Semantic Interoperability Solutions for the Essential Variables: Focus on biodiversity

<p>Essential Biodiversity Variables (EBVs) are harmonized biodiversity variables and their asssociated measurements needed for developing indicators of global biodiversity change. EBVs can serve the important purpose of aligning biodiversity monitoring efforts, much as Essential Climatic Variables (ECVs) help align allied efforts in climate science. One of six initially proposed EBV classes is devoted to species' traits, since traits form the crucial link between the evolutionary history of organisms, their assembly into communities, and the nature and dynamic functioning of ecosystems. Despite their importance, prevalence, and scientific promise, the biodiversity community is still developing the conceptual, informatics, technical, and legal frameworks required for the large scale implementation and uptake. As part of an international consortium called GLOBIS-B, and in coordination with the The Group on Earth Observations Biodiversity Observation Network (GEO BON; geobon.org), we report on recent efforts to synthesize current efforts in trait data collection and trait datasets, computational workflows, ways to standardize data and metadata, and assessments of the openness and accessibility of existing species trait datasets. Members of the GLOBIS-B (www.globis-b.eu/) consortium also produced a set of candidate EBVs within the broader trait class ('Phenology', 'Organism morphology', 'Reproduction', 'Physiology' and 'Movement'). In this presentation, we begin by introducing the concept of EBVs, the current working definition of traits in the context of the EBV process, and workflows that have been developed for other EBV classes ('Species Populations') and the importance of standardizing EBV classes and the trait class, in particular. Building on this introduction, we discuss how the EBV concept is operationalized, focusing on workflows for trait integration, and the importance of data and metadata standards,following work from Kissling et al. 2017 (http://onlinelibrary.wiley.com/doi/10.1111/brv.12359/full). On the legal front, we suggest that the Creative Commons (CC) framework provides effective tools for designating legally interoperable and open data, especially when trait data are in the public domain (CC0, CC PDM) or assigned with a CC BY license, and metadata citation and other forms of attribution are available in both human and machine-readable form. We also suggest how EBVs can inform policy at national and global scales. Moving forward, renewed efforts of repeated trait data collection as well as standardised protocols for data and metadata collection are needed to improve the empirical basis of species traits EBVs. Moreover, open data as well as computational workflows are required for comprehensively assessing progress towards conservation policy targets and sustainable development goals. We conclude with a call to action for the TDWG community to consider their role in further developing, implementing, and scaling biodiversity monitoring under the EBV framework.</p>

Taking stock of nature: Essential biodiversity variables explained

The concept of Essential Biodiversity Variables (EBVs) for monitoring changes in biodiversity and tracking policy goals has substantially advanced in the last decade. I synthesize crucial information for developing an integrated European-wide biodiversity monitoring framework using EBVs to better inform environmental policies. This includes an overview of which EBVs have been prioritized for a modern and efficient European biodiversity observation network (Junker et al. 2023) and how these EBVs link to the EU policy framework and to different types of monitoring methods (Kissling et al. 2024). Generating EBVs requires designing and developing workflows for integrating primary observations from multiple data streams into aggregated and harmonized datasets that can then be modelled to derive spatially explicit EBV products with a specific spatial and temporal resolution (Kissling et al. 2018, Fernández et al. 2020). This requires information on data collection and sampling, data integration, and modelling for each EBV (Fig. 1). data collection and sampling, data integration, and modelling for each EBV (Fig. 1). To achieve this at a European scale, an improved data coverage, enhanced transnational coordination, adoption of advanced monitoring technologies, and a digital infrastructure with open data and interoperable standards is needed (Kissling et al. 2024, Moersberger et al. 2024, Morán-Ordóñez et al. 2023, Santana et al. 2023). To develop an effective EU-wide spatial sampling design, existing monitoring sites need to be incorporated, and spatial gaps need to be filled to achieve a broad representation of European biodiversity. This can be accomplished by combining a stratified random selection of sites (e.g. grid cells) across Europe with local sampling designs that consider randomisation, replication and stratification and co-location of monitoring activities using various field survey methods (Kissling et al. 2024). This can ensure a representative sampling across different environments, human impacts and policy or management interventions. Close collaborations between ecologists, data scientists, monitoring practitioners, and national and EU bodies, and the adoption of open science practices and FAIR guiding principles will be crucial to advance EBV implementation at a continental scale. This will support the establishment of a European Biodiversity Observation Coordination Centre (EBOCC) that is proposed for the coordination of monitoring activities and data management across Europe (Liquete et al. 2024).

