Global changes are leading to widespread species redistribution. Comprehensive assessments of range shift dynamics and their drivers are difficult, partly due to the variation in range shift detection over space and taxa. Here we compile documented range shift records for 1,758 butterfly species from 105 countries and territories, representing ~10% of the known diversity of these insects. Most species (80%) experienced range expansions, and most range shifts (79%) were associated with climate change and extreme weather events. A substantial proportion of species in our dataset contracted their ranges (27%) or shifted along elevational gradients (22%). We report widespread horizontal range expansions and contractions across tropical countries, with less evidence for elevational range shifts. We show that a clearer picture of range shift dynamics emerged only through the combination of different types of data, with expert assessments and non-English studies alleviating potential biases. Our findings of climate-driven range shifts call for concerted efforts to improve inclusive data monitoring and conservation efforts, especially for tropical countries, where human-induced land-use changes exert additional critical pressure.

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.

ABSTRACT The conservation community sorely lacks a global indicator of change in insect populations. Given widespread insect declines, addressing this gap is key for conservation and policy targets. We suggest that butterfly monitoring programs can serve as the foundation for an effective global network of insect monitoring. To assess this potential, we bring together an international consortium and calculate a “Global Butterfly Index” using the Living Planet Index approach. Based on 10,386 population trends of 213 univoltine species, we found that overall declines in butterfly populations are predictable based on species traits. Our effort should pave the way for the development of a global network of butterfly population monitoring schemes. Since butterflies are the best monitored insects and have strong emotional value for the public, a global infrastructure for butterfly monitoring can be a flagship for insect conservation, informing policymaking and spurring societal transitions towards sustainable futures.

Aim To examine how butterfly population trends respond to climate change and urbanisation at a continental scale, and whether responses differ between urban and rural environments. Location 869 sites across 12 European countries, spanning six bioclimatic zones. Time Period 1976-2021. Major Taxa Studied Butterflies (Lepidoptera). Methods We analysed long-term monitoring data from > 8400 populations of 145 species representing a wide range of ecological and life-history traits. Population trends were modelled in relation to climate variables (temperature, precipitation and aridity), urbanisation (built-up surface), and their interactions with urban context (urban vs. rural) and species traits (trophic specialisation, body size, reproductive rate and thermal adaptation). Results Climate warming and aridification were consistently linked to population declines in both rural and urban contexts, while precipitation effects varied by location and species. Urbanisation alone did not predict trends, but the urban-rural context strongly modulated species' responses to warming, indicating potential synergies between climate change and urbanisation. The stronger impact of warming in urban populations likely reflects elevated baseline temperatures and reduced habitat suitability and connectivity in urban landscapes, limiting thermal buffering. Species with colder thermal niches and lower reproductive rates were most vulnerable to warming, as warming exceeds the thermal optima of cold-adapted species and lower reproductive rates limit their capacity to buffer climate-driven population declines. Under aridification, which can reduce host-plant availability, trophic specialists declined more in urban areas, whereas generalists unexpectedly declined more in rural sites, suggesting context-dependent constraints under increasing water limitation. Main Conclusions Our findings highlight the complex interplay between climate change, urban context, and species traits in driving population dynamics. Importantly, our results suggest that urbanisation generally amplifies the negative impact of climate change on insect population trends.

