The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) Thematic Assessment Report on Invasive Alien Species and Their Control presents a “conceptual diagram of management-invasion continuum”, introducing a versatile framework to support decision-making on the management of biological invasions. Drawing on an extensive synthesis of current knowledge, this IPBES invasion-management framework has been developed to broaden the scope of existing invasion curves—which primarily overlay generic management objectives onto a sigmoid curve depicting the expansion of the affected area over time—and to illustrate the applicability of the concept of effective management at different stages of the biological invasion process. To introduce the IPBES invasion-management framework to a wider audience, this paper explains the features of the framework and defines the invasion-stage-based management approaches and the potential outcomes envisaged therein. Reflecting the currently limited management options for biological invasions in marine and other connected-water systems, unlike in terrestrial and closed-water systems, the IPBES invasion-management framework clearly distinguishes between these two groups of systems. For each, it presents management approaches including three key factors that decision-makers should consider concurrently: management objectives, targets, and actions. This framework supports informed decision-making in the management of biological invasions in all ecosystems.

OneSTOP is a 3.5 year EU Horizon funded project focussing on enhancing systems and technologies to deliver approaches to achieve One Biosecurity and mitigate the impacts of invasive alien species. The need to deliver pioneering innovative and holistic approaches to biosecurity for invasive alien species is widely recognised and there is an increasing awareness of the interconnections between animal, plant, human and environmental health. However, it is critical that the barriers posed by dispersed and fragmentary processes, policies, and knowledge are addressed. The strategic actions recommended for integrated governance of biological invasions in the IPBES Thematic assessment report on invasive alien species and their control (IPBES 2023) underpin the work plan of OneSTOP. OneSTOP explores the use of current and emerging tools and technologies to deliver methods for identification, early detection and surveillance of terrestrial invasive alien species. Through seven work packages involving 20 partner organisations, OneSTOP aims to integrate interdisciplinary approaches, recognising the relevance of social and ecological science, to deliver cutting-edge approaches to minimise the introduction, establishment and spread of invasive alien species. Specifically, we are developing detection methods, underpinned by evidence-based prioritisation and robust models, alongside stakeholder engagement to inform decision-making and facilitate knowledge exchange. The outcomes will be relevant for invasive alien species policy across local, national, and regional scales. By adopting a holistic and interconnected approach, OneSTOP will document approaches to inform strategies to achieve rapid and transformative progress in detecting, eradicating and controlling terrestrial invasive alien animals and plants. Here we provide an overview of OneSTOP and outline the opportunities for collaboration.

Abstract This article provides an overview of the biological invasion process, the impact of invasive alien species, and management strategies. The process of biological invasion involves the movement of species, through human activities, from their native range to regions of the world where they would not naturally occur, thereby becoming alien (non-native) species. The number of alien (or non-native) species – animals, plants, and microorganisms that have been transported and introduced to a new place – is rising globally, largely driven by expanding trade and transport networks over the last century. A small proportion of these alien species become invasive, establishing, spreading, and negatively impacting nature and people. The most effective way to mitigate the threat of invasive alien species is to prevent their arrival by managing pathways of introduction through robust biosecurity. When prevention fails, early detection and rapid response can limit the establishment and spread of invasive alien species. If this also fails, then long-term management may be required to reduce the negative impacts. There are many frameworks, including voluntary codes of conduct but also legislation, underpinning action against invasive alien species. However, implementation remains uneven across the world. Target 6 of the Kunming-Montreal Global Biodiversity Framework outlines the commitment of nations to ambitiously reduce the establishment and impacts of invasive alien species. Mitigating the threat of invasive alien species is possible, but there is a need for implementation of biosecurity through integrated governance including cross-border cooperation and collaboration.

There is growing evidence that human-induced climate change and habitat loss are having negative impacts on insect populations. New technologies have a vital role in improving and expanding global biodiversity monitoring capacity to understand where change is happening and to support restorationMonitoring of insects traditionally needs entomologists in the field, but insect camera traps powered by AI are emerging as a scalable approach to monitoring semi-autonomously. These systems attract, detect, and identify insects using a Raspberry Pi, camera and UV lights. AI algorithms are also being developed by a network of researchers across the world to help with identification, notably in Europe and North America.The first version of a system for monitoring nocturnal insects was developed by Bjerge et al. 2021. An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning. Here we describe the second generation of the system as an open-source solution. This paper aims to enable anyone to build their own system, and to iterate and improve the design for their needs. This system captures images at set intervals or based on motion detection to monitor insects that are attracted to lights at night. The UKCEH Automated Monitoring of Insects System (UKCEH AMI-system) is an insect camera trap designed using a single board computer, USB camera and attractant lights as the primary components along with peripheral accessories to make an autonomous system capable of long-term deployment in the field. Nearly 200 UKCEH AMI-systems have been deployed to date in over 30 countries around the world.

