Scale Resolution Research Articles

Hamiltonian learning in quantum field theories

Quantum field theories (QFTs) as relevant for condensed-matter or high-energy physics are formulated in continuous space and time, and typically emerge as effective low-energy descriptions. In atomic physics, an example is given by tunnel-coupled superfluids, which realize the paradigmatic sine-Gordon model, and can act as quantum simulators of continuous QFTs. To quantitatively characterize QFT simulators, or to discover the Hamiltonian governing the dynamics of a continuous many-body quantum system, we discuss Hamiltonian learning as a method to systematically extract the operator content and the coupling constants of Hamiltonians from experimental data. In contrast to Hamiltonian learning for lattice models with a given lattice scale, we learn QFT Hamiltonians on a resolution scale set by the experiment. Varying the resolution scale gives access to QFTs at different energy scales, and allows to learn a flow of Hamiltonians reminiscent of the renormalization group. Applying these techniques to available experimental data from a tunnel-coupled quantum gas experiment allows a definite distinction between a free quadratic theory from an interacting sine-Gordon model, as the underlying QFT description of the system. Published by the American Physical Society 2024

Habitat Suitability in the Eyes of the Beholder: Using Random Forest Models to Predict Land Cover Type and Scale of Selection Through Avian Functional Traits

Ecologists have long sought to identify the scales at which avian species select habitats from their surroundings. However, this is a challenging undertaking given the complex hierarchical nature of the processes involved in avian habitat selection and also given the selection of data scales (resolution and extents) available in satellite-derived land cover. Past research has largely neglected to consider how grain size limitations are related to species’ functional traits. Fortunately, with the increased ubiquity of available land cover maps and open-access datasets detailing avian functional traits, tackling these questions is becoming more feasible. Using data from the Ontario Land Cover Compilation v2, the Ontario Breeding Bird Atlas (2001–2005), and functional trait data from the AVONET dataset, we trained Random Forest models to predict scale-dependent land cover preferences based on avian functional traits. To capture changing scales, we used increasing pixel sizes from the land cover map of our study area which sought to replicate the different perceptual ranges of avian species. Our Random Forest models showcase the ability to accurately predict between natural and human-modified land cover with varying predictive accuracies. Notably, we observed heightened accuracy at smaller pixel sizes, with a subtle decline as grain size increased. By revealing the relationship between avian traits and habitat selection across multiple scales, our study advances our understanding of species–environment interactions, offering valuable insights for conservation strategies and a deeper understanding of avian habitat selection.

The crossroads of clonal evolution, differentiation hierarchy and ontogeny in leukemia development.

Transformative technologies to sequence tumor genomes at large scale and single-cell resolution have exposed the repertoire of genetic alterations that are present in leukemia genomes, the timing of their acquisition and patterns of their co-occurrence. In parallel, single-cell multi-omics technologies are allowing us to map the differentiation paths and hierarchical structures of malignant cells and giving us a glimpse into hematopoietic development in prenatal life. We propose that interrogating how the genetic evolution, differentiation hierarchy and ontogeny of malignant myeloid cells intersect with each other, using new experimental systems and multimodal technologies, will fuel the next generation of research breakthroughs.

One step 4× and 12× 3D-ExM enables robust super-resolution microscopy of nanoscale cellular structures.

Super-resolution microscopy has become an indispensable tool across diverse research fields, offering unprecedented insights into biological architectures with nanometer scale resolution. Compared with traditional nanometer-scale imaging methods such as electron microscopy, super-resolution microscopy offers several advantages, including the simultaneous labeling of multiple target biomolecules with high specificity and simpler sample preparation, making it accessible to most researchers. In this study, we introduce two optimized methods of super-resolution imaging: 4-fold and 12-fold 3D-isotropic and preserved Expansion Microscopy (4× and 12× 3D-ExM). 3D-ExM is a straightforward expansion microscopy technique featuring a single-step process, providing robust and reproducible 3D isotropic expansion for both 2D and 3D cell culture models. With standard confocal microscopy, 12× 3D-ExM achieves a lateral resolution of <30 nm, enabling the visualization of nanoscale structures, including chromosomes, kinetochores, nuclear pore complexes, and Epstein-Barr virus particles. These results demonstrate that 3D-ExM provides cost-effective and user-friendly super-resolution microscopy, making it highly suitable for a wide range of cell biology research, including studies on cellular and chromatin architectures.

