Atmospheric and Oceanic Sciences

Introduction Leaf area index (LAI) estimation is sensitive to sensor field of view (FOV), within-plot spatial heterogeneity, and sampling layout. Because LAI influences canopy radiation transmission, microclimate and vegetation–atmosphere exchange, robust field estimation is important for biometeorological and ecosystem research. Methods We evaluated these effects in mature Norway spruce [ Picea abies (L.) H. Karst] stands in the Czech Republic across 15 sites at elevations of 407–1,019 m a.s.l., using a combined gap-fraction approach based on LAI-2200 PCA measurements and digital hemispherical photography. At each site, a measured 9 × 9 grid of 81 below-canopy measurement points with 2-m spacing was used as an operational within-plot benchmark for mean optically derived LAI and spatial structure. Monte Carlo subsampling was then used to compare how reduced layouts, including random, row-wise, column-wise, block-wise, and spatially balanced block–row–column layouts, reproduced the full-grid benchmark mean. Results Narrower FOVs produced higher stand-level optically derived LAI estimates and greater within-plot variability. The full grids also showed directional spatial structure, with stronger autocorrelation along the north–south column direction than along the west–east row direction, although this contrast weakened under the narrowest FOV. Reduced layouts with more even spatial coverage outperformed random sampling. The spatially balanced block–row–column layout performed most consistently, whereas the row-wise layout provided little improvement. Approximately 25–36 well-distributed below-canopy measurements were sufficient to keep reduced-layout LAI deviations within 0.15 m 2 m −2 of the full-grid benchmark, whereas 7–9 measurements were enough to remain within 5% of this benchmark in most stands. Discussion Spatially balanced sampling can therefore improve the robustness and efficiency of stand-level optically derived LAI estimation, with relevance to forest biometeorology, ecosystem monitoring, and the validation of satellite-derived LAI products.

The ingestion of nanoplastics (NPs) poses a growing environmental health threat, yet how intrinsic host factors modulate their intestinal fate remains poorly defined. This study tests the hypothesis that dietary patterns govern NP bioaccumulation by differentially regulating gut motility and barrier integrity. Mice were fed a control (CD), high-fat (HFD), or high-fiber diet (HFib) and exposed to 0, 5, or 25 mg/kg/day of deuterium-labeled polystyrene NPs for 8 weeks. Dietary composition profoundly altered colonic NP accumulation: compared to CD-fed mice, an HFD exacerbated the burden by 2.83-fold (328.6 ± 23.5 μg/g dry weight), whereas a HFib attenuated it to 34% (38.9 ± 7.6 μg/g). This differential accumulation was linked to barrier damage and motility suppression, most severe under HFD. Multiomics analysis revealed that HFD promoted gut dysbiosis and deficiency of short-chain fatty acids, particularly butanoic acid. This metabolic deficit was associated with disrupted enteric nervous system signaling, notably suppressed serotonergic pathways. Integrative path modeling delineated two mechanistic landscapes: a barrier-centric pathogenic cascade driven by HFD and a microbiota-led protective network sustained by HFib. Our findings establish host nutrition as a potent modifier of NP intestinal fate and accumulation, highlighting dietary fiber as a plausible nutritional strategy to enhance intestinal resilience.

Terrestrial laser scanners provide both geometric and radiometric information, and terrestrial laser scanning (TLS) intensity is influenced by surface optical properties and the angle of incidence. This study presents a piecewise polynomial Lambert–Beckmann (PPLB) model to enhance TLS intensity characterization for broad-leaved surfaces. The PPLB model incorporates the Beckmann law, thus enabling a data-driven estimation of transition angles and enhancing the fitting flexibility across diverse leaf surfaces. TLS measurements were conducted on adaxial and abaxial leaf surfaces of five tree species using a RIEGL VZ-400 scanner. The results indicated that the TLS intensity consistently declined as the angle of incidence increased; however, different species and surfaces showed distinct angular response patterns. Across all ten tested species–surface combinations, the PPLB model achieved low root mean square error values of 0.0095–0.0183 and yielded three physically meaningful parameters: specular reflection contribution (ks), surface roughness (m), and transition angle (θT). Excluding the θT = 0° fitted result for the Golden Shower Tree abaxial surface, the estimated nonzero θT values ranged from 30° to 64°, indicating substantial variability in threshold angles among leaf surfaces beyond the commonly assumed value of 45°. These findings highlight the importance of incorporating surface-specific threshold angles for improved leaf characterization.

