regardless of research goal, we found that metazoan 18S sequences produced by 454 pyrosequencing should be trimmed to 375–400 bp and sequence quality filtering should be relaxed (1.5 ≤ maximum expected error ≤ 3.0, Phred score = 10). Clustering and denoising were only viable for estimating species richness, because these processing steps made some species undetectable at low sequence abundances which would not be useful for early detection of AIS. With parameter sets optimized for early detection of AIS, 90% of AIS were detected with fewer than 11 target sequences, regardless of whether clustering or denoising was used. Despite developments in next-generation sequencing, sequence processing remains an important issue owing to difficulties in balancing false-positive and false-negative errors in metabarcoding data