Biodiversity is declining in many parts of the world. Biological diversity measurement and monitoring are fundamental to the assessment of the causes and consequences of environmental changes, identification of key areas for the protection of biodiversity or ecosystem services, determining the effectiveness of actions, and the creation of decision-support tools critical to maintaining a sustainable planet. Biodiversity measurement is rapidly changing due to advances in citizen science, image recognition, acoustic monitoring, environmental DNA, genomics, remote sensing, and AI. In this perspective, we outline the exciting opportunities these developments offer but also consider the challenges. Our key recommendations are to 1) Capitalize on the ability of novel technology to integrate data sources 2) agree to standard methods for data collection 3) ensure new technologies are calibrated with existing data; 4) fill data gaps by using emerging technologies and increasing capacity, especially in the tropics; 5) create living safeguarded databases of trusted information to reduce the risk of poisoning by AI hallucinated, or false, information; 6) ensure data generation is valued; 7) ensure respectful incorporation of Indigenous Knowledge; 8) ensure measurements enable the quantification of effectiveness of actions, and 9) increase the resilience of global datasets to technical and societal change. Radical new collaborations are needed between computer scientists, engineers, molecular biologists, data scientists, field ecologists, citizen scientists, Indigenous peoples, policymakers, and local communities to create the rigorous, resilient, accessible biodiversity information systems required to underpin policies and practices that ensure the maintenance and restoration of ecological systems.

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.

Habitat assessment at local scales — critical for enhancing biodiversity and guiding conservation priorities — often relies on expert field surveys that can be costly, motivating the exploration of AI-driven tools to automate and refine this process. While most AI-driven habitat mapping depends on remote sensing, it is often constrained by sensor availability, weather, and coarse resolution. In contrast, ground-level imagery captures essential structural and compositional cues invisible from above and remains underexplored for robust, fine-grained habitat classification. This study addresses this gap by applying state-of-the-art deep neural network architectures to ground-level habitat imagery. Leveraging data from the UK Countryside Survey covering 18 broad habitat types, we evaluate two families of models - convolutional neural networks (CNNs) and vision transformers (ViTs) - under both supervised and supervised contrastive learning paradigms. Our results demonstrate that ViTs consistently outperform state-of-the-art CNN baselines on key classification metrics (Top-3 accuracy = 91%, MCC = 0.66) and offer more interpretable scene understanding tailored to ground-level images. Moreover, supervised contrastive learning significantly reduces misclassification rates among visually similar habitats (e.g., Improved vs. Neutral Grassland), driven by a more discriminative embedding space. Finally, our best model performs on par with experienced ecological experts in habitat classification from images, underscoring the promise of expert-level automated assessment. By integrating advanced AI with ecological expertise, this research establishes a scalable, cost-effective framework for ground-level habitat monitoring to accelerate biodiversity conservation and inform land-use decisions at a national scale.

Insect camera traps are a rapidly developing technology for automated insect monitoring. However, little has been reported on improving the attractants used for daytime flying insects on such cameras. This study compares the attractiveness of 3D-printed artificial flowers with traditional attractants (pan traps and coloured paper squares). We hypothesised that artificial flowers would attract higher insect abundance and diversity by more accurately mimicking flowers, and additionally examined colour preference and landing duration. Artificial flowers, dry pan traps and paper squares, painted in UV-induced fluorescent yellow, white, or blue paint, were filmed simultaneously to observe wild insect behavioural responses (landings and approaches). The results indicate an overall preference for artificial flowers over traditional attractants, and colour preferences for blue and yellow. Analysed by group, hoverflies preferred landing on artificial flowers over the other attractants. Bumblebees preferred approaching artificial flowers, and 'small insects' preferred landing and approaching artificial flowers over the other attractants. 'Other flies' preferred landing on pan traps and paper over artificial flowers. Hoverflies, 'small insects', wasps, and 'solitary bees' responded more to yellow than the other colours, while bumblebees responded more to blue. Analysis of landing probability broadly mirrored these attractant preferences. Landing duration showed limited effects across groups, though 'small insects' spent longer on artificial flowers and pan traps than paper, and hoverflies spent longer on yellow than the other colours. These results suggest artificial flowers could offer an efficient attractant for insect camera traps as they attracted higher abundances of key pollinating insects (hoverflies and bumblebees) without reducing attraction rates for other insect groups (excluding 'other flies').

