The opportunities of CS to fulfill the monitoring needs of the GBF are slowly being recognised. However, the role of CS in monitoring still varies across scales, countries and monitoring targets. The impact of CS is most diverse at the local scale, but successful integration of data into national and international scale analyses is still largely restricted to taxa such as birds and butterflies, especially in western countries. Successful case studies and tested workflows could offer templates for how CS could be applied elsewhere.
Here, we offer an interdisciplinary session about research and practice on CS as a tool for local and national biodiversity indicators. Relevant topics could include:
- Pipelines to translate CS data into indicators
- Examples of co-design or novel forms of engagement
- Approaches for including new tools and technologies
- Challenges and opportunities within different forms of CS
- Adaptive sampling or similar novel approaches for integrated monitoring
- Evidence on social outcomes from CS
Citizen science has emerged as a powerful approach for enhancing biodiversity monitoring, particularly in small water bodies such as ponds, streams, and wetlands that are often underrepresented in large-scale environmental and biodiversity surveys. These smaller waters play crucial roles in supporting aquatic and terrestrial biodiversity, nutrient cycling, and ecosystem connectivity, yet they remain highly vulnerable to pollution, land-use change, and climate impacts. Engaging local communities in data collection can significantly expand spatial and temporal coverage of monitoring efforts while fostering environmental awareness and stewardship. With an active community of more than 800 citizen scientists, we were able to disclose the ecological water quality of more than 9000 smaller water bodies in the Netherlands over a period of five years. Comparison with data from formal monitoring framework showed that citizen science collected data yielded comparable results, reflecting that nearly 80% of the waters are in a moderate to poor ecological state. Our study examines the role of citizen science in assessing biodiversity and ecological water quality within smaller water systems, highlighting its potential to generate high-quality ecological data, identify local conservation priorities, and inform management decisions. Challenges such as data validation, methodological consistency, and participant training are discussed alongside opportunities provided by digital tools, open-access platforms, and cross-sector collaborations. Our study shows that such large-scale citizen scientist efforts have the potential to reveal blind spots in government mandated monitoring schemes, given proper training and validation is in place. One of those underrepresented sites are the rainwater and sewage overflow systems, which are also in the Netherlands, not monitored regularly. Citizen scientists have selected together with municipalities and water boards relevant sites, whcih are typically located in urban enviornment. At those sites a rich set of indicators in bi-weekly intervals, such as nutrients, water depth, water clarity but also photos of algae, vegetation and invertebrates.
The integration of citizen science into formal monitoring frameworks offers an additional cost-effective and socially inclusive pathway toward improved understanding and protection of small-scale aquatic ecosystems.
How to cite: Dekker, S., van Halsema, R., Teurlincx, S., and de Senerpont Domis, L.: Leveraging the power of citizen science for aquatic biodiversity monitoring , World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-137, https://doi.org/10.5194/wbf2026-137, 2026.
Reliable information on the state of biodiversity depends on long-term, spatially representative, and taxonomically valid monitoring data. Citizen science (CS) is increasingly considered a complementary and valuable component of biodiversity monitoring, yet its suitability for meeting specific monitoring needs has rarely been assessed systematically. To address this, we conducted a nationwide anonymous survey among about 350 stakeholders involved in professional and CS-based biodiversity monitoring in Germany. Respondents evaluated key structural needs for improvement in current monitoring programs and assessed the perceived ability of CS to address these needs.
To synthesize the responses, we systematically compared perceived monitoring needs with the perceived suitability of CS for the same set of criteria. This comparison yields a data-driven, conceptual gap framework identifying three key areas of i) strong potential, where CS aligns well with high monitoring needs; ii) critical gaps, where needs are high, but CS is considered unsuitable without additional support; and iii) intermediate zones, where CS may contribute under certain conditions.
High needs in monitoring were reported for improving data continuity and quality, spatial and temporal coverage, methodological consistency, and taxonomic expertise. CS was considered particularly well-suited to contributing to areas of strong potential, such as expanding sampling coverage, supporting long-term datasets, and offering opportunities for training, engagement and piloting new methods. Conversely, critical gaps emerged for tasks requiring specialized taxonomic expertise, rigorous validation or strict standardization—areas in which CS would require additional infrastructures, professional oversight, or hybrid models to contribute effectively.
