A holistic approach to evaluating climate vulnerability of French Polynesia pearl oyster farming: Bridging communities and scientific knowledge

Building evidence regarding nature-based solutions indicators and their implications for policy – the case of air quality

Assessing parallel urban dynamics through local-scale morphological and activity indicators in German and Spanish cities

Wastewater-based epidemiology (WBE) is a valuable tool for monitoring emerging disease trends in a community. Specifically, early predictions of hospitalization in a community can help reduce the strain on healthcare services and facilitate better planning and preparation. This study examines the use of SARS-CoV-2 RNA concentrations in wastewater to predict COVID-19 hospitalizations in South Carolina. We analyzed SARS-CoV-2 RNA concentration collected from six wastewater treatment plants (WWTPs) across South Carolina from April 19, 2020 to February 2, 2021 to predict COVID-19-related hospitalizations across WWTPs and 43 associated ZIP codes. Poisson regression and random forest models were utilized to forecast 7-day, 14-day, and 21-day ahead COVID-19 hospitalizations. Model performance was validated against statewide hospitalization claims data. Model accuracy was strongest for 14-day ahead prediction, with the random forest models achieving a median percentage agreement (PA) of 91.16% (IQR = 86.49-91.84%) across WWTPs and 78.12% (IQR = 67.99-84.53%) across ZIP codes. These findings demonstrate that WBE offers a robust and timely approach for predicting hospitalizations at fine geographic scales. This modeling framework can be adapted to other infectious diseases to enhance surveillance and response efforts.

Accurate prediction of urban land use changes at fine spatial scales is essential for developing healthy and sustainable cities, yet traditional simulation models struggle to capture local dynamics due to limited availability of fine-grained data and insufficient complexity in modeling urban systems. To address these limitations, we propose a novel approach that leverages advances in pre-trained vision-language foundation models combined with spatial dynamic modeling to forecast detailed urban land use patterns. Specifically, we collected a spatially dense collection of street view images (SVIs) throughout Shenzhen, China, and applied UrbanCLIP, a specialized vision-language prompting framework, to perform zero-shot inference of urban land use directly from images without labeled datasets and model retraining. The resulting fine-grained classifications delineate eight distinct urban land use types, producing a detailed urban functional map. These high-resolution patterns were then integrated into a spatial dynamic model enhanced by polynomial regression to simulate urban evolution toward 2035. This approach effectively captures neighborhood influences, socioeconomic drivers, and urban planning policies. Our simulation provides actionable insights for sustainable development in Shenzhen by identifying areas for balanced growth, targeted infrastructure investments, and ecological preservation. Compared to conventional methods, our methodology significantly improves predictive accuracy and spatial granularity. By incorporating foundation models, our approach addresses traditional data constraints, offering scalable and robust tools for informed urban governance and decision-making. • Proposed a VLM-enhanced framework to predict fine-grained urban land use changes. • Achieved zero-shot land use inference based on street view images. • Produced high-resolution simulations of Shenzhen's urban dynamics toward 2035.

Improving lightning representation in global model through convective cloud based Price and Rind 1992 scheme.

Recent advances in statistical and machine learning (ML) methods have improved the prediction of soil attributes at fine spatial scales, yet the comparative performance and reliability of these techniques remain unclear. This study compared Ordinary Kriging (OK), Inverse Distance Weighting (IDW), and ML algorithms in predicting and spatializing soil attributes, while also evaluating prediction uncertainty and computational processing time. Conducted in Minas Gerais State (Brazil), the analysis used Euclidean distance based predictors derived from X-Y coordinates and regular grids with 5, 7, and 10 divisions. Soil attribute maps (CEC, phosphorus, sand, and clay) were generated using OK, IDW, Random Forest (RF), Cubist, Support Vector Machine (SVM), and Earth. Model performance was assessed using R2, RMSE, MAE, and the coefficient of variation. IDW and OK showed the lowest predictive accuracy (R2 = 0.52–0.58), whereas ML methods, especially RF and SVM achieved superior performance (R2 = 0.62–0.70). Among ML algorithms, Earth performed worst, while RF produced the highest accuracy for all attributes except sand, for which SVM performed best. Processing time was shortest for IDW, followed by OK; among ML models, Earth was fastest, followed by RF, SVM, and Cubist. Larger regular grids improved ML prediction and spatialization but increased computational cost. ML methods thus outperform traditional geostatistical interpolators, benefiting from the use of numerous covariates and flexible algorithmic structures, although requiring greater computational time. These findings demonstrate the robustness and practical potential of ML approaches for soil attribute mapping.