Climate change and increasing human activities are impacting ecosystems and their biodiversity. Quantitative measurements of essential biodiversity variables (EBV) and essential climate variables are used to monitor biodiversity and carbon dynamics and evaluate policy and management interventions. Ecosystem structure is at the core of EBVs and carbon stock estimation and can help to inform assessments of species and species diversity. Ecosystem structure is also used as an indirect indicator of habitat quality and expected species richness or species community composition. Spaceborne measurements can provide large-scale insight into monitoring the structural dynamics of ecosystems, but they generally lack consistent, robust, timely and detailed information regarding their full three-dimensional vegetation structure at local scales. Here we demonstrate the potential of high-frequency ground-based laser scanning to systematically monitor structural changes in vegetation. We present a proof-of-concept high-temporal ecosystem structure time series of 5 years in a temperate forest using terrestrial laser scanning (TLS). We also present data from automated high-temporal laser scanning that can allow upscaling of vegetation structure scanning, overcoming the limitations of a typically opportunistic TLS measurement approach. Automated monitoring will be a critical component to build a network of field monitoring sites that can provide the required calibration data for satellite missions to effectively monitor the structural dynamics of vegetation over large areas. Within this perspective, we reflect on how this network could be designed and discuss implementation pathways.

The United Nations Framework Convention on Climate Change (UNFCCC) has identified climate change, pollution, and biodiversity loss as the most critical and interconnected challenges facing humanity today, collectively known as the "triple planetary crisis." Each of these issues uniquely impacts the ocean, and monitoring these effects requires both advanced and traditional technologies working in tandem. Since 2012, the Essential Ocean Variables (EOV) framework has been central to global ocean observation efforts. This framework standardizes parameters, methodologies, and data documentation, making it possible to ensure data compatibility on a global scale and creating a solid foundation for collaborative research. In Germany, three Helmholtz Research Centers—Alfred Wegener Institute (AWI), GEOMAR Helmholtz Centre for Ocean Research Kiel, and the Helmholtz-Zentrum Hereon—have united their expertise in marine and polar research to establish the Strategic Helmholtz Infrastructure MUSE (Marine Environmental Robotics and Sensor Technologies for sustainable Research and Management of Coastal, Ocean, and Polar Regions). With decades of experience in environmental observation, these centers are pooling resources to create MUSE as a cutting-edge infrastructure. MUSE aims to advance robotic sensor technology, sustainable energy solutions, and data systems, transforming current robotic platforms into an integrated and cohesive fleet for comprehensive ocean monitoring. A central focus of MUSE is the development of modular, state-of-the-art environmental sensors, power sources, and data management systems. These innovations will enhance the ability of robotic ocean observation systems to measure the full spectrum of Essential Ocean Variables, especially those that are vital for studying biodiversity and pollution. This aligns with the standards set by the Global Ocean Observing System (GOOS) working groups and addresses critical needs in biodiversity and pollution research. By building upon these modular systems, MUSE is positioned to tackle immediate and emerging challenges in ocean observation. MUSE also has a strong emphasis on accessibility and affordability, aiming to create modular and cost-effective solutions that can be deployed by the global oceanographic community. This approach seeks to democratize access to high-quality ocean data, ultimately making a tangible impact worldwide. By following the principles of FAIR data management—ensuring that data is findable, accessible, interoperable, and reusable—MUSE upholds transparency and collaboration in ocean science. To support data-sharing efforts, MUSE will leverage the DataHub, a comprehensive data infrastructure initiative that aligns with Germany’s national data strategy (NFDI). DataHub provides an innovative framework for managing data and metadata, facilitating seamless data flow and interoperability on a global scale. Through this initiative, MUSE's contributions will not only advance scientific understanding of the ocean but will also enable informed decision-making, conservation efforts, and climate action. In essence, MUSE represents a bold leap forward in ocean observation, combining expertise, cutting-edge technology, and open-access principles to confront the urgent challenges posed by the triple planetary crisis. By advancing ocean monitoring capabilities and promoting global collaboration, MUSE is set to make a transformative impact on sustainable research and the stewardship of marine environments.

