Incorporating EO technologies and resulting information into the framework of the GBF requires close interdisciplinary collaboration and the exchange of knowledge among specialists of various backgrounds. Within this session, we aim to facilitate the exchange of ideas and collaborations among biodiversity practitioners, scientists (EO- and non-EO specialists), policy makers, and industry experts. We work toward filling gaps between the knowledge and technologies available to researchers and the information needed for large scale reporting. We invite abstracts that address current needs and gaps in biodiversity monitoring, that demonstrate the potential of EO technology to fill monitoring gaps, combine different data sources including remote sensing technologies for large scale biodiversity monitoring, and demonstrate current limitations. We welcome abstracts including but not limited to the GBF indicators, Essential Biodiversity and Ecosystem Variables, and for effective biodiversity conservation on large scales.
Biodiversity is declining at alarming rates worldwide despite ongoing conservation efforts. In 2022, the Convention on Biological Diversity identified 23 targets and 4 goals to counter this trend, aiming to achieve them by 2030 and 2050, respectively. Islands are both biodiversity hotspots and particularly vulnerable to biodiversity loss, hosting half of the world’s threatened species and 75% of extinctions since colonial times. Yet, achieving these targets and goals on islands has received little attention to date. Our objectives are to explore historical rates of biodiversity change on islands globally and predict whether current trends align with the biodiversity targets for 2030 as outlined in the Kunming-Montreal Global Biodiversity Framework (KM-GBF). We compiled 303 biodiversity and socio-economic indicators using three main sources (KM-GBF indicators, Geobon indicators, and all indicators listed by Tittensor et al., 2014) and evaluated them against six criteria (relevance, credibility, time span, time resolution, availability, global and island cover). Of these, 36 indicators were included. However, 7 targets (30.4%) lack an indicator. We used the KM-GBF indicators categories (i.e., headline, binary, component, and complementary) to assess how well included indicators correspond to the target (i.e., alignment). The indicator that best aligns with a target was used to assess coverage (i.e., how well a set of indicators measures progress toward a target). Four targets had only poorly aligned indicators and thus poor coverage. We then used time series spanning from 1950 to the present to model current and future trends for each indicator, aggregated by target. Preliminary results indicate that 24 indicators (66.7%) appear to be on track to meet their respective targets. However, almost half of the targets have either poor coverage or no indicators at all (n = 11, 47.8%). Additionally, several limitations exist in quantifying progress toward the KM-GBF targets on islands. We urge the development of missing indicators, as well as the design of indicators and long-term monitoring schemes tailored to islands.
How to cite: Mologni, F., Essl, F., and Lenzner, B.: Meeting International Biodiversity Targets on Islands: Current Status and Challenges, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-63, https://doi.org/10.5194/wbf2026-63, 2026.
The widespread degradation of nature has increased pressure on corporations and financial institutions to report and mitigate their impacts on biodiversity. Despite the increasing abundance and accessibility of biodiversity data and measurement technologies, there remains a lack of datasets suitable for deriving comprehensive summary measures, such as Essential Biodiversity Variables (EBV’s), that can underpin robust impact assessments and reporting aligned with the Kunming-Montreal Global Biodiversity Framework (GBF). One way to assess biodiversity risk and impact at scale could be to link local biodiversity data with large-scale measurements of environmental variables, for instance from satellite Earth Observation (EO) platforms, to provide model-based estimates of biodiversity feeding into GBF indicators. However, extrapolation of patterns of biodiversity is a notoriously difficult task, and is often associated with high errors when predicting patterns to new spatial locations. A review of datasets and tools currently used by corporations and financial institutions shows that extrapolation to local sites from global datasets, or using only proxies, is the currently dominant approach. Here, we test the reliability of such assessments by combining high resolution EO time series data that capture seasonal dynamics with large scale biodiversity time series data from two countries, using machine learning algorithms to predict five EBVs. We show that while reasonable predictive performance can be achieved at sites with local data, performance declines considerably when modelling measures at new sites. We draw on these results to argue that biodiversity patterns are hard to generalise to sites where no local data has been collected, and conclude with a proposed biodiversity data hierarchy framework focusing on data quality. Because biodiversity risk and impact reporting is still evolving, corporations now have a critical window of opportunity to showcase measurable incremental improvements in data sources and data quality that underpin the Global Biodiversity Framework impact assessments and reporting.