Additionally, an exploratory subset of species and data experts investigated these patterns qualitatively and more in detail. Lastly, respondents identified key criteria in successful CS projects.
Together, these findings offer a structured, evidence-based framework for where CS can meaningfully contribute to national biodiversity monitoring and indicators and where targeted investment, coordination, or expertise are required. The results provide actionable guidance for designing scalable monitoring architectures aligned with national and GBF targets.
How to cite: Ewert, S. P., Voigt-Heucke, S., Warnk, T., and Krämer, R.: Bridging Monitoring Needs and Citizen Science Potentials: A National Assessment of Opportunities and Gaps for Biodiversity Monitoring in Germany, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-525, https://doi.org/10.5194/wbf2026-525, 2026.
Citizen science (CS) has become an important contributor to biodiversity research, expanding the spatial and temporal coverage of observations and strengthening the interface between science, policy, and society. Millions of biodiversity records collected by volunteers now complement formal monitoring systems, support conservation assessments, and feed into national and international reporting processes. Despite these achievements, the European citizen science landscape remains highly fragmented. Numerous local, regional, and national platforms exist, yet they operate with different standards, tools, and data models. This fragmentation limits data interoperability, hinders cross-border collaboration, and reduces the overall impact and visibility of citizen-generated biodiversity information. At the same time, the contributions of non-professional observers, who form the backbone of many biodiversity datasets, often remain insufficiently acknowledged.
The EU-funded RIECS-Concept project (Research Infrastructure for Excellent Citizen Science) aims to address these gaps by designing the first European research infrastructure dedicated to citizen science across disciplines, with biodiversity as a key use case. RIECS-Concept explores how citizen-generated biodiversity data can be better connected to scientific databases, monitoring networks, and policy-relevant platforms. Guided by the FAIR data principles (Findable, Accessible, Interoperable, Reusable) and the broader open science agenda, the project investigates how to improve data accessibility, interoperability and long-term usability.
A central element of RIECS-Concept is its co-design process, involving citizen science networks, researchers, NGOs, educational systems, research infrastructures, policymakers, and citizen science participants themselves from across Europe. Engaging these stakeholders will enable the project to identify the technical requirements, governance models, and ethical frameworks needed for an inclusive and trustworthy infrastructure. This includes exploring how tools used by citizens (e.g., smartphones, apps, or web platforms) can interface with scientific repositories, how data quality and validation can be supported, and how contributors can be properly recognized.
We will present a first synthesis of the results of the first European co-design phase and propose first solutions on integrating tools, data flows, and diverse actors to create a connected and participatory European infrastructure, supporting citizen science in biodiversity.
How to cite: Stressmann, F., Doran, C., Anticoli, A., and Wu, K.-T.: Towards a connected Europe: RIECS-Concept and the future of citizen science for biodiversity, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-910, https://doi.org/10.5194/wbf2026-910, 2026.
Citizen or community science is becoming an increasingly important component of biodiversity monitoring in cities. While the term is associated with a multitude of interpretations, citizen science involves the engagement of volunteers in the scientific process, with the goal of generating knowledge that addresses a relevant problem or research question. Citizen science has the potential to improve scientific literacy, facilitate experiences with nature, and provide valuable training for careers in STEM. Outside of direct benefits to volunteers, citizen science has yielded global biodiversity datasets, species discoveries and rediscoveries, and early detections of invasive species. As urban areas grow, citizen science presents even more opportunities to monitor and conserve biodiversity in cities. In particular, urban ecologists have paid increasing attention to engaging audiences not traditionally represented in ecology. Lack of adequate representation reinforces systemic inequalities, creates gaps in data, and leaves out research questions that are important to participating communities. By implementing an ‘ecology with cities’ approach, citizen science has immense potential to cultivate a more inclusive generation of scientists and advance urban ecological knowledge. Thus, assessing the status of urban citizen science projects and how they engage volunteers is of critical importance. Herein, we review over 400 citizen science projects monitoring urban biodiversity globally. We describe the breadth of urban citizen science (CS) projects which monitor biodiversity in terms of their scope, scale, and aims; the variety of approaches employed for biodiversity monitoring; and the audiences targeted for participation in urban CS projects. Finally, we present case studies representing a range of geographic and cultural contexts to illustrate the various challenges and opportunities associated with improving representation in urban CS projects. We summarize recommendations informed by these case studies that citizen science practitioners can use to inform their own efforts to reach audiences not traditionally represented in ecology. We chart out future research directions for citizen science to innovate community-engaged research methods, promote conservation, and advance biodiversity monitoring in cities.