Photoreversible Color-Switching Cu-Doped TiO2 Nanoparticles for High-Contrast Rewritable Printing.

Light-printable rewritable paper that can be used multiple times has attracted extensive attention because of its potential benefits in reducing environmental pollution and energy consumption. Developing rewritable paper with high black-to-colorless contrast, lasting legibility, and a fast response is fascinating but challenging. Here, we integrate the redox chemistry of Cu2+ ions into photoreductive TiO2 nanoparticles to produce Cu-doped TiO2 nanoparticles capable of highly photoreversible switching between colorless and black with excellent contrast and color stability. Incorporating such nanoparticles into hydroxyethyl cellulose produces a rewritable paper with the same appearance as that of conventional paper. More importantly, it demonstrates great features promising for practical applications, including high black-to-colorless contrast, fast light-printing (<20 s), long legible time (>3 days), high reversibility (>50 cycles), high resolution (90 μm), and large scale (A4 size) applicability.

Daily Light Integral Maps for Agriculture Lighting Design in Spain

The Daily Light Integral (DLI) is a key metric to optimise plant cultivation, influence photosynthesis, morphology, yield, and nutritional quality. Our study focuses on (i) the creation of the spatiotemporal DLI map for a representative Mediterranean country, Spain, (ii) the presentation of the improved semi-automatic DLI mapping workflow, (iii) the challenge of the comparability of different DLI value scalings in global (continent) and local context (country). DLI maps provide essential insights into the spatial and temporal distribution of natural and artificial light, critical for optimizing agricultural practices and enhancing crop yields. Spain's diverse topography and climate create significant regional and seasonal variations in DLI values. Using data from SunTracker Technologies and global weather stations, we developed detailed DLI maps at 30-meter resolution. These maps reveal high DLI values in coastal and southern regions, especially in summer, and lower values in northern and interior regions during winter. Our findings emphasize the importance of tailored lighting strategies in agriculture, suggesting higher scaling (from 5 to 2 mol·m−2·d−1) resolution for European contexts. This research underpins the development of smart agricultural systems that both quantitatively and qualitatively regulate lighting conditions (supplementary light and shading management) to maximize crop productivity and sustainability. Integrating DLI maps with IoT and AI technologies offers predictive insights for precision agriculture, highlighting the need for advanced training to leverage these tools effectively.

Stress, Anxiety, and Depression in the First-year Students of Medical Education: A Prospective Cohort Study from a Women's Medical College in South India.

Medical students around the world have been found to have high rates of depression and anxiety as compared to the general population. This study aimed to assess these in medical students immediately after they joined medical school and six months later. This study also aimed to assess if there was any association with stress, anxiety, and depression scores at six months follow-up with coping styles, self-esteem, personality, family functioning, and academic performance. We enrolled 154 first-year undergraduate medical students in this study with a baseline assessment including sociodemographic factors and Depression, Anxiety, Stress Scale 21 (DASS 21). They were followed up at six months with assessments of DASS 21, family functioning using the Family Adaptability, Partnership, Growth, Affection, and Resolve Scale (APGAR), coping styles using the Brief Coping Orientation to Problems Experienced (Brief COPE) scale, personality factors using the Big Five Inventory (BFI-10) and self-esteem using Rosenberg Self-Esteem Inventory (RSES). Change in scores in DASS 21 was measured. The DASS 21 scores at six months were correlated with other scale scores using appropriate statistical tools. Logistic regression analysis was done to study the effect of different variables on the outcomes. Mean DASS scores at baseline fell within the normal range. There was a significant increase in mean DASS scores six months after joining. Despite this, only three students reported receiving treatment for mental health problems. DASS scores showed positive correlations with neuroticism and emotion-focused coping styles. About 36.6% of students reported failing in at least one subject. Academic performance did not show any association with levels of psychological distress. Students showed a striking rise in psychological distress six months after joining medical school. This suggests that the medical school environment could play a role. To meet students' needs, a change in medical school culture and the provision of accessible and flexible mental health services are required.

