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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?
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.