How to cite: Pham, M., Roy, H., Pocock, M., Werenkraut, V., Gardiner, M., and Bowler, D.: Unraveling urban biodiversity through citizen science , World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-537, https://doi.org/10.5194/wbf2026-537, 2026.
Citizen science is playing an increasingly important role in biodiversity monitoring, providing unprecedented volumes of observations while engaging society in conservation efforts.
Yet many plant monitoring surveys still rely on expert-based field studies, which are time-consuming, costly, and difficult to scale up spatially and temporally. These constraints limit our capacity to keep pace with current rapid environmental changes. In this context, vision-based identification tools built on citizen science data such as Pl@ntNet, offer a promising opportunity to enable automated extraction of ecological information from images.
For this study, we developed a new workflow that integrates high-resolution plot imagery of annual vegetation communities, analysed with the Pl@ntNet model and filtered on the basis of phytosociological data to monitor plant communities at local scale. While Pl@ntNet has traditionally been used for single-species observations, we adapt its capacities to multi-specimen plot images, extending the scope of citizen science tools to support ecological studies at larger scale.
We evaluate our approach on a collection of standardised plot images collected in the Mediterranean ecosystem and precisely annotated by botanical experts. Using the model outputs, we derived multiple biodiversity indicators and compared them to expert-based estimates, focusing on commonly used metrics such as species richness, Shannon and Simpson diversity indices, Raunkiaer life-form types and community-weighted means of functional traits.
To further understand model performance and assess its reliability, we investigated how the accuracy of predicted indicators varied with ecological and methodological factors, including habitat type, seasonality, and image resolution. Discrepancies not explained by these factors can be attributed either to the intrinsic limitations of the model for which we will discuss avenues for future improvement, or, importantly, to observation bias, as botanical relevés are inherently subject to inter-observer error.
Our results demonstrate the potential of leveraging citizen-science infrastructures such as Pl@ntNet to generate scalable, repeatable biodiversity indicators from plot imagery. This approach offers a promising pathway for integrating citizen-science tools into local and national monitoring schemes, ultimately contributing to more efficient and inclusive biodiversity assessment workflows.
How to cite: Martellucci, G., Vinatier, F., Goëau, H., Bonnet, P., and Joly, A.: How image-based applications can measure plant biodiversity: A multi-specimen plot analysis based on Pl@ntNet citizen science platform., World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-650, https://doi.org/10.5194/wbf2026-650, 2026.
Fragmented datasets, sampling bias and inconsistent observation protocols often limit the use of citizen science data for indicator development. Citizen science data are often collected opportunistically without a design for use in biodiversity metrics. However, the large volume of data, and the broad spatial and taxonomic coverage, provide an invaluable source of biodiversity information at scale.
Here, we present a pipeline that integrates heterogeneous datasets to generate large scale maps of biodiversity metrics. These maps form a basis for management relevant information tools. We apply integrated species distribution modelling (iSDM) to correct for sampling bias and differences in data collection methods. We use the large number of open datasets available through aggregators such as GBIF.
The workflow has four main steps. These are data acquisition, data integration, integrated species distribution modelling (iSDM) and the production of derived outputs. Input data include structured surveys, opportunistic observations and environmental covariates. We standardise these inputs and combine them in a common iSDM framework. This produces species intensity maps, associated uncertainty estimates and sampling effort maps. We further process these outputs to identify biodiversity hotspots and to summarise species environment relationships.
