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  • This service aims at modeling trophic positions as a function of environmental drivers (e.g., through GAMs, etc.) by using the "Bioclim_predic.csv" file. It represents the final step (Step 5) of the Crustaceans Workflow within the Internal Joint Initiative.

  • A service that aims at verifying the coordinates (latitude and longitude in column 3 and 4 of file “Pop_trop_SIA.csv”) match with the location (“country” and “location” in column 1 and 2 of file “Pop_troph_SIA.csv”) using the “SIAShapefile.shp”. This is very important because these geographic coordinates will be used in other services to extract and retrieve environmental data on the location of the species observation/occurrence. So scientists need to ensure these coordinates are correct. It represents the Step 3.2 of the Crustaceans Workflow within the Internal Joint Initiative.

  • Background Monitoring hard-bottom marine biodiversity can be challenging as it often involves non-standardised sampling methods that limit scalability and inter-comparison across different monitoring approaches. Therefore, it is essential to implement standardised techniques when assessing the status of and changes in marine communities, in order to give the correct information to support management policy and decisions, and to ensure the most appropriate level of protection for the biodiversity in each ecosystem. Biomonitoring methods need to comply with a number of criteria including the implementation of broadly accepted standards and protocols and the collection of FAIR data (Findable, Accessible, Interoperable, and Reusable). Introduction Artificial substrates represent a promising tool for monitoring community assemblages of hard-bottom habitats with a standardised methodology. The European ARMS project is a long-term observatory network in which about 20 institutions distributed across 14 European countries, including Greenland and Antarctica, collaborate. The network consists of Autonomous Reef Monitoring Structures (ARMS) which are deployed in the proximity of marine stations and Long-term Ecological Research sites. ARMS units are passive monitoring systems made of stacked settlement plates that are placed on the sea floor. The three-dimensional structure of the settlement units mimics the complexity of marine substrates and attracts sessile and motile benthic organisms. After a certain period of time these structures are brought up, and visual, photographic, and genetic (DNA metabarcoding) assessments are made of the lifeforms that have colonised them. These data are used to systematically assess the status of, and changes in, the hard-bottom communities of near-coast ecosystems. Aims ARMS data are quality controlled and open access, and they are permanently stored (Marine Data Archive) along with their metadata (IMIS, catalogue of VLIZ) ensuring data fairness. Data from ARMS observatories provide a promising early-warning system for marine biological invasions by: i) identifying newly arrived Non-Indigenous Species (NIS) at each ARMS site; ii) tracking the migration of already known NIS in European continental waters; iii) monitoring the composition of hard-bottom communities over longer periods; and iv) identifying the Essential Biodiversity Variables (EBVs) for hard-bottom fauna, including NIS. The ARMS validation case was conceived to achieve these objectives: a data-analysis workflow was developed to process raw genetic data from ARMS; end-users can select ARMS samples from the ever-growing number available in collection; and raw DNA sequences are analysed using a bioinformatic pipeline (P.E.M.A.) embedded in the workflow for taxonomic identification. In the data-analysis workflow, the correct identification of taxa in each specific location is made with reference to WoRMS and WRiMS, webservices that are used to check respectively the identity of the organisms and whether they are introduced.

  • 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.

  • A service that aims at analyzing the trophic position of the “invader” by using a Bayesian model to estimate the NA values of the trophic position that corresponds to the column 13 “TP” of the ""Tax_validated_SIA.csv” file by means of a loop that allows extracting data of single populations. It represents the Step 2 of the Crustaceans Workflow within the Internal Joint Initiative.

  • An R package to get downloads from the EurOBIS database. In 2019, development started for the eurobis R package, to serve as an easy to use interface to download EurOBIS data in R. Currently, the main functions and documentation are being developed and are working, but need some further testing and user feedback before it can be officially released.

  • This service aims at creating a vector from the coordinates in the “Pop_troph_SIA.csv” file and convert it into a shapefile. It represents the Step 3.1 of the Crustaceans Workflow within the Internal Joint Initiative.

  • The service aims at harvesting species occurrences from GBIF based on a keyword for the geographic region (two letter country code) and a time interval, both entered by the user. It represents the Step 1a of the Biotope vulnerability 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.

  • It is a Support Vector Machine, pixel-based, classifier trained for a multi-class problem. It represents the Step 4 of the Ailanthus Workflow within the Internal Joint Initiative.