Traits as Essential Biodiversity Variables

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).

The Earth system is enduring multiple, interacting stressors causing immense and irreversible change to its biosphere. The unprecedented magnitude of human influence on the planet is a cause for much concern, but also an opportunity to mitigate undesirable impact by instituting socio-ecological management strategies and monitoring their effectiveness. To do so, local and global policy developers and decision makers must have access to clear and globally consistent information streams that serve as planetary diagnostics. This need is being addressed by several international, multi-agency networks which have developed sets of "Essential Variables" (EVs) to improve consistency in the observation of key phenomena within their domains of operation. For example, the implementation of the Essential Climate Variables (ECVs) led by the Global Climate Observing System (GCOS) has set an example being followed in the marine and biodiversity domains by, respectively, the Global Ocean Observing System's (GOOS) Essential Ocean Variables (EOVs) and the Group on Earth Observation Biodiversity Observation Network (GEO BON)'s Essential Biodiversity Variables (EBVs). Each of these EV collections has a subset dedicated to biological and ecological phenomena. This is greatly encouraging for biodiversity science at large, but also runs the risk of parallel and conflicting developments occuring in the EV communities addressing thecross-cutting theme of biodiversity. As a consequence, there is an urgent need for these systems, and the communities behind them, to pursue tight conceptual and technical interoperability in aid of a coherent treatment of biodiversity in planetary-scale monitoring. In this contribution, we will describe our current research and implementation of a semantic interoperability layer for the EVs, with a focus on biology and ecology. Applying the best practices and core infrastructure of the Open Biological and Biomedical Ontology (OBO) Foundry and Library, we have leveraged both mature and emerging reference ontologies targeting the domains of ecosystems and environment, population and communities, and high-level ecological phenomena. This interoperable collection of ontologies constitutes a foundation for highly expressive EV knowledge representation and management through robust, FAIR-compliant technologies. Further, we are coordinating these efforts with semantic technology adopted by UN agencies, representing global directives and indicators of the UN Sustainable Development Agenda for 2030. With open editorial models behind each of these ontologies, we aim to offer a scalable and inclusive system to bridge data and information products across the EVs through machine-accessible knowledge representation.

Satellite remote sensing (SRS) provides huge potential for tracking progress towards conservation targets and goals, but SRS products need to be tailored towards the requirements of ecological users and policymakers. In this viewpoint article, we propose to advance SRS products with a terrestrial biodiversity focus for tracking the goals and targets of the Kunming-Montreal global biodiversity framework (GBF). Of 371 GBF biodiversity indicators, we identified 58 unique indicators for tracking the state of terrestrial biodiversity, spanning 2 goals and 8 targets. Thirty-six shared enough information to analyse their underlying workflows and spatial information products. We used the concept of Essential Biodiversity Variables (EBV) to connect spatial information products to different dimensions of biodiversity (e.g. species populations, species traits, and ecosystem structure), and then counted EBV usage across GBF goals and targets. Combined with published scores on feasibility, accuracy, and immaturity of SRS products, we identified a priority list of terrestrial SRS products representing opportunities for scientific development in the next decade. From this list, we suggest two key directions for advancing SRS products and workflows in the GBF context using current instruments and technologies. First, existing terrestrial ecosystem distributions and live cover fraction SRS products (of above-ground biomass, ecosystem fragmentation, ecosystem structural variance, fraction of vegetation cover, plant area index profile, and land cover) need to be refined using a co-design approach to achieve harmonized ecosystem taxonomies, reference states and improved thematic detail. Second, new SRS products related to plant physiology and primary productivity (e.g. leaf area index, chlorophyll content & flux, foliar N/P/K content, and carbon cycle) need to be developed to better estimate plant functional traits, especially with deep learning techniques, radiative transfer models and multi-sensor frameworks. Advancements along these two routes could greatly improve the tracking of GBF target 2 (‘improve connectivity of priority terrestrial ecosystems), target 3 (‘ensure management of protected areas’), target 6 (‘control the introduction and impact of invasive alien species’), target 8 (‘minimize impact of climate change on biodiversity’), target 10 (‘increase sustainable productivity of agricultural and forested ecosystems’) and target 12 (‘increase public urban green/blue spaces’). Such improvements can have secondary benefits for other EBVs, e.g. as predictor variables for modelling species distributions and population abundances (i.e. data that are required in several GBF indicators). We hope that our viewpoint stimulates the advancement of biodiversity monitoring from space and a stronger collaboration among ecologists, SRS scientists and policy experts.

