This workflow aims to streamline the integration of phytosociological inventory data stored in multiple XML files within a ZIP archive into a MongoDB database. This process is crucial for effective data management within the project's Virtual Research Environment (VRE). The workflow involves extracting XML files from the ZIP archive, converting them to JSON format for MongoDB compatibility, checking for duplicates to ensure data integrity, and uploading the data to increment the inventory count. This enhances the robustness and reliability of the inventory dataset for comprehensive analysis. Background Efficient data management is crucial in phytosociological inventories, necessitating seamless integration of inventory data. This workflow addresses a key aspect by facilitating the importation of phytosociological inventories stored in multiple XML files within a ZIP archive into the MongoDB database. This integration is vital for the project's Virtual Research Environment (VRE), providing a foundation for robust data analysis. The workflow comprises two essential components: converting XML to JSON and checking for inventory duplicates, ultimately enhancing the integrity and expansiveness of the inventory database. Introduction In phytosociological inventories, effective data handling is paramount, particularly concerning the integration of inventory data. This workflow focuses on the pivotal task of importing phytosociological inventories, stored in multiple XML files within a ZIP archive, into the MongoDB database. This process is integral to the VRE of the project, laying the groundwork for comprehensive data analysis. The workflow's primary goal is to ensure a smooth and duplicate-free integration, promoting a reliable dataset for further exploration and utilization within the project's VRE. Aims The primary aim of this workflow is to streamline the integration of phytosociological inventory data, stored in multiple XML files within a ZIP archive, into the MongoDB database. This ensures a robust and duplicate-free dataset for further analysis within the project's VRE. To achieve this, the workflow includes the following key components: - ZIP Extraction and XML to JSON Conversion: Extracts XML files from the ZIP archive and converts each phytosociological inventory stored in XML format to JSON, preparing the data for MongoDB compatibility. - Duplicate Check and Database Upload: Checks for duplicate inventories in the MongoDB database and uploads the JSON files, incrementing the inventory count in the database. Scientific Questions - ZIP Archive Handling: How effectively does the workflow handle ZIP archives containing multiple XML files with distinct phytosociological inventories? - Data Format Compatibility: How successful is the conversion of XML-based phytosociological inventories to the JSON format for MongoDB integration? - Database Integrity Check: How effective is the duplicate check component in ensuring data integrity by identifying and handling duplicate inventories? - Inventory Count Increment: How does the workflow contribute to the increment of the inventory count in the MongoDB database, and how is this reflected in the overall project dataset?

This workflow focuses on enhancing spatial interpolation of meteorological variables by incorporating altitude. The process involves importing geolocated meteorological data from shapefiles, downloading a Digital Elevation Model (DEM) for elevation data, and utilizing 3D Kriging for interpolation. This method improves the accuracy of meteorological data interpolation across various elevations, providing comprehensive spatial coverage. Key components include precise data import, effective DEM integration, and accurate 3D Kriging, addressing scientific questions about data import precision, DEM integration, Kriging accuracy, and spatial coverage enhancement. Background Interpolating geolocated meteorological variables is crucial for obtaining comprehensive insights into environmental conditions. This workflow, comprising three components, focuses on importing shapefile data containing geolocated meteorological variables. The primary objective is to perform a 3D interpolation, considering altitude as a significant factor. To achieve this, the workflow downloads a Digital Elevation Model (DEM) to incorporate elevation information and utilizes 3D Kriging for interpolation. Introduction Interpolating meteorological variables in geospatial datasets is essential for understanding environmental conditions. This workflow aims to enhance the accuracy of such interpolations by importing shapefile data, obtaining elevation data from a DEM, and performing a 3D interpolation using Kriging. The resulting dataset provides interpolated meteorological values for locations not covered by the original sampling. Aims The primary aim of this workflow is to achieve accurate 3D interpolation of meteorological variables, considering altitude, to enhance spatial coverage. The workflow includes the following key components: ∙Shapefile Data Import: Imports geolocated meteorological variables from a shapefile, preparing the data for 3D interpolation. ∙Digital Elevation Model (DEM) Download: Downloads a Digital Elevation Model (DEM) to obtain elevation information for the interpolation process. ∙3D Kriging Interpolation: Utilizes 3D Kriging to interpolate meteorological variables, incorporating altitude information for enhanced accuracy. Scientific questions ∙Data Import Precision: How precise is the workflow in importing geolocated meteorological variables from the shapefile data? ∙DEM Download and Integration: How effectively does the workflow download the DEM and integrate elevation information into the interpolation process? ∙3D Kriging Accuracy: How accurate is the 3D Kriging interpolation in providing reliable meteorological values, considering altitude as a key factor? ∙Enhancement of Spatial Coverage: To what extent does the 3D interpolation process enhance spatial coverage, providing interpolated values for locations not originally sampled?

