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  • The main aim of this workflow is to detect likely NIS species with metabarcoding. Detailed questions that will be answer with this tool and for each metabarcoding sample are: 1) What was the taxonomic resolution achieved with the marker (species, genus, family) and pipeline used? And how many OTUs were not attributed to any taxa? 2) Which taxa were listed? 3) Which species are listed as nonnative invasive (NIS) in the checklist for the country published by GBIF? 4) Which species are not listed as NIS (i.e. N - native in the region/country or newNIS - new detected taxa)? 5) What is the number or reads of each taxa in the newNIS list? 6) How much does the species not listed as NIS overlap with its known distribution area?

  • Biological invasions are to date acknowledged as significant environmental and economic threats, yet the identification of key ecological traits determining species invasiveness has remained elusive. One unappreciated source of variation concerns dietary flexibility and the ability to shift trophic position within invaded food webs. Trophic plasticity may greatly influence invasion success as it negates resource availability as a functional constraint for introduced individuals; in addition, the impact of an invader might increase with its dietary plasticity since a greater proportion of the resident assemblage of species and multiple trophic levels are affected. The validation case will focus on two invasive crustaceans widely distributed in marine and freshwater European waters, i.e., the Atlantic blue crab Callinectes sapidus and the Louisiana crayfish Procambarus clarkii. Key questions that will be addressed are: a) does the trophic position of C. sapidus vary between invaded Mediterranean and native west Atlantic habitats, testifying the occurrence of significant post-invasion shifts in dietary habits? b) does the trophic position of P. clarkii vary in invaded habitats, and which are the main ecological factors involved? c) do bioclimatic drivers influence broad‐scale spatial patterns of variation in the trophic position of C. sapidus and P. clarkii?

  • To address the today’s ecological challenges, it is necessary to use data coming from different disciplines and providers. Discovery and integration of data, especially from the ecological domain, is highly labour-intensive and often ambiguous in semantic terms. To improve the location, interpretation and integration of data based on its inherent meaning vocabularies can help in harmonizing and enriching descriptions of data, providing a formal mechanism for the definition of terms and their relationships. To satisfy these emerging needs LifeWatch ERIC developed a Semantic Resources Catalogue (EcoPortal) focused on the Biodiversity and Ecosystem Research. This effort will help to support the community in the management and integration/alignment of their semantics and subsequently also of their data. The main goal of the EcoPortal initiative is to provide a unique platform for vocabularies in the ecological and domain for different kinds of stakeholders.

  • The ARMS European project is a Genomics Observatory and a collaboration of ~20 European institutes. Our network of Autonomous Reef Monitoring Structures (ARMS) consists of ~25 observatories who deploy individual settlement units in the vicinity of marine stations and Long-term Ecological Research sites in European coastal waters and Ant/arctica. After a few months they are brought up, and visual, photographic, and genetic assessments are made of the lifeforms that settled on them. These data will be used to assess the status of, and changes in, the hard-bottom communities of these near-coast environments. This project began in 2019 and will run for as long as funding allows. The scientific goals are: - to identify newly arrived Non-Indigenous Species (NIS) at each ARMS site; - to track the migration of already known NIS in European continental waters; - to monitor hard-bottom community composition over longer periods; - to identify the Essential Biodiversity Variables for hard-bottom fauna, including NIS.

  • Ailanthus altissima is one of the worst invasive plant species in Europe. It reproduces both by seeds and asexually by vegetative sprouts. The winged seed can be dispersed by wind, water and machinery, while its robust root system can generate numerous suckers and progenity plants. A. altissima typically occurs in very dense clumps, resulting from even-aged seedling establishment or from clonal expansion through root sprouting, and occasionally it growth as widely spaced or single stems. It grows on a broad range of anthropogenic to 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 plant species and reducing their growth. In the last decades, the species is quickly spreading and multiplying in the Alta Murgia national Park (South Italy) mostly characterized by dry grassland and pseudo-steppe, wide-open spaces with low vegetation, whose tendency is to be easily invaded. Ailanthus altissima causes serious direct and indirect damages to ecosystems, replacing and altering communities that have a great conservation value, producing severe ecological environmental and economic effects and causing natural habitat loss and degradation. This treat is likely to increase in the future, unless robust action is taken at all levels to control the advancement and spread. In a recent working document of the European Commission, it has been evidenced that the costs of controlling and eliminating invasive species in Europe amounts to 12 billion of euro per year. Two relevant questions then arises: whether it is possible or not to fully eradicate or at least to reduce the impact of an invasive species and how to do that at a minimum cost, in terms of both environmental damage and economic resources. The Life Programme, the European Commission financial instrument for environmental and nature conservation, funded the Life Alta Murgia project (LIFE12BIO/IT/000213) which has, as main objective, the eradication of the invasive exotic tree species from the Alta Murgia national Park and which provided both the expert knowledge and the infield data for the case study we have started. Actually, there is a single map of Ailanthus A. presence, provided by the Life Alta Murgia project, and it is dated at 2012, before the starting of the on-going eradication program. Due to the lack of data, predicting the extent of invasion and its impacts are extremely difficult as well as assessing the efficacy of any control measures. If static models based on statistical fitting cannot predict any spatial–temporal dynamics, (e.g. where and when the Ailanthus trees may repopulate a zone), mechanistic models which incorporate growth and spread of a plant, would require a precise parametrization that is extremely difficult with the few available data. Due to these limitations, we rely on a relatively simple mechanistic model, a diffusion model, which is validated against the current plant spatial distribution deduced by satellite images. The effect of an eradication program is taken into account with a reaction term, which simulates the plant eradication during a control program. The value of our predictive mechanistic model would be to provide an automatic tool for an a-priori estimate of the effectiveness of a planned control action under temporal and budget constraints. In our study, the interest is focused on finding the best budget allocation both in space and in time for the Park area maintenance, this aiding in determining whether a control policy needs to be improved. Within the activities of the ECOPOTENTIAL H2020 project, we developed an automatic tool that can get the plant presence information from the satellite and use that information for predicting the best action of the park manager. Indeed, many satellite data are not being used to their full potential and there are optimisation methods that are powerful but not being used on-ground. We are linking the two methods and providing an avenue to make them accessible and useful to managers. We are currently estimating the uncertainty of the model and working at a validation of the overall approach by testing it on different areas, by using aerial images coming from a regional project. The final aim is to provide a robust tool to be easily transferred to other geographical areas and potentially to different species. The approach might incorporate the effect of changes (climate, land cover): this is still an open research issue, which will largely benefit from the exploitation of the LifeWatch ERIC infrastructure.

