Regional Scale Research Articles

Predicting wheat yield gap and its determinants combining remote sensing, machine learning, and survey approaches in rainfed Mediterranean regions of Morocco

Wheat plays a crucial role in Morocco’s food security, economic stability, and livelihoods of farming communities. Assessing key vegetation indices (as yield predictors), along with understanding potential yield, yield gap, and major determinants for this gap at regional and national scales, is vital for improving food security with resilience in variable climatic conditions. Analysing the yield gap and its causes during drought and optimal weather conditions can reduce crop failure risks and enhance productivity specially in variable rainfed production systems. This study aimed to develop scalable methodology to predict field- and landscape-level yield and yield gaps for wheat and their underlying causes examplifying Morocco’s rainfed production environment combining remote sensing, machine learning, and ground information. By analysing six vegetation indices (EVI2, CGVI, MSR, NDVI, OSAVI, and RVI) derived from Sentinel-2 satellite imagery (10 m resolution) over three successive growing seasons (2018–2019, 2019–2020, and 2020–2021), the study employed advanced vegetation index models for accurate prediction of wheat yields and yield gaps at plot and on a larger regional scale within the Rabat-Sale-Kenitra region. To identify the determinants of yield gap, climate and soil datasets were merged with crop management information and the random forest model was fine-tuned and assessed for each season and cumulatively. The findings highlighted that RVI, GCVI, and NDVI vegetation indices were particularly effective in predicting wheat yields, showing the highest R2 and the lowest prediction errors (RMSE). Such predictive methodologies are crucial for policymakers to proactively plan and mitigate risk minimization and adaption plans at regional and national levels. The models predicted rainfed potential yields of 5.99, 1.53, and 4.66 t ha−1, with corresponding yield gap of 3.38, 0.73, and 1.58 t ha−1 for the seasons of 2018/2019 (favorable); 2019/2020 (drought) and 2020/2021 (favorable), respectively. Across three periods, critical factors determining yield include soil moisture, total rainfall during the crop growing period, evapotranspiration, and soil texture and carbon content. To minimize drought risks and maximize benefits during variable rainfall conditions, it is essential to implement pre-season drought forecasts, customize seeding dates based on soil moisture, adopt technologies that enhance soil moisture retention, and utilize climate-adapted farming practices in the semi-arid and arid rainfed regions.

Parsimonious estimation of hourly surface ozone concentration across China during 2015–2020

Surface ozone is an important air pollutant detrimental to human health and vegetation productivity, particularly in China. However, high resolution surface ozone concentration data is still lacking, largely hindering accurate assessment of associated environmental impacts. Here, we collected hourly ground ozone observations (over 6 million records), remote sensing products, meteorological data, and social-economic information, and applied recurrent neural networks to map hourly surface ozone data (HrSOD) at a 0.1° × 0.1° resolution across China during 2015–2020. The coefficient of determination (R2) values in sample-based, site-based, and by-year cross-validations were 0.72, 0.65 and 0.71, respectively, with the root mean square error (RMSE) values being 11.71 ppb (mean = 30.89 ppb), 12.81 ppb (mean = 30.96 ppb) and 11.14 ppb (mean = 31.26 ppb). Moreover, it exhibits high spatiotemporal consistency with ground-level observations at different time scales (diurnal, seasonal, annual), and at various spatial levels (individual sites and regional scales). Meanwhile, the HrSOD provides critical information for fine-resolution assessment of surface ozone impacts on environmental and human benefits.

Interactions Between Anthropogenic Greenhouse‐Gas and Aerosol Emissions Will Shape Extreme Precipitations Over the Qinghai‐Tibet Plateau

AbstractAttributing intensification extreme precipitation to anthropogenic factors on the regional scale is challenging, given the large fluctuations and the complexity of quantifying interactions among these anthropogenic factors. Here, we propose a new variance‐based method to investigate the roles of human‐induced greenhouse gas (GHG), aerosol (AER), and their interactions (GA) in shaping extreme precipitation on the Qinghai‐Tibet Plateau (QTP) at stational scale. In terms of contribution, GHG has the greatest impact on total wet‐day precipitation and simple daily intensity. In terms of significance, GA, and AER exert significant effects on all 10 extreme indices (P; < 0.05) over 48.3% and 44.8% of all stations, while GHG affects less (25.9%). Overall, GHG is not the only dominant factor, and GA and AER are expected to play vital roles in intensifying extreme precipitation over the QTP under SSP2‐4.5. These findings challenge the conventional insights that GHG is the primary anthropogenic driver of extreme precipitation.