Rapid Building Damage Estimates From the M7.8 Turkey Earthquake Sequence via Causality-Informed Bayesian Inference From Satellite Imagery

On February 6, 2023, a major earthquake of 7.8 magnitude and its aftershocks caused widespread destruction in Turkey and Syria, causing more than 55,000 deaths, displacing 3 million people in Turkey and 2.9 million in Syria, and destroying or damaging at least 230,000 buildings. Our research presents detailed city-scale maps of landslides, liquefaction, and building damage from this earthquake, utilizing a novel variational causal Bayesian network. This network integrates InSAR-derived change detection with new empirical ground failure models and building footprints, enabling us to (1) rapidly estimate large-scale building damage, landslides, and liquefaction from remote sensing data, (2) jointly attribute building damage to landslides, liquefaction, and shaking, (3) improve regional landslide and liquefaction predictions impacting infrastructure, and (4) simultaneously identify damage degrees in thousands of buildings. For city-scale, building-by-building damage assessments, we use building footprints and satellite imagery with a spatial resolution of approximately 30 meters. This allows us to achieve a high resolution in damage assessment, both in timeliness and scale, enabling damage classification at the individual building level within days of the earthquake. Our findings detail the extent of building damage, including collapses, in Hatay, Osmaniye, Adıyaman, Gaziantep, and Kahramanmaras. We classified building damages into five categories: no damage, slight, moderate, partial collapse, and collapse. We evaluated damage estimates against preliminary ground-truth data reported by the civil authorities. Our results demonstrate the accuracy of our classification system, as evidenced by the area under the curve (AUC) scores on the receiver operating characteristic (ROC) curve, which ranged from 0.9588 to 0.9931 across different damage categories and regions. Specifically, our model achieved an AUC of 0.9931 for collapsed buildings in the Hatay/Osmaniye area, indicating a 99.31% probability that the model will rank a randomly chosen collapsed building higher than a randomly chosen non-collapsed building. These accurate, building-specific damage estimates, with greater than 95% classification accuracy across all categories, are crucial for disaster response and can aid agencies in effectively allocating resources and coordinating efforts during disaster recovery.

Development and Validation of a Measure of the Resolution Phase of the Sexual Response Cycle: The Sexual Resolution Scale (SRS)

ABSTRACT We developed and validated a measure of the depth of the resolution experience – the Sexual Resolution Scale (SRS). Portuguese-speaking participants responded to an online survey including the SRS whose eight items were derived from the literature on post-sex states: 1) deeply relaxed, 2) with elevated heart rate, 3) with intensified emotions, 4) more aware of body, 5) in a profound peace, 6) more imaginative, 7) more predisposed to talk about emotionally charged topics, 8) more predisposed to listen to emotionally charged topics. They also reported the intensity of satisfaction, desire, arousal, and orgasm, during the last sexual activity, and completed the Perceived Relationship Quality Components Inventory, and the Female Sexual Function Index or the International Index of Erectile Function. Principal Component Analysis and Confirmatory Factor Analysis revealed two dimensions: Internal Experience (items 1 to 6) and Openness to Communication (items 7 and 8). The two dimensions had good reliability. Invariance was confirmed between women and men. Nomological validity evidence was demonstrated: the two dimensions correlated with better sexual function during the last sexual activity and over the past 4 weeks. The SRS appears to be usable to measure the enjoyable aspects of the resolution phase of sexual intercourse.

Development and validation of the Closure and Resolution Scale (CRS)

ABSTRACT This research comprises three studies centered on the development and validation of a self-report measure of psychological closure and resolution, the Closure and Resolution Scale (CRS). Study 1 used exploratory factor analysis (EFA) in two samples to probe the factor structure for closure using original (Study 1a N = 284) and revised (Study 1b N = 158) item pools. Study 2 (N = 159) examined model fit using confirmatory factor analysis (CFA), and revealed a good-fitting simple structural solution. The CRS consists of 34 items assessing six facets of event resolution: (1) finality, (2) understanding, (3) felt distance, (4) emotional relief, (5) mental release, and (6) behavioural deactivation. Study 3 (N = 182) examined convergent and discriminant validity for the CRS and provided evidence of construct validity. Participants resided in North America and primarily identified as White (68.7%−77.5%), male (44.3%−59.3%) or female (40.1%−55.7%), with post-secondary education (69.3%−71.7%). We offer a novel measure of psychological closure and resolution with preliminary evidence of good psychometric properties.