Effective decision-making on biodiversity restoration would greatly benefit from baseline data on intraspecific genetic diversity, the ability to integrate it across species for each location, and efficient systems for monitoring changes of genetic diversity in response to management interventions and environmental dynamics. This requires large sets of well-curated information-rich FAIR (Findable, Accessible, Interoperable, Reusable) Digital Objects (FDOs; Schultes and Wittenburg 2019), implemented as, for instance, Digital Extended Specimens (DES), which are representing digitized field samples, their derived genomic data and associated information (Hardisty et al. 2022). These use-case-driven sets of structured (meta)data from many providers need to be merged, modified and further extended on demand. Existing workflows and work environments have to be redesigned to accelerate this process and achieve seamless integration to be able to scale to the needs of efficient and effective worldwide monitoring under the United Nations Kunming-Montreal Global Biodiversity Framework. Contributing to data and infrastructure development processes in support of global monitoring is also the global effort to generate high-quality reference genomes. The Earth BioGenome communities, including the European Reference Genome Atlas (ERGA) and Biodiversity Genomics Europe (BGE) communities, drive the development of standardization and harmonization for well-designed sampling and comprehensive FAIR and CARE (Collective Benefit, Authority to Control, Responsibility, Ethics) (meta)data (Buzan et al. 2025). Their goal is reproducible analytical pipelines that can be assembled on the fly for implementing sophisticated statistical approaches and algorithms. Such research-focused pipelines prepare the development and application of globally adopted workflow templates for the calculation of Essential Biodiversity Variables (EBVs). These templates are under development within the Group on Earth Observations Biodiversity Observation Network (GEO BON) (Lumbierres et al. 2025). Their output can be used for globally aligned and interpretable planning, monitoring, reporting and reviewing (see CBD/COP/16/L.33). Achieving global agreement on a (small) set of jointly used interoperable vocabularies and ontologies for (meta)data and machine-actionable operations is a communication, community-capacity development, and negotiation process that requires significant resources, engagement across sociocultural groups and geographies, patience and time. Ongoing processes towards these goals continue to be organized and promoted by, e.g., Biodiversity Information Standards (TDWG), the Global Biodiversity Information Facility (GBIF), GEO BON, and the Ocean Biodiversity Information System (OBIS), as well as large continental networks. We propose to bridge the gap before standardization and harmonization are in place and thereby facilitate and accelerate such efforts. Taking a pragmatic approach, our objective is to contribute a lightweight "pocket" dataspace that provides interoperability and data governance in connection with a digital platform for global genetic monitoring (Fig. 1). The pocket dataspace would allow existing platforms and tools to be connected easily through community-provided mappings between workflow element-specific formats, terms, data and operations stored in an open repository. This approach would enable users to take advantage of the core strengths of existing software products and the expertise of their associated communities, while quickly sharing data and their work between specialized solutions. At the same time, data would be FAIRified and CAREd-for, promoting attribution, transparency and responsibility. The functions of the pocket dataspace can be prerequisites for a transition to machine-actionable operations usable to agentic AI. As a general-purpose interlinking and translation component, the pocket dataspace aims to be the missing link between distributed, federated, non-standard-compliant and undocumented data, governance regimes, provenance logs and software output, and the need for transparent, well-governed and versatile conservation applications. One of these conservation applications will be the proposed platform for monitoring global genetic diversity. The platform will aggregate and visualize externally-linked population-genetic data and summary metrics that are the results of analysis pipelines enabled by, e.g., the pocket dataspace. Its aim is to provide visualization and support dataset and analysis management for local to global conservation efforts. It would store uploaded or linked genetic diversity metrics, perform selected automated analyses for continuously updated genetic diversity measures, as well as provide a starting point for user-designed analyses. The objective of our initial use case is to analyze three basic measures of population-genetic diversity based on genome-wide sequencing data as a first step towards operationalizing genetic monitoring at scale. Together, the pocket dataspace and monitoring platform for genetic diversity data have the goal to support a digital ecosystem that is foremost flexible, requiring low investments by users, and be able to quickly integrate both inter- and transdisciplinary data as well as existing powerful platforms and well-tested analytical pipelines and functionality.

The goal was to model irrigated (IBY) and rainfed (RBY) bean yields in central (Culiacán) and southern (Rosario) Sinaloa state as a function of the essential climate variables soil moisture, temperature, reference evapotranspiration, and precipitation. For Sinaloa, for the period 1982–2013 (October–March), the following were calculated: (a) temperatures, (b) average degree days for the bean, (c) cumulative reference evapotranspiration, and (d) cumulative effective precipitation. For essential climate variables, (e) daily soil moisture obtained from the European Space Agency and (f) IBY and RBY from the Agrifood and Fisheries Information Service were used. Multiple linear regressions were significant for predicting IBY–RBY (dependent variables) as a function of essential climate variables (independent variables). The four models obtained were significantly predictive: IBY–Culiacán (Pearson correlation (PC) = 0.590 > Pearson critical correlation (CPC) = |0.349|), RBY–Culiacán (PC = 0.734 > CPC = |0.349|), IBY–Rosario (PC = 0.621 > CPC = |0.355|), and RBY–Rosario (PC = 0.532 > CPC = |0.349|). Due to the lack of irrigation depth data, many studies only focus on modeling RBY; this study is the first in Sinaloa to predict IBY and RBY based on essential climate variables, contributing to the production of sustainable food.

