英国鸟类信托

Global efforts to mitigate anthropogenic pressures on biodiversity and ecosystems will often be realised through management at landscape-scales (i.e., in the range of 100s–1000 s km2). In consequence, we need to measure biodiversity responses at landscape-scales to ensure mitigations are effectively protecting and restoring ecosystems. Yet many countries currently lack monitoring programmes that can generate indicators of biodiversity at these scales. Localised monitoring (e.g., 1 km2) is often amalgamated into national-scale indicators, however, this leaves a substantial gap in the middle of this spatial gradient, limiting availability of information at decision-relevant scales. Here, using the United Kingdom as a case study, we explored the suitability of seven sources of biodiversity data which could be used to construct landscape-scale indicators. We surveyed 70, mostly UK-based, monitoring experts for their opinions on structured and unstructured in-person surveys, camera traps, eDNA, drones, passive acoustic recorders, and satellite remote sensing. We assessed data source utility to construct indicators reflecting Essential Biodiversity Variables, i.e., as holistic measures of taxa or ecosystems rather than assessments of individual management interventions. All seven data sources were deemed suitable, and experts expected developments in technology and infrastructure to greatly increase this potential over the next decade. However, there are technical, analytical, logistical and financial barriers to establishing monitoring networks that could yield the requisite data for landscape-scale indicators. Resolving these issues requires substantial research, policy commitment and investment, but landscape-scale indicators will be essential for the UK to undertake adaptive management and monitor nature recovery.

Achieving the goals of the Kunming-Montreal Global Biodiversity Framework (GBF) requires monitoring systems that can transform heterogeneous observations into consistent, decision-relevant knowledge. Yet current biodiversity data are fragmented, uneven in quality, and seldom comparable across space or time. Existing standards such as Darwin Core, Findable, Accessible, Interoperable, and Reusable (FAIR) and Collective Benefit, Authority to Control, Responsibility, and Ethics (CARE) principles provide important foundations, but they do not connect the full chain from field observation to policy reporting. We introduce the Biodiversity Monitoring Standards Framework (BMSF)-a unifying architecture that links ethical principles, standardized data collection, accredited analytical workflows, and transparent reporting into a single auditable "chain of evidence." The framework's novelty lies in its tiered and federated design, enabling national agencies, Indigenous knowledge holders, local communities, and private-sector actors to operate under shared principles while maintaining data sovereignty. By integrating Essential Variables, accredited analytical methods, and open-source implementation pathways, the BMSF allows locally generated data to be aggregated into credible, comparable indicators aligned with GBF targets. Concrete application, such as a national forest-connectivity assessment, demonstrates how the BMSF improves reproducibility, transparency, and policy relevance relative to existing approaches. Implemented generally, this framework would convert fragmented monitoring efforts into a coordinated, scalable system capable of tracking and guiding collective progress toward halting and reversing biodiversity loss.

Despite increasing awareness of invasive non‐native species (INNS) and enhanced biosecurity controls in many countries, INNS are still arriving and establishing in new destinations, remaining a globally acknowledged threat to native biodiversity. Preventing the introduction of INNS, as opposed to controlling them once they have arrived, is recognised as the most effective approach to their management. Horizon scanning represents one of the key tools to identify high‐risk INNS that have yet to arrive within a region and has been applied in many contexts around the world, but to date there have been no studies that systematically assess the effectiveness of this approach. Here, we revisit the horizon scan for Great Britain conducted in 2013 that assessed the likelihood of high‐risk INNS arriving within the next 10 years, establishing and having an impact on biodiversity and ecosystems. We evaluated the success of this exercise in predicting arrival of these species within the subsequent 10 years. Ninety‐two species were shortlisted in the 2013 horizon scan. In total, 31 of the 92 species identified in the 2013 horizon scan had arrived by 2023. We found that 12 of the top 20 species had arrived within 10 years. In predicting arrival, there was a significant effect of species having arrived previously to Great Britain, and the number of countries in Western Europe and Baltic countries in which an INNS was found prior to 2013. Policy implications : We conclude that horizon scanning provides a rapid, affordable and successful mechanism to predict the arrival of high‐risk INNS. We highlight the importance of citizen science, including biological recording, and of local expertise for detecting and documenting arrival of INNS. We discuss knowledge gaps that could help inform and improve future horizon scanning. In addition, we recommend regularly repeating horizon scanning exercises to support biosecurity and awareness raising for INNS.

Biodiversity monitoring at large spatial and temporal scales is essential for informing conservation policies. The International Waterbird Census (IWC) is one of the longest-running global citizen science monitoring schemes, providing critical information to several international agreements. However, analyzing IWC count data poses statistical challenges, including zero inflation, overdispersion, spatial autocorrelation, and missing data. While various modeling approaches have been used to estimate waterbird population size and trends, their ability to handle these issues and the implications for trend estimates remain unassessed. Using IWC count data from five species in the East Atlantic Flyway, we compared four modeling approaches: TRIM (TRends and Indices for Monitoring data), LORI (Low-Rank Interactions), and two generalized linear mixed models (GLMMs) with simple or optimized parametrizations. We benchmarked their performance in addressing zero inflation, overdispersion, and spatial autocorrelation across different realistic sampling designs (i.e., alternative dataset configurations). Our results highlight significant limitations in commonly used methods. Simple GLMMs, TRIM, and LORI generally failed to mitigate both zero inflation and overdispersion. In contrast, optimized GLMMs improved model convergence and better addressed these issues by selecting appropriate probability distributions. However, no single distribution performed consistently well across species and sampling designs. Spatial structures were effective in reducing spatial autocorrelation in most cases. We recommend a careful species-specific selection of statistical methods when analyzing count data, as inadequate models may misrepresent population trends and thus misguide conservation efforts. Future research should explore the integration of advanced hierarchical and spatio-temporal models to improve inference from large-scale citizen science datasets.

Summary Biodiversity monitoring is essential to inform the state of wildlife populations, and the impacts of environmental change, conservation intervention, and sustainable development policies and actions. We review the current state of bird monitoring across Africa using public questionnaires and semi-structured interviews. We received 87 questionnaire responses from 46 (of 54) countries and, additionally, 24 in-depth interviews were carried out. Multiple data collection methods were reported with total counts of individuals being most frequent, but all-species surveys, essential for quantifying ecosystem health, were restricted to bird atlases and Common Bird Monitoring (CBM) projects in Kenya, Uganda, and Botswana. Data collection relied largely on volunteers, but their motivation, recruitment, training, and retention is a continuing challenge. The most sustainable programmes were driven by clear policy objectives (e.g. waterbird monitoring under the Ramsar Convention or the Convention on the Conservation of Migratory Species), monitoring of individual groups (e.g. raptors, vultures, bustards), specific threatened species, and where clear national priorities had been set within government agencies. Use of monitoring data by governments in country biodiversity reports or National Biodiversity Species Action Plans (NBSAPs) varied widely and, for many countries, simply did not exist. A lack of skilled analysts and a comprehensive approach to data curation and ownership were identified as major limitations. A more strategic approach to funding and monitoring is needed, whereby smaller funders collaborate to reduce costs associated with applying for small amounts of money, and bird (and biodiversity) monitoring is explicitly integrated with sustainable development goals to exploit broader funding streams.