This session will explore practical and innovative indicators and metrics for farmland biodiversity, with a focus on novel technologies that improve monitoring and standardise data collection. Contributions may include traditional field-based methods, as well as cutting-edge technologies such as satellite or airborne remote sensing, passive acoustic monitoring, environmental DNA (eDNA) and artificial intelligence (AI)-assisted species identification. These technologies can provide rapid, cost-effective and scalable biodiversity assessments. We particularly welcome contributions that link these indicators to agri-environmental policy instruments such as result-based payment schemes, to global biodiversity targets or to sustainability certifications.
By showcasing scalable monitoring strategies and highlighting how data can be translated into actionable guidance, this session aims to bridge science, policy, and practice. We encourage contributions that connect farmers, nature conservation organisations, researchers, and policymakers, and that identify pathways to integrate farmland biodiversity monitoring into broader biodiversity observing systems.
Biodiversity loss driven by agricultural expansion and intensification is well documented, and the Global Biodiversity Framework calls for transforming agroecosystems toward sustainable management. Yet, cultivated land remains a major blind spot in global, open-access biodiversity data, leading to persistent knowledge gaps and reporting biases. At the same time, rapid advances in sensor technologies, digital tools, and artificial intelligence are reshaping how biodiversity and ecosystems are monitored, while novel farming technologies are transforming food production, with significant implications for agrobiodiversity. Despite these developments, little research has examined how such technologies can support agri-environment schemes, the primary policy instrument for biodiversity conservation in agricultural landscapes. Most existing agri-environment schemes are action-based, rewarding farmers for implementing specific practices or management rather than for achieving ecological outcomes. Result-based payments offer an alternative by linking rewards directly to biodiversity results, but their application has been limited – particularly for mobile species – due to monitoring challenges. This presentation explores how technologies can enhance agri-environment schemes by expanding conservation targets in result-based payments, improving the targeting and effectiveness of existing action-based schemes, enabling novel state-dependent designs, and strengthening monitoring of both outcomes and compliance. It also outlines key challenges, including high monitoring costs, remaining uncertainty in biodiversity assessments, data-privacy concerns, and farmers’ skepticism toward new technologies. Additional risks which as discussed in the presentation include technology-driven conservation goal setting, profit-oriented innovation that overlooks impacts on agrobiodiversity, and the marginalization of farmers lacking access to advanced tools. A particular focus in the presentation is placed on Passive Acoustic Monitoring as a promising approach for novel agri-environment schemes that is low-cost, scalable, and capable of detecting the presence, activity, and diversity of sound-producing animals. The presentation demonstrates how Passive Acoustic Monitoring can be integrated into result-based payment frameworks, from identifying suitable biodiversity targets and indicators to evaluating cost implications and understanding farmers’ perspectives.
How to cite: Cord, A. and Wätzold, F.: Digital biodiversity monitoring for next generation agri-environment schemes, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-865, https://doi.org/10.5194/wbf2026-865, 2026.
In contrast to traditional conservation management which relies on generalized practices or historical data, potentially leading to inefficient use of resources, the concept of state-dependent conservation promises an adaptive management strategy that uses (near) real-time ecosystem or species presence data to make targeted decisions about implementing conservation measures. However, given that the monitoring requirements for state-dependent conservation are much higher, the concept has largely been dismissed as being unfeasible. With the rise of automated monitoring technology and increasing capabilities of AI assisted species classification, overcoming this barrier might have become possible. Against this backdrop, we set out to revisit the concept of state-dependent conservation.
This study integrates Passive Acoustic Monitoring (PAM) technologies, AI assisted species classification and cost-effectiveness analysis in agri-environment schemes (AES). We present a proof-of-concept case study where PAM is deployed across agricultural plots to detect meadow-nesting birds during their breeding season that will be conducted in the Spreewald Biosphere Reserve in spring 2025. We assume varying probabilities of bird presence and analyze the costs of monitoring and compensating farmers for state-dependent and conventional conservation measures. We also demonstrate early results for a custom AI classifier trained not to detect all species, but rather specific bird calls which indicate bird breeding behaviour (and which can thus inform conservation actions). This state-dependent approach, facilitated by novel monitoring technologies, theoretically allows more targeted conservation actions only when necessary, potentially reducing overall expenditure or freeing up resources for other conservation targets.
Comparative analysis with conventional AES approaches will investigate trade-offs between fixed conservation payments and dynamic, monitoring-informed actions. Our analysis aims to identify conditions under which state-dependent AES can outperform conventional approaches in maximizing conservation outcomes relative to cost.
