Borassus flabellifer or Asian Palmyra palm is an important crop for local economies in the South and Southeast Asia for its fruit and palm sugar production. Archeological and historical evidence indicated the presence of this species in Southeast Asia dating back at least 1500 years. B. flabellifer is believed to be originated in Africa, spread to South Asia and introduced into Southeast Asia through commercial routes and dissemination of cultures, however, the nature of its invasion and settlement in Thailand is unclear.B. flabellifer accessions across Thailand using 17 EST-SSR and 12 gSSR polymorphic markers. Clustering analysis revealed that the population consisted of two genetic clusters (STRUCTURE K = 2). Cluster I is found mainly in southern Thailand, while Cluster II is found mainly in the northeastern. Those found in the central are of an extensive mix between the two. These two clusters are in moderate differentiation (FST = 0.066 and NM = 3.532) and have low genetic diversity . The minimum numbers of founders for each genetic group varies from 3 to 4 individuals, based on simulation using different allele frequency assumptions. These numbers coincide with that B. flabellifer is dioecious, and a number of seeds had to be simultaneously introduced for obtaining both male and female founders.Here, we analyzed genetic data of 230 B. flabellifer in Thailand. B. flabellifer was likely brought through the Straits of Malacca to be propagated in the southern Thailand as one of the invasive events before spreading to the central Thailand. The second event likely occurred in Khmer Empire, currently Cambodia, before spreading to the northeastern Thailand.From these data and geographical and historical evidence, we hypothesize that there were at least two different invasive events of The online version of this article (10.1186/s12863-017-0554-y) contains supplementary material, which is available to authorized users. Borassus flabellifer , as of African origin [B. flabellifer was spread out from Africa to South Asia before being introduced into Southeast Asia through commercial routes in the past [B. flabellifer is one of the oldest domesticated fruit crops estimating that it has been in Thailand since 1500 years ago. Almost all parts of the plant are used by the locals, and its fruit is widely consumed. Especially, sap from the inflorescence flower is the source for palm sugar production and alcoholic beverages. As a result, B. flabellifer was distributed and grown in many regions of Thailand in various soil and climatic conditions.Biological invasions occurred both naturally and unnaturally, and the rate of invasions has been elevated since the rise of global trades –3. Althon origin , is one the past , 13. In B. flabellifer, a dioecious monocotyledonous woody perennial tree in the family Arecaceae, is a massive palm with a single stem reaching 30 m in height and large fan-shaped leaves spanning 4–6 m in diameters [B. flabellifer grows very slow, taking up to 12–20 years to reach its maturity and produce its first inflorescence flowers. Sex determination based on plant morphology is not possible, and that based on molecular markers is currently unachievable. Previous genetic studies using various types of DNA markers showed that B. flabellifer populations have very low genetic diversity [iameters . B. flabiversity –17, and B. flabellifer should be a key consideration for its agricultural sustainability or it should be acknowledged that this had occurred through selection and domestication processes a long time ago. Here, we analyzed the genetic diversity and population structure of B. flabellifer across Thailand using microsatellite markers. The phylogeography was also observed, and the minimum numbers of founder individuals introduced into Thailand were estimated. Geographical and historical evidence was taken into account to form theoretical invasive events of B. flabellifer in Thailand and Southeast Asia.A standing question is whether the narrow genetic diversity of B. flabellifer population was analyzed by 17 EST-SSR and 12 gSSR polymorphic markers  using PowerMarker version 3.25 . The seqB. flabellifer was initially grouped according to the sampling locations: provinces and geographical parts of Thailand. Assuming a population admixture model, 100,000 burn-ins and 100,000 Markov chain Monte Carlo (MCMC) replicates for K varied from 1 to 10 were performed. Twenty independent runs were performed for each K. The best K was then inferred based on lnP(D) and delta K [Ho) and expected (HE) heterozygosity, the number of observed alleles and the number of effective alleles were calculated using POPGEN version 1.31. Allelic richness (Ar) and pairwise FST among groups were calculated using FSTAT version 2.9.3.2 [NM) was estimated using an equation according to Wright [NM = 1(1/FST-1)/4.EST-SSR and gSSR data were used to infer the most likely number of population genetic clusters (K), based on a Bayesian approach implemented in STRUCTURE version 2.3.4 . Each of delta K . A hiera delta K  to test  2.9.3.2 . The numo Wright : NM = 1FCT) was used.Although a number of methods for estimating a minimum number of founders based on genetic data are available, these are unsuitable for our dataset as they require genetic information from the source population and assume no admixture between introduced populations. Thus, to estimate the minimum number of founders, we used an approach developed by Rasner et al. , which rCustom-written scripts  increased continually as the K increased (data not shown). The delta K statistics based on Evanno et al. [The genotypic data of the d et al.  was unabo et al.  was thenFCT  followed by among populations (7.6%) and smallest among clusters (6.27%). Therefore, the grouping as two clusters was used for subsequent analyses. By mapping the clusters and sample sizes, it revealed that B. flabellifer individuals from cluster I dominate southern Thailand, while those from cluster II dominate the northeastern  showed that grouping based on STRUCTURE assisted by the three sampling regions was the most suitable for these samples as indicated by the highest , effective alleles, observed heterozygosity and heterozygosity (Table p < 0.0001). This is most likely because the B. flabellifer is dioecious, allowing gene flow between the two clusters within the same sampling sites. The FST and NM values between both clusters were 0.066 and 3.532, respectively, indicating that these clusters are in moderate differentiation with a moderate gene flow.The microsatellite data of the two clusters based on STRUCTURE clustering were separately analyzed. It showed that 120 and 110 individuals of the cluster I and II, respectively, had low numbers of observed allelesty Table . EstimatB. flabellifer was introduced into Thailand, and, thus, could be originated from a considerably small number of founders. The number of B. flabellifer founders was estimated using the microsatellite data. As expected, the total number of alleles included in the analysis affected the estimated minimum number of founders . However, the recent evidence favors the first hypothesis as five of six Borassus species are found in Africa, and, specifically, B. aethiopum and B. akeassii have similar morphology to that of B. flabellifer [B. flabellifer was spread to India and its subcontinent and later to Southeast Asia because of its values in palm sugar production and alcohol products.Currently, there are two hypotheses regarding the origin of ontinent  and thenontinent , 13. An bellifer –32. It cB. flabellifer was brought to Southeast Asia most likely through the commercial routes and dissemination of cultures a long time ago. Based on our results, we hypothesize that the B. flabellifer was introduced into the areas, which are currently parts of Thailand, from two directions. Based on geographical reasons and historical commercial routes, one could be initially introduced into the southern part of Thailand, possibly from the Straits of Malacca as the world important shipping route since the past [the past . AnotherB. flabellifer in Thailand since at least 1500 years ago. This evidence includes the discovery of a stone sealing in the Dvaravati period  showing a man climbing a palm tree [B. flabellifer pollen in archeological specimens aged ~1500 years in Songkhla province, southern Thailand [Historical and archeological evidence indicated the presence of alm tree  and an iThailand . We specB. flabellifer dating back approximately 1400–1500 years [B. flabellifer in Laos in the past 1500 years [B. flabellifer in the northeastern Thailand was likely introduced from the areas, where it is now Cambodia, rather than Laos or Vietnam. This is because our result showed that the cluster II is concentrated at the lower part of the northeastern Thailand next to the Cambodia border, which has no major natural barriers. Noting that, in 7th centuries, this area in the northeastern Thailand belonged to the Khmer Empire. Furthermore, because there are long mountain ranges that separate between Vietnam and Laos, it is unlikely that B. flabellifer was introduced into Thailand through Vietnam, Laos and Mekong river. More samples from the neighboring countries are required to obtain a clearer picture of the B. flabellifer introduction route in Southeast Asia.Our finding of the second settlement in the northeastern Thailand was firstly unexpected. Nonetheless, there is a number of evidence supporting this finding, including archeological studies in Angkor Borei in southern Cambodia that found pollen and tissues of 00 years , 37. Alt00 years , 39, we B. flabellifer requires 12–20 years to reach its flowering stage and considering the 1500 years of settlement, this means that the species has been reproducing in Thailand for at least 125 generations. Thus, it is not surprising that our observed allele diversity was very low when considering the number of generation and genetic variation indexes. Likewise, no clear phenotypic variations among the population have been identified. Furthermore, the genetic study of this species in India, as thought to be the origin of B. flabellifer in Southeast Asia, using RAPD markers also showed low genetic diversity [Since iversity , 40–43.B. flabellifer is dioecious, and both male and female seeds are required for successful propagation and reproduction. Because there was no mean for sex determination and it takes at least 12 years to verify the sex of individual plants, a number of seeds had to be simultaneously introduced. This number could have been as low as 3–4 seeds to cover a potential outcome for both sexes. This might suggest that, in such case, the introduction was likely occurred through a human activity for exploiting B. flabellifer. In