The overarching objective of OneSTOP is to pioneer an innovative and joined-up approach to biosecurity for terrestrial invasive alien species, strengthening the interconnections between animal, plant, human and environmental health. OneSTOP aims to harness current technologies and citizen science, while overcoming challenges posed by dispersed and fragmentary processes, policies, and knowledge, to deliver methods for identification, early detection and surveillance of invasive alien species. OneSTOP aims to achieve transformative results to minimise the introduction, establishment and spread of invasive alien species by integrating cutting-edge detection methods, underpinned by prioritisation and robust models, alongside stakeholder engagement to inform harmonised policies and facilitate knowledge exchange. The outcomes will be relevant for invasive alien species policy, noting the importance of enhancing collaboration and coordination across local, national, and regional scales, recognising that geographic boundaries do not confine the impact of these species. By adopting a holistic and interconnected approach, OneSTOP seeks to establish a strategy to achieve rapid and transformative progress in detecting, eradicating and controlling invasive alien animals and plants, ultimately contributing to a more secure and resilient environment. Throughout, OneSTOP is based upon the strategic actions recommended for integrated governance of biological invasions in the recently published IPBES Thematic assessment report on invasive alien species and their control (IPBES 2023).

AimThere is compelling evidence that drivers and patterns of biodiversity and ecosystem functioning vary across multiple spatial scales, from global to regional, landscape and patch. However, macroecological processes impacting freshwater biodiversity are poorly understood compared to marine and terrestrial ecosystems. Despite step changes in data availability, we have a fragmented view beyond the local scale of how hydrological and landscape connectivity interact with ecosystem stressors to shape freshwater biodiversity and functioning. While macroecological patterns can vary substantially among taxonomic groups, previous studies have focussed on individual habitat types, sites or taxonomic groups within landscapes, hindering direct comparisons. We present a cross-landscape, multi-species analysis of the interactive effects of landscape and hydrological connectivity and stressors on standing freshwater quality and the diversity of several major freshwater taxonomic groups.LocationGreat Britain (United Kingdom).Time Period2000-2016.Major Taxa StudiedPhytoplankton chlorophyll-a, macrophytes, molluscs, Coleoptera, Odonata, fish and birds.MethodsUsing random forests and generalised additive modelling, we quantified the interactive effects of landscape and hydrological connectivity and stressors on water quality (phytoplankton chlorophyll-a) and the diversity of selected taxa in standing freshwaters.ResultsWe found evidence of connectivity changing from positive to negative relationships with biotic responses with increasing human-induced stress levels. Some species groups showed the inverse, reflecting complexities of modelling at large, cross-landscape scales. Almost all responses were affected by stress or connectivity, often interacting and with non-linear relationships.Main ConclusionsPatterns in stressor-connectivity interactions differed across taxa, but were important in shaping 6 of 8 biotic responses. This emphasises the need for taxon-specific analyses to resolve freshwater ecological responses to stressors, connectivity, and their interactions. Our results also highlight that connectivity effects must be integrated in landscape-scale, evidence-led decision-making, designed to reduce impacts of stressors on water quality and biodiversity.

The integration of language models into ecological workflows is opening new possibilities for automated species monitoring. Classification systems are especially relevant in this context, as the high volume of data generated by automated systems requires efficient tools to support expert curators. Multimodal approaches, which incorporate textual information alongside visual or acoustic data, have shown potential to improve classification performance and interpretability. However, for many insect taxa, structured and usable textual descriptions remain scarce or difficult to access. In this work, we present a tool for retrieving and merging textual information about moth species from official repositories and citable sources. The resulting descriptions can be used to enrich multimodal classification models across different taxonomic levels or to build structured databases for species comparison and discovery.