Monitoring cetacean populations is essential for understanding the status of marine communities. In this regard, environmental DNA (eDNA) metabarcoding is an increasingly popular innovative and non-invasive genetic tool for marine biodiversity monitoring, particularly useful for assessing the populations of highly mobile species with a wide distribution range, such as cetaceans. In this study, we compared cetacean species detection and distribution patterns obtained through eDNA metabarcoding with the sighting records obtained during simultaneous traditional visual monitoring. A total of 258 seawater samples was analysed, collected at 129 sampling stations during the SCANS-IV survey carried out in oceanic waters of the North Atlantic Ocean. Nine cetacean species were identified by eDNA metabarcoding and ten by visual monitoring; three species were detected solely by eDNA, while four were exclusively detected by visual observation, demonstrating the complementarity of the two methods. The four most frequently sighted species were also the most frequently detected species in eDNA samples, showing similar detection frequencies and distribution patterns at broad and intermediate spatial scales. At a finer scale, however, species occurrence did not overlap between methods. Additionally, eDNA provided new insights into striped dolphin (Stenella coeruleoalba), which may be under-represented in traditional monitoring data. Overall, the results support the use of eDNA metabarcoding as a valuable complementary monitoring tool for assessing cetacean diversity and distribution, providing additional information on species presence throughout the study area. This is particularly relevant for species that are rarely recorded visually and for which there is insufficient information to design and implement appropriate management strategies.

Male mammals are of particular interest for molecular systematics as their cells contain two non-recombinant markers, the mitochondrial genome and the male-specific Y chromosome (MSY), which provide information on maternal and paternal evolutionary histories, respectively. Here, we assembled four single-copy MSY genes (AMELY, DDX3Y, SRY and ZFY), three homologs on theXchromosome, the mitogenome, and 21 autosomal introns from whole genome sequencing (WGS) data available for 123 male giraffes. We detected several instances of introgression between giraffe subspecies involving the mitogenome, MSY, and X-linked genes. The analysis of MSY haplotypes supports a deep separation in Africa between northern giraffes (subspecies antiquorum, peralta, rothschildi, and reticulata) and southern giraffes, with a large gap between intragroup and intergroup DNA distances (referred to as the 'MSY barcoding gap'). At a finer scale, southern giraffes can be divided into two geographic MSY groups that are distributed in East Africa (comprising the subspecies tippelskirchi and thornicrofti) and southern Africa (comprising the subspecies giraffa and wardi). These relationships are all supported by several exclusive synapomorphies in most DNA datasets. Our results provide strong support for two species of Giraffa, i.e., G. camelopardalis (northern giraffes) and G. giraffa (southern giraffes), but with ZFX alleles showing evidence of ancient introgression between the two taxa. The delimitation of Giraffa in two species is consistent with skull morphology and the evolution of highly distinctive phenotypes (reticulated versus Masai) in the hybrid zone between northern and southern species in southern Kenya which may have promoted the reinforcement of prezygotic isolation, thus limiting gene flow between them.

Contest competition for mates and female reproductive energetics influence body size and sexual dimorphism across many primates; nevertheless, some monomorphic species defy these patterns. These deviations may reflect biological anthropology's focus on body mass and adult size as defining features of sexual dimorphism. Using ontogenetic data, we test whether sexual monomorphism in adult body mass necessitates monomorphism across all traits and evaluate whether mate competition and female energetics explain sex-specific developmental patterns and adult morphology in a monomorphic primate. We built sex-specific growth curves using generalized additive mixed models to examine levels of sexual dimorphism in 14 morphological measurements across development in Verreaux's sifaka (Propithecus verreauxi) at Ankoatsifaka Research Station in western Madagascar. Sifaka exhibited male-biased dimorphism in upper arm and thigh circumference at adulthood and across growth, but not in other contest-related traits. Adult females had significantly longer hindlimbs and thighs and exhibited bimaturism and increased growth rate in these and several other traits. We found limited support for our hypotheses that female reproductive energetics or male-male contest competition drive adult size, sex differences, and growth trajectories in Verreaux's sifaka. However, male-male contest competition likely drives male-biased dimorphism in muscle mass, and longer female hindlimbs may represent an adaptation to infant carrying, reflecting a species-specific suite of dimorphic traits. This study demonstrates that sexual dimorphism exists at finer scales even in monomorphic species, and that adult size and sex differences are the result of a mosaic of selective pressures acting on individual traits.