addition, this may suggest that, at that period, the human might have learned that B. flabellifer requires both male and female trees for fruit production and breeding.Simulation for the minimum number of founders estimated that 3–4 individuals settled in the southern and northeastern Thailand represent the founders of the cluster I and II, respectively. B. flabellifer is an invasive plant successfully spreading throughout Thailand and Southeast Asia countries in spite of low genetic diversity. The low genetic diversity of invasive species is commonly referred as the genetic paradox of invasive organisms [B. flabellifer may be explained as this plant is useful for human uses, consumption and, perhaps, has tolerant capacities to insects, diseases and environmental challenges at the invading areas. Hence, the successful invasion of B. flabellifer was assisted through human activities.rganisms . The genB. flabellifer individuals collected throughout Thailand using 17 EST-SSR and 12 gSSR polymorphic markers. The population was divided into two clusters according to STRUCTURE analysis  based on three sampling regions, supporting by AMOVA (maximum FCT = 0.06266). The cluster I was found predominately in the central and southern Thailand, while the cluster II was found mostly in the northeastern. The minimum number of founders was estimated using the microsatellite data, and it was likely that up to four individuals of each cluster were introduced into two different regions in Thailand. This study proposes the origin of B. flabellifer that was introduced into two different locations in the past, and this may also explain the low genetic diversity of the population in Thailand.In this work, we analyzed the genetic data of 230 Additional file 1:B. flabellifer samples in Thailand (DOCX 14 kb)Locations of the Additional file 2:Representative polyacrylamide electrophoresis gels for the polymorphic loci (DOCX 9268 kb)Additional file 3:An R script for simulating different numbers of founder genotypes (DOCX 24 kb)


This service greatly improves the sharing and reuse of genomics tools and promotes interoperability with similar projects through emerging web service standards developed by the Global Alliance for Genomics and Health (GA4GH).As genomic datasets continue to grow, the feasibility of downloading data to a local organization and running analysis on a traditional compute environment is becoming increasingly problematic. Current large-scale projects, such as the ICGC PanCancer Analysis of Whole Genomes (PCAWG), the Data Platform for the U.S. Precision Medicine Initiative, and the NIH Big Data to Knowledge Center for Translational Genomics, are using cloud-based infrastructure to both host and perform analysis across large data sets. In PCAWG, over 5,800 whole human genomes were aligned and variant called across 14 cloud and HPC environments; the processed data was then made available on the cloud for further analysis and sharing. If run locally, an operation at this scale would have monopolized a typical academic data centre for many months, and would have presented major challenges for data storage and distribution. However, this scale is increasingly typical for genomics projects and necessitates a rethink of how analytical tools are packaged and moved to the data. For PCAWG, we embraced the use of highly portable Docker images for encapsulating and sharing complex alignment and variant calling workflows across highly variable environments. While successful, this endeavor revealed a limitation in Docker containers, namely the lack of a standardized way to describe and execute the tools encapsulated inside the container. As a result, we created the Dockstore ( PCAWG’s initial core goal was to consistently analyze approximately 2,800 cancer donors , an effort that culminated in the re-alignment and somatic variant calling for these donors. This effort used considerable computational resources. At its peak, 14 cloud and HPC environments were utilized with over 16,000 cores in total, resulting in a cumulative dataset of nearly 1 Petabyte in size.The Dockstore project has its roots in the large-scale ICGC PanCancer Analysis of Whole Genomes  and to install software on them using Linux Bash setup scripts and, later, Ansible playbooks (https://www.ansible.com). We found that the use of cloud APIs and scripts to be a cumbersome and error prone way to move algorithms to the data. Over time dependencies and software versions would change, resulting in frequent failures of the setup scripts, or mysterious downstream analytical failures. Docker, a relatively new lightweight virtualization technology, mitigated these issues by providing a mechanism to encapsulate tools and their dependencies in a highly portable way (https://www.docker.com). This meant PCAWG workflow authors could create and set up their environments within a Docker image, including tools, library dependencies, reference files, and so forth, and then copy that image from cloud to cloud for analysis of data in place. This allowed us to very quickly create cloud-based VMs, install Docker, pull the current version of the Docker-based workflows, and be ready to perform analysis within a few minutes, highly simplifying our deployment strategy. The consistent, portable execution environment provided within a Docker container meant we could avoid issues caused by differences between cloud environments. Furthermore, the inherent portability of Docker images allowed us to leverage a multitude of computational environments, including non-cloud environments that were previously inaccessible to the project.Our initial approach for PCAWG was to utilize cloud Application Program Interfaces (APIs) to build computational worker nodes from scratch, rather than use the Docker virtualization technologyhttps://dockstore.org), generalizes the PCAWG approach in an easy-to-use web application that any tool developer or tool end user can utilize. The concept extends popular services used in Information Technology (IT) fields, in particular commercial sites, such as Quay.io (https://quay.io) and DockerHub (https://hub.docker.com), which provide hosted Docker registries where anyone can upload images containing tools or services.Given our positive experience using Docker to distribute analytical tools, we began exploring a generalized method for other projects to leverage the same approach. Our creation, the Dockstore (https://dx.doi.org/10.6084/m9.figshare.3115156.v2) or Workflow Definition Language  tool definition syntaxes to define the commands available inside a Docker image, how to parameterize them, to describe their inputs/outputs and their resource requirements. Dockstore also supports linking multiple tools together using CWL or WDL workflows; these multi-image workflows can then be registered on the site and used as building blocks to create more complex systems. The result is that Dockstore-based tools and workflows can be programmatically addressed and executed, enabling a new level of modularity, automation and integration.Dockstore’s key innovation is its bridging of Docker image registries with a new, standardized approach to describing tools inside images. Up to this point, tools inside Docker images have had no standardized way to document how to call them, leading to the convention of using human-readable README files to describe tool invocation. This has made automation and integration among Docker images and execution systems cumbersome given the lack of machine-readable tool definitions. To solve this, we used the Common Workflow Language . This Tool Registry API is being developed as part of a larger effort by the GA4GH Containers and Workflows task team to create a container registry API standard. Its implementation in Dockstore, and other sites, is a key goal of the standards effort and will allow for federated searches across tool registries that implement the GA4GH API.Finally, Dockstore is supported by the Global Alliance for Genomics and Health (GA4GH) organizationThe Dockstore implementation can be divided into four facets: a tool and workflow registration process aimed at authors, a RESTful web API used to power the site, a web application that uses this API, and, finally, a command line utility that interacts with, and launches, tools and workflows present on Dockstore.Tool registration process. The Dockstore does not itself act as a Docker image host or provide services to build Docker images automatically from source. These services are already provided reliably and at scale by sites, such as Quay.io and DockerHub. Instead, Dockstore provides a registry to link Docker-based tools hosted on Quay.io or DockerHub with tool metadata described in CWL or WDL and checked into a source control repository at GitHub or Bitbucket. It also acts as a workflow registry for CWL or WDL-based workflow definitions hosted on GitHub or Bitbucket. CWL and WDL provide the emerging standard for describing tools and their parameterizations . For that reason, registering workflows in either CWL or WDL format is simpler, and only requires the workflow document to be checked into source control in BitBucket or GitHub. It can then be found and registered in the Dockstore (ckstore .RESTful application programming interface (API). The Dockstore web and command line interfaces are driven by a RESTful web API (DOI:10.5281/zenodo.154185). This API includes endpoints that conform to the emerging GA4GH Tool Registry API standard , a comments system, and links to share entries via various social media sites.d tools . The sead tools . The sitd tools . TogetheDevelopers wishing to share their tools on Dockstore can log in using GitHub as an identity provider. Upon first login, they are presented with an onboarding wizard that assists in linking third party services that provide source code hosting (in order to host CWL and WDL descriptors) and Docker registries (in order to host Docker images). For source code, GitHub is linked to by default while Bitbucket is also supported. For Docker images, Quay.io is supported (DockerHub linking is not required since an API is not offered). Once linked, the developer is prompted to download and configure the Dockstore command line tool and is presented with an API token to be used with the underlying Dockstore web service. Developers wishing to build on top of Dockstore can use this token to authenticate against the Dockstore API and use it to make secure requests to GitHub, Bitbucket, and Quay.io.Following login through GitHub and the onboarding process to set up linked accounts and obtain the command line and API token, the developer is presented with a listing of the Docker images they have previously built with Quay.io. In the recommended build process, we link to the