Biodiversity underpins ecosystem resilience and human well being, yet conventional field inventories alone cannot monitor its rapid change across large areas. Earth observation fills this gap by providing consistent, repeatable measurements for assessing biodiversity change. Lidar is particularly well suited to biodiversity assessment because it captures three dimensional (3D) vegetation structure, which is linked to habitat complexity. The Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar mission delivers consistent, globally distributed 3D structure measurements, enabling explicit inference at extents unattainable from aircraft. Here we review 29 studies that use GEDI for biodiversity assessment across ecosystems and taxa. We first map geographic and ecosystem coverage, then synthesize how GEDI data have been used for evaluating aspects of biodiversity. We next assess approaches to link GEDI information and biodiversity variables, and evaluate which aspects of biodiversity have been targeted and their effectiveness. We discuss key limitations and chart future directions, including hypothesis driven data fusion, uncertainty propagation, and cross mission synergies. We find that the use of GEDI for biodiversity applications has increased steadily since 2019, where most work has focused on alpha diversity and species–habitat relationships in forest ecosystems, with a strong emphasis on birds and trees. GEDI structural metrics are commonly fused with complementary predictors from optical, radar, topography, and climate datasets for continuously mapped structure measures, which generally improves model performance relative to GEDI only inputs. By contrast, applications of beta/gamma taxonomic patterns, functional or phylogenetic diversity, non forest systems, and other taxa (e.g. bats, insects, small mammals) remain rare and are represented mainly by isolated case studies. Most analyses are conducted at local to regional scales thus global assessments are still limited. Key methodological gaps include standardized in-situ validation, explicit uncertainty treatment, and temporal analyses beyond single snapshots. Overall, GEDI has become a valuable baseline for biodiversity assessment, yet broader uptake—particularly across ecosystems, taxa, and scales—will benefit from continued multi sensor fusion, improved validation frameworks, and follow on spaceborne lidar missions with greater spatial continuity and accuracy.

Citizen science is increasingly important in the collection of biological data. However, to understand the broader utility of the growing number of citizen-derived records, we need to understand exactly how recorder behaviour affects the geographic distribution of records made. Here, we apply an optimal foraging model to citizen science data from the UK to determine how likely a recorder (predator) is to visit any given kilometre square and record a butterfly (prey). By defining the square with the highest density of an individual's records as their 'origin', we show that the probability of visiting a given site depends on its distance from the origin and the rarity-weighted species richness of the species thought to be present. This pattern of behaviour differs between recorders visiting more than or fewer than five squares, termed broad and narrow-range foragers. The model shows that recorder behaviour is driven, in part, by a simple trade-off between distance travelled and the rarity-weighted species richness. This collective behaviour helps explain over-recording by broad-ranging foragers in protected areas at distance and under-recording, by narrow-range foragers, in the wider countryside. It also implies that estimating parameters describing rare species' distributions (e.g. mean occupancy) will be challenging, since sample inclusion depends on occupancy itself. Mapping rare species' distributions should be simpler, since the sites at which they can be found tend to be well-sampled, but the same is unlikely to be true of common species, which also occupy areas that are unlikely to be sampled. More work is needed to understand how widely our results can be generalised beyond the UK and the dataset considered.

Scalable biodiversity monitoring remains a critical challenge for global conservation, particularly in ecologically rich but underrepresented regions with limited data infrastructure. The AMBER project (Automated Monitoring of Biodiversity using Edge and Remote Sensing) addresses this gap by integrating lightweight, compressed machine learning models with the AMI insect-monitoring system to enable on-device species identification. We focus on moth classification as a tractable use case and evaluate two end-to-end inference pipelines: a full-featured, server-based baseline and a compressed, edge-optimised alternative. To support field deployment on low-power devices, we apply quantisation, and model distillation techniques and evaluate trade-offs between full-featured server-based inference and resource-efficient edge deployment strategies. Our results show that compressed models retain strong classification performance while drastically reducing computation and bandwidth needs, enabling scalable, real-time monitoring in remote settings. This work lays the foundation for scalable, real-time ecological monitoring through trustworthy edge AI systems.

The NASA Harmonized Landsat Sentinel-2 (HLS) data provides global coverage atmospherically corrected surface reflectance with a 30m cloud and cloud shadow mask derived using the Fmask algorithm applied to top-ofatmosphere (TOA) reflectance. In this study we demonstrate, as have other researchers, low Sentinel-2 Fmask performance, and present a solution that applies a deep learning Swin-Unet model to the HLS surface reflectance to provide unambiguously improved cloud and cloud shadow detection. The model was trained and assessed using 30m HLS surface reflectance for the 13 Sentinel-2 bands and corresponding CloudSEN12+ annotations, that define cloud, thin cloud, clear, and cloud shadow, and is the largest publicly available expert annotation set. All the CloudSEN12 annotations with coincident HLS Sentinel-2 data were considered. A total of 8672 globally distributed 5 x 5 km data sets were used, 7362 to train the model, 464 for internal model validation, and 846 to independently assess the classification accuracy. The HLS Sentinel-2 Fmask had F1-scores of 0.832 (cloud), 0.546 (cloud shadow), and 0.873 (clear), and the Swin-Unet model had higher performance with F1-scores of 0.891 (cloud and thin cloud combined), 0.710 (cloud shadow), and 0.923 (clear) despite the use of surface and not TOA reflectance. The Swin-Unet thin cloud class had low accuracy (0.604 F1-score) likely due to atmospheric correction issues and thin cloud variability that are discussed. The comprehensively trained model provides a solution for users who wish to improve the HLS Sentinel-2 cloud and cloud shadow masking using the available HLS Sentinel-2 surface reflectance data.