How to cite: Goodsell, R., Granqvist, E., Christiaen, C., and Ronquist, F.: Limits to Extrapolating Biodiversity Patterns From EO: Implications for Model-Based EBV Estimation and Corporate Reporting, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-350, https://doi.org/10.5194/wbf2026-350, 2026.
The Kunming–Montreal GBF emphasizes the need for robust and scalable measures of genetic diversity (Headline Indicator 4.2). Recent work highlights that population estimates, via population delineation combined with density estimates, or effective population size (Ne) can be used for measuring genetic diversity indicators. While streamlined R workflows have been proposed in earlier studies, and an online tool (BON in a Box) is currently being beta-tested by Genes from Space, there remains a critical need to understand how such methods are applied across diverse taxa and what analytical considerations are required for consistent implementation.
To address this need, we measured the Genetic Diversity Indicator for South Korea across eight major taxonomic groups (plants, insects, birds, fish, mammals, non-insect invertebrates, amphibians, and algae). We conducted two full-day workshops per taxon (16 total workshops), each with three subject-matter experts. These workshops generated key insights into species-selection criteria, essential data requirements, and methodological challenges unique to each group. We also performed sensitivity analyses examining within and among user variation, the influence of prior exposure to genetic diversity literature on population estimate, and the effect of adding environmental layers (e.g., temperature, elevation) on increasing the accuracy of population estimates. Given abstract length limits, we focus here only on the resulting analytical protocol.
We developed a unified QGIS-based workflow that allows users to flexibly incorporate both the quality (e.g., high-resolution raster) and quantity (e.g., elevation, bathymetry) of available spatial data. Analysts generate environmental envelopes and draw polygons informed by multiple, taxon-appropriate layers, increasing the ecological accuracy of population delineation and subsequent population estimates. To support repeatability and future monitoring, we embedded self-evaluation metadata within each species’ geopackage, including user confidence scores, perceived data sufficiency, and recommendations for expert reassessment. Furthermore, a quantitative method was implemented to detect species distribution change between two time points.
By synthesizing lessons learned across eight taxonomic groups and formalizing a transparent, adaptable, and repeatable analytical protocol, this work provides practical guidance for countries preparing to implement Genetic Diversity Indicator 4.2 and contributes to emerging global efforts to standardize genetic diversity monitoring.
This study was funded by the National Institute of Biological Resources, South Korea(NIBR202405203,NIBR202505103,NIBR202505203.
How to cite: Kim, K. and Koo, K. A. and the Korea Genetic Diversity Mapping Working Group (K-GENMAP): Lessons from Implementing the Genetic Diversity Indicator Across Multiple Taxa; From Conceptual Guidance to Taxon-Specific and Technical Workflow Recommendations, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-432, https://doi.org/10.5194/wbf2026-432, 2026.
Ecosystems are fundamental to the planet’s environmental and socio-economic systems, and assessing their condition and services begins with understanding their extent. Developing reliable, standardized and scalable methods for mapping ecosystem extent and change is urgently needed, yet remains a significant challenge. Ecosystems’ inherent diversity, their natural complexity and the variation in their definition complicate efforts to delineate boundaries clearly and represent individual cases correctly.
The World Ecosystem Extent Dynamics (WEED) platform is a globally applicable, open-source toolbox designed to support countries and regions to generate extent maps of terrestrial, freshwater and coastal ecosystem types and their temporal dynamics, according to different typologies. Provided as an Earth Observation (EO)-integrated, end-to-end processing system, WEED runs on cloud computing infrastructures and complies with Open and FAIR principles. It offers both graphical and command-line interfaces to support users with varying technical capacities, enabling data upload, analysis, visualization, export, and access to intermediate results with full workflow transparency and reproducibility.