Understanding human impacts on marine ecosystems is increasingly urgent as climate change and other anthropogenic pressures intensify, particularly at high latitudes. Long-term records are essential for distinguishing recent impacts from natural variability, yet most ecological observations span only the recent past. Using bulk and compound-specific stable carbon and nitrogen isotope analysis of amino acids in bone collagen from Steller sea lions ( Eumetopias jubatus ) and northern fur seals ( Callorhinus ursinus ) recovered from ancestral Unangax̂ midden sites in the Aleutian Islands, we were able to extend the ecological perspective across the past 5,500 years. Despite climatic variability across the Late Holocene, isotopic baselines and trophic structure remained broadly stable through time, suggesting long-term resilience in the Aleutian marine system. However, trophic position estimates derived from amino acids varied depending on the calculation approach, highlighting persistent challenges in reconstructing trophic ecology in high-trophic marine mammals. Elevated and variable phenylalanine δ 15 N values, commonly assumed to reflect baseline signals with minimal enrichment, suggest that this amino acid can also respond to metabolic strain or physiological stress. Patterns observed in glycine, serine, and threonine further indicate that amino acid isotope values reflect not only trophic transfer and baseline variability but also metabolic routing and physiological state. Because these amino acids constitute a major fraction of the bulk collagen this complicates not only amino acid-specific trophic estimates but also has major implications for bulk collagen studies.

Abstract With climate change intensifying and growing concerns over greenhouse gas (GHG) emissions, accurate accounting of emissions has become crucial towards identifying hotspots and opportunities for reduction. Higher Education Institutions have taken a large initiative in carbon accounting by reporting scope 1, 2, and 3 emissions. However, scope 3 emissions remain particularly challenging due to its large scope and the diversity of available models and methodologies for calculating. This case study compares several commonly used models (WARM, EIO-LCA, IPCC Waste Tool, and SIMAP) for scope 3 GHG emissions accounting, focusing on GHG protocol category 5: waste generated in operations subcategory. Using a consistent dataset of solid waste metrics from the University of Wisconsin–Madison, differences in results are evaluated to assess the variability across models. Utilizing multiple models and scenarios within those models across a single multi-year data set enables identification of key assumptions and system boundaries that drive variability. The analysis revealed variation in scope 3 estimates across models, with total emissions ranging from approximately metric tons of −6474 to 8642 metric tons of CO 2 equivalents across the 2023 analysis year and models. These findings highlight the need to understand the different implications on projections of environmental impact generated by utilizing different available models with the same dataset and different settings within those models. And highlight the need to understand the different tradeoffs with respect to model assumptions and calculations alongside the effort needed to generate results with each model. This is relevant for considering the comparison among universities’ impacts as well, as these may change significantly based on their choice of model. This is relevant for universities as well when they are comparing their emissions to those of their peers.

ABSTRACT Rural women smallholder farmers (RWSF) play a central role in agriculture in sub‐Saharan Africa (SSA); however, their potentials for adaptation to climate change (CC) remain underused. In this study, we identified and prioritized best practices for including RWSF in climate resilience‐building efforts through the teaching of ecological coping strategies and adaptation to CC. Employing a mixed‐method approach, the study combines a systematic review of 78 articles with participatory workshops that included 100 RWSF, 10 NGOs, and 3 local authorities from a climate‐sensitive district in Benin. The review reported 16 adaptation strategies, including conservation agriculture, climate‐smart approaches, and income generation projects. Furthermore, 13 inclusion practices were identified, including incremental learning, awareness campaigns, land and finance access, and recognition of women as agents of change. The workshops allowed stakeholders to prioritize the reported practices given their local context. The results revealed significant differences in the rankings established by the stakeholders. Although RWSF prioritized incremental learning and land access, NGOs emphasized capacity building and women's development, and local authorities stressed awareness campaigns and access to climate finance. Meaningful inclusion of RWSF in teaching ecological coping and climate adaptation strategies can challenge gender norms, promote socio‐economic empowerment, and enhance environmental sustainability. The study offers evidence‐based recommendations for policymakers, NGOs, and communities to integrate gender‐sensitive approaches into adaptation interventions.