This approach may have implications for policy-makers and conservation managers seeking economically sustainable and ecologically effective AES solutions. By leveraging advancements in monitoring technology, state-dependent conservation schemes could potentially improve biodiversity outcomes in managed agricultural landscapes.
How to cite: Bennett, D.: Can Automated Monitoring Enable State-Dependent Conservation?, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-777, https://doi.org/10.5194/wbf2026-777, 2026.
Healthy soil underpins terrestrial biodiversity by supporting microbial food webs, regulating nutrient and water cycling, and enabling diverse plant communities that form the foundation of higher trophic levels. Both farmers and conservation organisations monitor soil properties but historically for different objectives. In agriculture, measuring soil moisture, temperature, electrical conductivity (EC), pH, and soil respiration (CO2) is used to optimise production outcomes, particularly irrigation scheduling and fertiliser efficiency, enabling producers to maximise yield while reducing water, nutrient inputs, and costs.
In contrast, conservation organisations monitor the same variables to assess restoration effectiveness and ecosystem recovery. Soil data evaluate interventions including landscape rehydration works, revegetation programs, and grazing management, and provide early warning indicators of ecosystem stress, such as declining soil moisture or increasing salinity prior to visible vegetation loss during drought.
Despite their value for decision-making, long-term in-situ soil monitoring has been constrained by high equipment and deployment costs. Expenses arise from ruggedised, low-power sensors and hardware, data transmission fees, installation and maintenance in remote environments, and logistical challenges in terrain lacking power and reliable communications. Consequently, most monitoring relies on short-term sampling incapable of capturing long-term trends, seasonal variability, or ecological tipping points at landscape scale.
Baseliner Soil is a real-time, in-situ system developed with ecologists and scientists for conservation and restoration. Using affordable, lab-verified sensors, it measures soil moisture at multiple depths, soil temperature, EC, pH, and CO2, transmitting data to the internet to generate long-term time-series datasets able to detect soil condition changes overlooked by short-duration monitoring.
The system is power-optimised for long-term deployment, ruggedised for harsh field conditions, offers multiple communications options for remote sites, and is cost-optimised for scalable monitoring networks.
By adopting conservation monitoring practices, farmers enhancing on-farm biodiversity can assess regenerative actions such as cover cropping, reduced tillage, rotational grazing, riparian restoration, tree belts, and organic amendments. Improvements in soil carbon, respiration (CO2), aggregate stability, moisture retention, and reduced salinity can provide early evidence of recovering soil food webs, increasing plant diversity, and expanding habitat quality, bridging agriculture and conservation toward sustainable, biodiversity-positive farming systems.
How to cite: Plucinski, J. and Wang, A.: Long-Term In-Situ Soil Monitoring as a Bridge Between Agricultural Productivity and Biodiversity Conservation, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-676, https://doi.org/10.5194/wbf2026-676, 2026.
Alpine and subalpine pastures are semi-natural, high-biodiversity grassland ecosystems shaped over centuries of livestock grazing. In South Tyrol (Italy), socio-economic and technological factors over the past century have contributed to land abandonment in less-favoured areas. This has resulted in shrub and tree encroachment into meadows and pastures, leading to a decline in grassland biodiversity. A key challenge is to understand how grazing intensity affects soil biodiversity and whether clear thresholds exist beyond which such changes can substantially impact the ecosystem.
To investigate this, we selected four high-altitude sheep pastures (ranging from 2100 to 2500 m a.s.l.) under three distinct management regimes: intensive grazing, moderate grazing, and abandonment (defined as no grazing for three or more years). For each regime, we chose three plots and collected ten soil samples from each (5–10 cm depth). We measured soil physical and chemical parameters, including aggregate stability, total organic carbon, total nitrogen, dissolved organic carbon, pH, nitrate, and available phosphate. Additionally, we studied belowground biodiversity through environmental DNA metabarcoding targeting soil bacteria (16S rRNA gene), fungi (ITS2 region), and soil fauna (COI gene).
We expect that high grazing pressure will significantly influence microbial communities, especially fungi, thereby affecting carbon and nitrogen cycling. Indeed, previous studies show that grazing disturbance and repeated nutrient inputs from urine and faeces increase soil nitrogen availability and stimulate nitrification processes. Conversely, grazing abandonment causes successional vegetation shifts and low biomass removal, as shown in comparable subalpine grasslands, with cascading effects on soil microbial biodiversity. Moderate grazing, on the other hand, has historically fostered the highest biodiversity in semi-natural grasslands in the Alps by maintaining ecological heterogeneity, which prevents competitive exclusion among plant species. This aboveground heterogeneity is closely linked to soil microbial patterns and can therefore sustain higher biodiversity compared to both intensive grazing and abandonment. Considering all these elements together, we aim to identify indicators and thresholds that signal a tipping point toward grassland recovery or degradation. Such information can support sustainable grazing management practices to preserve biodiversity and maintain ecosystem functions in alpine meadows.