We use Norway as a case study. Norway has extensive opportunistic citizen science programs. We produced detailed maps of species richness, biodiversity hotspots, uncertainty and sampling intensity. Our results show the potential of pipelines that integrate disparate datasets. Our example also reveals important limitations in the current body of data. In particular, it is not possible to infer and correct for sampling bias without data that allow estimation of the probability of occurrence. In practice this means data that include information on both what was observed and what was not observed. Our study therefore demonstrates a clear need to incorporate more structured approaches into citizen science data. This should not replace opportunistic, curiosity driven activity. It should add to it and support both the large data volumes and the high level of public engagement.
How to cite: Finstad, A., Perin, S., Mostert, P., Adjei, K., Togunov, R., and O'Hara, B.: Addressing data fragmentation in biodiversity citizen science data: Pipelines for integrated species distribution Models , World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-643, https://doi.org/10.5194/wbf2026-643, 2026.
Nature is inherently variable—its patterns shift across landscapes and through seasons, often in ways that challenge our ability to keep up. Yet traditional biodiversity monitoring has remained comparatively rigid, with protocols that stay fixed for long periods even as ecological and social conditions evolve. The value of routine, regular monitoring is clear in the sense that it is the best way to truly estimate a statistically robust biological trend. However, routing monitoring over long periods depends on continuously funding large monitoring projects that span social trends and political cycles.
On the other hand, more opportunistic data is being fueled by technology and flowing in from all directions in the form of camera traps, acoustic sensors, eDNA, satellite imagery, drones, and citizens science. These more ‘rapid’ data serve a complementary role and eventually could also produce long-term patterns. However, so far, most of these efforts are missing the longer, broader perspective and the carefully crafted goals and targets required of traditional ‘adaptive monitoring’.
We must reimagine adaptive monitoring for these new technologies. We first introduce a framework ‘Routine-Opportunistic Adaptive Monitoring’ (ROAM) that is a hybrid framework that combines responsive, short-term sampling within a design that still supports long-term trend detection. We outline general examples from phenology, stream disruption, disease surveillance, wildlife observation and animal movement, and a more detailed example using citizen science. Using results from the first summer of Blitz the Gap (blitzthegap.org) in Canada, we demonstrate the power of incentivized sampling to strategically fill in missing data gaps in a way that answers the goals of monitoring networks and, more specifically, the needs of reporting trends and indicators in a coordinated way across entire countries.
We find that small, strategic adjustments to sampling and engagement can make monitoring efforts more intelligent, scalable and policy-relevant. We illustrate an example using Key Biodiversity Areas (KBAs), which are designated in part when key species are found in an area. By guiding citizen science to these species, more observations of those species were added and new KBAs can be established. Overall, if done well, adaptive monitoring can pave the way for adaptive action.
How to cite: Pollock, L. and Hebert, K.: Reimagining adaptive sampling in the age of technology and crowd sourced science, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-920, https://doi.org/10.5194/wbf2026-920, 2026.
The dynamic nature of biodiversity conservation and management demands flexibility in biodiversity research. Yet there are many practical barriers to research flexibility, so it is important to find new ways to foster flexibility in conservation research. I report here on recent synthesis research proposing that citizen science offers strong yet untapped potential for research flexibility. I draw on diverse examples of citizen science initiatives documented in the literature to outline five underlying attributes that generate a strong innate capacity for flexibility in citizen science. These include: rigorous planning and evaluation frameworks, varied participant motivations, diverse knowledge to inform research priorities, boundary spanning institutions and professionals, and networks for capacity building at scales from local to global. I further explore eight common strategies for leveraging this capacity for flexibility, including: participant nudges, program expansion, diversification, flexible tools, repurposing outputs, facilitator partners, hubs, and new initiatives.
Based on this synthesis, I propose that citizen science has a strong underlying capacity for flexibility, and that further cultivating and leveraging this capacity can increase the impact of citizen science on conservation and generate a stronger response to emergent conservation issues. By shining a light on sources of and strategies for research flexibility in citizen science, I aim to establish a shared frame of reference for researchers and practitioners in both conservation and citizen science to explore and leverage this untapped potential. I further discuss trade-offs for citizen science programs that are considering opportunities to practice flexibility, as well as ways that citizen science practitioners might leverage flexibility to meet additional program objectives, including inclusivity and integration of participant perspectives. Finally, I close with an overview of considerations for conservation researchers and practitioners, as well as governance institutions and professional organizations, which may increase their ability to address dynamic and emerging research needs with citizen science.