The integration of wave energy converters with offshore wind foundations offers a potential route to improving the utilization of offshore renewable energy infrastructure. This study numerically investigates the power-load characteristics of fixed oscillating water column (OWC) chambers intended for potential installation near offshore wind jacket foundations. A preliminary jacket comparison is first used to delimit the scope, after which the main parametric study is performed on isolated OWC chambers so that pneumatic response and local chamber loads can be compared consistently. The simulations are conducted under regular waves with a wave height of H = 0.05 m, a water depth of h = 1.6 m, and wave periods of T = 0.9–1.7 s. Three baseline geometries, namely cylindrical, sandglass-shaped, and bottle-shaped OWCs, are first screened in order to identify the most suitable reference chamber family. The cylindrical chamber is then retained as the reference configuration for subsequent local parameter studies of the frustum-contraction parameter D2 and the front-wall draft d2. The results indicate that the geometric effect is strongly dependent on the incident-wave period. The sandglass-shaped and bottle-shaped chambers can enhance short-period pneumatic power or reduce loads at longer periods, whereas the cylindrical chamber provides a more consistent reference response over the tested range. Under the wave conditions adopted in this study, further analysis reveals that D2 exerts a non-monotonic tuning effect varying with wave period. For the selected frustum-shaped configuration, increasing d2 reduces hydrodynamic loads yet simultaneously weakens pneumatic power output and CWR. Because the air phase is treated as incompressible and the orifice represents an orifice-only damping condition rather than a turbine-controlled PTO system, the reported Pe should be interpreted as a pneumatic/hydrodynamic comparison metric and not as wave-to-wire electrical power. The conclusions are therefore positioned as regular-wave geometry-tuning trends for the present model scale rather than as full coupled jacket-OWC design rules.

Introduction Micronutrient availability in arid agricultural soils is highly variable and difficult to predict due to the complex interactions among soil physical and chemical properties. This study explored the geochemical associations of iron (Fe), zinc (Zn), manganese (Mn), and copper (Cu) across a large soil dataset from North Africa. Methods The study used a combination of statistical and modeling approaches, including principal component analysis (PCA), machine learning (XGBoost), and structural equation modeling (SEM). Results The soils were predominantly neutral to moderately alkaline (mean pH = 7.68) and showed considerable variation in soil organic carbon (SOC), cation exchange capacity (CEC), and texture. Clear patterns emerged across analyses. Texture played a central role: clay-rich soils were associated with higher SOC and CEC, while sandy soils showed reduced nutrient retention. Micronutrients followed distinct relationships: Fe was moderately associated with Zn, whereas Mn and Cu were strongly coupled and closely linked to CEC and clay content. The machine learning model identified Fe as the strongest predictor of Zn variability, followed by Mn and sand content. Structural equation modeling suggested negative associations of pH with Fe, Mn, and Cu, while SOC showed positive associations with Fe and Zn but negative associations with Mn and Cu. CEC was strongly associated with Mn and Cu, highlighting the importance of soil charge properties. Conclusion The results point to the presence of two contrasting micronutrient systems: one associated with Fe and Zn, and another dominated by Mn and Cu. These findings suggest that micronutrient variability in arid soils is unlikely to reflect a single factor, but instead reflects the combined associations with soil texture, chemical properties, and geochemical interactions. This integrated approach provides a clearer understanding of micronutrient behaviour in dryland systems and offers a useful framework for improving soil fertility management in arid agricultural landscapes.