ABSTRACT Aims Environmental filtering is a key framework in vegetation science; yet, disentangling the relative roles of habitat and soil‐mediated filters remains challenging in Antarctic cryptogamic communities. At the regional scale in Antarctica, climate is the primary driver of plant community diversity and structure. However, at a local scale, topography and soil properties are also crucial. This study aimed to assess how habitat‐mediated (including substrate type and nesting‐related disturbance), and soil properties act as environmental filters shaping cryptogamic species richness and community composition on rocky outcrops. We evaluated the relative contribution of habitat structure and edaphic variation to plant distribution across substrate gradients. Location Harmony Point, Nelson Island, Maritime Antarctica. Methods We conducted detailed vegetation and soil surveys across three rocky outcrops with varying nesting activity. Plant frequency, coverage, and richness were quantified in 121 plots using a grid‐based approach. Soil samples were collected and analyzed for physical and chemical properties. To quantify environmental gradients, we summarized edaphic variables using multivariate analysis. Rarefaction/extrapolation curves, community composition analysis, and linear mixed‐effects models were used to test whether habitat‐mediated and soil‐mediated filters predict species richness and community composition variability. Results Habitat‐mediated filtering strongly influenced species richness and turnover. Sites with active giant petrel nests exhibited higher nutrient availability and distinct vegetation assemblages compared to areas without nesting activity. Substrate type also played a key role, with species composition differing markedly between rocky and soil substrates. These patterns indicate that both soil fertility and microhabitat heterogeneity contribute to the spatial variability in community structure. Conclusions Our results further reveal that changes in substrate type across the outcrops promote high species turnover between the three rocky fragments. Contrary to patterns commonly reported for Maritime Antarctica, soil properties alone did not explain richness or composition, suggesting that habitat‐level processes override edaphic constraints at a fine spatial scale.

ABSTRACT Functional cross-border integration in Europe has intensified over recent decades as liberalization processes have increased border permeability. Given its policy relevance, the underlying mechanisms have been widely studied. However, existing approaches to measuring cross-border functionality and identifying cross-border functional areas (CBFAs) remain fragmented and methodologically diverse. The lack of pan-European data at a fine scale has also limited comprehensive quantitative analysis. Building on a scoping review of functional cross-border integration, this article examines how cross-border functionality can be measured. To address gaps in the literature, the study measures cross-border functionality across Europe using municipal-level data and a multi-sectoral set of indicators designed to capture cross-border interactions as accurately as possible. It also presents the first systematic approach to delineating CBFAs across EU and EFTA countries. By providing a robust empirical basis, the study supports targeted border-regional policies through a better understanding of cross-border functional patterns.

Non-pharmaceutical interventions (NPIs) such as city-level curfews and local lockdowns were implemented to control SARS-CoV-2 transmission, yet their effectiveness at fine spatial scales remains uncertain. We evaluated a rotational lockdown policy in Bogotá, Colombia, applied at the locality level-an administrative aggregation of neighborhoods. Mobility patterns derived from mobile phone data were analyzed to quantify commuting changes, defined as relative variations in movement compared to a pre-intervention baseline (one month before restrictions). We distinguished between external mobility (between localities) and internal mobility (within localities). Using epidemiological surveillance data, we estimated the effective reproductive number ([Formula: see text]) and assessed its association with mobility reductions. A compartmental transmission model simulated counterfactual epidemic trajectories without NPIs, comparing predicted and observed infections, mortality, and [Formula: see text]. The intervention reduced inter-locality mobility by up to 40% but only minimally affected within-locality movement (median change < 5%), a descriptive result based on mobility data. Early lockdown cycles produced the largest declines in transmission (up to 27% reduction in cases), while subsequent rounds showed diminishing effects. Socioeconomic heterogeneity explained substantial spatial variability in transmission dynamics, revealing stronger associations between mobility and [Formula: see text] in localities with lower socioeconomic status. Our findings demonstrate that fine-scale NPIs can transiently reduce community transmission, but their impact depends on the spatial distribution of mobility and socioeconomic inequalities across the urban landscape.

Near-surface atmospheric moisture is a key component of the climate and environment systems, exerting significant influences on both nature and human beings. However, existing moisture data are often limited by sparse observations and low spatial/temporal resolution, which restricts their applicability at fine scales, particularly in populated and urbanized regions with strong moisture variability, such as the North China Plain (NCP). Here, we construct a high-resolution (daily and 1 km) near-surface atmospheric moisture index collection comprising six different indicators over the NCP during 2003-2020 (HiMIC-NCP). HiMIC-NCP is generated by the Light Gradient Boosting Machine(LightGBM) algorithm by integrating meteorological observations and multiple covariates, including 2-meter air temperature, land surface temperature, water vapor, topography, and population density. The dataset exhibits a high accuracy with R² values ranging from 0.879 to 0.988, and mean absolute error and root mean square error remaining within reasonable ranges. The dataset also exhibits high consistency with ground observations across spatial and temporal regimes, demonstrating its robustness and reliability, and thereby provides a high-quality foundation for fine-scale climate change assessment, agricultural management, and public health studies.