Species populations naturally fluctuate, yet long-term trend analysis can reveal patterns of success, decline, or stability under global change pressures. While responses to climate change are well-documented, its synergy with another major global driver, urbanization, remains understudied. Here, we analyzed long-term monitoring data from over 8,400 populations of 145 butterfly species across Europe, representing a high diversity of species traits, to assess population trends in response to climate change and urbanization. We examined how population responses vary between urban and rural contexts, providing insights into the influence of site-specific conditions. Climate warming was associated with population declines, which were more pronounced in urban areas. The effect of precipitation varied between environments: increases in precipitation generally benefited populations in rural areas but had detrimental effects in urban ones. Aridity consistently drove population declines across environments, with slightly stronger effects in urban areas. Species with colder climatic niches declined the most in response to warming, increased aridity, and reduced precipitation, while trophic specialists were particularly vulnerable to aridity and precipitation changes in urban environments. Although increasing urbanization did not explain overall population trends, its effects became evident when considering species traits, with certain traits being more vulnerable to urbanization. Specifically, species with narrow climatic niches declined the most in response to urbanization in rural areas, while those and larger body sizes decline the most in urban environments. Our findings highlight the complex interplay between environmental change, landscape context, and species traits in shaping biodiversity outcomes. Importantly, our results suggest that urbanization generally amplifies the impact of climate change on insect population trends. ### Competing Interest Statement The authors have declared no competing interest.

Efficient tools for monitoring pollinator populations are urgently needed to address their reported declines. Here, we review advanced technologies focusing on image recognition and DNA-based methods to monitor bees, hoverflies, moths, and butterflies. Insect camera traps are widely used to record nocturnal insects against uniform backgrounds, while cameras studying diurnal pollinators in natural vegetation are in early stages of development. Depending on context, insect camera traps can assess occurrence, phenology, and proxies of abundance for easily recognisable and common species. DNA-based techniques can drastically decrease the costs of sample processing and speed of specimen identification but strongly depend on the completeness of reference DNA databases, which are continually improving. Molecular analyses are becoming more affordable as uptake increases. Image-based methods for identification of dead specimens show promising results for some invertebrates, but image reference databases for pollinators are far from complete. Building image reference databases with expert entomologists is a priority. Lidar and acoustic sensors are emerging technologies although it is still uncertain which insect taxa can be separated in data from these sensors and how well. By improving accessibility to novel technologies and integrating them with existing approaches, monitoring of pollinators and other insects could deliver richer, more standardised and possibly cheaper data with benefits to future insect conservation efforts.

Spatially synchronised population dynamics are driven by a combination of shared environmental conditions among sites and the movements of individuals between sites. Untangling the drivers of population synchrony requires investigation of how populations are correlated across space and time in relation to climate and mobility-related attributes. Here, we use species survey data from over four decades to investigate average levels and temporal trends in population synchrony for 58 British bird and butterfly species. We first show that population synchrony is significantly associated with synchrony in seasonal climatic variables. After accounting for spatiotemporal climatic patterns, we determine whether temporal trends in population synchrony are shaped by mobility-related attributes. We test this through an interspecies comparison using three variables correlated with mobility: biotope specialism, estimated species mobility, and local abundance change, which is known to affect emigration rate. We find that temporal trends in population synchrony are most marked for generalist butterfly species, butterflies with high estimated mobility, and butterflies that had changed in their mean abundance. For birds, we find changes in population synchrony are associated with specialist bird species and those that increased in abundance over time. Our results reveal a widespread effect of mobility attributes and abundance patterns on population synchrony over time, suggesting that variation in dispersal is a key factor determining the extent to which population dynamics are synchronised.