How to cite: Fracasso, I., Zimmermann, F., Zanon, T., Borruso, L., Mimmo, T., and Wellstein, C.: Tipping Points of Biodiversity and Sustainability in Traditional Alpine Pastures (BIOPAS), World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-708, https://doi.org/10.5194/wbf2026-708, 2026.
We developed an ecosystem account for a 38,603-hectare farm in southern Amazonia, Brazil, using integrated spatial, field, and farm-supplied data. About half of the property is covered by native forest designated as legal reserve, which provides an opportunity to assess how on-farm forests compare with reference conditions in the same biome. Our assessment focused on three pillars of ecosystem accounting using SEEA UN (System of Environmental Economic Accounting - United Nations) framework: extent, condition, and services.
To evaluate forest condition, we used several indicators, including net primary productivity (NPP) as a measure of forest growth dynamics, the percentage of area burned by fires, and the evolution of aboveground carbon (AGB). For biodiversity specifically, we relied on a single spatially derived indicator: tree species richness, obtained from a regional species-distribution dataset and validated with field observations across the property. Some indicators were benchmarked against values from a nearby pristine forest. Comparisons showed that on-farm forests maintain species richness and forest growth rates close to those of intact forest, suggesting that the legal-reserve area retains high ecological quality.
Still on biodiversity assessment, we used farm-provided management data to build an additional condition indicator: Aggregated Total Applied Toxicity (ATAT). This metric captures the cumulative toxicity of pesticides applied to groups of fauna and flora species. Mapping ATAT over time revealed rising toxicity loads in recent years and highlighted how some of the most affected farm's crop fields are spatially proximate to river catchments, underscoring potential risks to aquatic ecosystems.
To deepen the assessment of ecosystem services, we conducted a 3-hectare forest inventory. More than 90 different tree species were identified along with their associated ecological functions, including potential pollinators, mycorrhizal fungi, and documented human uses (food, timber, medicine). While this inventory provided qualitative evidence of multiple potential ecosystem services, it also exposed methodological gaps. Current frameworks lack robust, quantitative tools for valuing the diverse services generated by species-rich tropical forests.
How to cite: de Souza Batista, F.: Integrating Spatial, Field, and Farm Data for Tracking Farmland Biodiversity and Ecosystem Condition, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-340, https://doi.org/10.5194/wbf2026-340, 2026.
This research aimed to determine the readiness of agricultural producers toward evolving industry expectations and requirements for biodiversity and natural capital accounting. The main objectives were to 1) establish biodiversity baseline information, 2) assess growers’ knowledge and motivations to enhance biodiversity, 3) understand internationally recognised metrics and how they relate to agricultural producers (Australian grain growers), and 4) evaluate the suitability of available natural capital accounting tools. Interviews with grain growers and supply chain stakeholders were completed along with a systematic evaluation of biodiversity metrics and natural capital accounting tools.
A key finding is that linking producer knowledge to globally available datasets is key. Global stakeholders routinely use Earth observations data to assess biodiversity and other environmental risks of producers, which in many cases do not align with local or regional information. However, several data sources are readily available for growers to access, at a scale that allows farm-level assessment for key metrics like Ecosystem Extent, Mean Species Abundance (“MSA”), and whether a farm is located in a Key Biodiversity Area (“KBA”). Many growers use sophisticated, GPS-based farm management apps, but the data collected to inform management operations is not typically informative about biodiversity.
On-farm interventions that are most effective in enhancing biodiversity include several practices that Australian grains growers already commonly integrate into their operations, including crop rotation with legumes, soil health monitoring, and maintaining native vegetation strips for refuge habitat.
Growers were most informed on carbon-related or emissions standards, due to impending supply chain requirements, and less so on biodiversity measurement or how the supply chain monitors biodiversity practices. No particular biodiversity accounting or natural capital accounting tool could currently be considered fully fit for purpose.
This talk will detail the spectrum of data sources for monitoring biodiversity that are considered by supply chain and ESG advisory stakeholders, data collected and reported by producers, and identified solutions for bridging the data gaps, including advances in the use of Earth Observations data along the value chain. The presentation will include findings and recommendations to increase biodiversity best practice and measurement against a backdrop of shifting industry and regulatory expectations.