Near-Time Digital Mapping for Geoforensic Searches

Inadequate base maps with poor scale or low resolution demonstrate the need for contemporary topographic maps when conducting geological mapping. In neotectonic regimes and areas of dynamic geomorphology, archival or large-scale maps require time-consuming, on-site manual updating while mapping bedrock and superficial geology. In contrast, stable ground conditions may have suitable legacy maps in some locations but not in others, such as where surveying is absent, incomplete or subject to legal restrictions. The geologist tasked with mapping may have to do this on short notice at their first site visit with no time to search for or create digital or physical copies of background maps on a suitable scale. The field mapper may encounter any of the above scenarios, especially the Geoforensic specialist tasked with Search and Rescue, hazard assessment or preparation and desktop study for subsequent search teams or law enforcement. Drone-derived orthoimagery and digital surface modelling can be conducted on-site in near real time to provide high resolution georeferenced maps for direct input of geological information, thus bypassing either non-existent or unsuitable base maps.

The recombination landscape of the barn owl, from families to populations.

Homologous recombination is a meiotic process that generates diversity along the genome and interacts with all evolutionary forces. Despite its importance, studies of recombination landscapes are lacking due to methodological limitations and limited data. Frequently used approaches include linkage mapping based on familial data that provides sex-specific broad-scale estimates of realized recombination and inferences based on population LD that reveal a more fine scale resolution of the recombination landscape, albeit dependent on the effective population size and the selective forces acting on the population. In this study, we use a combination of these two methods to elucidate the recombination landscape for the Afro-European barn owl (Tyto alba). We find subtle differences in crossover placement between sexes that leads to differential effective shuffling of alleles. LD based estimates of recombination are concordant with family-based estimates and identify large variation in recombination rates within and among linkage groups. Larger chromosomes show variation in recombination rates, while smaller chromosomes have a universally high rate which shapes the diversity landscape. We find that recombination rates are correlated with gene content, genetic diversity and GC content. We find no conclusive differences in the recombination landscapes between populations. Overall, this comprehensive analysis enhances our understanding of recombination dynamics, genomic architecture, and sex-specific variation in the barn owl, contributing valuable insights to the broader field of avian genomics.

Spectral Heat Transfer Coefficient for Thermal Design Analysis—Part 1: Augmenting the Cooling Law for Non-Isothermal Wall

Abstract There are two major issues of interest in relation to Newton's law of cooling. The first is its applicability to flow bounded by a nonisothermal wall where the wall surface temperature is nonuniform. The second is the restriction by the basic linear assumption. In terms of the first issue, a general Green's function-based framework exists but its implementation as a working method has been lacking, attributable to the inherent locality of Green's function. Instead of setting up and solving the local–local influence and response, a new spectral heat transfer coefficient (SHTC) method takes a different avenue. It sets up and solves global-to-local temperature-heat flux influences for a small number of low order spectral modes of wall temperature disturbances. The SHTC approach covers a range of physically relevant and numerically resolvable length scales, which have been missing in the conventional cooling law. The present work is aimed at applying the SHTC methodology to turbine blade aerothermal analysis. Two aerothermal regimes are considered, respectively. In the first part (Part 1 of the two-part article), the SHTC approach is described and case-studied for a linear aerothermal regime where the flow energy equation behaves linearly and the corresponding temperature (thermal) field is passively dictated by the velocity (momentum) field. In the companion paper (Part II), the methodology will be extended to a nonlinear regime, where the temperature field will be actively interacting with (rather than passively influenced by) the velocity field.

Effect of Part Size, Displacement Rate, and Aging on Compressive Properties of Elastomeric Parts of Different Unit Cell Topologies Formed by Vat Photopolymerization Additive Manufacturing.

Due to its ability to achieve geometric complexity at high resolution and low length scales, additive manufacturing (AM) has increasingly been used for fabricating cellular structures (e.g., foams and lattices) for a variety of applications. Specifically, elastomeric cellular structures offer tunability of compliance as well as energy absorption and dissipation characteristics. However, there are limited data available on compression properties for printed elastomeric cellular structures of different designs and testing parameters. In this work, the authors evaluate how unit cell topology, part size, the rate of compression, and aging affect the compressive response of polyurethane-based simple cubic, body-centered, and gyroid structures formed by vat photopolymerization AM. Finite element simulations incorporating hyperelastic and viscoelastic models were used to describe the data, and the simulated results compared well with the experimental data. Of the designs tested, only the parts with the body-centered unit cell exhibited differences in stress-strain responses at different part sizes. Of the compression rates tested, the highest displacement rate (1000 mm/min) often caused stiffer compressive behavior, indicating deviation from the quasi-static assumption and approaching the intermediate rate response. The cellular structures did not change in compression properties across five weeks of aging time, which is desirable for cushioning applications. This work advances knowledge on the structure-property relationships of printed elastomeric cellular materials, which will enable more predictable compressive properties that can be traced to specific unit cell designs.