source code repository for the automated build in order to locate tool descriptors. By default the developers’ images are “unpublished” and not publicly visible in Dockstore. Valid images (images that can be linked to a WDL/CWL descriptor) can be toggled to “published”, making them visible to any Dockstore user . The devCommand line interface. The Dockstore command line utility provides the registration and search functionality offered by the web interface, and additionally provides assistance for file provisioning and local execution of tools and workflows registered within the system. This functionality allows Dockstore users to find workflows and tools of interest and quickly execute them using a completely standardized approach. Since every tool and workflow in Dockstore is described with CWL or WDL, the local execution of these tools is always done using the same command line and same parameterization process, greatly simplifying the learning curve for using any particular tool or workflow from Dockstore.https://github.com/common-workflow-language/cwltool) or Cromwell  is called to invoke the tool in Docker or the workflow on the local host; and 3) output files are collected and staged to a final location. The parameterization of the Docker-based tool or workflow is encoded in a JSON document, a template of which can be created with the Dockstore command line. The command line launcher supports file downloads from HTTP/HTTPS, Amazon S3, FTP/SFTP, and local file paths, while file uploads are supported for Amazon S3, FTP/SFTP, and local file paths. The Dockstore command line supports file provisioning, since provisioning of files is beyond the scope of the specifications for CWL and WDL. The ability to execute tools from Dockstore is of particular value for development and user evaluation purposes and the command line supports a batch processing mode as well. We anticipate that other systems, both open source and commercial, and through a standard API, will ultimately enable larger-scale concurrent analysis with Dockstore-registered workflows and tools.Local execution functionality proceeds through three distinct steps: 1) input files are staged; 2) cwl-runner , testing coverage prediction with Coveralls , and community engagement with Gitter (https://gitter.im/ga4gh/dockstore). The Dockstore web application,https://dockstore.org, will remain an open and free site for users to register their public tool images and workflows. As an open source project5, we also encourage others to customize and install instances of Dockstore (both the UI and RESTful web API) at their own sites. Modifications to the source should be submitted back to the project via the standard GitHub “pull request” mechanism. We hope sites with sharable content participate in our federated network of GA4GH Tool Registry API compliant services, seehttps://github.com/ga4gh/tool-registry-schemas.Dockstore follows best practices for software development, including using source control through GitHub, continuous integration testing with Travis CI  tool can be found in theThe developer use case focuses on providing a standardized, best-practice development process for building portable tools and workflows. Using Dockstore necessitates that a tool or workflow author uses source control, leverages a Docker build/hosting service, and provides a standardized description of how to invoke the tool/workflow. This development process ensures a given tool or workflow is ready for distribution in a transparent and portable way. Standardized descriptor formats (in WDL or CWL) mean that the tool or workflow is self-documenting, easing the documentation burden on developers. Example Dockerfile, CWL-descriptor, and JSON parameterization files for the BAMStats  project, will be able to benefit from Dockstore infrastructure. In these projects, Dockstore, or sites supporting the GA4GH Tool Registry standard, provide a standardized way to develop and share portable tools and workflows. Developers and researchers creating analytical tools and workflows for these projects can build, test, and distribute these tools/workflows using Dockstore. This is decoupled from the environments that run the tools and workflows, allowing tool and workflow authors to focus on their scientific content rather than compatibility with execution sites. For those tasked with executing Dockstore-based tools and workflows at scale, their inherent consistency means execution environments shown to run a given Dockstore-based tool or workflow are very likely to be able to run any other Dockstore-based tool or workflow. This separation of concerns, through the consistency provided by Dockstore and portability provided by Docker and standards like CWL and WDL, mean large-scale projects are much more likely to be successful in their distributed computing goals than a model where every tool and workflow needs to be validated across all compute environments used by the distributed project. This is particularly important when environments are changed, added, or removed over the life of the distributed project, or there are a large and dynamic number of tools and workflows being employed, such as in the Dream challenges .Large-scale, distributed computational projects are a special form of the developer and user use cases above. Since Dockstore was inspired directly from the lessons learned in the highly-distributed PCAWG project, we feel other large-scale, distributed analysis efforts, such as the upcoming ICGCmed  with Docker images hosted on high-quality commercial services. Together these two features allow tools to be utilized in a variety of automated systems, programmatically discovered, built into larger workflows, and shared with the community. These features are key to supporting the next generation of large-scale genomics analysis projects, such as ICGCmed which require a robust mechanism to encapsulate and move algorithms to data, integrate the efforts of multiple developers, and handle change management in a dynamic environment.6 and Bioconda (https://bioconda.github.io) provide methods for describing and linking tools, but do not use Docker to abstract the execution environments. Hence, the Dockstore approach combines the cloud-based flexibility and elasticity of Docker with the modularity of tool repositories like Galaxy Toolshed.In contrast with generic Docker repositories, such as DockerHub, the Dockstore provides mechanisms to interpret the contents of one or more Docker images, link them together, and execute them on a variety of HPC and cloud environments without modification. Projects like Galaxy Toolshed7, Bioboxes8, and BioDocker (http://biodocker.org), focus on encapsulating bioinformatics tools in Docker images in a way similar to Dockstore. BioDocker encourages the use of bioinformatics tools in Docker images by curating them in a single GitHub repository that collaborators can contribute to. Bioboxes defines guidelines (https://github.com/bioboxes/rfc) for particular types of software, such as assemblers or binning applications, allowing for easy benchmarking and interoperability between tools in bioinformatics pipeline. BioShaDock is the most similar to Dockstore and provides a fully controlled environment to build and publish bioinformatics software. It also hosts Docker images locally. Dockstore, like these existing efforts, encourages the use of Docker as a technology for packaging and distributing bioinformatics tools. However, unlike Bioboxes and BioDocker, Dockstore has a heavy focus on CWL/WDL in order to collect Docker images that can be used as part of larger workflows. Unlike BioShaDock, Dockstore is a lightweight registry that focuses on deep integration with commercial source code providers and the Quay.io Docker image registry. We believe that the combination of a standardized descriptor for bioinformatics tools and integration with third party services allows for a great deal of flexibility by allowing for a robust software development experience, which will enable execution of tools in any CWL/WDL-compatible cloud environment. Furthermore, integration with commercial providers allows for a convenient registration experience that mimics popular services focused on the general software development community, such as Coveralls  and Travis CI (https://travis-ci.org/).A number of existing projects, such as BioShaDockhttp://biodocker.org) can flourish independently, but benefit from supporting the emerging GA4GH API standard. Such a network stands a good chance of gaining the critical mass to make scientific tool sharing a popular realityIn the future, it should be possible to leverage multiple open source user interfaces  and commercial platforms  to provide a friendly environment for finding, combining, and executing Dockstore-based tools and workflows. To further this goal, the creation of the Tool Registry API standard through the GA4GH will be key for future interoperability between tool registries and the systems that scale the execution of tools they contain. The Dockstore is the first implementation of this emerging standard. We hope that other tool repositories will implement the standard, allowing the creation of a tool sharing network of registries. Multiple sites that have different models of how Docker-based tools should be built, shared, and secured, such as BioShaDock, Bioboxes, and BioDocker (https://icgcmed.org/) and future DREAM challenges , where Dockstore can enable the seamless interchange and execution of software tools across a variety of computer environments.Future features of Dockstore will include the support of testing frameworks and execution environments. The ability to specify test datasets for each tool and workflow will be extended providing users with “known good” sample inputs for testing and instructional purposes. We will also add support for signed Docker images, providing a mechanism to support “verified” Dockstore entries that are validated to come from trusted sources. This will complement private registry support in Dockstore in order to facilitate sharing Docker-based tools and workflows with a select set of collaborators. A long term evolution of the Dockstore site will include a central registry index, complete with faceted search, for querying across the network of GA4GH-compliant tool registries described previously. Dockstore will also integrate with the related and complementary GA4GH Workflow and Task Execution API standards currently in development, enabling the use of compute resources to run Dockstore-based tools and workflows through standardized APIs. Dockstore’s support of these features, and emerging standards, will support future successors to large scale, distributed analysis projects such as PCAWG. This may include efforts, such as the ICGCmed (https://dockstore.org/Software available at:https://github.com/ga4gh/dockstore (web UI:https://github.com/ga4gh/dockstore-ui)Dockstore source code available from the Global Alliance for Genomics and Health (GitHub):https://zenodo.org/record/154185, DOI:10.5281/zenodo.1541859Archived source code for Dockstore 1.0 release:License: Apache 2.0 The article is very well written and discusses the implementation of a valuable tool for the community. The use of Docker is currently very popular, and the combination with CWL/WDL is very good. I have a minor comment for the on-line tool and the presented workflows (https://dockstore.org/search-workflows). Currently, there does not seem to be description for the presented workflows. Example: https://dockstore.org/workflows/ICGC-TCGA-PanCancer/wdl-pcawg-bwa-mem-workflow "No description associated with this workflow. " This makes it difficult for users to select a workflow. Adding a short description would be very helpful.