The WEED solution combines data-driven (Machine Learning) methods with expert-based approaches to analyse geospatial proxy data, including EO imagery, environmental datasets, crowdsourced inputs and local measurements. Its modular design creates a federated system that integrates state-of-the-art components, including the ARIES semantics platform, the OpenEO processing platform, and the Laco-WIKI reference data platform, applying a digital twin concept. Co-developed with six countries, WEED allows users to upload national reference data for model training and extent mapping tailored to local contexts. In addition to ecosystem extent maps, the toolbox delivers outputs for key policy indicators, such as the Ecosystem Extent accounting tables (UN SEEA-EA) and the Global Biodiversity Headline Indicator A.2 (UN CBD GBF), which can also be extended to other indicators.
The WEED platform development is a project funded by the European Space Agency (ESA), and a technology contribution to the Global Earth Observation Atlas initiative (GEO-Atlas). The presentation will introduce participants to the upcoming toolbox (planned release in 2026) and show practical results from the co-creation with countries.
How to cite: Smets, B., Buchhorn, M., Balbi, S., Bulckaen, A., Meyer, C., Tregubova, P., Remelgado, R., McCallum, I., De Roo, B., and Villa, F.: World Ecosystem Extent Dynamics (WEED), a toolbox for countries to report on GBF Headline indicator A.2, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-493, https://doi.org/10.5194/wbf2026-493, 2026.
Effective implementation of the GBF monitoring framework requires bridging the gap between research methods and the information that parties must report at a national scale. Genetic diversity is an important aspect of the GBF monitoring framework but remains the least developed for large-scale monitoring.
Genetic diversity indicators depend on knowledge of population stability and change, which is difficult to obtain repeatedly across many species and large areas. Although the parties to the CBD have recently adopted genetic diversity indicators that do not rely on DNA sequencing, established alternative workflows still rely on periodic ground-based assessments that can be costly, especially at national scales. Continuous, openly accessible data from satellites can be used to estimate habitat properties relevant for population status, thereby reducing dependence on sparse field observations and providing assessments that support national reporting. The project Earth Observation (EO) for Genetic Indicators demonstrates how EO can be integrated practically into workflows for monitoring genetic diversity indicators aligned with policy requirements.
An interdisciplinary team of experts in Earth observation, conservation genetics, and indicator development created a workflow that incorporates EO data-derived habitat extent and thus supports genetic diversity indicator calculation. An online tool, which was co-designed with end users, integrates public EO data to standardize and support indicator calculations for national-scale reporting. The workflow uses population locations and EO-based land cover data as core inputs from which it determines changes in population-specific habitat. We evaluate the presented workflow through a set of multi-country use cases across multiple continents. Using existing ground-based data from multiple partner projects, we assess how EO-derived habitat information can complement or update population-level information where ground data are limited. We demonstrate the workflow's feasibility, its alignment with existing ground-based approaches, and the ability to support repeated indicator calculations for various plant and animal species.
The presented approach provides a practical means to supply consistent, repeatable inputs for national genetic diversity reporting. Our project aims to strengthen the connection between EO and conservation genetics communities and move genetic diversity monitoring closer to large-scale operational use within the GBF monitoring framework.
How to cite: Schweizer, N., Helfenstein, I., Pahls, S., Nocera, F., Team, G. F. S., Selmoni, O., Schuman, M., and Roeoesli, C.: Earth Observation for Genetic Indicators: Bridging Ground-Based and Satellite Data for Genetic Diversity Monitoring in the GBF Framework, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-702, https://doi.org/10.5194/wbf2026-702, 2026.
The effective management of ASEAN Heritage Parks (AHPs) and ASEAN Protected Areas is fundamental to conserving the region’s rich biodiversity and safeguarding the ecosystem services that support human well-being. Recognizing the ecological importance of Southeast Asia’s terrestrial and marine environments, the ASEAN Biodiversity Dashboard was developed as a regional, open-access platform to monitor biodiversity trends and species occurrences across the ASEAN region. This platform illustrates how data-driven approaches can enhance protected area management by supporting scientific data integration, spatial analysis, and the deployment of digital tools for conservation decision-making.