Microbial eukaryotes play a vital role in biogeochemical cycles and aquatic food webs. Over past decades, their taxonomic diversity has been investigated using short-read DNA metabarcoding, which, while effective, is hampered by the limited resolution of the targeted regions. In this study, we utilised both short-read (Illumina) and long-read (PacBio) metabarcoding approaches to analyse eukaryotic diversity in a lake ecosystem over one year, allowing for a direct comparison of the results obtained from each method. Our metabarcoding analysis revealed a high degree of congruence between long-read and short-read data at broad taxonomic levels. However, at finer taxonomic scales, such as the genus level, long-read sequencing achieved higher resolution, enabling more precise identification of microbial eukaryotes. This enhanced taxonomic resolution proved especially valuable for tracking seasonal dynamics within key groups, including ciliates and chytrid fungi. The resolution offered by long-read sequencing provides a more detailed picture of the microbial eukaryotic community, thereby facilitating the exploration of ecological interactions at a finer scale. For example, this approach allowed us to monitor the dynamics of the chytrid genus Zygorhizidium in relation to various diatom genera, which are primary targets for chytrid parasitism.

Abstract Background Weather and fuels are among the critical, interacting factors that drive wildland fire behavior, and thus are primary factors in fire operations planning and decision support tools. In mesic forests, variation in stand structure may lead to heterogeneous microclimate and fuel moisture conditions in the understory where fires often ignite and spread. However, such variation in fuel availability is often overlooked by fire behavior models that assume spatially uniform weather and fuel conditions. In this study, we analyze a combination of field-based understory meteorology and fuel moisture data with terrestrial laser scanning (TLS) and traditional forest inventory data to develop new knowledge about relationships between forest structure, microclimate, and dead fuel moisture in USA northern conifer forests that can inform fire operations planning and decision support. Results We found that open canopy plots were significantly warmer (+ 7.82 °C average daily maximum air temperature) and drier (-24.1% average daily relative humidity) and with drier fuels (+ 8.03% fuel moisture) at midday in summer compared to closed canopy plots. Directly using microclimate variables (i.e., air temperature and relative humidity) resulted in better predictions of dead fuel moisture content (mean R 2 = 0.88) than using forest structure variables such as canopy openness (R 2 = 0.60). Furthermore, forest structure variables derived from TLS were better predictors of dead fuel moisture content (R 2 = 0.74) than traditional forest inventory metrics. Conclusions Our study used multi-modal measurements to demonstrate that dense forest cover linearly reduces fuel availability by buffering microclimate and maintaining fuel moisture. This research can be used to develop thinning prescriptions to achieve certain thresholds of understory temperature, relative humidity, and dead fuel moisture. Moreover, our results highlight the microclimate buffering effect of shaded fuel breaks used in fire suppression and containment tactics. Finally, our work suggests that tools like TLS can be used to fine tune fuel-weather relationships in fire behavior models that use spatially explicit fuels data to inform planning and predict fuels treatment effectiveness. This research enhances fire managers’ ability to plan and implement fuel treatments by highlighting how changes in forest stand structure drive fine scale heterogeneity in fuel availability.

ABSTRACT This study presents the first continent‐wide assessment of long‐term (1974–2023) summer heat stress in Africa using the Universal Thermal Climate Index (UTCI) derived from ERA5‐HEAT reanalysis datasets. Employing a grid‐specific definition of summer, we analysed UTCI trends across spatial (continental to city scale) and temporal (decadal to hourly) dimensions. While annual UTCI anomalies range 0.2°C–1.6°C, substantial intensification emerges at finer scales. The frequency, duration and spatial extent of ‘very strong’ (38°C–46°C) and ‘extreme’ (≥ 46°C) heat stress have increased markedly, particularly, in the Sahel. Heat‐stressed hours (UTCI &gt; 32°C) rose by 2%–25% across most countries, with extreme events doubling in 3–5 and 6–8 days episodes. City‐level analyses reveal escalating risks in large urban centres like Cairo, Lagos and Kano. As climate projections indicate further intensification, the findings underscore the urgent need for targeted heat‐health adaptation and early warning systems to protect vulnerable populations across the continent.