Earth observation imaging technologies, particularly multispectral sensors, produce extensive high-dimensional data over time, thus offering a wealth of information on global dynamics. These data encapsulate crucial information in essential climate variables, such as varying levels of soil moisture and temperature. However, current cutting-edge machine learning models, including deep learning ones, often overlook the treasure trove of multidimensional data, thus analyzing each variable in isolation and losing critical interconnected information. In our study, we enhance conventional convolutional neural network models, specifically those based on the embedded temporal convolutional network framework, thus transforming them into models that inherently understand and interpret multidimensional correlations and dependencies. This transformation involves recasting the existing problem as a generalized case of N-dimensional observation analysis, which is followed by deriving essential forward and backward pass equations through tensor decompositions and compounded convolutions. Consequently, we adapt integral components of established embedded temporal convolutional network models, like encoder and decoder networks, thus enabling them to process 4D spatial time series data that encompass all essential climate variables concurrently. Through the rigorous exploration of diverse model architectures and an extensive evaluation of their forecasting prowess against top-tier methods, we utilize two new, long-term essential climate variables datasets with monthly intervals extending over four decades. Our empirical scrutiny, particularly focusing on soil temperature data, unveils that the innovative high-dimensional embedded temporal convolutional network model-centric approaches markedly excel in forecasting, thus surpassing their low-dimensional counterparts, even under the most challenging conditions characterized by a notable paucity of training data.

Abstract. The World Meteorological Organization (WMO) established Regional Climate Centres (RCCs) around the world to create science-based climate information on a regional scale within the Global Framework for Climate Services (GFCS). The paper introduces the satellite component of the WMO Regional Climate Centre on Climate Monitoring (RCC-CM) for Europe and the Middle East. The RCC-CM product portfolio is based on essential climate variables (ECVs) as defined by the Global Climate Observing System (GCOS), spanning the atmospheric (radiation, clouds, water vapour) and terrestrial domains (snow cover, soil moisture). In the first part, the input data sets are briefly described, which are provided by the EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) Satellite Application Facilities (SAF), in particular CM SAF, and by the ESA (European Space Agency) Climate Change Initiative (CCI). In the second part, the derived RCC-CM products are presented, which are divided into two groups: (i) operational monitoring products (e.g. monthly means and anomalies) based on near-real-time environmental data records (EDRs) and (ii) climate information records (e.g. climatologies, time series, trend maps) based on long-term thematic climate data records (TCDRs) with adequate stability, accuracy and homogeneity. The products are provided as maps, statistical plots and gridded data, which are made available through the RCC-CM website (www.dwd.de/rcc-cm).

Global Navigation Satellite System (GNSS) is an established atmospheric observing system for atmospheric water vapour with high spatiotemporal resolution. Water vapour is under-sampled in the current meteorological and climate-observing systems, and thus obtaining and exploiting more high-quality humidity observations is essential.  The operational GNSS data processing at GFZ provides all tropospheric products, zenith total delays (ZTD), precipitable water vapour (PWV), slant total delays (STD) and tropospheric gradients in the framework of different meteorological projects like e.g. E-GVAP ("The EUMETNET EIG GNSS Water Vapour Programme", http://egvap.dmi). E-GVAP is in charge of the collection and quality control of operational GNSS tropospheric products for numerical weather prediction. GFZ is one of the E-GVAP Analysis Centres and processes about 600 GNSS stations in near real-time. The tropospheric products provided by GFZ are used by European weather services for operational forecasts.  GFZ contributes to climate research within the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN). Established in 2006, GRUAN, is an international reference observing network of sites measuring essential climate variables above the Earth's surface. Currently, this network comprises 33 reference sites worldwide, designed to detect long-term trends of key climate variables such as temperature and humidity. GFZ hosts a central processing facility for the GNSS data and is responsible for the installation of GNSS hardware, data transfer, processing and archiving, as well as derivation of GNSS-PWV products according to the GRUAN requirements. A complementary small scale regional climate station network is the Austrian WegenerNet, which provides since 2007 measurements of hydrometeorological variables with very high spatial and temporal resolution. GNSS-PWV measurements have been included as a priority one measurement of the essential climate variable water vapour to both GRUAN and WegenerNet climate station networks.GNSS-derived tropospheric products and results of selected validation studies will be presented.