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. In this manuscript, the authors describe the motivation, design, architecture, and merit of Dockstore.org, a community-focused utility for sharing Docker-based tools and workflows for the sciences. The authors should be commended for their overview of the significant challenges facing large-scale genomics efforts, such as maintaining consistent, reproducible analyses across environments, as well as the solution they’ve architected. They highlight important considerations that must be addressed in order to accelerate scientific progress and the improvement of human health. The technical description of the ICGC PCAWG project is illuminating for researchers and organizations wanting to organize or participate in large-magnitude informatics projects. Overall, we recommend that the manuscript be accepted pending minor revisions. Each revision item is discussed below: The description of Dockstore architecture is thorough and each design decision is justified and informative to the reader. A few additions, however, may benefit audiences which are less conversant in Docker or cloud architecture. For example, though container-based workflow descriptions are becoming increasingly common, many researchers may not yet be familiar with CWL and WDL. A more detailed description of the container-tool-workflow relationship and the benefit of modularizing workflows into containerized tools (as opposed to have whole workflows in a single container) may be helpful to newcomers.https://dx.doi.org/10.6084/m9.figshare.3115156.v2, as stated oncommonwl.org.1 For WDL, we have previously cited their GitHub repository (https://github.com/broadinstitute/wdl) though a more appropriate citation may now exist and could be provided by their development team. We request that the authors cite the Common Workflow Language and Workflow Description Language as appropriate. For CWL, the appropriate citation is   The authors mention that cloud APIs and scripts resulted in analytical failures. The manuscript may benefit from brief discussion of any design constraints when using containers and workflows that may introduce similar risks. If there are none or relatively few, please elucidate why such a technological advantage exists to the reader. Figure 2 may benefit from streamlining, as there are duplicate images and the discussion items (A-C) are mentioned out of order. Regarding the use of GitHub for automated builds and workflow descriptions, the reader may benefit from a small description of best practices in a supplement. For example, how does Dockstore handle tagging of Dockerfiles and how should users make use of them? This can be brief, but it may be helpful to better describe how to augment the value add that Dockstore brings to reproducibility with good git practices. On page 7, “Dream” should be “DREAM”.I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


Lavandula latifolia, and compare genetic and epigenetic characteristics of plants in two adjacent subplots, one experimentally disturbed and one left undisturbed, 20 years after disturbance. Experimental setup was comparable to an unreplicated ‘Before‐After‐Control‐Impact’ (BACI) design where a single pair of perturbed and control areas were compared. When sampled in 2005, plants in the two subplots had roughly similar ages, but they had established in contrasting environments: dense conspecific population (‘Undisturbed’ subpopulation) versus open area with all conspecifics removed (‘Disturbed’ subpopulation). Plants were characterized genetically and epigenetically using amplified fragment length polymorphism (AFLP) and two classes of methylation‐sensitive AFLP (MSAP) markers. Subpopulations were similar in genetic diversity but differed in epigenetic diversity and multilocus genetic and epigenetic characteristics. Epigenetic divergence between subpopulations was statistically unrelated to genetic divergence. Bayesian clustering revealed an abrupt linear boundary between subpopulations closely coincident with the arbitrary demarcation line between subplots drawn 20 years back, which supports that genetic and epigenetic divergence between subpopulations was caused by artificial disturbance. There was significant fine‐scale spatial structuring of MSAP markers in both subpopulations, which in the Undisturbed one was indistinguishable from that of AFLP markers. Genetic differences between subpopulations could be explained by divergent selection alone, while the concerted action of divergent selection and disturbance‐driven appearance of new methylation variants in the Disturbed subpopulation is proposed to explain epigenetic differences. This study provides the first empirical evidence to date suggesting that relatively mild disturbances could leave genetic and epigenetic signatures on the next adult generation of long‐lived plants.Little is known on the potential of ecological disturbance to cause genetic and epigenetic changes in plant populations. We take advantage of a long‐term field experiment initiated in 1986 to study the demography of the shrub  It is well‐known that ecological disturbances, defined as the punctuated killing or damaging of individuals that creates opportunities for new individuals to become established Sousa , can infEpigenetic variation  can complement genetic variation (based on DNA sequence variants) as a source of phenotypic and functional variation in plants . The main aims of the study are (1) to assess the extent to which the genetic and epigenetic characteristics  of adult plants naturally reestablished after a disturbance differed from those of nearby adult plants established naturally under undisturbed conditions; (2) to evaluate the degree of independence of the genetic and epigenetic correlates of disturbance; and (3) to compare the magnitude and spatial pattern of postdisturbance differences in genetic and epigenetic diversity, which may shed light on the role of disturbance as both a selective agent and a releaser of epigenetic variation.Epigenetic and genetic variation may or may not be independent, and their ecological and evolutionary implications are also expected to differ Richards . EstabliL. latifolia , and each half‐plot was randomly assigned to control and treatment experimental levels (‘Undisturbed’ and ‘Disturbed’ hereafter respectively).This study was conducted at the ‘Aguaderillos‐2’ site of Herrera , 1991, l Herrera ,b, 1991;L. latifolia plants growing within the Disturbed subplot were removed by hand in July 1986. Particular care was taken to minimize disturbances of the upper soil layers. The subplot was thoroughly screened until all L. latifolia plants, including first year seedlings, had been removed. Surveys were later performed there on August 1989 and October 1990, and all new L. latifolia juveniles ≥1 year old found were mapped and individually tagged. Reestablishing plants were initially distributed fairly homogeneously over the Disturbed subplot, although spatially heterogeneous mortality eventually led to a clustered distribution. A demographic analysis for the period 1990–2008 of this naturally reestablished subpopulation was presented by Herrera and Jovani (L. latifolia plants within the Undisturbed subplot bearing ≥10 leaves (roughly ≥2 year in age) were mapped and tagged in August 1986. Preliminary demographic data for this subpopulation for the period 1986–1989 were presented in Herrera . First‐year, undamaged mature leaves were collected from each plant in September 2005, placed in paper envelopes and dried immediately at ambient temperature in sealed containers with silica gel. Those sampled plants that died naturally during 2006–2014 were aged by ring counting Herrera . From thL. latifolia plants sampled were assessed by fingerprinting them using amplified fragment length polymorphism . Fragment separation and detection was made using an ABI PRISM 3130xl DNA sequencer , and the presence or absence of each AFLP fragment in each individual plant was scored manually by visualizing electropherograms with GeneMapper 3.7 software . Only fragments ≥150 base pairs in size were considered to reduce possible biases arising from size homoplasy  and the manufacturer protocol. AFLP and MSAP analyses were conducted on the same DNA extracts. The AFLP analysis was performed using standard protocols involving the use of fluorescent dye‐labeled selective primers. Four HpaII and MspI in parallel runs in combination with another restriction enzyme, commonly EcoRI or MseI. MseI was used here because of better repeatability of results . Fragment separation and detection was made using an ABI PRISM 3130xl DNA sequencer, and the presence or absence of HpaII/MseI and MspI/MseI fragments in each sample was scored manually by visualizing electropherograms with GeneMapper 3.7 software. MSAP genotyping error rates were estimated for each primer combination by running repeated HpaII/MseI and MspI/MseI analyses for 32 plants , and computed as the ratio of the number of discordant scores in the two analyses  to twice the product of the number of plants by the number of scored markers. Mean genotyping error rate (±SE) for the four MSAP primer combinations used was 3.1 ± 0.4% (Table S1).Methylation‐sensitive amplified polymorphism is a modification of the standard AFLP technique that uses the methylation‐sensitive restriction enzymes –absence matrices for MSAP fragments were obtained with the four HpaII‐MseI and MspI‐MseI primer combination pairs (Table S1). Different MSAP ‘scoring’ methods can be used to obtain from these data the sample x marker matrix containing information on the methylation status of cytosines at the CCGG end of fragments (MSAP ‘fingerprint’ matrix)  and HMeCCG methylation  markers. Plants sampled were characterized