The concept of Essential Biodiversity Variables (EBVs) was conceived to study, report, and manage biodiversity change. The EBV netCDF structure was developed in order to support publication and interoperability of biodiversity data. This standard is based on the Network Common Data Format (netCDF). Additionally, it follows the Climate and Forecast Conventions (CF, version 1.8) and the Attribute Convention for Data Discovery (ACDD, version 1.3). The standard allows several datacubes per netCDF file (see Fig. 1). These cubes have four dimensions: longitude, latitude, time and entity, whereby the last dimension can, for example, encompass different species or groups of species, ecosystem types or other aspects. The usage of hierarchical groups enables the coexistence of multiple EBV cubes (see Fig. 2). The first level (netCDF group) are scenarios, e.g., the modelling for different Shared Socioeconomic Pathways (SSP) scenarios. The second level (netCDF group) are metrics, e.g., the percentage of protected area per pixel and its proportional loss over a certain time span per pixel. All metrics are repeated per scenario, if any are present. The result is a rather complex raster dataset (see example dataset in Fig. 3). This is where the ebvcube R package comes into play. This R package enables scientists to create their own netCDFs in the EBV cube standard. Its functionality covers the creation, opening/reading and visualizing the EBV netCDFs. The ebvcube package is part of the overall EBV infrastructure and works together with the EBV Data Portal. Users can work with the downloaded EBV netCDFs or upload their own EBV netCDFs to the portal. Generally, the package aims to condense the output for the users and assist in the understanding of the file structure to overcome the complexity. The output is reduced to the necessary information, e.g., not displaying coordinate variables or any technical attributes. Moreover, functionality for a quick data exploration is implemented.

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.

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.

Tropical forests host at least two-thirds of the world’s flora and fauna diversity and store 25% of the terrestrial above and belowground carbon. However, biodiversity decline due to deforestation and forest degradation of tropical forest is increasing at an alarming rate. Biodiversity dynamics due to natural and anthropogenic disturbances are mainly monitored using established field survey approaches. However, such approaches appear to fall short at addressing complex disturbance factors and responses. We argue that the integration of state-of-the-art monitoring approaches can improve the detection of subtle biodiversity disturbances and responses in changing tropical forests, which are often data-poor. We assess the state-of-the-art technologies used to monitor biodiversity dynamics of changing tropical forests, and how their potential integration can increase the detail and accuracy of biodiversity monitoring. Moreover, the relevance of these biodiversity monitoring techniques in support of the UNCBD Aichi targets was explored using the Essential Biodiversity Variables (EBVs) as a framework. Our review indicates that although established field surveys were generally the dominant monitoring systems employed, the temporal trend of monitoring approaches indicates the increasing application of remote sensing and in -situ sensors in detecting disturbances related to agricultural activities, logging, hunting and infrastructure. The relevance of new technologies (i.e., remote sensing, in situ sensors, and DNA barcoding) in operationalising EBVs (especially towards the ecosystem structure, ecosystem function, and species population classes) and the Aichi targets has been assessed. Remote sensing application is limited for EBV classes such as genetic composition and species traits but was found most suitable for ecosystem structure class. The complementarity of remote sensing and emerging technologies were shown in relation to EBV candidates such as species distribution, net primary productivity, and habitat structure. We also developed a framework based on the primary biodiversity attributes, which indicated the potential of integration between monitoring approaches. In situ sensors are suitable to help measure biodiversity composition, while approaches based on remote sensing are powerful for addressing structural and functional biodiversity attributes. We conclude that, synergy between the recent biodiversity monitoring approaches is important and possible. However, testing the suitability of monitoring methods across scales, integrating heterogeneous monitoring technologies, setting up metadata standards, and making interpolation and/or extrapolation from observation at different scales is still required to design a robust biodiversity monitoring system that can contribute to effective conservation measures.