This workflow aims to enhance water resource management by combining temporal precipitation and temperature data from various sources. It performs essential hydroclimatic calculations, including potential evapotranspiration (ETP), useful rainfall (LLU), runoff (ESC), and infiltration (INF). Using data integration and interactive HTML graph generation, the workflow provides dynamic visual representations of precipitation trends, ETP dynamics, and correlations between temperature and precipitation. This comprehensive approach facilitates a deeper understanding of hydroclimatic patterns and supports effective water management decisions. Background Water resource management necessitates a comprehensive understanding of hydroclimatic patterns. This series of workflows addresses the amalgamation of temporal precipitation and temperature data from distinct sources to facilitate an integrated analysis. By unifying these datasets, the workflows perform initial processing and calculations, including the determination of potential evapotranspiration (ETP), useful rainfall (LLU), runoff (ESC), and infiltration (INF). The subsequent components generate interactive HTML graphs, providing valuable insights into hydroclimatic dynamics. Introduction Effective water resource management hinges on the ability to synthesize disparate datasets into a cohesive analysis. This series of workflows not only consolidates temporal precipitation and temperature data from various locations but also performs essential calculations to derive key hydroclimatic parameters. The resulting interactive graphs offer a dynamic visual representation of the cumulative deviation from the mean precipitation, temporal trends in precipitation (including ESC, INF, LLU, and total precipitation), ETP, daily and cumulative precipitation, temperature (maximum and minimum), and monthly precipitation. Aims The primary objectives of this workflow are tailored to address specific challenges and goals inherent in the analysis of ETP: ∙Data Integration: Unify temporal precipitation and temperature data from various sources into a coherent dataset for subsequent analysis. ∙Hydroclimatic Calculations: Calculate potential evapotranspiration (ETP), useful rainfall (LLU), runoff (ESC), and infiltration (INF) based on the integrated dataset. Note: ETP is calculated using formulas from different authors, including Hargreaves, Hamon, Jensen–Haise, Makkink, Taylor, Hydro Quebec, Oudin, and Papadakis. ∙Interactive Graph Generation: Utilize HTML to create interactive graphs representing cumulative deviation from the mean precipitation, temporal trends in precipitation (including ESC, INF, LLU, and total precipitation), ETP, daily and cumulative precipitation, temperature (maximum and minimum), and monthly precipitation. Scientific questions This workflow addresses critical scientific questions related to ETP analysis: ∙Temporal Precipitation Trends: Are there discernible patterns in the temporal trends of precipitation, and how do they relate to runoff, infiltration, and useful rainfall? ∙Potential Evapotranspiration (ETP) Dynamics: How does ETP vary over time using different authors' methods, and what are the implications for potential water loss? ∙Relationship Between Precipitation and Temperature: Are there significant correlations between variations in temperature (maximum and minimum) and the quantity and type of precipitation? ∙Seasonal Distribution of Precipitation: How is precipitation distributed across months, and are there seasonal patterns that may influence water management?

The aim of the (Taxonomic) Data Refinement Workflow is to provide a streamlined workflow environment for preparing observational and specimen data sets for use in scientific analysis on the Taverna platform. The workflow has been designed in a way that, accepts input data in a recognized format, but originating from various sources (e.g. services, local user data sets), includes a number of graphical user interfaces to view and interact with the data, the output of each part of the workflow is compatible with the input of each part, implying that the user is free to choose a specific sequence of actions, allows for the use of custom-built as well as third-party tools applications and tools. This workflow can be accessed through the BioVeL Portal here http://biovelportal.vliz.be/workflows?category_id=1 This workflow can be combined with the Ecological Niche Modelling Workflows. http://marine.lifewatch.eu/ecological-niche-modelling Developed by: Biodiversity Virtual e-Laboratory (BioVeL) (EU FP7 project) Technology or platform: The workflow has been developed to be run in the Taverna automated workflow environment.

