The conceptual framework of Essential Biodiversity Variables (EBVs) aims to capture the major dimensions of biodiversity change by structuring biodiversity monitoring and by ruling data collection amongst different providers. Amongst the research infrastructures adopting and implementing the EBV framework, LTER-Europe - the European node of ILTER (International Long-Term Ecological Research) - follows the approach to compare site-based biodiversity observations within and across its networks. However, a synoptic overview of their contributions with EBVs-relevant data is still missing, since data are not made available for several reasons. In this paper, we assess the capacity of LTER-Italy, one of the richest and heterogeneous networks of LTER sites in Europe, to provide data to “Species Distribution” and “Species Abundance” EBVs without inspecting and downloading their contents. To this aim, we mine the EBVs information which is publicly structured and shared by LTER site managers through DEIMS-SDR, the LTER-Europe online metadata repository. We classify the sites according to two types of contributions: (i) the actual contribution, based on metadata of datasets and (ii) the potential contribution, based on metadata of sites. Through these assessments, we investigate if LTER-Italy monitoring activities can provide EBVs measures and which sites currently provide datasets. By comparing the two contributions, we pinpoint the factors hampering the accessibility of LTER-Italy data and suggest solutions to increase the discoverability and reusability of LTER-Italy EBVs measurements. The research provides the first overview of EBVs monitored in LTER-Italy and the corresponding data management practices, as well as an evaluation of the interoperability of this network with respect to other research organisations for legal and technical aspects.

Essential Biodiversity Variables (EBVs) and Biological and Ecosystem Essential Ocean Variables (BioEco EOVs) are two cooperative conceptual frameworks which help harmonize and process multi-source marine biodiversity observations into robust indexes, in order to measure progress toward policy conservation goals. Long-term monitoring networks are encouraged to contribute to these frameworks by mobilizing historical times series which are suitable for detecting impacts of management policies. In this paper, we identify specific recommendations for increasing reuse in the EV frameworks of the biodiversity historical data collected and maintained by the Gulf of Venice (GOV) site, i.e., the monitoring facility that is selected as case study in the Italian Long-Term Ecological Research network (LTER-Italy). The recommendations are obtained through a practical approach comprising two phases. In the first phase, a literature review helps extract the guidelines for implementing the principles representing the most recent attempt to unify management of EBV and BioEco EOV data, i.e., Benson’s tenets. In the second phase, we compare the guidelines to the data management practices enacted by the selected monitoring site in order to recommend curation interventions. The outputs of the analysis are discussed in order to verify if the approach and the recommendations are general enough to be replicated in the marine component of monitoring networks to coordinate the LTER data contribution to the EV frameworks.

This contribution outlines the design of a Knowledge Hub of heterogeneous documents related to the UNEP/MAP Barcelona Convention system. The Knowledge Hub is intended to serve as a resource to assist public authorities and users with different backgrounds and needs in accessing information efficiently; users should be able to either formulate natural language queries or to navigate a knowledge graph that is automatically generated to find relevant documents. The ad hoc retrieval task and the Knowledge Hub creation are defined based on state-of-the-art Large Language Models (LLMs). Specifically, this contribution focuses on a user-evaluation experiment that tested publicly available pretrained foundation Large Language Models (LLMs) for retrieving a subset of documents with varying lengths and topics.