This study investigates stochastic income convergence across Italian regions at two spatial scales—NUTS-2 (regions) and NUTS-3 (provinces)—in a context of an affluent North and the less-developed South. Using dynamic panel data models, both non-spatial and spatial, for 2001–2021, the analysis explores the roles of persistence, unobserved regional heterogeneity, and spatial dependence in income gap dynamics. Models without fixed effects show near-unit-root behavior, indicating high persistence or divergence, whereas including fixed effects substantially reduces persistence, providing evidence of conditional stochastic convergence at both spatial scales. Spatial dependence is limited at the NUTS-2 level, with coefficients generally small and insignificant, but at the NUTS-3 level, spatial interactions are economically meaningful and significant, highlighting localized spillovers in provincial income disparities. Ignoring these interactions at the finer scale can overstate persistence. To correct for Nickell bias in dynamic fixed-effects models, System GMM estimators are employed. Spatial System GMM results show strong but stable persistence, with coefficients around 0.86–0.87. The implied half-life of income shocks is roughly five years at the regional level and four to four-and-a-half years at the provincial level, indicating faster adjustments locally. Overall, the study finds weak but robust conditional stochastic convergence, emphasizing the importance of accounting for both spatial dependence and unobserved heterogeneity in understanding regional income dynamics in Italy.Unlike previous studies, our results quantify the persistence and half-life of income shocks, highlighting the difference between NUTS-2 and NUTS-3

Air pollution remains a critical environmental and public health threat, particularly in highly populated urban areas such as the Lisbon Metropolitan Area (LMA). This study provides a refined and detailed assessment of the spatial distribution of air pollution and associated attributable mortality across the LMA. High-resolution (1 km2) annual mean concentrations of key pollutants (PM2.5, PM10 and NO2) for 2022 and 2023 were estimated by integrating outputs from the URBAIR dispersion model with ground-based monitoring observations using advanced geostatistical data-fusion techniques. Air pollutant concentrations were combined with gridded population data and age-stratified baseline mortality rates within a Geographic Information System framework to quantify spatial variations in health impacts. Using the World Health Organization AirQ+ framework and established concentration–response functions, we estimated a total of 3195 air-pollution-attributable deaths across the Lisbon Metropolitan Area (LMA) in 2022, increasing to 4010 deaths in 2023. Fine particulate matter (PM2.5) was identified as the dominant contributor, accounting for more than 40% of the total health burden. At a high spatial resolution (1 km2 grid), estimated mortality exhibited substantial variability, ranging from 0 to 29 deaths per cell in 2022 and from 0 to 36 deaths per cell in 2023. These results highlight the importance of fine-scale spatial analysis, revealing intra-urban disparities that are not captured by aggregated estimates of total attributable mortality. The proposed methodological framework, integrating dispersion modelling, data fusion, and spatially explicit health impact assessment at fine spatial scales, provides a robust and transferable approach to support evidence-based air quality management and urban health policy development in European metropolitan contexts. This integrated approach enhances comparability, improves exposure assessment accuracy, and strengthens the scientific basis for designing targeted mitigation strategies that could prevent hundreds of premature deaths annually while addressing documented spatial inequalities in pollution exposure.

The symbiosis between clownfishes (or anemonefishes) and their host sea anemones ranks among the most recognizable animal interactions on the planet. Found on coral reef habitats across the Indian and Pacific Oceans, 28 recognized species of clownfishes adaptively radiated from a common ancestor to live obligately with only 10 nominal species of host sea anemones. Are the host sea anemones truly less diverse than clownfishes? Did the symbiosis with clownfishes trigger a reciprocal co-evolutionary response to the mutualism? To address these questions, we combined fine- and broad-scale biogeographic sampling with multiple independent genomic datasets for the bubble-tip sea anemone, Entacmaea quadricolor-the most common clownfish host anemone throughout the Indo-West Pacific. Fine-scale sampling and restriction site associated DNA sequencing (RADseq) throughout the Japanese Archipelago revealed three highly divergent cryptic species: two of which co-occur throughout the Ryukyu Islands and can be differentiated by the clownfish species they host. Remarkably, broader biogeographic sampling and bait-capture sequencing reveals that this pattern is not simply the result of local ecological processes unique to Japan, but part of a deeper evolutionary signal where some species of E. quadricolor serve as host to the generalist clownfish species Amphiprion clarkii and others serve as host to the specialist clownfish A. frenatus. In total, we delimit six cryptic species in E. quadricolor that have diversified within the last five million years. The rapid speciation of E. quadricolor combined with functional ecological and phenotypic differentiation supports the hypothesis that this diversification is an evolutionary response to mutualism with clownfishes. Clownfishes are not merely settling in locally available hosts but recruiting to specialized host lineages with which they have co-evolved. These findings have important implications for understanding how the clownfish-sea anemone symbiosis has evolved and will shape future research agendas on this iconic model system.