This workflow streamlines the export, preprocessing, and analysis of phytosociological inventories from a project database. The workflow's goals include exporting and preprocessing inventories, conducting statistical analyses, and using interactive graphs to visualize species dominance, altitudinal distribution, average coverage, similarity clusters, and species interactions. It also calculates and visualizes the fidelity index for species co-occurrence. This workflow addresses key scientific questions about dominant species, distribution patterns, species coverage, inventory similarity, species interactions, and co-occurrence probabilities, aiding efficient vegetation management in environmental projects. Background Efficient vegetation management in environmental projects necessitates a detailed analysis of phytosociological inventories. This workflow streamlines the export and preprocessing of vegetation inventories from the project database. Subsequently, it conducts various statistical analyses and graphical representations, offering a comprehensive view of plant composition and interactions. Introduction In the realm of vegetation research, the availability of phytosociological data is paramount. This workflow empowers users to specify parameters for exporting vegetation inventories, performs preprocessing, and conducts diverse statistical analyses. The resulting insights are visually represented through interactive graphs, highlighting predominant species, altitudinal ranges of plant communities, average species coverage, similarity clusters, and interactive species interactions. Aims The primary objectives of this workflow are tailored to address specific challenges and goals inherent in the analysis of phytosociological inventories: 1. Export and Preprocess Inventories: Enable the export and preprocessing of phytosociological inventories stored in the project database. 2. Statistical Analyses of Species and Plant Communities: Conduct detailed statistical analyses on the species and plant communities present in the inventories. 3. Interactive Graphical Representation: Utilize interactive graphs to represent predominant species, altitudinal ranges of plant communities, and average species coverage. 4. Similarity Dendrogram: Generate a dendrogram grouping similar phytosociological inventories based on the similarity of their species content. 5. Interactive Species Interaction Analysis: Visualize species interactions through interactive graphs, facilitating the identification of species that tend to coexist. 6. Calculation and Visualization of Fidelity Index: Calculate the fidelity index between species and visually represent the probability of two or more species co-occurring in the same inventory. Scientific Questions This workflow addresses critical scientific questions related to the analysis of phytosociological inventories: - Dominant Species Identification: Which species emerge as predominant in the phytosociological inventories, and what is their frequency of occurrence? - Altitudinal Distribution Patterns: How are plant communities distributed across altitudinal ranges, and are there discernible patterns? - Average Species Coverage Assessment: What is the average coverage of plant species, and how does it vary across different inventories? - Similarity in Inventory Content: How are phytosociological inventories grouped based on the similarity of their species content? - Species Interaction Dynamics: Which species exhibit notable interactive dynamics, and how can these interactions be visualized? - Fidelity Between Species: What is the likelihood that two or more species co-occur in the same inventory, and how does this fidelity vary across species pairs?

Background Information about the incidence and impact of Non-indigenous and Invasive Species (NIS) are often scattered across different spatial, temporal and taxonomic scales and, therefore, it can be difficult to draw any comprehensive conclusion about the most vulnerable ecosystems or map the areas more at risk of biological invasion. Occurrence data are usually collected using a variety of sampling approaches and the impact of NIS can affect ecosystems at any biological scale (e.g., individuals, populations, communities, etc.) and with different degrees of severity. The heterogeneity of species occurrence data and the complexity of multiple effects acting at different biological scales and determining ecosystem-dependent changes make estimations of NIS incidence and impact difficult on large geographical scales. Introduction Comprehensive, standardised and modular methods to assess both incidence and impact of NIS at different spatial scale (e.g., continental, regional, local) are required to support management and conservation actions and to prioritise areas of intervention. To achieve this objective, researchers have developed two standardised approaches to quantify the incidence and the impact of NIS on ecosystems respectively by means of: occurrence cubes and analysis of the Cumulative IMPacts of invasive ALien species (CIMPAL). Occurrence cubes consist of species occurrence data aggregated on a three-dimensional space (cube) whereby the three dimensions considered are taxonomic, temporal and spatial. Data cubes allow the homogenisation and aggregation of heterogeneous data collected using different methods and standards. The CIMPAL model allows the mapping of cumulative negative impacts of NIS on different ecosystems (marine, freshwater, terrestrial) on the basis of existing evidence. NIS impacts can be additionally mapped according to the main associated pathways of introduction and the relative importance of species on cumulative impacts can be inferred. Using these two standardised methodologies, vulnerability map of biotopes can be produced in order to identify hot spots particularly threatened by NIS and that, in turn, would require special protection and maintenance. Aims This validation case aims at using the occurrence cube approach and the CIMPAL model to map ecosystem and habitat type vulnerability at continental scale, inferring the relevance of key risk factors (e.g. vectors of invasion) and intrinsic resistance/resilience components (e.g. native biodiversity, food web structure, etc…) and design scenarios of change, in the context of expected climate changes, for ecosystem and habitat types found highly vulnerable to NIS. The workflow developed offers a valuable tool that may assist policy makers and managers in their efforts to develop strategies for mitigating the impacts of invasive species and improving the environmental status of marine waters. The method, although tested on the the marine environment, can easily be transferred to the terrestrial environment as well.