How to cite: Seidel, V., Dourte, D., Madani, S., Chihava, E., and Osorio, D.: Biodiversity Metrics in Agricultural Supply Chains: Lessons Learned from the Australian Grain-Growing Industry, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-541, https://doi.org/10.5194/wbf2026-541, 2026.
Biodiversity is facing unprecedented threats from human activities, leading to alarming declines in species and ecosystems worldwide. This decline undermines ecosystem stability and reduces resilience to environmental changes, leading to significant ecological, economic, and societal impacts, and the world is seeking for effective strategies to tackle these challanges. This research focuses on Colombia, where sustainable management is essential to support agricultural productivity while safeguarding natural heritage. The study aims to provide decision-makers with a comprehensive assessment methodology to measure the impact of interventions on biodiversity conservation. By critically examining the current status of biodiversity the research seeks to inform evidence-based strategies for sustainable socio-economic development. A dynamic indicator will be used to support evidence-based decision-making, explicitly examining the conversion of traditional agricultural systems into Urban and Peri-Urban Agriculture (UPA). Data from the municipality of Bogotá with varying agricultural densities were used to validate the indicator. The tool is based on a SDM approach and it quantify the impact of management measures on biodiversity using the Red List Index (RLI) and shapefiles from the IUCN database. The research employs a technical questionnaire, evaluated through a weighted process, to generate a numerical indicator for the effectiveness of proposed interventions. The methodology also involves a tailored survey for local stakeholders and expert to gather insights on biodiversity and agricultural practices and validate the scientific data. This methodology provides a versatile framework for assessing biodiversity impacts and supports the development of sustainable strategies to enhance biodiversity conservation amidst environmental challenges. Results indicate a significant potential for UPA to mitigate agricultural pressures on biodiversity by introducing sustainable agricultural practises and land management. The results showed a marked improvement in biodiversity scores following UPA implementation, with a Simulated Red List Index (SRLI) demonstrating a 7% improvement related to a 59% progress on UPA system implementation. Furthermore, the tailored survey validates the scientific outcome and reveals a growing perception of biodiversity benefits linked to UPA practices. These findings suggest that UPA systems, if managed effectively, can play a pivotal role in biodiversity conservation, offering a replicable framework adaptable to other regions facing similar challenges
How to cite: Falda, M., Caucci, S., Karthe, D., and Guzman, J.: A Dynamic Indicator for Assessing Biodiversity Impacts in Natural Resource Management: Insights from Urban and Peri-Urban Agriculture in Colombia, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-829, https://doi.org/10.5194/wbf2026-829, 2026.
Bird populations reflect ecosystem health and serve as early warning signals for ecological changes. In Luxembourg, rising temperatures and increasing habitat loss and fragmentation threaten these indicator species and their functions. To monitor early ecosystem degradation, we selected six bird species representing forest, farmland, or open land habitats. However, with multiple interacting drivers of change, a single metric cannot assess future vulnerability. Starting with a risk-based approach allows evaluation of both climatic changes and whether habitat availability and protection amplify or reduce species exposure. While many studies have examined climate-driven range shifts, the combined effects of land-cover constraints and species-level risk assessments remain underexplored, despite their importance for conservation planning. Most existing studies describe future suitability but do not translate projections into measurable risk for decision-makers. Integrating a risk index allows us to identify which species face the greatest ecological threats and prioritise conservation actions accordingly.
To address these research gaps, we modelled current and future ranges for the six key bird species using an ensemble of machine-learning algorithms (Random Forest, XGBoost, LightGBM, CatBoost, and SVM). These models included historical climate and high-resolution land-cover data, ensuring both climate and landscape structure were considered. For future projections, we applied the SSP2-4.5 and SSP5-8.5 scenarios and filtered results with a 30 m habitat-specific mask to ensure ecological validity. Linking our predictions for 2021–2040 and 2041–2060 to the protected-area network, we incorporated these into a risk index. This index combined the percentage of habitat lost, climate stress under each scenario, and the proportion of remaining suitable area within protected areas. The resulting risk score is intended to guide national biodiversity prioritisation by highlighting species at the highest risk, especially those with limited conservation protection.