epigenetically by means of the presence–absence scores for u‐type, h‐type and m‐type MSAP markers, using the Extract_MSAP_epigenotypes function from Schulz et al. (u‐type markers obtained (N = 158) were nearly perfectly correlated with m‐type (N = 122) and h‐type (N = 153) ones  × subpopulation (Undisturbed‐Disturbed) two‐way contingency tables were constructed for each marker, and P‐values obtained from Fisher exact‐probability tests were used to identify nonrandom marker‐subpopulation associations. Storey and Tibshirani's , AFLP marker score (presence‐absence) × MSAP marker score (presence‐absence) two‐way contingency tables were constructed for all possible pairs of markers whose frequencies differed significantly between subpopulations. The set of P‐values obtained from Fisher exact‐probability tests were used to identify possible AFLP and MSAP marker pairs exhibiting nonrandom covariation between subpopulations.Whether genetic and epigenetic correlates of ecological disturbance were independent of each other was examined by testing whether (1) multilocus genetic and epigenetic variation was uncorrelated across plants; (2) multilocus epigenetic differences between subpopulations persisted after multilocus genetic differences were statistically accounted for; and (3) AFLP and MSAP markers nonrandomly distributed between subpopulations were uncorrelated. For testing (1), correlations were run between individual pairwise dissimilarity matrices for AFLP and MSAP markers, and statistical significance determined using Mantel tests. Test (2) was accomplished with partial Mantel tests, which involved estimating the correlation between two matrices while controlling for the effect of another (Legendre and Legendre K = 2). Then we assessed whether the resulting cluster memberships were actually separated by the linear boundary between subplots, as would be expected if observed genetic and epigenetic divergence between subpopulations was caused by disturbance. Fifty independent runs of the MCMC function were run, and maps of posterior probabilities of cluster membership for each pixel of the spatial domain were obtained for the model with the lowest mean log posterior probability.Differentiation between subpopulations could reflect some previous spatial gradient(s) in genetic and/or epigenetic composition across the plot, rather than population substructuring due to experimental disturbance Meirmans . This keh‐type and MSAP m‐type markers. SPAGeDi version 1.4 . For all plants combined, 43.0% of AFLP markers and 53.5% of MSAP markers were polymorphic , the difference between marker types being statistically significant . Epigenetic diversity also exceeded genetic diversity when subpopulations were considered separately, the difference being particularly marked in the Disturbed one (Table Plants sampled in 2005 were fingerprinted using 230 AFLP and 173 MSAP fragments (Table S1). The majority of MSAP fragments (85.0%) produced r = −0.045, P = 0.82), m‐type , and h‐type MSAP markers . The contrasting pattern of variation between subpopulations in genetic and epigenetic diversity (Table h‐type markers (Table N = 99) and MSAP (N = 147) markers will be considered in subsequent analyses.Epigenetic variation was  unrelated to genetic variation across plants, as shown by negligible and statistically nonsignificant correlations between the pairwise dissimilarity matrix for AFLP markers and each of the corresponding dissimilarity matrices for all  was consistently smaller than differentiation between Disturbed and Undisturbed subpopulations  and epigenetic (MSAP) differences between subpopulations Table , which ars Table . Differens Table .m‐type MSAP markers were still significantly correlated with the binary matrix of subpopulation membership . The dissimilarity matrix based on h‐type MSAP markers (which did not differ between subpopulations) was uncorrelated with subpopulation membership after controlling for genetic dissimilarity .Epigenetic divergence between subpopulations was unrelated to genetic differences. After controlling for genetic differences , dissimilarity matrices based on all and m‐type MSAP  markers differed significantly between subpopulations. Among h‐type MSAP markers, none was found to be nonrandomly distributed between subpopulations. The Boruta analysis identified three AFLP and four MSAP markers with significant predictive value to discriminate between subpopulations (Table S2). With the single exception of an h‐type MSAP marker, all of these were also shown by marker × subpopulation association analyses to be nonrandomly distributed between subpopulations (Table S2). Unequal distribution between subpopulations of three representative discriminatory markers is illustrated in Figure P ≥ 0.36; Fisher exact‐probability tests), which further supports the independence of genetic and epigenetic differences between subpopulations.Several AFLP and MSAP markers were nonrandomly distributed between subpopulations, as revealed by both marker x subpopulation association tests and Boruta analyses (Table S2). The marker × subpopulation association tests revealed that the frequency of six AFLP  and five Bayesian clustering on AFLP and MSAP markers nonrandomly distributed between subpopulations (Table S2) defined two distinct spatial clusters of individuals whose geometry and distribution matched Disturbed and Undisturbed subpopulations .Ecological investigations on the effects of disturbance have concentrated on the impact of devastating agents such as fire or hurricanes, but milder perturbations may also have significant consequences for populations and communities  and Undisturbed (N = 19) subpopulations were resampled and MSAP analyses repeated. Results showed that epigenetic differences between subpopulations reported here persisted essentially unaltered almost one decade later, and that longitudinal changes exhibited by some plants were quantitatively negligible in relation to differences between subpopulations . Genetic and epigenetic differences between subpopulations found in this study should thus most likely be related to the contrasting ecological backgrounds prevailing during seedling emergence and early growth of plants rather than to slight differences in age.Because of its faster ‘demographic pace’, the Disturbed subpopulation was able to catch up with the Undisturbed one (in terms of proportion of plants flowering and inflorescence production) only 10 years after disturbance. Over the next 10 years the two subpopulations became phenotypically undistinguishable even to the advised observer, and their reproductive parameters fluctuated annually in unison. By 2005, when leaf samples were collected for this study, plants from the Disturbed subpopulation were, on average, 5 years younger than those from the Undisturbed one. Age difference between plants from the two subpopulations might have contributed to their epigenetic divergence if cytosine methylation varied with age in adult Genetic diversity did not differ between subpopulations, but these did differ in multilocus genetic characteristics. The AMOVAs revealed significant differences, and up to six AFLP markers were nonrandomly distributed between subpopulations. Such genetic differences were partly due to an underlying isolation‐by‐distance pattern occurring across the whole plot Meirmans , as reveL. latifolia . In contrast, epigenetic differentiation between subpopulations was largely caused by m‐type markers (CG‐context methylation). Since spatial genetic patterns discussed above deny a major role of immigrant seeds in the reestablishment of the Disturbed subpopulation, the latter's epigenetic differentiation and increased diversity most likely resulted from a combination of isolation‐by‐distance effects, postdisturbance divergent selection and appearance of new epigenetic variants through transgenerational modification of methylation marks. New epigenetic variants could have arisen spontaneously  unrelated to genetic differences. This finding, and the unrelatedness of AFLP and MSAP dissimilarity matrices, suggest that epigenetic variation was largely autonomous from genetic variation in the sample of m‐type markers was virtually identical to that for AFLP markers. Since dispersal patterns of nuclear genetic and epigenetic markers should by definition be identical, a parsimonious interpretation of this result is that in the Undisturbed subpopulation the transgenerational inheritance of m‐type and AFLP markers had been sufficiently similar during a number of prior generations as to produce identical isolation‐by‐distance signatures , while the significant differentiation between subpopulations would reflect divergent selection on non‐neutral m‐type markers . The shallower slope of the kinship‐distance regression for m‐type markers in the Disturbed subpopulation is compatible with these interpretations, since both selection and reduced heritability act reducing the strength of isolation‐by‐distance patterns . This suggests that disturbance enhanced intraspecific functional diversity through increased genetic and epigenetic diversity at the population level. AMOVAs revealed that, 20 years after disturbance, differences between subpopulations contributed 2.5% and 1.4% to local genetic and epigenetic diversity (Disturbed + Undisturbed subpopulations combined), respectively, which is remarkable given the restricted spatial scale considered.Results of this study also highlight other aspects that should be considered by future investigations on the effects of ecological disturbance and, more generally, by investigations in the currently expanding field of ecological epigenetics  and CG (m‐type markers) contexts in maintenance mechanisms and predominant genomic location (genes vs. transposable elements) (Cokus et al. The contrasting results obtained for different MSAP marker types is another aspect deserving consideration by future studies on natural epigenetic variation of nonmodel plants. Differential spatial patterns for different marker types were also reported by Schulz et al.  in an inAn increasing number of studies show that epigenetic variation in natural plant populations may be spatially structured at the landscape and regional levels, but explicitly spatial methods have been only rarely used to examine fine‐scale spatial structuring (Herrera et al. 10.5061/dryad.v4192.AFLP and MSAP data, and spatial coordinates of sampled plants, deposited at DRYAD doi:None declared.Table S1. Primer combinations, scoring errors, number of markers, and polymorphism levels for AFLP and MSAP analyses.Click here for additional data file.Table S2. AFLP and MSAP markers nonrandomly distributed between Undisturbed and Disturbed subpopulations.Click here for additional data file.