Global sustainability policy demands coordination among domain‐specific policies, yet a major challenge persists in policy monitoring and reporting, where various, often uncoordinated indicator initiatives exist. To address this challenge, we apply a network approach to investigate linkages between the indicators of the Sustainable Development Goal 15 (SDG 15, Life on Land), the indicators of the Aichi Biodiversity Targets and those used in assessments of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES), and the Essential Biodiversity Variables (EBVs). Network analysis is a well‐known tool but to the best of our knowledge remains largely unexplored to analyse indicator linkages. Our findings indicate limited interconnectivity (number and type of cross‐set linkages) among the selected indicator sets. Nevertheless, we identify direct network linkages showing that most SDG 15 indicators could draw upon data or other information (e.g., methodological) from Aichi indicators, IPBES indicators, or EBVs. Findings show the potential of EBVs to deliver policy‐relevant information, but this warrants further research. We identify indicators and EBVs with a key role in the network and discuss advantages and limitations of our network approach. We show the utility of the approach to linking multiple policy agendas, and argue that—if applied more widely—it can promote a more coordinated sustainability policy monitoring and reporting.

Using essential biodiversity variables (EBVs) as a framework for coordination between research and monitoring networks: a case study with phenology

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite‐based sensors can repeatedly record the visible and near‐infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100‐m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short‐wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14‐bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3‐d repeat low‐Earth orbit could sample 30‐km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.

Trait-based approaches are key to understanding eco-evolutionary processes but rarely account for animal behaviour despite its central role in ecosystem dynamics. We propose integrating behaviour into trait-based ecology through movement traits-standardised and comparable measures of animal movement derived from biologging data, such as daily displacements or range sizes. Accounting for animal behaviour will advance trait-based research on species interactions, community structure and ecosystem functioning. Importantly, movement traits allow for quantification of behavioural reaction norms, offering insights into species' acclimation and adaptive capacity to environmental change. We outline a vision for a 'living' global movement trait database that enhances trait data curation by (1) continuously growing alongside shared biologging data, (2) calculating traits directly from individual-level data using standardised, consistent methodology and (3) providing information on multi-level (species, individual, within-individual) trait variation. We present a proof-of-concept 'MoveTraits' database with 52 mammal and 97 bird species, demonstrating calculation workflows for 5 traits across multiple timescales. Movement traits have significant potential to improve trait-based global change predictions and contribute to global biodiversity assessments as Essential Biodiversity Variables. By making animal movement data more accessible and interpretable, this database could bridge the gap between movement ecology and biodiversity policy, facilitating evidence-based conservation.

The Convention on Biological Diversity's Kunming-Montreal Global Biodiversity Framework (GBF) sets ambitious goals to protect and restore biodiversity. It includes a monitoring framework that mandates countries to track progress toward these goals using indicators that summarize biodiversity trends. Calculating indicators is challenging for countries because of fragmented biodiversity monitoring efforts, technical barriers, a lack of available data and tools, and capacity bottlenecks. The BON in a Box platform for biodiversity monitoring and indicator calculation, developed by the Group on Earth Observations Biodiversity Observation Network, was created to address these challenges by providing open, transparent, and reproducible analysis pipelines that convert data into essential biodiversity variables and indicators. These pipelines are built by experts and contributed by the community, follow FAIR principles, and help scientists apply their research to coordinate biodiversity monitoring efforts, build capacity to track progress toward the GBF, and affect policy change.