How to cite: Mandeville, C.: Achieving impact in a dynamic conservation landscape: Unlocking potential for research flexibility in citizen science, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-491, https://doi.org/10.5194/wbf2026-491, 2026.
Citizen science (CS) has developed as a form of public participation in conservation decision-making. Contributory CS that reports species occurrence records to online platforms has broadened the opportunities for a diverse audience freely to record and share species observations. However, given the common lack of spatial or temporal guidelines, this open and unstructured participation amplifies observer-driven biases in where and how biodiversity is recorded, shaped by who participates, their socioeconomic backgrounds, residential locations, and how far they travel to record. Understanding these socio-spatial dynamics of CS participants is critical for uncovering, and potentially correcting, the resultant implications for the design of representative and inclusive conservation actions.
While prior studies examined socio-demographic characteristics of CS participants, there remains a limited understanding of how these factors influence their spatial recording behaviour and data contribution patterns. This study addresses this gap by conducting an online cross-sectional survey of iNaturalistUK registered users, generating 2,500 responses. We collected socio-demographic and residence location data of the users, which we then linked to their observation records from 2008 to 2024. For each user, we first computed the maximum distance, mean distance and the overall spread of recordings from their residence location, then applied DBSCAN and k-means clustering to identify distinct recorder clusters based on the three spatial metrics. The resulting clusters were statistically modelled with socio-demographic variables to determine predictors of cluster membership, followed by analysis of recording volume and species diversity across clusters.
Our results revealed five distinct recorder clusters ranging from local recorders, who are likely to record only near their home or neighbourhood, to wide-range travellers, who are likely to explore large geographic areas and or travel long distances from their residence for recording. Socio-demographic variables such as age group, income, gender, marital status, dog ownership and physical or mental health condition significantly predicted cluster membership. Notably, wide-range travellers and regional roamers who record across regional landscapes tend to contribute more records and a greater variety of species. These findings underscore the critical role of human dimensions in shaping data biases in citizen science data.
How to cite: Bandara, D., Elliott, L., Lovell, R., and Gaston, K.: Socioeconomic Differences and Spatial Recording Behaviour among Contributory Citizen Science Participants, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-187, https://doi.org/10.5194/wbf2026-187, 2026.
Insects are vastly under-represented in biodiversity monitoring, but different forms of heterogeneous data have indicated long-term declines. Unstructured citizen science monitoring, generating occurrence records collected without a common protocol, have been particularly valuable for deriving large-scale trends at the species-level. Such species-level trend estimates are essential for defining conservation priorities and understanding implications of declines for ecosystems. However, unstructured monitoring data contain multiple challenges related to potential confounding effects of sampling variability. New monitoring approaches are needed to deliver the robust insect data needed to reverse insect loss and maintain associated ecosystem services. Semi-structured monitoring, relying on protocol reporting than protocol standardization, has emerged as a promising and more flexible alternative to structured monitoring, retaining broad participation of citizen scientists. While semi-structured monitoring has already been highly successful for birds, via apps such as eBird, its potential application to insects is less clear. In this project, we examined the potential opportunities and barriers for semi-structured monitoring of insects, using dragonfly monitoring in the UK as a case study. We explored the recording practises of citizen scientists and the modelling performance of semi-structured data. We found that many insect recorders already follow attributes of structured surveying, reporting complete species lists, collecting abundance data and revisiting the same sites, but existing platforms are not sufficiently retaining all the information to make use of the structure. We also found that most recorders are willing to report more comprehensive metadata, such as survey effort, but only a subset would follow a fully standardised protocol. In our modelling experiments, we contrasted different approaches to making use of available semi-structured data streams, either by filtering data to those of the highest quality, or by building integrated models that combines the information within different data streams. Results suggest that a bias-variance trade-off is faced when making such analytical decisions. Overall, our findings highlight the overlooked structure within citizen science monitoring data that could be leveraged for more robust analyses of insect biodiversity change.