Molecular signatures of cortical expansion in the human foetal brain

The third trimester of human gestation is characterised by rapid increases in brain volume and cortical surface area. Recent studies have revealed a remarkable molecular diversity across the prenatal cortex but little is known about how this diversity translates into the differential rates of cortical expansion observed during gestation. We present a digital resource, μBrain, to facilitate knowledge translation between molecular and anatomical descriptions of the prenatal brain. Using μBrain, we evaluate the molecular signatures of preferentially-expanded cortical regions, quantified in utero using magnetic resonance imaging. Our findings demonstrate a spatial coupling between areal differences in the timing of neurogenesis and rates of neocortical expansion during gestation. We identify genes, upregulated from mid-gestation, that are highly expressed in rapidly expanding neocortex and implicated in genetic disorders with cognitive sequelae. The μBrain atlas provides a tool to comprehensively map early brain development across domains, model systems and resolution scales.

Homonukleare Super‐Resolution NMR‐Spektroskopie

AbstractIn homonuklearen 1H NMR ‐Spektren wie dem [1H,1H]‐NOESY‐Spektrum (Nuclear Overhauser Enhancement Spectroscopy), einem historischen Eckpfeiler der biomolekularen NMR‐Strukturbiologie, treten innerhalb eines Quadrats von etwa 100 ppm2 hunderte bis tausende von Kreuzpeaks auf, was zu einer starken Signalüberlappung führt. Die spektrale Auflösung ist daher ein limitierender Faktor für die eindeutige Zuordnung der chemischen Verschiebung und die Interpretation der Daten für die Dynamik und Strukturaufklärung. Die Aufnahme der Spektren bei höheren Magnetfeldern, z. B. bei einer 1.2 GHz 1H‐Frequenz, hilft, die spektrale Überlagerung zu verringern, da die Auflösung proportional zur Magnetfeldstärke skaliert. Hier zeigen wir, dass die Linienbreiten von Kreuzpeaks in [1H,1H]‐NOESY‐ und [1H,1H]‐TOCSY‐Spektren durch Super‐Resolution Spektroskopie in jeder Dimension um einen Faktor 2 bis 3 weiter reduziert werden können. In der indirekten Dimension wird eine zusammengesetzte, exponentiell‐kosinus‐gewichtete Anzahl von Scans entlang der Zeitinkremente gemessen und durch eine Windowfunction digital geglättet, während in der direkten Dimension ein Produkt aus Exponential‐ und Kosinusfunktion alsApodisierungsfunktion angewendet wird. Darüber hinaus wird eine Zeitersparnis bei der Messung durch Reduced‐Acquired Super‐ Resolution (RASR) eingeführt. Die Anwendung auf das 20 kDa Protein KRAS zeigt, dass hochaufgelöste NMR‐Spektren, die sich für eine automatisierte Analyse eignen, in weniger als 3 Stunden aufgenommen werden können. Die Methode eröffnet einen Weg zur automatisierten Zuordnung chemischer Verschiebungen, Dynamik und Strukturbestimmung von unmarkierten kleinen und mittelgroßen Proteinen innerhalb von 24 Stunden.

Homonuclear Super-Resolution NMR Spectroscopy.

In homonuclear 1H NMR (nuclear magnetic resonance) spectra such as [1H,1H]-NOESY (Nuclear Overhauser Enhancement spectroscopy), which is a historic cornerstone spectrum for biomolecular NMR structural biology, hundreds to thousands of cross peaks are present within a square of approximately 100 ppm2 leading to a lot of signal overlap. Spectral resolution is thus a limiting factor for unambiguous chemical shift assignment and data interpretation for dynamics and structure elucidation. Acquiring the spectra at higher magnetic fields such as at a 1.2 GHz 1H frequency helps to reduce spectral crowding, since resolution scales proportionally to the magnetic field strength. Here, we show that the linewidths of cross peaks in [1H,1H]-NOESY and [1H,1H]-TOCSY spectra can be further reduced by a factor of 2-3 in each dimension by super-resolution spectroscopy. In the indirect dimension a composite exponential-cosine weighted number of scans along the time increments are recorded and digitally smoothened by a window function, while in the direct dimension an exponential-cosine window function is applied. Furthermore, measurement time saving by reduced-acquisition super-resolution (RASR) is introduced. Application to the 20 kDa protein KRAS shows that highly resolved NMR spectra suitable for automated analysis can be acquired within less than 3 hours. The method opens an avenue towards automated chemical shift assignment, dynamics and structure determination of unlabeled small and medium size proteins within 24 hours.