In the temperate region, inter-annual variation of air temperature affects leaf phenology, i.e., timings of leaf emergence and growth in spring and defoliation in autumn. These changes have significant impacts not only on the canopy of dominant trees of forest ecosystems, but also on the seasonal light environment within the forest understory which further influences the growth and survival of tree seedlings, shrubs, and herbaceous species. Consequently, global warming is expected to influence biodiversity by altering species-specific growth responses to the environmental shifts, affecting primary production and hence the progress of vegetation succession. Therefore, in order to comprehensively monitor and assess the state and changes in forest ecosystems across wide geographical and decadal scales, it is important to observe leaf phenology at both the species and ecosystem scales, which is considered one of Essential Biodiversity Variables (EBVs). The objective of this study is to investigate the decadal-scale change of the leaf phenology in deciduous forest in Japan. We examined 20-year changes of the dates of leaf emergence, leaf area index (LAI) reached its maximum, and defoliation by using in-situ and satellite data. The in-situ remote sensing has been conducted by a spectroradiometer and automated digital cameras on a canopy tower since 2003 at a deciduous forest in Takayama site, located in the cool-temperate region in the central Japan. The system is part of the Phenological Eyes Network (PEN). We estimated the dates of leaf emergence, maximum LAI, and defoliation based on the seasonal pattern on the Green-Red Vegetation Index (GRVI). These dates exhibit notable inter-annual variations, and notably, the date of maximum LAI occurrence tended to shift earlier over the 20-years period from 2004 to 2023. Those inter-annual variations in the leaf phenology were strongly related to the air temperature. Based on the knowledge gained at the Takayama site, we then examined the spatial distribution and annual changes of phenology of the deciduous forests in Honshu Island with satellite-GRVI. We will discuss the spatial and temporal changes in phenology along the environmental gradient and rising air temperature due to global warming, and evaluate the sensitivity or tolerance of these forests by focusing on species composition and geographical characteristics. The authors thank PEN for sharing the data of spectral reflectance and canopy images.

Recent advances in large vision–language models (VLMs) offer new opportunities to extract structured, biologically relevant information from unstructured, image-like data, including earth observation satellite and ocean observation marine remote sensing modalities. We present a lightweight pipeline that uses few-shot, instruction-aligned VLMs to convert visual inputs into concise, schema-based JSON records capable of capturing specific, but flexible scene content such as habitat characteristics, disturbance events, species labels, data-quality attributes, Essential Biodiversity Variables (EBV) or any user-defined ecological indicators. This system enables rapid generation of consistent annotations and structured data extraction across massive archives without task-specific model training, complex engineering, or human labels. We demonstrate the usefulness of this approach through practical applications to automated quality-control tagging, low-level land-use and habitat-type classification, species classification, and numerical feature estimation from 2-D images generated by satellite platforms as well as complementary sensors such as shipboard acoustics (EK60) and S-band radar. We optimize performance generally and on a task-specific basis through the incorporation of human-like spatial reasoning via grid-referenced subregion analysis and prompt-optimization frameworks such as DSPy for declarative prompt programming and self-improvement. By producing interpretable, reproducible and harmonized annotations at scale, our approach substantially reduces the manual screening effort required to curate multi-sensor datasets, prioritizes scenes for higher-fidelity processing and supports sophisticated cross-platform analysis aligned with biodiversity applications. VLM technologies are rapidly reshaping environmental data management, and our results provide an early, practical demonstration of how VLM-based visual interpretation can enhance the flexibility, scalability, and interoperability of remote-sensing pipelines for biodiversity monitoring. Moreover, these capabilities directly support key reporting needs under the Kunming–Montreal Global Biodiversity Framework, particularly Targets 1, 2, 4, and 19 that require scalable information on ecosystem extent, condition, disturbance, and data accessibility. They also contribute to SDG indicators (e.g., 15.1.1, 15.3.1, 14.2.1) by enabling rapid, harmonized extraction of habitat, land-use, and marine-ecosystem attributes from multi-sensor Earth observation archives.