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.

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.

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.