Our results demonstrate significant mid-century habitat losses under SSP5-8.5. For example, Lanius collurio, Oenanthe oenanthe, and Vanellus vanellus lose over 90% of their suitable area, while Ficedula hypoleuca and Accipiter gentilis lose 70–80%. Lullula arborea loses 47.9%. While forest refugia remain protected, open-land and ecotone species are highly vulnerable. These findings show that integrating high-resolution data, land-cover constraints, and risk assessments effectively targets climate-smart conservation efforts and advances BIOFIN-EU goals.
How to cite: Eddamiri, S., Sai, A. R., and Brewster, C.: Risk Assessment of Indicator Bird Species Under Climate and Land-Cover Change Using Ensemble Species Distribution Models, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-332, https://doi.org/10.5194/wbf2026-332, 2026.
Climate change is intensifying water scarcity across Europe, raising urgent questions about how to improve the water-use efficiency (WUE) and ecological resilience of agricultural systems. Crop rotation—an essential form of agroecosystem biodiversity—has long been recognized as a cornerstone of sustainable land management, yet its large-scale contributions to improving WUE remain poorly quantified. Here, we integrate multi-year remote sensing observations with a Europe-wide crop rotation dataset to assess how rotational biodiversity influences agroecosystem WUE at 300m resolution across Europe. By leveraging multi-source satellite data and causal explainable machine learning, we show that greater rotational diversity substantially enhances WUE relative to monoculture systems, with the strongest improvements emerging in water-limited environments of Southern and Eastern Europe. Biodiversity in crop sequences increased WUE primarily by boosting gross primary productivity while stabilizing evapotranspiration under highly variable climatic conditions. Regions practicing cereal–legume/oilseed crop rotations exhibited the largest gains, highlighting the role of nitrogen-fixing and break crops in strengthening ecosystem water–carbon coupling and promoting functional diversity. Moreover, climate anomalies amplified the benefits of biodiverse rotations: during drought years, fields with high rotational diversity maintained significantly greater WUE resilience than continuous cropping systems. Our findings provide the first continental-scale evidence that agroecosystem biodiversity, expressed through crop rotational diversity, is a powerful and climate-robust strategy for enhancing agricultural water-use efficiency. As Europe faces escalating hydroclimatic extremes, promoting biodiversity-driven crop rotations represents a critical pathway toward sustainable, resource-efficient, and climate-resilient agroecosystems.
Keywords: Crop rotations; Biodiversity; Climate change; Agroecosystems; Water-use efficiency; Europe
How to cite: Wang, S., Liu, D., and Guan, K.: Optimal crop rotation benefits agroecosystem water-use efficiency across Europe, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-564, https://doi.org/10.5194/wbf2026-564, 2026.
Oil palm plantations (OPP) have often been linked to deforestation and loss of biodiversity. However, understanding how the surrounding landscape influences wildlife is very important to support sustainable and biodiversity-friendly plantation management. This study was carried out to compare the diversity of different biodiversity taxa in OPP with different landscape structures. The types of landscapes studied were plantations with no forest, plantations with forest patches, and plantations located next to forest reserves.
The study was conducted in four FGV plantations, namely FGV Setiu in Terengganu, FGV Lepar in Pahang, FGV Tenggaroh in Johor, and FGV Aring in Kelantan. Sampling methods vary across taxa within 1km radius at each landscape type. Each site represented a different level of forest connectivity, allowing comparison of wildlife presence in relation to surrounding habitat conditions. The overall survey recorded 48 species of mammals, 92 species of birds, 27 species of herpetofauna, and 594 species of insects. Among the species detected were several iconic and threatened wildlife, including the sun bear, Asian elephant, Malayan tiger and Malayan Tapir. These findings show that OPP can still support a wide range of wildlife species, depending on how the landscape is structured and managed.
Plantations located next to forest reserves showed the highest number of species followed by plantations with forest patches (HCVs). The lowest number of species was found in plantations with no forest at all. The same trend was seen for threatened species such as Endangered, Vulnerable, and Near-Threatened species, which were more common in plantations next to forests.
Among the sites, FGV Aring showed the highest conservation value as several threatened species were recorded there. The results indicate that forest and HCV areas are an important component of OPP because they help to reduce biodiversity loss and provide refuge for many species. Therefore, maintaining and restoring forest patches and High Conservation Value areas within plantations are essential steps toward achieving sustainable palm oil production and long-term wildlife conservation.
How to cite: Ramli, Z., Kasim, A. S., Mohd Taib, F. S., and Syed Abdullah, S. N. A.: Forest and High Conservation Value Areas Enhance Wildlife Diversity in Oil Palm Landscapes: Evidence from FGV Plantations, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-134, https://doi.org/10.5194/wbf2026-134, 2026.