The InsectAI COST action will support insect monitoring and conservation at the national and continental scale in order to understand and counteract widespread insect declines. The Action will bring together a critical mass of researchers and stakeholders in image-based insect AI technologies to direct and drive the research agenda, build research capacity across Europe and support innovation and application.There is mounting evidence that populations of insects around the world are in sharp decline. Understanding trends in species and their drivers is key to knowing the size of the challenge, its causes and how to address it. To identify solutions that lead to sustainable biodiversity alongside economic prosperity, insect monitoring should be efficient and provide standardised and frequently updated status indicators to guide conservation actions.The EU Biodiversity Strategy 2030 identifies the critical challenge of delivering standardised information about the state of nature and image-based insect AI can contribute to this. Specifically, the EU Nature Restoration Law will likely set binding targets for the high resolution data that cameras can provide. Thus, outputs of the Action will contribute directly to EU policies implementation, where biodiversity monitoring is considered a key component.The InsectAI COST Action will organise workshops, conferences, short-term scientific missions, hackathons, design-sprints and much more, across four Working Groups. These groups will address how image-based insect AI technologies can best address Societal Needs, support innovation in Image Collection hardware, create standardised approaches for Image Processing and develop novel Data Analysis and Integration methods for turning data into actionable insights.

the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) released the most comprehensive global synthesis of the current knowledge on the biological invasion process and the impacts of invasive alien species, i.e., the Thematic Assessment Report on Invasive Alien Species and their Control (hereafter IPBES-IAS assessment, IPBES 2023a).This assessment includes data and knowledge from existing databases, peer-reviewed and gray literature, and knowledge from Indigenous Peoples and local communities to gain a global perspective on biological invasions across regions, ecosystems, and taxa (Figs.1,2).Here we place the IPBES-IAS assessment in the continuum of invasion science and policy history, describe the assessment process, and discuss the results.While Charles Darwin introduced a remarkable number of concepts relevant to invasion science

We investigated the plant-pollinator interactions of the Mexican grass-carrying wasp Isodontia mexicana-native to North America and introduced in Europe in the 1960s-through the use of secondary data from citizen science observations. We applied a novel data exchange workflow from two global citizen science platforms, iNaturalist and Pl@ntNet. Images from iNaturalist of the wasp were used to query the Pl@ntNet application to identify possible plant species present in the pictures. Simultaneously, botanists manually identified the plants at family, genus and species levels and additionally documented flower color and biotic interactions. The goals were to calibrate Pl@ntNet's accuracy in relation to this workflow, update the list of plant species that I. mexicana visits as well as its flower color preferences in its native and introduced ranges. In addition, we investigated the types and corresponding frequencies of other biotic interactions incidentally captured on the citizen scientists' images. Although the list of known host plants could be expanded, identifying the flora from images that predominantly show an insect proved difficult for both experts and the Pl@ntNet app. The workflow performs with a 75% probability of correct identification of the plant at the species level from a score of 0.8, and with over 90% chance of correct family and genus identification from a score of 0.5. Although the number of images above these scores may be limited due to the flower parts present on the pictures, our approach can help to get an overview into species interactions and generate more specific research questions. It could be used as a triaging method to select images for further investigation. Additionally, the manual analysis of the images has shown that the information they contain offers great potential for learning more about the ecology of an introduced species in its new range.

Although invasive alien species have long been recognized as a major threat to nature and people, until now there has been no comprehensive global review of the status, trends, drivers, impacts, management and governance challenges of biological invasions. The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) Thematic Assessment Report on Invasive Alien Species and Their Control (hereafter 'IPBES invasive alien species assessment') drew on more than 13,000 scientific publications and reports in 15 languages as well as Indigenous and local knowledge on all taxa, ecosystems and regions across the globe. Therefore, it provides unequivocal evidence of the major and growing threat of invasive alien species alongside ambitious but realistic approaches to manage biological invasions. The extent of the threat and impacts has been recognized by the 143 member states of IPBES who approved the summary for policymakers of this assessment. Here, the authors of the IPBES assessment outline the main findings of the IPBES invasive alien species assessment and highlight the urgency to act now.

Insects represent half of all global biodiversity, yet many of the world's insects are disappearing, with severe implications for ecosystems and agriculture. Despite this crisis, data on insect diversity and abundance remain woefully inadequate, due to the scarcity of human experts and the lack of scalable tools for monitoring. Ecologists have started to adopt camera traps to record and study insects, and have proposed computer vision algorithms as an answer for scalable data processing. However, insect monitoring in the wild poses unique challenges that have not yet been addressed within computer vision, including the combination of long-tailed data, extremely similar classes, and significant distribution shifts. We provide the first large-scale machine learning benchmarks for fine-grained insect recognition, designed to match real-world tasks faced by ecologists. Our contributions include a curated dataset of images from citizen science platforms and museums, and an expert-annotated dataset drawn from automated camera traps across multiple continents, designed to test out-of-distribution generalization under field conditions. We train and evaluate a variety of baseline algorithms and introduce a combination of data augmentation techniques that enhance generalization across geographies and hardware setups. Code and datasets will be made publicly available.