  • The Phytoplankton Traits Computation service provides a user-friendly Graphical User Interface (GUI) that allow researchers to run a workflow wrapped into R code for the computation of morphological and demographic traits, such as hidden dimension, biovolume, surface area, surface-volume ratio, cell carbon content, density, carbon content and total biovolume. The service works on datasets structured according to the Phyto template based on the "LifeWatch Italy Data Schema" that can be selected by the GUI or uploaded by the researchers. The input file is in CSV format with some mandatory fields according to the computation type. Before selecting or uploading the input file, users have to specify some parameters (e.g., the computation type, the traits to be computed, the aggregation level, etc.). The service provides as output a file that includes the original records with the computed traits. Moreover, users can restrict the set of records of the output file by exploiting the "Data Selecting" service, which is integrated in the workflow, that allows to exclude rare taxa based on their ranked distribution considering the density (n of cells per liter), or the biovolume per liter (μm3 of cells per liter) or the carbon content per liter (pgC per liter) as size unit. To do that, users need to specify the threshold value (between 0 and 1) and to choose the size unit (density, biovolume per liter or carbon content per liter). Both output files could be used in the other services provided within the Phyto VRE.

  • The LifeWatch Italy national node has realised the Phytoplankton Virtual Research Environment (Phyto VRE) for supporting researchers to address basic and applied studies on phytoplankton ecology at a level of resolution going from individual cells to whole assemblages. The Phyto VRE enables researchers to: - produce harmonised data on taxonomy and morphological traits by using the Atlas of Phytoplankton, Atlas of Shapes and Phytoplankton Traits Thesaurus; - access, download, and select LifeWatch Italy datasets (published through the LifeWatch Italy Data Portal and distributed by the LifeWatch ERIC Metadata Catalogue) or upload their own datasets structured according to the Phyto template based on the LifeWatch Italy Data Schema in order to execute the services included in the VRE; - faciliatate the computation of morphological and demographic traits (such as hidden dimension, biovolume, surface area, surface-volume ratio, cell carbon content, etc.) and investigate their distribution patterns at different levels of data aggregation (i.e. spatial, temporal, taxonomic) by means of services which automate a set of operations written in the R language.

  • Assessing ecosystem and habitat type vulnerability to invasion/colonisation of non indigenous species (NIS) is key to focus conservation actions protecting them from the negative impacts related to NIS invasiveness. This validation case aims at mapping 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. This workflow aims at highlighting, at the scale of Europe, where the incidence of invasive alien species is the strongest and what areas (or habitats when possible) are the most vulnerable to the negative impact of invasive alien species. At present, the scale considered is the one of European countries and respective marine regions.

  • This service provides a user-friendly Graphical User Interface (GUI) that allow researchers to run a workflow wrapped into R code for: - the reshaping of the input dataset in order to obtain alien species and native specie richness for each family at the habitat and site level. If more that 1 EUNIS habitat is present in a site, the richness will be calculate for the two (or more that 2) habitats in the site; - the selection of the best fitting model, by calling a set of R functions from the packages lme4 and MuMIn. Initially, a full GLMM model is calculated including both richness and level-1 EUNIS habitat as fixed factor. Subsequently, reduced models are calculated and compared with the full model using the Akaike Information Criteria (AIC). The model showing the best AIC is used to create the output (tables and graph); - the plot of the rarefaction curves on the reshaped dataset.

  • The World Register of Introduced Marine Species (WRiMS) records which marine species in the World Register of Marine species (WoRMS) have been introduced deliberately or accidentally by human activities to geographic areas outside their native range. As a user or developer you can use the WoRMS webservice to feed your own application with standard WoRMS data, including WRiMS data. A non-exhaustive list of applications: - get the AphiaID for your taxon - check the spelling of your taxa - get the authority for your taxa - get the full classification for your taxa - resolve your unaccepted names to accepted ones - get all synonyms for a taxon - fuzzy/near match your species list - resolve a common name/vernacular to a scientific name - get the common name(s)/vernacular(s) for a taxon - get the sources/references for a taxon - get the WoRMS citation for a taxon - get the direct children for a taxon - get all taxa modified during a time interval - get an external identifier for a taxon - get the AphiaID for an external identifier/database - get all distributions for a taxon - get all attributes for a taxon