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  • This service aims at enabling the dataset uploading from the user. It represents the Step 1b of the Biotope vulnerability Workflow within the Internal Joint Initiative.

  • This service aims to download the sequences files (fasq files) that have been choosen in Step 4 (ARMS Choose and Parameterize) in order to run PEMA. It represents the Step 5.1 of the ARMS Workflow within the Internal Joint Initiative.

  • This service aims at normalizing the user datasets to be consistent with the accepted formats. It represents the Step 3 of the Biotope vulnerability Workflow within the Internal Joint Initiative.

  • This service aims at building two datacubes based on the occurrences. The datacube aggregates (=counts) occurrences on a regular grid. This count has a stochastic component that takes the uncertainty field into account. All this process has been developed in R in the frame of the TrIAS project. It represents the Step 4 of the Biotope vulnerability Workflow within the Internal Joint Initiative.

  • This service aims at getting the ARMS collection file from the IMIS database. Since the URL of the ARMS file changes each time the file is updated, and this update is transmitted to the IMIS metadata record, it is necessary to always access the URL of the file from the field in the json format of the IMIS metadata record rather than via a fixed URL. This service represents the Step 1 of the ARMS Workflow within the Internal Joint Initiative.

  • This step of the ARMS IJI workflow does the following: - Input: the OTU tables from the previous step (running the omics pipeline), containing taxa (usually genus or species names) obtained from the databases used by the pipeline; - Check: using the WoRMS taxon match webservice to check for the WoRMS match to those taxa; - Output: the WoRMS match (yes or not) and, if matched, the scientific name and aphiaID. It represents the Step 7 of the ARMS Workflow within the Internal Joint Initiative.

  • This service aims to reformat and produce final output in variuos formats for human and machine2machine reading. It represents the Step 9 of the ARMS Workflow within the Internal Joint Initiative.

  • This service aims at extracting the Deciduous Vegetation Layer from multiclass land cover map at 30 meters, resampling it at 2 meters and masking all the pixels, of the 2 meters stack, not overlaid by the deciduous vegetation layer. It represents the Step 8 of the Ailanthus Workflow within the Internal Joint Initiative.

  • This service allows to filter the list of samples in the MasterARMS csv file on date range (start and end date in ISO format) and geographic location (lat and lon in decimal degrees). Moreover, also the selection of specific samples is permitted. It represents the Step 3 of the ARMS Workflow within the Internal Joint Initiative.

  • 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.