Salmo trutta) as a model to study piscivorous life history and its genetic characteristics in environmentally contrasting habitats; a large lake ecosystem with one major large and stable tributary, and several small tributaries. Data from 550 fish and 13 polymorphic microsatellites (He = 0.67) indicated ecotype‐specific genetic differentiation  among Bayesian assigned small riverine resident and large, lake migrating brown trout (>35 cm), but only in the large tributary. In contrast, large trout did not constitute a distinct genetic group in small tributaries, or across riverine sites. Whereas life history data suggest a small, river resident and a large migratory piscivorous ecotype in all studied tributaries, genetic data indicated that a genetically distinct piscivorous ecotype is more likely to evolve in the large and relatively more stable river habitat. In the smaller tributaries, ecotypes apparently resulted from individual plasticity. Whether different life histories and ecotypes result from individual plasticity or define different genetic types, have important consequence for conservation strategies.Ecotype variation in species exhibiting different life history strategies may reflect heritable adaptations to optimize reproductive success, and potential for speciation. Traditionally, ecotypes have, however, been defined by morphometrics and life history characteristics, which may be confounded with individual plasticity. Here, we use the widely distributed and polytypic freshwater fish species brown trout ( Available river habitat for spawning is restricted to the lower 300 m of the river by an upstream waterfall , mean flow (1961–1990) 83 million m3 year−1, is about 5 m wide and 1.5 km long up to Lake Rambergvatnet (756 m a.s.l.), providing an estimated rearing area of 7500 m2. River Halldalsåi (P1), the second small tributary, mean flow (1961–1990) 29 million m3 year−1, is a 4.5 km long and about 4 m wide stream up to a dam at the outlet of Lake Halldalsvatnet (846 m a.s.l.). It provides an estimated rearing area of 18,000 m2. Mean annual water runoff (1987–2007) in two locations within the PALs watershed is 26.7 L s−1 m−2  and 15.4 L s−1 m−2 , indicating a twofold to threefold variation in annual flow within the alpine watershed. Recruits from all tributaries typically migrate downstream to the common feeding habitat in Lake PAL and TUN at age 2+ or 3+  at water depth 5–23 m (T1), immediately downstream the subterranean anthropogenic channel. Available spawning and recruitment habitat size and associated hydraulics fluctuate substantially with water regulations in Lake PAL and TUN. An additional two small tributaries make up the majority of the remaining spawning and recruitment habitat in TUN. In River Rødungselva , upstream migration is restricted by a natural barrier 1.5 km up the about 6 m wide river, resulting in roughly 9,000 m2 of available rearing habitat. The small River Tunhovdbekken (T3), mean flow <4 million m3 year−1 and located close to T2, is 1 km long and about 2 m wide, providing about 2,000 m2 of recruitment habitat. Upstream migration access to T3 varies with annual variations in TUN water level.Three contrasting tributaries in each of the two lakes Figure  are poteSalvelinus alpinus L.) and European minnow (Phoxinus phoxinus L.), both since the two latter were introduced around 1920. No other forage fish species occur, and diets of adult trout in both lakes often consist of terrestrial insects and a variety of zooplankton and zoobenthos. Crustaceans like Gammarus pulex and Lepidurus arcticus, known to favor growth, are present in limited amounts  from the County Governor of Buskerud. All efforts were made to ameliorate suffering of animals. Fin clips of 30–40 wild riverine resident trout (R) across year classes were sampled after electroshocking  at each of the six recruitment sites  during 2005–2007. Samples were collected from the highest regulated water level to 300–800 m upstream, and represent 3‐ to 7‐year classes within sites , but only age classes 0+ to 2+ in P3. The majority of fish from all sites were nonmature. Wild trout (verified by present adipose fin) from both lakes  were caught annually 2006–2008, by beach seines and gillnets throughout both lakes. Resampled individuals were excluded from further analysis, based on genetic composition (n = 14). Sample L consisted of 324 trout (200 trout >350 mm), mean total length (LT) 404 mm , 122 males and 202 females, determined from physical characteristics, gonads and presence of running egg/milt in ripe trout, and across 14‐year classes (age 3+ to 16+). Individual ages of a random sample of riverine resident fish and all lake caught fish were determined by scale and otolith readings, followed by back calculation of age, assuming proportionality among fish length and age structure . Allelic richness (Ar) among samples was estimated in FSTAT v.2.9.3.2  equilibrium for all loci within riverine sites and globally across loci within sites, as well as tests for linkage disequilibrium across sites, were performed in FSTAT, after 104,000 and 780,000 permutations, respectively. The program BOTTLENECK v.1.2.2  estimate with >15,000 permutations, calculated in FSTAT  that assigned to the riverine sites in GENECLASS2 , 200,000 replications of burn‐in, 500,000 MCMC replicates, and admixture model with correlated allele frequencies. The number of population clusters (K) was estimated by maximum‐likelihood measures (Ln P(D)) and their variance and by ΔK  were assigned to riverine (R) and lake (L) partitions of population clusters identified by STRUCTURE, based on individual membership coefficients (q). Population hybrids as in the studied lakes  and assignment criteria. A stringency level of q = 0.8 from the STRUCTURE analyses was implemented in assigning individuals, excluding trout with lower assignment from populations. The conservative low‐assignment threshold  and assignment (q) of population partitions in R v.3.3.2 (http://www.R-project.org), only considering comparisons of samples with n ≥ 5. Tests implementing all sizes (LT > 13.7 cm) and LT of 40–50 cm in the lake sample L evaluated the robustness of the defined size limit for FST differentiation in the Bayesian design. The suitability of the implemented threshold for assignment in this design was tested by running additional FST tests implementing q‐levels from 0.7 to 0.9.The sequential Bonferroni correction procedure Rice,  was used33.1SD ± 11.7, Table p > .012 within loci, p > .346 across loci), and nonrandom associations of alleles among loci were not found across samples (p > .0008). No significant differentiation in He or Ar was found among riverine sites . The test for bottlenecks within sites did not indicate recolonization or strong genetic drift between age classes (p > .271).Amplification and allele calling were obtained in 99.2% of the cases, and consistent with secondary amplification, and controls. Number of alleles per locus averaged 14.1 . Pairwise FST estimates were somewhat larger among R and L in P3 , compared to other sites , but did not indicate ecotype variation within any of the six sites after correction for multiple tests  for K = 10. The population clusters were named p12, t23, and p3, named according to their most likely origin. Clusters p12 and t23 corresponded mainly to the two smaller tributaries in PAL (P1 and P2) and the small tributaries in TUN (T2 and T3), respectively, whereas population cluster p3 corresponded to the large river (P3) in PAL. Sample T1 and L had a more mixed genetic origin, with the latter assigning slightly less to the population cluster corresponding to the small tributaries in TUN  of lake caught trout differed among population clusters , than in Lp12  and Lt23 . Pairwise comparisons indicated no size difference between Lp12 and Lt23 (p = .198). Ages were similarly differentiated (p < .001) among population clusters, considerably larger in Lp3 , but no difference (p = .536) between Lp12  and Lt23 )  were highest in the two ecotype partitions in p3 . With respect to ecotype partitioning within population clusters, q only differed among partitions in t23 (p = .020).Mean size  Figure . Self‐as3 Figure  and diffLT ≤ 35 cm and q < 0.8), respectively, excluded 124 and 295 trout, combined retaining 208 trout across population partitions , than in Lp12  and Lt23 . Pairwise comparisons indicated similar size (p = .185) in Lp12 and Lt23. Ages were less differentiated for this subsample  were still highest in the two ecotype partitions in p3 and differed among population partitions in total .The two exclusion criteria (FST estimates indicated that ecotype large lake caught trout (>35 cm) were significantly different from ecotype riverine resident trout in population p3 (Table p < .001), that is, in the large tributary P3. In contrast, lake and river caught trout were genetically similar in the two other populations , corresponding to the smaller tributaries. Consequently, PCoA plots of clusters with subsets of population partitions based on size‐ and assignment criteria, where 82.9% of the genetic variation was captured by the first two components, visualized contrasting ecotypes in p3 only . Lake caught trout were genetically different across populations for both large  and small  fish, corroborating the absence of evidence for a common piscivorous ecotype across populations, as indicated by PCoA plots , and greater, but not significant variance to differences among ecotype partitions across the tripartite population structure  than among R and L within populations . Excluding populations one by one only changed the pattern of low but significant variation among partitions within populations when removing p3 , suggesting the significant difference among R and L was caused by population p3‐specific ecotype differentiation.AMOVA results attributed the majority of allelic variation to individuals , but not within other populations, for these size limits. FST test implementing q‐level 0.7 indicated partition differentiation in p3 and t23 , whereas a q‐level of 0.9 only differentiated partitions in p3 . Thus, the exclusion criteria of q = 0.8 seemed appropriate for hypothesis testing in this system, characterized by high straying rates and occasionally environmental obstacles for spawning migration, as often evident in northern alpine communities of piscivorous trout.Implementing size limits of 40, 45, and 50 cm for lake caught trout did not change the pattern of size‐ or assignment differentiation among population partitions (data not shown). Gene frequencies of partitions within populations were also consistently differentiated in p3  was explained by ecotypes , to a minor extent also water flow. Thus, evolution of large piscivorous ecotypes is most likely positively correlated with water flow. Longevity may also induce self‐energizing effects of ecotype differentiation. Achieved length and age of trout in our studied populations equal or even surpass that from other populations defined as large piscivorous brown trout  is large, and river length is short. Although several exceptions exist, fish are generally larger bodied in large and fast flowing rivers  may hamper evolution of locally adapted habitat specialists (Crispo, In contrast, in smaller habitats selection for large size, as a reproductive strategy may be traded off against harsh climatic conditions (Borgstrom & Museth,  Crispo, .5The study demonstrated genetic differentiation of large individuals in a large, stable river habitat. A piscivorous life history did not, however, involve a genetic signature within more unstable habitats or across populations. Rather, it results from individual plasticity. Available river habitat for spawning and recruitment varied considerably among sites, and also indicated a larger proportion of large piscivorous trout in stable habitat. The study corroborates the accumulating evidence that genetic constitution and heritability of life history traits are influenced by environmental stability (Charmantier & Garant, None declared.All authors; JW, JH, and KH conceived the study and participated in the project design. JW conducted the field and laboratory work, and did the genetic analyses. All authors wrote and approved the final manuscript.