By bringing together biodiversity information from ASEAN Member States and global data partners, the Dashboard strengthens evidence-based policymaking and fosters meaningful regional collaboration. It plays a vital role in advancing biodiversity conservation strategies by offering harmonized, accessible, and policy-relevant datasets.
The Dashboard consolidates a wide range of biodiversity indicators including species distribution data, habitat condition assessments, conservation status, and human pressure metrics into an interactive interface designed for protected area managers, decision-makers, and technical experts. For terrestrial protected areas, it supports habitat monitoring, species assessments, and land-use planning. At the same time, it provides valuable insights for both terrestrial and marine sites engaged in integrated coastal zones and fisheries management.
More than a simple visualization tool, the ASEAN Biodiversity Dashboard enables forward-thinking conservation planning by ensuring that strategies are informed by up-to-date, scientifically robust information. It enhances regional coordination by promoting standardized biodiversity monitoring practices and cultivating a shared sense of responsibility among ASEAN Member States in managing their natural resources.
The platform also integrates complementary technologies such as remote sensing, geographic information systems (GIS), and biodiversity informatics. It highlights the importance of standardized data protocols, the use of Essential Biodiversity Variables (EBVs), and alignment with global frameworks, including the Kunming-Montreal Global Biodiversity Framework (KM-GBF). Through these features, the ASEAN Biodiversity Dashboard serves as a foundation for strengthening biodiversity governance and supporting long-term conservation outcomes across the ASEAN Region.
How to cite: Elloran, C., Reblora, E. R., Alano, J., Gauch, N. H., Villavelez, E., and Maglangit, E. P.: ASEAN Biodiversity Dashboard as a data sharing platform to monitor biodiversity trends and species occurrences in the ASEAN Region, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-742, https://doi.org/10.5194/wbf2026-742, 2026.
Genetic diversity is essential for the adaptive potential and resilience of species and ecosystems. Recognizing its importance, target 4 of the Kunming-Montreal Global Biodiversity Framework (GBF) aims to monitor and conserve genetic diversity and includes two species-specific genetic diversity indicators. The headline indicator “Ne> 500” (ratio of populations with an effective population size over 500, a conventional threshold to safeguard adaptive potential) and the supplementary indicator “populations maintained” (PM) (proportion of populations that are still present) are traditionally assessed by conducting field studies and collecting and processing lots of genetic samples. These indicators should be monitored for a taxonomically diverse set of ca. 100 species per country. Because the established DNA-based approach to estimate effective population sizes requires substantial investment of time, expertise and money per sample, it is not practical to obtain sufficient data for reporting on the GBF genetic diversity indicators based solely on DNA data. The ISSI “Genes From Space” project developed a new method to calculate these indicators based on existing data (if any) and/or local knowledge complemented with Earth Observation (EO) data. The approach uses EO data combined with species occurrence data to estimate habitat size and structure, and density information to estimate whether the effective population size is above or below the Ne=500 threshold. Making use of already available and processed satellite data products, this new approach is time- and cost-effective and only requires basic computing power and internet access. By using data from 30 terrestrial plant and animal species on five different continents, a sensitivity analysis was conducted to evaluate the method’s sensitivity to different input parameters. The results showed that the calculation of indicator values was most robust with well-dispersed populations and species with a large range. The method is, however, most sensitive for species with low dispersal capacities and very low population densities.
How to cite: Nocera, F., Pahls, S., Helfenstein, I., Schweizer, N., Team, G. F. S., Selmoni, O., Schuman, M. C., and Roeoesli, C.: Genes from Space: Monitoring genetic diversity indicators with the help of EO data, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-677, https://doi.org/10.5194/wbf2026-677, 2026.