Background Ailanthus altissima is one of the worst invasive plants in Europe. It reproduces both by seeds and asexually through root sprouting. The winged seeds can be dispersed by wind, water and machinery, while its robust root system can generate numerous suckers and cloned plants. In this way, Ailanthus altissima typically occurs in very dense clumps, but can also occasionally grow as widely spaced or single stems. This highly invasive plant can colonise a wide range of anthropogenic and natural sites, from stony and sterile soils to rich alluvial bottoms. Due to its vigour, rapid growth, tolerance, adaptability and lack of natural enemies, it spreads spontaneously, out-competing other plants and inhibiting their growth Introduction Over the last few decades, Ailanthus altissima has quickly spread in the Alta Murgia National Park (Southern Italy) which is mostly characterized by dry grassland and pseudo-steppe, wide-open spaces with low vegetation, which are very vulnerable to invasion. Ailanthus altissima causes serious direct and indirect damages to ecosystems, replacing and altering communities that have great conservation value, producing severe ecological, environmental and economic effects, and causing natural habitat loss and degradation. The spread of Ailanthus altissima is likely to increase in the future, unless robust action is taken at all levels to control its expansion. In a recent working document of the European Commission, it was found that the cost of controlling and eliminating invasive species in Europe amounts to €12 billion per year. Two relevant questions then arise: i) whether it is possible or not to fully eradicate or, at least, to reduce the impact of an invasive species and ii) how to achieve this at a minimum cost, in terms of both environmental damage and economic resources. A Life Program funded the Life Alta Murgia project (LIFE12BIO/IT/000213) had, as its main objective, the eradication of this invasive exotic tree species from the Alta Murgia National Park. That project provided both the expert knowledge and valuable in-field data for the Ailanthus validation case study, which was conceived and developed within the Internal Joint Initiative of LifeWatch ERIC. Aims At the start of the on-going eradication program a single map of A. altissima was available, dating back to 2012. Due to the lack of data, predicting the extent of invasion and its impacts was extremely difficult, making it impossible to assess the efficacy of control measures. Static models based on statistics cannot predict spatial–temporal dynamics (e.g. where and when A. altissima may repopulate an area), whereas mechanistic models incorporating the growth and spread of a plant would require precise parametrisation, which was extremely difficult with the scarce information available. To overcome these limitations, a relatively simple mechanistic model has been developed, a diffusion model, which is validated against the current spatial distribution of the plant estimated by satellite images. This model accounts for the effect of eradication programs by using a reaction term to estimate the uncertainty of the prediction. This model provides an automatic tool to estimate a-priori the effectiveness of a planned control action under temporal and budget constraints. This robust tool can be easily applied to other geographical areas and, potentially, to different species.