How to cite: Bowler, D., Pocock, M., and Pearce-Higgins, J.: Ecological and modelling opportunities of semi-structured insect monitoring , World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-462, https://doi.org/10.5194/wbf2026-462, 2026.
Large carnivores are threatened globally, with most extant taxa having suffered significant historical range contractions. Due to this imperiled status, as well as increased scientific interest in top-down ecological processes, ecologists and conservationists have dedicated renewed efforts towards large carnivore preservation and management. As part of these efforts, reliable and transparent population monitoring is critical both to evaluating population dynamics as well as to detecting policy and management effects on large carnivore populations. Therefore, it is imperative to evaluate how methodological changes to monitoring regimes may affect the bias and uncertainty of estimates, especially with cryptic and politically contentious taxa like large carnivores. We describe methodological changes in Wisconsin gray wolf (Canis lupus) censusing techniques by the Wisconsin Department of Natural Resources (DNR), paying particular attention to a citizen science program where volunteers conducted winter wolf track surveys separately from DNR trackers. We hypothesize how changes to volunteer training and participation in winter wolf counts may have resulted in several methodologically distinct time series of wolf population estimates. To investigate this hypothesis, we use a Bayesian mixed effects model to analyze how volunteer and DNR trackers counted wolves during a relatively methodologically consistent period from 2003 to 2011 and find that volunteers counted 83% (95% CI: [74%-92%]) as many wolves as DNR trackers. Therefore, we conclude that changes in relative volunteer involvement before and after that period must necessarily affect the bias and precision of wolf population estimates. We hypothesize possible reasons for this discrepancy between volunteer and DNR trackers, including differences in tracking aptitude, potential biases among trackers, and differences in survey timing. We also simulate volunteer and DNR wolf counts as if both tracker types had surveyed all blocks across all years to compare our reproducible wolf count uncertainties to DNR-reported uncertainties. We end with recommendations for more transparent and reproducible wolf counting by the DNR and broader recommendations for ecological citizen science initiatives.
How to cite: Putrevu, K., Chappell, R., and Treves, A.: Community Counting Carnivores: Discrepancies between volunteer and DNR winter gray wolf counts in Wisconsin 2003-2011 , World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-203, https://doi.org/10.5194/wbf2026-203, 2026.
The growing use of community science platforms has led to an exponential increase in biodiversity data stored in global repositories. However, our understanding of species distributions remains patchy. This is primarily due to the fact that biodiversity monitoring programs and citizen science activities remain heavily biased, both taxonomically and geographically, with most data for birds coming from Europe and North America. With the widespread use of smartphones, many people share their biodiversity observations on social media platforms, yet such data remains underutilised in conservation efforts. Incorporating social media data can help fill existing gaps, especially in megadiverse countries with limited records, but whether such data can substantially enhance our understanding of species range shifts is still uncertain. In my presentation, I will discuss how integrating Facebook data can reduce biodiversity data gaps, improve understanding of invasive species distributions, clarify the range dynamics of a highly range-shifting butterfly, and support conservation assessments. We collected species distribution records from Facebook and the Global Biodiversity Information Facility (GBIF), grouping them into GBIF-only and combined (GBIF and Facebook) datasets. We scraped nearly 45,000 unique georeferenced records covering 967 species, with a median of 27 records per species. About 12% of the distribution data related to threatened species, representing 27% of all species. We also gathered data for 56 Data Deficient species in Bangladesh. These findings highlight the importance of using social media data to fill knowledge gaps, track species redistributions, and inform spatial planning under the Kunming–Montreal Global Biodiversity Framework. While most assessments focus on Bangladesh, our method can be adapted for other countries, particularly those lacking detailed biodiversity databases. A key research priority now is to develop methods for extracting and analysing biodiversity data from social media. Community efforts are crucial to achieving the goals of the Kunming–Montreal Global Biodiversity Framework, especially in megadiverse tropical countries that lack current, reliable species distribution data.
How to cite: Chowdhury, S. and the iEcology: Social media records can reduce tropical biodiversity data gaps and inform conservation, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-281, https://doi.org/10.5194/wbf2026-281, 2026.