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.

Global changes in climate and land use are threatening natural ecosystems, biodiversity, and the ecosystem services people rely on. This is why it is necessary to track and monitor spatiotemporal change at a level of detail that can inform science, management, and policy development. The current constellation of multiple Landsat and Sentinel-2 satellites collecting imagery at predominantly ≤30-m spatial resolution affords an opportunity for the generation of global medium- resolution products every few days. Our goal is to both identify the information needs and provide direction towards the generation of a suite of global, high-level, systematically-generated, medium-resolution products designed for both management and science. Our vision builds on the success of the NASA MODIS/VIIRS product suite, while recognizing the unique strengths of medium-resolution satellite data given their higher spatial resolution and longer time series. We propose a suite of 13 essential products that enable the characterization of the current state and changes in the biosphere, cryosphere, and hydrosphere, and would fill information needs identified by the Committee on Earth Observation Satellites for the Global Climate Observing System and the Global Terrestrial Observing System, by the National Research Council of the US National Academies in the decadal survey, and by others. These products are: land cover, land cover change, burned area, forest loss, vegetation indices, phenology, dynamic habitat indices, albedo, land surface temperature, snow cover, ice extent, surface water extent, and evapotranspiration. Furthermore, we provide a list of desirable products poised for addition to the essential products (e.g., crop type, emissivity, and ice sheet velocity). Lastly, we suggest aspirational products requiring further algorithm development (e.g., forest structure and crop yield). For the identified essential products, algorithms are in place, making it feasible to begin generating products systematically. These products should be accompanied by quality and accuracy assessments undertaken following consensus protocols. Five decades after the first Landsat satellite, and two decades after the MODIS products were first produced, it is time now for readily available, standardized, and consistent high-level products built upon medium-resolution imagery, thereby fulfilling the promise and the vision that inspired the Landsat program since its inception.

The Harmonized Landsat Sentinel-2 (HLS) data, harmonizing Landsat-8/9 and Sentinel-2 imagery, offers frequent 30 m resolution multispectral observations but is often contaminated by clouds, shadows, and snow that reduce the availability of good-quality surface observations. Traditional techniques for reconstructing HLS time series, such as polynomial, logistic, or harmonic functions that model seasonal reflectance changes struggle with complex changes violating the function fitting assumptions. We propose a data-driven time series reconstruction framework based on Transformer, termed self-supervised learning for interpolation (SSLI) with a smoothing constraint to model seasonal reflectance change patterns without any annotated labels. In this study, a year of HLS 30 m data were processed into 3-day surface reflectance composites (i.e., 122 composites). SSLI was trained by using randomly selected 70% of the good-quality 3-day composites in a time series to reconstruct the reflectance for the remaining 30%. The random masking was undertaken independently for each HLS pixel time series and for each training iteration so that the selected composite periods cover all the good-quality periods evenly. The methodology was tested on five diverse regions in the Conterminous United States (CONUS) each using three HLS tiles. Two versions of SSLI were evaluated. SSLI i was trained using time series samples from one tile per region to impose data independence, and SSLI ii was trained using samples from all the tiles to simulate data availability in real-world applications. The results were compared with those of three state-of-the practice approaches for gap-filling reflectance and Normalized difference vegetation index (NDVI) time series, i.e., the fill-and-fit (FF), the dynamic temporal smoothing (DTS), and the double logistic (DL) algorithms. The superior performance of SSLI, reflected in lower RMSE (SSLI i: 0.0192, SSLI ii: 0.0164) and higher R2 (SSLI i: 0.9150, SSLI ii: 0.9349) compared to the other algorithms, demonstrates much higher accuracy in reconstructing complex phenological changes with multiple greenness peaks (e.g., in cropland) and robustness to temporal cadence variations and time series noise. The potential of adapting SSLI for land cover mapping and global-scale time series reconstruction is discussed. The developed codes and training data were made publicly available.