Global conservation organizations invest heavily in protected areas and management actions to support biodiversity outcomes, often with a particular focus on target species. However, they require more flexible, efficient means to monitor the impact of these interventions at scale. Lack of responsive tools that can regularly track progress and impact of conservation actions can limit effective resourcing and planning for future work. While sufficient in situ data may exist for specific regions, and species, conservation organizations require standardized measurements and aggregate metrics to support decision-making across their global portfolio and countries of activity.
This presented work addresses these challenges by leveraging the advanced and proven species-level models and metrics of Map of Life (MOL). This effort utilizes NASA-supported remote sensing workflows and the digital infrastructure that enables calculations of Species Habitat Scores and Species Habitat Indices – formally adopted indicators of Goal A of the UN Global Biodiversity Framework.
These indicators rely on species-habitat association calculations, historically developed from expert-informed range maps validated against a single independent source of species presence data. To enhance the accuracy and reliability of these measures, we will implement emerging modelling techniques that integrate diverse data streams, including citizen science records as well as visual, acoustic, and GPS tracking data. Combining species occurrence information from multiple data sources substantially improves model predictions and resulting conservation insights, but these approaches have yet to be widely adopted by conservation practitioners for large-scale decision-making.
We show results for implementing these models for mammal species based on camera trap data and citizen science data, with all routines designed to be easily extended to additional taxa. By integrating multi-source datasets with NASA and other Earth Observation products that capture fine-scale climatic and environmental conditions, we can provide annual, range-wide estimates of suitable habitat, habitat connectivity, protection adequacy, and estimate total population size trends for target species. Our advanced species distribution and occupancy modeling approaches enables us to offer both high-resolution maps and species-level trends alongside method-associated uncertainty that will directly support tracking progress towards 30x30 goals and guide adaptive management interventions.
How to cite: Jetz, W. and Palmer, M.: Measuring range-wide species population changes to support global conservation monitoring and decision-making, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-891, https://doi.org/10.5194/wbf2026-891, 2026.
The Kunming-Montreal Global Biodiversity Framework (GBF) emphasizes the need for standardized, transparent, and scalable indicators to evaluate national progress toward global biodiversity goals. As both an Essential Biodiversity Variable (EBV) and a component indicator for GBF Target 3, the Species Protection Index (SPI) measures how well species’ geographic distributions are represented within various forms of protected and conserved areas. Recent updates to the SPI build on advances in species distribution modeling, improved global habitat mapping, and expanded data coverage across taxa, creating new opportunities to strengthen national-level reporting.
In this contribution, we present ongoing efforts to enhance the SPI through integration of high-resolution species distribution models, refined habitat suitability layers, and emerging Earth-observation–derived land-cover datasets. These developments directly address long-standing challenges for GBF reporting: capturing ecological patterns at spatial grains relevant to management decisions and reflecting real-time changes in habitat integrity and species ranges resulting from land-use dynamics, climate pressures, and conservation interventions. Increasingly, new Earth Observation (EO) products driven by commercial data providers, combined with nationally produced EO datasets, require the SPI workflow to flexibly incorporate heterogeneous inputs while remaining globally consistent and reproducible.
We evaluate how these new data sources influence SPI calculations across regions and taxonomic groups, identifying where EO inputs most substantially improve national indicator values. We highlight trade-offs among spatial resolution, temporal frequency, and global completeness, and assess the local data and tools required for countries seeking consistent, repeatable metrics for their GBF monitoring cycles.
Finally, we outline current examples of SPI operationalization within national biodiversity monitoring systems and describe how updated workflows, supported by cloud-based computation, private data integration, and repeatable modeling pipelines, enable Parties to update their data and more easily integrate the SPI into routine reporting. These enhancements position the SPI as a more robust, scalable, and flexible indicator for assessing species-level outcomes under the GBF.
How to cite: Killion, A.: Advancing the Species Protection Index for GBF Reporting Through Integration of High-Resolution Species Distribution Models and New Earth Observation Data, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-936, https://doi.org/10.5194/wbf2026-936, 2026.
