RESUMO
The ecological assessment of European aquatic ecosystems is regulated under the framework directives on strategy for water and marine environments. Benthic macroinvertebrates are the most used biological quality element for ecological assessment of rivers, coastal-marines, and transitional waters. The morphological identification of benthic macroinvertebrates is the current tool for their assessment. Recently, DNA-based tools have been proposed as effective alternatives. The main current limits of DNA-based applications include the incompleteness of species recorded in the DNA barcode reference libraries and the primers bias. Here, we analysed the influence of the incompleteness of DNA barcode databases on species diversity indices, ecological indicators, and ecological assessment in transitional waters of the southeast Mediterranean, taking into account the availability of commonly sequenced and deposited genomic regions for listed species. The ecological quality status assigned through the potential application of both approaches to the analysed transitional water ecosystems was different in 27% of sites. We also analysed the inter-specific genetic distances to evaluate the potential application of the DNA metabarcoding method. Overall, this work highlights the importance to expand the barcode databases and to analyse, at the regional level, the gaps in the DNA barcodes.
RESUMO
BACKGROUND: The main objective of the project Common strategies and best practices to IMprove the transnational PRotection of ECOsystem integrity and services - IMPRECO is to enhance the safeguarding of ecosystems and ecosystem services. Additionally, the aim of this project is to tackle their environmental vulnerability by strengthening the potential of the Protected Areas in biodiversity, ecosystems and ecosystem services conservation. This is expected to be addressed by maintaining it through their transnational networking located in the European Adriatic-Ionian Macro-Region. NEW INFORMATION: The aim of this research is: 1) to characterise the habitats and ecosystems involved in the coastal-marine protected areas considered; 2) to set a biodiversity baseline; 3) to understand what current ecosystems' conditions are; 4) to build up a transnational biomonitoring programme of target species and habitats and 5) to assess their response to pilot actions. To do so, a transnational inventory of species, habitats, ecosystems and ecosystem services was established, starting with the seven coastal-marine protected areas involved in the project. Data collection was carried out using different sources of information: scientific literature, officially available data from NATURA 2000 Standard Data Forms, checklists from local biomonitoring programmes, personal observations and citizen science, historical maps and data from new in-field analyses. Data were filled in the transnational biodiversity geo-databases according to the NATURA 2000 standards about habitat features, species protection level and species features. The presence of alien species (non-indigenous species, NIS) was also acknowledged and references about data collection were provided in the databases according to the Darwin Core standards.
RESUMO
BACKGROUND: The use of location-based data in clinical settings is often limited to real-time monitoring. In this study, we aim to develop a proximity-based localization system and show how its longitudinal deployment can provide operational insights related to staff and patients' mobility and room occupancy in clinical settings. Such a streamlined data-driven approach can help in increasing the uptime of operating rooms and more broadly provide an improved understanding of facility utilization. OBJECTIVE: The aim of this study is to measure the accuracy of the system and algorithmically calculate measures of mobility and occupancy. METHODS: We developed a Bluetooth low energy, proximity-based localization system and deployed it in a hospital for 30 days. The system recorded the position of 75 people (17 patients and 55 staff) during this period. In addition, we collected ground-truth data and used them to validate system performance and accuracy. A number of analyses were conducted to estimate how people move in the hospital and where they spend their time. RESULTS: Using ground-truth data, we estimated the accuracy of our system to be 96%. Using mobility trace analysis, we generated occupancy rates for different rooms in the hospital occupied by both staff and patients. We were also able to measure how much time, on average, patients spend in different rooms of the hospital. Finally, using unsupervised hierarchical clustering, we showed that the system could differentiate between staff and patients without training. CONCLUSIONS: Analysis of longitudinal, location-based data can offer rich operational insights into hospital efficiency. In particular, they allow quick and consistent assessment of new strategies and protocols and provide a quantitative way to measure their effectiveness.