Pieris rapae in a subpart of the study area. Comparing simulated data for P. rapae with its normal dispersal ability and with a reduced dispersal ability, we also show that a higher dispersal ability can be an advantage to survive in an urban or highly fragmented environment. The results obtained here suggest that it is of high importance to account for population persistence, and confirm that it is crucial to maintain habitat size and connectivity in the context of land-use planning.In a simulation study of genotypes conducted over 100 generations for more than 1600 butterfly’s individuals, we evaluate how the increase of anthropogenic fragmentation and reduction of habitat size along urbanisation gradients (from 7 to 59% of impervious land cover) influences genetic diversity and population persistence in butterfly species. We show that in areas characterised by a high urbanisation rate (>56% impervious land cover), a large decrease of both genetic diversity  and population size  is observed over time. This is confirmed by empirical data available for the mobile butterfly species  During the Anthropocene and particularly the last five decades, human population growth and migration have led to an increased demand for housing, transport and infrastructure, leading to a large expansion of cities and to a growing impact of human activities on the environment . The lanP. rapae within four regions along a transect leading from the periphery to the city centre and showed decreased genetic diversity in urban versus nonurban sites. Similarly, Pieris (Pieris rapae and Pieris melete) in study areas from Japan and Korea. They showed that the genetic diversity is not directly significantly different in urban areas as compared to rural ones. However, important genetic variations can be observed among seasonal subpopulations in urban areas, whereas it is not the case in rural ones. As a consequence, when considering seasonal subpopulations, the genetic diversity in cities is reduced as compared to rural environments. As the reduction of population size and genetic diversity induces a higher risk of inbreeding  or other human-influenced areas , which reduced natural habitat size and therefore population size too . In addibreeding .Turdus merula, was able to colonise and adapt to urban environments. Despite a little reduction in expected heterozygosity within urban populations of this blackbird, no reduction of observed heterozygosity was noticed and no evidence of genetic differentiation was highlighted between urban and rural populations. Similarly, However, urban habitats can also harbour self-sustaining populations of native (and exotic) species  that areP. rapae); (2) to study the impact of a simulated reduction of the dispersal ability on this evolution; and (3) to compare spatially explicit simulation results with measured genetic diversity of P. rapae populations, estimated from an empirical data set (366 amplified fragment length polymorphism (AFLP) markers, In this context, our study combines empirical and simulated data to analyse the impact of landscape elements on the genetic diversity and on the persistence of butterfly populations in the region of Marseille, France. Butterflies, as airborne species that require open spaces to fly, are constrained in their movements in fragmented and human-influenced landscapes and are therefore interesting model species. In this study, we aim for the following: (1) to analyse the spatiotemporal evolution of the genetic diversity and population persistence as a function of the urbanisation level from simulated genetic data (500 single nucleotide polymorphism (SNP) loci over 100 generations) for a butterfly species with high dispersal capacity . In order to capture the landscape heterogeneity of this region, we combined vector and raster data describing the land cover of the Marseille area . These study areas were defined along four transects, each of 18 km in length and 4 km in width, leading from the Vieux-Port of Marseille (city centre) to the suburbs and therefore showing a high urbanisation gradient from densely populated urban to more natural areas. As a function of the urban densities, we divided lengthwise each transect into three parts: high urban density being the first 6 km along the transect from the city centre , 3–7% of green spaces (grasslands and parks), 10–22% of forests and 15–30% of other land cover types . The medium urbanised areas contain 8–41% of impervious land cover, 16–26% of green spaces, 19–46% of forests and 17–32% of other land covers. Finally, the areas with a low urbanisation level show 3–13% of impervious land cover, 36–66% of green spaces, 21–37% of forests and 3–28% of other surfaces .P. rapae to disperse in each type of land cover  to randomly assign 100 sites to potential habitats for P. rapae  within each of the 12 zones. The number of sites per zone (100) was chosen in order to obtain realistic distances between the sites but to avoid overestimating the number of potential habitats, notably in the city centre. In order to ensure the largest habitats—which are assumed to harbour important butterfly subpopulations—to be represented in the sampling design for the simulations, we applied a stepwise procedure. We first chose to randomly position three sites in each of the green spaces showing an area of at least five hectares (ha), and we then placed one site in each of the green spaces showing an area between 1 ha and 5 ha. All the other sites—required to achieve a total of 100 sites per zone—were randomly positioned within green spaces of at least 200 m2 (2 pixels). Due to partial overlapping between the zones (mainly in the city centre), some points were counted for two different areas. We ended up with a total of 1083 sites for simulations, with a median nearest-neighbour distance of about 320 m. In addition, in order to allow for connectivity and potential gene flow among populations from different transects, 550 sites were randomly distributed within green spaces situated between the transects, as illustrated in In order to simulate the evolution of populations and genotypes along the four transects, we first defined sites within potential habitat areas for Once the sampling sites were defined, we used the individual-based population genetics model software CDPOP 1.2.21  to simulP. rapae at the beginning of the simulations . The initial genotypes were randomly assigned. The simulation of dispersal and mating movement between the sites was then based on a cost–distance matrix indicating the cost of dispersing from one site to another. For this matrix, we defined the dispersal cost as the cumulative resistance of the least-cost path, computed using the software Graphab  over 100 generations. The data set is replicated 5 times (5 runs) for Hobs) as well as the average expected heterozygosity (Hexp) assuming Hardy–Weinberg equilibrium:Once the genetic data have been simulated over 100 generations, we analysed the level of genetic diversity within each of the 12 zones, based on measures of heterozygosity. A high heterozygosity indicates a lot of genetic variability, whereas a low heterozygosity indicates poor genetic diversity. We used here two indices: the average observed heterozygosity  is the frequency of presence of the first (respectively second) allele for the marker i and k is the total number of markers. The values of these measures range from 0  to 1 . The comparison of the values of these two indices can allow the identification of potential inbreeding. Indeed, when a population is facing high inbreeding, the fraction of heterozygotes observed will be less than what is expected under random mating. The difference between observed and expected heterozygosities can therefore be used to estimate the amount of current inbreeding (where breeding .N) and used this number as an estimate of the population persistence in the respective zone.During the simulations, habitat sites may become uninhabited, in particular if the cost of reaching sites is too high. As a result, the number of individuals per zone can change over time . We retrieved the number of individuals remaining in each zone at each generation (σ) and 95% confidence intervals (μ±1.96*σ/√n) for the three parameters  at each generation and we produced plots of their evolution over time. In order to compare the genetic diversity and number of individuals remaining in each zone at the end of the simulations, we also used the 5 values from the 5 simulation runs to compute a one-way ANOVA between the last values (generation 100) of Hobs (resp. Hexp and N) in each of the 12 zones . Post-hoc testing was then performed using a Scheffé’s test in order to highlight the significant differences between the zones. All computations were performed using the Matlab R2014b software (functions anova1 and multcompare).For both dispersal abilities and for each of the 12 zones, we used the five values resulting from the five simulation runs to compute mean (μ), s.d. . Assuming Hardy–Weinberg equilibrium with p representing the allele frequencies of the dominant allele and q the allele frequencies of the recessive allele, we have f=q2 and With the objective to compare the results of the genetic diversity obtained by simulations to an empirical case study, we used a published empirical data set of k is the total number of markers.where n nearest neighbours (n between 3 and 25), using formula (2).In order to compare simulated and empirical data, the expected heterozygosity for the simulated data was also computed along the same transect (transect B) by considering for each site the P. rapae with normal dispersal ability) and 2b . The initial value at generation zero is equal to 0.5, which corresponds to the theoretical maximum value for heterozygosity expected under Hardy–Weinberg equilibrium, resulting from the random distribution of genotypes at the beginning of the simulations. For both dispersal capacities, this value rapidly decreases in all transects and for all levels of urbanisation.The change over time of the genetic diversity, as measured by the observed heterozygosity is presented in P. rapae with normal dispersal ability, but the decline then stabilises and more than 75% of the initial heterozygosity level is still present after hundred generations  than that in the other areas with a similar urbanisation level (maximum 41.4% lost). This area is also showing the highest loss among the areas of low urbanisation levels with the normal dispersal ability.In the more rural areas (green lines), a loss of 6–7% of the initial heterozygosity can be observed after 10 generations for erations . SimilarP-values <10−6) and in transects B, C and D for reduced dispersal .In the areas with medium urbanisation (blue lines), a loss of 9–15% of the initial heterozygosity can be observed with the normal dispersal ability after 10 generations and of 20–32% at the end of the simulation. Once again, the decline is more pronounced for the reduced dispersal, where the loss already reached 25–36% at generation 10 and 56–76% at the end of the simulations. The values of observed heterozygosities in these medium urbanised areas (blue lines) are generally lower than those in the areas with low urbanisation (green lines). However, for some transects, the values are close and the confidence intervals sometimes overlap . The Scheffé’s tests computed on the values at generation 100 indeed indicate significant differences between the values of areas with low and medium urbanisation only in transects C and D for the normal dispersal . We can also notice that with the normal dispersal ability, the