Mixed traffic containing connected automated vehicles (CAVs) and human-driven vehicles (HDVs) is sensitive to loss of cooperative information and heterogeneous driver responses. This study develops a probability-weighted full-frequency transfer-function framework to evaluate longitudinal string stability under communication degradation. CAVs are represented by cooperative adaptive cruise control (CACC) when cooperative information is available and by adaptive cruise control (ACC) when such information is unavailable; HDV responses are represented by the intelligent driver model (IDM), optimal velocity model (OVM), and risk-sensitive model (RSM). The framework preserves frequency-dependent disturbance responses and interprets the conventional algebraic condition as a low-frequency approximation. Under the adopted single-lane, small-perturbation assumptions, passive CACC-to-ACC degradation raised the global critical CAV penetration threshold from about 87.3% in an ideal connected-upgrade benchmark to about 96.0% in the degraded baseline. HDV heterogeneity mainly compressed local stable-speed regions rather than changing the global threshold, and this pattern persisted under 100 Latin Hypercube samples with ±10% HDV parameter perturbations. A 5% functional communication-reliability loss increased the critical threshold to about 99.7% and compressed the stable domain; an equivalent response delay reduced the stable-area ratio even when the global threshold changed only slightly at 0.20 s. An NGSIM US101 trajectory replay supported the IDM-based HDV component, with median speed and spacing root mean square errors of 0.8456 m/s and 3.5078 m. These findings indicate that mixed-flow stability depends not only on CAV penetration but also on cooperative-information availability and HDV response heterogeneity, while the full speed-penetration stability map remains a model-based prediction.

ABSTRACT Loess is a widely distributed and collapsible soil on China's Loess Plateau, and due to its poor dynamic performance under cyclic loads, it poses significant challenges to the stability of infrastructure. However, systematic datasets linking calcium oxalate crystal‐induced modification, microstructural characteristics, and dynamic mechanical responses remain limited. The dataset includes compacted loess specimens prepared at oxalic acid concentrations of 0, 0.5, 1.5 and 2.0 mol/L and target moisture contents of 14%, 17% and 20%. Dynamic damping‐ratio curves were obtained using a hollow cylindrical torsional shear apparatus, while SEM observations and PCAS analysis were used to quantify pore‐size distribution, pore morphology, pore orientation and related microstructural descriptors. Microstructural characterisation was performed using JSM‐6700F scanning electron microscopy combined with the Pore and Crack Analysis System. The image‐based analysis provided quantitative information on pore‐size distribution, pore orientation, pore morphology and the bonding characteristics between crystals and soil particles. This dataset includes the basic physical properties of loess, the dynamic damping ratio curves versus shear strain for calcium oxalate crystal‐modified loess and untreated loess, scanning electron microscopy images of microstructures before and after dynamic shear, quantitative pore structure parameters derived from Pore and Crack Analysis System analysis, and Pearson correlation matrices linking oxalic acid concentration, microstructural parameters and dynamic damping ratios. This dataset provides reusable data for Earth system science and civil engineering research. It can be used to validate numerical models of soil modification, compare eco‐friendly stabilisation methods for collapsible loess and support further assessment of infrastructure performance in loess regions.

Drone-based shark surveillance has been implemented as a non-lethal mitigation method to minimise the risk of human–shark interactions along beaches of New South Wales (NSW), Australia. However, real-time misidentification remains problematic, often triggering unnecessary countermeasures due to marine animals that pose little to no risk to humans. We investigated shark misidentification in drone surveys by comparing real-time identification with post-flight verification across 900 flights. Post-flight analyses revealed false-positive detection rates of 53%, 79%, and 100% for bull (Carcharhinus leucas), white (Carcharodon carcharias), and tiger (Galeocerdo cuvier) sharks, respectively, which collectively are the ‘target’ sharks of mitigation measures in NSW. Of the 269 flights in which sharks were identified in real time as target sharks, 62% were confirmed post-flight as other sharks (i.e., whaler species, grey nurse, leopard, or wobbegong), sharks that could not be identified (unknown sharks), or non-shark species (i.e., guitarfish). Conversely, 25% of flights with target sharks identified post-flight were recorded in real time as ‘other’ or ‘unknown’ sharks. Overall, real-time classification overestimated the presence of target sharks, with an apparent prevalence approximately twice the true prevalence. Countermeasure activations based on real-time classification of target sharks were accurate in only 36% of instances. Non-shark species (i.e., guitarfish or gamefish) also triggered 39 countermeasures, including 28 water evacuations. Integrating artificial intelligence or other advances (e.g., higher-resolution video on larger screens) may enhance the effectiveness of drone-based surveillance by assisting pilots with real-time shark detection and identification.