Terrain analysis in forested areas: consequences of using DSM instead of DTM

Abstract. Relief mapping through dense tropical forest is a challenge, which can be met by processing radar images. Various global digital elevation models (DEMs) are available, either free or paid, the most widely used methods for producing them at global scale being photogrammetry and short wavelength radar interferometry. However, the resulting DEMs do not directly represent the ground surface but rather the canopy or something in between. Since most users need terrain models for a variety of applications in geoscience, water management etc. but use such canopy models for lack of anything better, it was relevant to assess the consequences of this inappropriate but unavoidable choice. This paper presents a study carried out in the Brazilian Amazon, where an airborne P-band radar interferometric DEM was used as a reference based on its accuracy assessed in previous studies. A well-established DEM (SRTM) and newer ones (Copernicus and Fabdem), as well as a DEM obtained from aerial X-bands radar data, were compared to the reference according to different criteria related to elevation (accuracy and a typical application, i.e., dam filling simulation) and to slope at different scales (accuracy and a typical application, i.e., terrain classification). The results highlighted the risks of using short wavelengths to represent the terrain and emphasized the importance of slope in different resolution scales.

FCSwinU: Fourier Convolutions and Swin Transformer UNet for Hyperspectral and Multispectral Image Fusion.

The fusion of low-resolution hyperspectral images (LR-HSI) with high-resolution multispectral images (HR-MSI) provides a cost-effective approach to obtaining high-resolution hyperspectral images (HR-HSI). Existing methods primarily based on convolutional neural networks (CNNs) struggle to capture global features and do not adequately address the significant scale and spectral resolution differences between LR-HSI and HR-MSI. To tackle these challenges, our novel FCSwinU network leverages the spectral fast Fourier convolution (SFFC) module for spectral feature extraction and utilizes the Swin Transformer's self-attention mechanism for multi-scale global feature fusion. FCSwinU employs a UNet-like encoder-decoder framework to effectively merge spatiospectral features. The encoder integrates the Swin Transformer feature abstraction module (SwinTFAM) to encode pixel correlations and perform multi-scale transformations, facilitating the adaptive fusion of hyperspectral and multispectral data. The decoder then employs the Swin Transformer feature reconstruction module (SwinTFRM) to reconstruct the fused features, restoring the original image dimensions and ensuring the precise recovery of spatial and spectral details. Experimental results from three benchmark datasets and a real-world dataset robustly validate the superior performance of our method in both visual representation and quantitative assessment compared to existing fusion methods.

Eyes on nature: Embedded vision cameras for terrestrial biodiversity monitoring

Abstract We need comprehensive information to manage and protect biodiversity in the face of global environmental challenges, and artificial intelligence is required to generate that information from vast amounts of biodiversity data. Currently, vision‐based monitoring methods are heterogenous; they poorly cover spatial and temporal dimensions, overly depend on humans, and are not reactive enough for adaptive management. To mitigate these issues, we present a portable, modular, affordable and low‐power device with embedded vision for biodiversity monitoring of a wide range of terrestrial taxa. Our camera uses interchangeable lenses to resolve barely visible and remote targets, as well as customisable algorithms for blob detection, region‐of‐interest classification and object detection to automatically identify them. We showcase our system in six use cases from ethology, landscape ecology, agronomy, pollination ecology, conservation biology and phenology disciplines. Using the same devices with different setups, we discovered bats feeding on durian tree flowers, monitored flying bats and their insect prey, identified nocturnal insect pests in paddy fields, detected bees visiting rapeseed crop flowers, triggered real‐time alerts for waterfowl and tracked flower phenology over months. We measured classification accuracies (i.e. F1‐scores) between 55% and 95% in our field surveys and used them to standardise observations over highly resolved time scales. Our cameras are amenable to situations where automated vision‐based monitoring is required off the grid, in natural and agricultural ecosystems, and in particular for quantifying species interactions. Embedded vision devices such as this will help addressing global biodiversity challenges and facilitate a technology‐aided agricultural systems transformation.