Inclusivity is fundamental to progress in understanding and addressing the global phenomena of biological invasions because inclusivity fosters a breadth of perspectives, knowledge, and solutions. Here, we report on how the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) assessment on invasive alien species (IAS) prioritized inclusivity, the benefits of this approach, and the remaining challenges.

Deep learning has advanced the content analysis of digital data, unlocking opportunities for detecting, mapping, and monitoring invasive species. Here, we tested the ability of open source classification and object detection models (i.e., convolutional neural networks: CNNs) to identify and map the invasive plant Cortaderia selloana (pampas grass) in mainland Portugal. CNNs were trained over citizen science images and then applied to social media content (from Flickr, Twitter, Instagram, and Facebook), allowing to classify or detect the species in over 77% of situations. Images where the species was identified were mapped, using their georeferenced coordinates and time stamp, showing previously unreported occurrences of C. selloana, and a tendency for the species expansion from 2019 to 2021. Our study shows great potential from deep learning, citizen science and social media data for the detection, mapping, and monitoring of invasive plants, and, by extension, for supporting follow-up management options.

Abstract Volunteer recorders generate large amounts of biodiversity data through citizen science which is used in conservation planning and policy decision‐making. Unstructured sampling, where the volunteer can record what they want, where they want, leads to spatial unevenness in these data. While there are many statistical techniques to account for the resulting biases, it may be possible to improve datasets by directing a subset of recorders to sample in the most informative locations, known as adaptive sampling. We investigated the potential for adaptive sampling to improve the performance of species distribution models built on citizen science data using simulated ecological communities. We simulated ecological assemblages across Great Britain based on current butterfly data and modelled the distributions of each species. We then simulated the sampling of new data based on five adaptive sampling methods (one empirical method based simply on gap‐filling, and four model‐based methods using various measures from the model outputs) and one non‐adaptive method (a method in which recording continued in the current pattern), and re‐ran the species distribution models. In these, we also varied the rate of recording effort that was distributed according to adaptive sampling. The model predictions using the original and adaptively sampled data were compared to true species distributions to evaluate the performance of each method. We found that all adaptive sampling approaches improved model performance, with greatest improvement for model‐based approaches compared to the empirical sampling method (i.e. simple gap‐filling). All four model‐based adaptive sampling approaches provided similar benefits for model outputs. Improvements in model performance were greatest when the amount of adaptive sampling changed from no uptake to 1% uptake, indicating that only a small amount of change in recorder behaviour is needed to improve model performance. Directing volunteer recorders to places where records are most needed, based on information from model outputs, can improve species distribution models built on citizen science data, even with minimal uptake of suggested locations. Our results therefore suggest that adaptive sampling by recorders could be beneficial for real‐world citizen science datasets.

Opportunistic species sightings submitted by citizen science volunteers are a valuable source of species data for trends analysis, as used in biodiversity indicators. However, projects collecting these data give people flexibility where and when to make records, and the recording behaviour of participants varies between individuals. Here we tested the effect of recorder behaviour on outputs of the analysis of temporal biodiversity trends. Using a large (c. 3 million records), 20 year unstructured citizen science dataset of butterfly records in Great Britain, we manipulated recorder behaviour by constructing biased 50% subsamples of the dataset by preferentially including different types of recorders (based on high and low values of four metrics independently describing the temporal, spatial and taxonomic attributes of recorder behaviour). We found that, in general, the three outputs (namely: occupancy trend, precision of the trend, and the estimate of occupancy) showed relatively little deviation from random expectation across most of the different types of recorder behaviour. Occupancy trends showed least deviation, while estimates of occupancy itself showed greatest deviation from the random expectation. Regarding the recorder behaviours, the outputs were most sensitive to variation in 'recorder potential', which describes the difference between 'thorough' and 'incidental' recorders. Importantly, by demonstrating the robustness of occupancy trends to differences in recorder behaviour, this study provides support for the appropriate use of occupancy trend modelling for unstructured citizen science. However, we did not consider change in recorder behaviour over time, so further research is required to assess the impact of this on trend modelling. This study highlights the value of developing solutions to further increase the robustness of biodiversity trend analysis. These solutions should include both analytical developments and enhancements in project design to engage participants.