value of observed heterozygosity reached at generation 100 in area 12  is significantly lower than that in the other highly urbanised areas . This area is also the one presenting the highest loss of observed heterozygosity with the reduced dispersal ability (−96.2%).Finally, for highly urbanised areas (red lines), more than 30% of the initial observed heterozygosity is already lost after 10 generations with the normal dispersal and the decline continues until the end of the simulations, after hundred generations, where only 28–40% of the initial observed heterozygosity remains . With thP. rapae with normal dispersal ability  with either dispersal abilities. The values of expected heterozygosity in the more rural areas (green lines) even occasionally drop below the value of the corresponding intermediate areas (blue lines), notably in transect A. With the normal dispersal ability, the differences between highly urbanised areas and the other levels of urbanisation are small but nevertheless significant , except for area 6 (transect B). With the reduced dispersal, only the areas 9 and 12 (transects C and D) show significantly lower values at generation 100 as compared to the other levels of urbanisation .When comparing the various levels of urbanisation, no significant difference can be highlighted for any transect between the areas of low or medium urbanisation, when considering the values of expected heterozygosity at generation 100 . However, with the reduced dispersal, the values are significantly different  except in transect B.For the areas with a medium urbanisation (blue lines), with the normal dispersal  the numbP-values <2 × 10−4).Finally, for the highly urbanised areas (red lines), with the normal dispersal, the number of individuals  rapidly For both the empirical and simulated data, we can observe a significant increase of expected heterozygosity with increasing distance from the city centre . Moreove2; P. rapae, which shows a decrease of the expected heterozygosity towards the city centre. Moreover, several previous studies have highlighted a similar negative influence of urban environment on dispersal , a low percentage of green spaces (<8%) and a reduced surface of green spaces entities <600 m2; . In thesispersal , gene flP. rapae individuals over time, especially in highly urbanised areas. This decrease suggests that the persistence of populations is threatened in urban environments. For the simulated data, a potential site may become unoccupied over time if the individuals living there are no longer able to find a congener to reproduce. Indeed, in this extreme case, individuals are isolated and the population is doomed to extinction. This negative impact of urban environment on the persistence of populations has already been highlighted in other urban areas and for other species  likely to be favourable for the species studied. However, this area also shows the highest percentage of impervious surfaces (12.7%), which could explain its disadvantage as compared to other regions of the periphery.As regard to the areas with low urbanisation, the highest loss of genetic diversity or number of individuals is generally observed in transect A. This area of transect A is characterised by a high percentage of green spaces (60%) and a large average surface for these entities (>3 km2), which indicates a higher fragmentation.For the areas with medium urbanisation, transect C often seems to be the most negative, especially for the species with the lower dispersal ability. This could be explained by the lower percentage of habitat areas (grassland and parks) in this region and also to the smaller average surface of habitat entities  but also a high percentage of water (11.8%) and forest (24.4%). These barriers may reduce the gene flow from the periphery to the city centre and therefore threaten the viability of the populations of the city centre. This shows that the fragmentation of the less urbanised suburb areas can also have a noticeable importance on the decrease of genetic diversity and population persistence of the urban populations.Finally, we note that in our case for all parameters , the differences highlighted between the transects  remain minor relative to the differences observed between the three levels of urbanisation .P. rapae and of a butterfly with a reduced dispersal ability, results show that the reduction of genetic diversity is much more pronounced for the less mobile species and that the persistence of the latter populations is also more threatened. This underlines that a higher dispersal capacity may be an advantage for species living in urban environments, which had already been highlighted by previous studies  resulting in the injection of uncertainty in the results obtained. This is an important reason why the combination with empirical data is particularly powerful since the latter provide landmarks to relieve the uncertainty mentioned above and enabling a complete analysis and a more confident interpretation of the results.Our results show that butterfly species can be strongly threatened in dense urban areas, highly fragmented environment or other human-influenced areas. For these species, in order to conserve and promote genetically stable and diverse populations, it is important to (1) preserve or restore suitable habitats and (2) maintain or increase the connectivity among them in order to allow dispersal also for species with limited dispersal capacities. As increasing the connectivity by the creation of dispersal corridors may be difficult to achieve due to the numerous constraints of urban or highly fragmented environments, the creation or preservation of stepping stone habitats is promising and of special importance (All simulated data have been submitted to Zenodo: doi:10.5281/zenodo.215722.


Podarcis tiliguerta with the aim of shedding more light on the evolutionary processes underlying the origin of Mediterranean island biodiversity.Mediterranean islands host a disproportionately high level of biodiversity and endemisms. Growing phylogeographic evidence on island endemics has unveiled unexpectedly complex patterns of intra-island diversification, which originated at diverse spatial and temporal scales. We investigated multilocus genetic variation of the Corsican-Sardinian endemic lizard 12S and nd4) and nuclear (acm4 and mc1r) gene fragments in 174 individuals of P. tiliguerta from 81 localities across the full range of the species in a geographic and genealogical framework. We found surprisingly high genetic diversity both at mitochondrial and nuclear loci. Seventeen reciprocally monophyletic allopatric mitochondrial haplogroups were sharply divided into four main mitochondrial lineages (two in Corsica and two in Sardinia) of Miocene origin. In contrast, shallow divergence and shared diversity within and between islands was observed at the nuclear loci. We evaluated alternative biogeographic and evolutionary scenarios to explain such profound discordance in spatial and phylogenetic patterning between mitochondrial and nuclear genomes. While neutral models provided unparsimonious explanations for the observed pattern, the hypothesis of environmental selection driving mitochondrial divergence in the presence of nuclear gene flow is favoured.We analysed DNA sequences of mitochondrial  contains supplementary material, which is available to authorized users. The Mediterranean Basin has long been recognised as one of the richest global biodiversity hotspots . A largeThese islands offer an ideal setting for investigating the evolutionary processes behind the origin and the structure of current patterns of insular biodiversity hotspots because: (i) they have a complex topography with a diversity of landscapes and microclimatic regions, spanning from Mediterranean to alpine climates , 6, whicIn the last decade, Corsican-Sardinian species have been the subject of intensive phylogeographic surveys, especially regarding amphibians and reptiles , 20–34, Podarcis tiliguerta offers an intriguing case study as previous genetic assessments uncovered extraordinarily high level of diversity with contrasting patterns between different genetic markers  which ahylogeny , 67 whicER 1.1.1  under thmated by  for the used see ). Similafragment . BEAST aIn order to identify the main phylogenetic discontinuities along terminal branches of the mitochondrial phylogeny we used the statistical parsimony network approach  implemen12S and cytb) showed two highly divergent mitochondrial lineages in P. tiliguerta (maximum cytb divergence = 15%) which neither form a monophyletic group nor have a resolved position in the Podarcis phylogeny. Therefore we used one representative of each P. tiliguerta lineage recovered from our BEAST analyses to infer their phylogenetic relationships within Podarcis. In doing so we sequenced 10 Podarcis species from the same two fragment used in this study and retrieved Genbank sequences from nine additional Podarcis species  analysis was also performed in Raxml GUI 1.1.3 . A Indeed,  suggestedriagae,  provided setting ; (ii) th setting , 20–34;  setting , 13, 29; setting , 27. ThuP. tiliguerta, it has been suggested that this species may represent a species complex [P. tiliguerta will be the result of long-term barriers to gene flow under neutral evolution; an assumption which received little support in our study. Despite mitochondrial structure, both this study and [P. tiliguerta showed high variability with a north–south cline of variation across the main islands rather than a sharp transition between geographic units [P. tiliguerta and convincing conclusions on the taxonomic value of the observed mitochondrial lineages can only be obtained after a fuller understanding of the evolutionary processes that led to the observed phylogeographic pattern.Based on the high value of genetic divergence observed between mitochondrial lineages of  complex . Interprtudy and  showed tic units . Bruschiic units  based onP. tiliguerta underline long-term isolation or local adaptation they indicate that this species does not represent in any case a single management and conservation unit. Therefore, each lineage should be individually targeted when setting strategies for the long-term conservation of P. tiliguerta and of its evolutionary potential.On the other hand, whether the distinct phylogeographic groups observed within Podarcis tiliguerta reveals surprising levels of genetic diversity underlining a complex phylogeographic pattern with a striking example of mito-nuclear discordance. These findings have considerable implications for the taxonomy and conservation of P. tiliguerta as well as for our understanding of the processes involved in the evolution of biodiversity hotspots within Mediterranean islands. Neutral models based on long-term vicariance provide unparsimonious explanations for the deep phylogeographic breaks observed in mtDNA within and between islands, both in this species as well in other Corsican-Sardinian endemics. Growing empirical evidence suggests a possible role for local adaptation along a smooth environmental gradient underlying the origin and persistence of highly divergent, geographically localized, mitochondrial groups found in endemic amphibian and reptiles as well as of their geographic patterns of nuclear variation. These hypotheses could be thoroughly tested taking advantage of genomic resources. In this respect, this study represents one more step in our long-term aim to fully understand the evolutionary mechanisms underlying the rising of the Mediterranean diversity hotspot.Our study on the multilocus genetic variation of