The importance of soil moisture (SM) in the global climate system has recently been outlined by the Global Climate Observing System (GCOS; http://www.wmo.int/pages/prog/ gcos/) by endorsing SM as an essential climate variable. It is a crucial variable for numerical weather and climate prediction and plays a key role in hydrological processes. A good representation of SM conditions can therefore help to improve forecasting of precipitation, droughts and floods, climate projections, and predictions. For many applications, globalor continental-scale SM maps are needed.

ABSTRACTSustained global ocean observations are needed to recognise, understand, and manage changes in marine biodiversity, resources and habitats, and to implement wise conservation and sustainable development strategies. To meet this need, the Global Ocean Observing System (GOOS), a network of observing systems distributed around the world and coordinated by the Intergovernmental Oceanographic Commission (IOC) has proposed Essential Ocean Variables (EOVs) that are relevant to both the scientific and the broader community, including resource managers. Building a network that is truly global requires expanding participation beyond scientists from well-resourced countries to a far broader representation of the global community. New approaches are required to provide appropriate training, and resources and technology should follow to enable the application of this training to engage meaningfully in global observing networks and in the use of the data. Investments in technical capacity fulfil international reporting obligations under the UN Sustainable Development Goal 14A. Important opportunities are emerging now for countries to develop research partnerships with the IOC and GOOS to address these obligations. Implementing these partnerships requires new funding models and initiatives that support a sustained research capacity and marine technology transfer.

Global satellite observations of column-averaged carbon dioxide and methane: The GHG-CCI XCO2 and XCH4 CRDP3 data set

Extreme weather and climate events have over the years impacted human society and have currently become a more complex challenge with the changing climate. Many more climate change consequences have been recognized due to various environmental and social stresses in the last few decades. One way to assess the impacts of observed climate change is by viewing the Earth from space. Monitoring climate change from space is one of the most important and challenging responsibilities. Satellite remote sensing measurements can make the task of monitoring the entire Earth relatively easy with low costs; thus, environment and weather satellite measurements provide the ability to observe and predict the Earth’s environment and weather while improving the accuracy. Satellite observations make a significant contribution to the Essential Climate Variables (ECVs) and their associated data sets and products of the Global Climate Observing System (GCOS) which was established to ensure that all users have access to the climate observations, data records, and information which they require to address pressing climate-related concerns. Generating climate data records with multi-mission and multi-instrument measurements is difficult and complex because of the spectral spatial, temporal, and angular coverage issues. Cross-sensor calibration and validation play a pivotal role relative to understanding the climate and its trends—an important issue. The applications of satellite data for climate change are summarized based on the 20 chapters in this book, and recent advances of climate change monitoring from space are discussed. Monitoring climate change from space presents major challenges that will be briefly summarized. The era of collecting satellite images from space is rapidly being replaced by the era of “Satellite Climatology” as the time series of satellite measurements has reached over 30 years of extended applications and use.

Surface albedo has been documented as one of the Essential Climate Variables (ECV) of the Global Climate Observing System (GCOS) that governs the Earth's Radiation Budget. The availability of surface albedo data is necessary for a comprehensive environmental modelling study. Thus, both temporal and spatial scale issues need to be rectified. This study reports about the availability of surface albedo data through in-situ and remote sensing satellite observations. In this paper, we reviewed the existing models for surface albedo derivation and various initiatives taken by related environmental agencies in order to understand the issues of climate with respect to surface albedo. This investigation evaluated the major activities on albedo-related research specifically for the retrieval methods used to derive the albedo values. Two main existing albedo measurement methods are derived through in-situ measurement and remotely sensed observations. In-situ measurement supported with number of instruments and techniques such aspyrheliometers, pyranometers and Baseline Surface Radiation Network (BSRN) and remotely sensed observations using angularly integrated Bi-directional Reflectance Distribution Function (BRDF) by both geostationary and polar orbit satellites. The investigation results reveals that the temporal and spatial scaling is the major issues when the albedo values are needed for microclimatic study, i.e. high-resolution time-series analyses and at heterogeneity and impervious surface. Thus, an improved technique of albedo retrieval at better spatial and temporal scale is required to fulfil the need for such kind of studies. Amongst many others, there are two downscaling methods that have been identified to be used in resolving the spatial scaling biased issues: Smoothing Filter-based Intensity Modulation (SFIM) and Pixel Block Intensity Modulation (PBIM). The temporal issues can be resolved using the multiple regression techniques of land surface temperature, selected air quality parameters, aerosol and daily skylight.