The Advanced Baseline Imager (ABI) sensors on the Geostationary Operational Environment Satellite-R series (GOES-R) broaden the application of global vegetation monitoring due to their higher temporal (5-15 min) and appropriate spatial (0.5-1 km) resolution compared to previous geostationary and current polar-orbiting sensing systems. Notably, ABI Land Surface Phenology (LSP) quantification may be improved due to the greater availability of cloud-free observations as compared to those from legacy GOES satellite generations and from polarorbiting sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). Geostationary satellites sense a location with a fixed view geometry but changing solar geometry and consequently capture pronounced temporal reflectance variations over anisotropic surfaces. These reflectance variations can be reduced by application of a Bidirectional Reflectance Distribution Function (BRDF) model to adjust or predict the reflectance for a new solar geometry and a fixed view geometry. Empirical and semi-empirical BRDF models perform less effectively when used to predict reflectance acquired at angles not found in the observations used to parameterize the model, or acquired under hot-spot sensing conditions when the solar and viewing directions coincide. Consequently, using a fixed solar geometry or even the geometry at local solar noon may introduce errors due to diurnal and seasonal variations in the position of the sun and the incidence of hot-spot sensing conditions. In this paper, a new solar geometry definition based on a Constant Scattering Angle (CSA) criterion is presented that, as we demonstrate, reduces the impacts of solar geometry changes on reflectance and derived vegetation indices used for LSP quantification. The CSA criterion is used with the Ross-Thick-Li-Sparse (RTLS) BRDF model applied to North America ABI surface reflectance data acquired by GOES-16 (1 January 2018 to 31 December 2020) and GOES-17 (1 January 2019 to 31 December 2020) to normalize solar geometry BRDF effects and generate 3-day two-band Enhanced Vegetation Index (EVI2) time series. Compared to the local solar noon geometry, the CSA criterion is shown to reduce solar geometry reflectance and EVI2 time series artifacts. Further, comparison with contemporaneous VIIRS NBAR (Nadir BRDFAdjusted Reflectance) EVI2 time series is also presented to illustrate the efficacy of the CSA criterion. Finally, the CSA-adjusted EVI2 time series are shown to produce LSP results that agree well with PhenoCam-based observations, with no obvious systematic bias in onsets of vegetation maturity, senescence, and dormancy dates compared to about 10-day bias found with local solar noon adjusted EVI2 time series.

Climate change and habitat loss present serious threats to nature. Yet, due to a lack of historical land-use data, the potential for land-use change and baseline land-use conditions to interact with a changing climate to affect biodiversity remains largely unknown. Here, we use historical land use, climate data and species observation data to investigate the patterns and causes of biodiversity change in Great Britain. We show that anthropogenic climate change and land conversion have broadly led to increased richness, biotic homogenization and warmer-adapted communities of British birds, butterflies and plants over the long term (50+ years) and short term (20 years). Biodiversity change was found to be largely determined by baseline environmental conditions of land use and climate, especially over shorter timescales, suggesting that biodiversity change in recent periods could reflect an inertia derived from past environmental changes. Climate–land-use interactions were mostly related to long-term change in species richness and beta diversity across taxa. Semi-natural grasslands (in a broad sense, including meadows, pastures, lowland and upland heathlands and open wetlands) were associated with lower rates of biodiversity change, while their contribution to national-level biodiversity doubled over the long term. Our findings highlight the need to protect and restore natural and semi-natural habitats, alongside a fuller consideration of individual species’ requirements beyond simple measures of species richness in biodiversity management and policy.

Biodiversity is declining rapidly. The most important causes of biodiversity loss are climate change, land- and sea-use change, invasive alien species, pollution, and overexploitation of natural resources. This unprecedented deterioration of the biosphere has profound and far-reaching consequences for insects, who play many important roles within ecosystems, including pollination and decomposition. Declines in the abundance and distribution of insects threaten these essential ecosystem functions. While there is no doubt that urgent and immediate measures are needed to address biodiversity loss and climate change, monitoring of insects is a priority to underpin and inform ongoing conservation action. Citizen science has emerged as an important tool for monitoring insects. In this primer, we explain the application of citizen science for monitoring insects and emerging approaches using digital technologies.