Background Biological invasions are acknowledged to be significant environmental and economic threats, yet the identification of key ecological traits determining invasiveness of species has remained elusive. One unappreciated source of variation concerns dietary flexibility of non-native species and their ability to shift trophic position within invaded food webs. Trophic plasticity may greatly influence invasion success as it facilitates colonisation, adaptation, and successful establishment of non-native species into new territories. In addition, having a flexible diet gives the introduced species a better chance to become invasive and, as a consequence, to have a strong impact on food webs, determining secondary disruptions such as trophic cascades and changes in energy fluxes. The deleterious effects can affect multiple trophic levels. Introduction Crustaceans are considered the most successful taxonomic group of aquatic invaders worldwide. Their ability to colonise and easily adapt to new ecosystems can be ascribed to a number of ecological features including their omnivorous feeding behaviour. This validation case study focuses on two invasive crustaceans widely distributed in marine and freshwater European waters: the Atlantic blue crab Callinectes sapidus and the Louisiana crayfish or red swamp crayfish Procambarus clarkii. Callinectes sapidus and Procambarus clarkii are opportunistic omnivores that feed on a variety of food sources from detritus to plants and invertebrates. For this reason, they represent a good model to investigate the variation of trophic niches in invaded food webs and their ecological impact on native communities. The ecological consequences of the invasion and establishment of these invasive crustaceans can vary from modification of carbon cycles in benthic food webs to regulation of prey/predator abundance through bottom-up and top-down interactions. Understanding how the trophic ecology of these invasive crustaceans shapes benthic food webs in invaded ecosystems is crucial for an accurate assessment of their impact. The analysis of stable isotopes can provide important clues on the trophic effects of invasive species within non-native ecosystems by evaluating changes in their trophic position and characteristics of their trophic niche. Aims This validation case uses a collection of stable isotopes (δ13C and δ15N) of C. sapidus and P. clarkii and their potential prey in invaded food webs to quantify changes in the trophic position of the invaders and to assess post-invasion shifts in their dietary habits. This case study additionally evaluates the main environmental drivers involved in trophic niche adaptations and whether such bioclimatic predictors influence broad-scale patterns of variation in the trophic position of the invader.

This workflow integrates the MEDA Toolbox for Matlab and Octave, focusing on data simulation, Principal Component Analysis (PCA), and result visualization. Key steps include simulating multivariate data, applying PCA for data modeling, and creating interactive visualizations. The MEDA Toolbox combines traditional and advanced methods, such as ANOVA Simultaneous Component Analysis (ASCA). The aim is to integrate the MEDA Toolbox into LifeWatch, providing tools for enhanced data analysis and visualization in research. Background This workflow is a template for the integration of the Multivariate Exploratory Data Analysis Toolbox (MEDA Toolbox, https://github.com/codaslab/MEDA-Toolbox) in LifeWatch. The MEDA Toolbox for Matlab and Octave is a set of multivariate analysis tools for the exploration of data sets. There are several alternative tools in the market for that purpose, both commercial and free. The PLS_Toolbox from Eigenvector Inc. is a very nice example. The MEDA Toolbox is not intended to replace or compete with any of these toolkits. Rather, the MEDA Toolbox is a complementary tool that includes several contributions of the Computational Data Science Laboratory (CoDaS Lab) to the field of data analysis. Thus, traditional exploratory plots based on Principal Component Analysis (PCA) or Partial Least Squares (PLS), such as score, loading, and residual plots, are combined with new methods: MEDA, oMEDA, SVI plots, ADICOV, EKF & CKF cross-validation, CSP, GPCA, etc. A main tool in the MEDA Toolbox which has received a lot of attention lately is ANOVA Simultaneous Component Analysis (ASCA). The ASCA code in the MEDA Toolbox is one of the most advanced internationally. Introduction The workflow integrates three examples of functionality within the MEDA Toolbox. First, there is a data simulation step, in which a matrix of random data is simulated with a user-defined correlation level. The output is sent to a modeling step, in which Principal Component Analysis (PCA) is computed. The PCA model is then sent to a visualization module. Aims The main goal of this template is the integration of the MEDA Toolbox in LifeWatch, including data simulation, data modeling, and data visualization routines. Scientific Questions This workflow only exemplifies the integration of the MEDA Toolbox. No specific questions are addressed.

The Ecological Niche Modelling Workflows offer an extensible framework for analyzing or predicting the impact of environmental changes on the distribution of biodiversity. Especially in combination with data aggregation workflows like the Taxonomic Data Refinement Workflow, the Ecological Niche Modelling workflows facilitate the analysis of species distribution patterns over large geo-temporal, taxonomic, and environmental scales. Examples for applications are studies of species adaptations to climate change, dynamic modeling of ecologically related species, identification of regions with accumulated risk for invasion, potential for restoration, or natural protected areas. Developed by: The Biodiversity Virtual e-Laboratoy (BioVeL) (EU FP7 project) Technology or platform: These workflows have been developed to be run in the Taverna automated workflow environment (https://incubator.apache.org/projects/taverna.html). In their current form, the workflow files (with the .t2flow extension) can be loaded and executed in the workbench variant of Taverna. They have been tested with Taverna Workbench version 2.4. These workflows can also be run in BioVeL Portal, a light weight user interface which allows browsing, reviewing and running Taverna Workflows without the need of installing any software.