Investigating the impact of coupling HARMONIE-WINS50 (cy43) meteorology to LOTOS-EUROS (v2.2.002) on a simulation of NO2 concentrations over the Netherlands

Abstract. Meteorological fields calculated by numerical weather prediction (NWP) models drive offline chemical transport models (CTMs) to solve the transport, chemical reactions, and atmospheric interaction over the geographical domain of interest. HARMONIE (HIRLAM ALADIN Research on Mesoscale Operational NWP in Euromed) is a state-of-the-art non-hydrostatic NWP community model used at several European weather agencies to forecast weather at the local and/or regional scale. In this work, the HARMONIE WINS50 (cycle 43 cy43) reanalysis dataset at a resolution of 0.025° × 0.025° covering an area surrounding the North Sea for the years 2019–2021 was coupled offline to the LOTOS-EUROS (LOng-Term Ozone Simulation-EURopean Operational Smog model, v2.2.002) CTM. The impact of using either meteorological fields from HARMONIE or from ECMWF on LOTOS-EUROS simulations of NO2 has been evaluated against ground-level observations and TROPOMI tropospheric NO2 vertical columns. Furthermore, the difference between crucial meteorological input parameters such as the boundary layer height and the vertical diffusion coefficient between the hydrostatic ECMWF and non-hydrostatic HARMONIE data has been studied, and the vertical profiles of temperature, humidity, and wind are evaluated against meteorological observations at Cabauw in The Netherlands. The results of these first evaluations of the LOTOS-EUROS model performance in both configurations are used to investigate current uncertainties in air quality forecasting in relation to driving meteorological parameters and to assess the potential for improvements in forecasting pollution episodes at high resolutions based on the HARMONIE NWP model.

A Regional Shallow Décollement at the Front of the Mexican Fold and Thrust Belt: Microstructure of the Santiago Shale

Abstract This contribution analyses the role played by the mechanical properties of a decollément shale layer in the evolution of the Mexican Fold and Thrust Belt (MFTB). The mobility of overpressured shales can accommodate large strains by grain-scale plastic mechanisms, and affect the folding and thrusting styles of the overburden. Research on shale deformation mechanisms is necessary to improve the knowledge of these processes and their influence on the structural style of fold and thrust belts. The ductile behavior of rocks involving grain-scale plasticity was documented in the Jurassic Santiago shale sequence using geological mapping, microstructural observations on thin-oriented sections, and scanning electron microscopy (SEM) imaging. Structural styles such as detachment folding, fault-bend folding, and shale-cored fold-thrusts were observed at the regional scale. At the outcrop scale, the shale developed strong foliation and pencil cleavage, with immersed packstone boudins. Observed structures include thrusting, soft and open folds, and buckle folding. In thin section, the ductile textures include a strong penetrative foliation with lenticular and wavy-parallel laminae composed of carbonates, ribbons of reoriented clays and organic matter (clay+OM), s-c structures, porphyroblasts microtextures, development of oblique cleavage concerning folded foliation (crenulation cleavage), and carbonates dissolution. The Santiago shale shows also evidence of brittle deformation including calcite-filled fractures and cataclastic gouges. X-ray diffraction (XRD) analysis of the clay size fraction suggests that the authigenic calcareous shale was deformed in conditions of the deep diagenetic zone (between 100 and 200°C) and fluid overpressure (>70 MPa). The results help to improve the understanding of ductile microstructure and its role in shale deformation cretaceous cover, promoting the formation of localized fault propagation folds in the overburden. This study aims to open new perspectives in the kinematics and rheology interpretations for this sector of the MFTB, highlighting the role of the décollement layers during the progression of the orogen.

Spatially evolving cascades in wall turbulence with and without interface

Direct numerical simulations of channel flow and temporal boundary layer at a Reynolds number $Re_{\tau } = 1500$ are used to assess the scale-by-scale mechanisms of wall turbulence. From the peak of turbulence production embedded at the small scales of the near-wall region, spatially ascending reverse cascades are generated that move through self-similar eddies growing in size with the wall distance. These fluxes are followed by spatially ascending forward cascades through detached eddies thus reaching sufficiently small scales where eventually scale energy is dissipated. This phenomenology is shared by both boundary layer and channel flow and is recognized as a robust physical feature characterizing wall turbulence in general. Specific features related to the flow configuration are indeed identified in the outer region. In particular, the central region of channels is characterized by a generalized Richardson energy cascade where large scales are in equilibrium with small scales at different wall distances through a combined forward cascade and spatial flux. On the contrary, the interface region of boundary layers is characterized by an almost two-dimensional physics where spatially ascending reverse cascades sustain long and wide interface structures with a forward cascade that survives only in the wall-normal scales. The overall scenario consists in a variety of scale motions that while protruding from the turbulent core towards the external region, squeeze at the interface thus sustaining vertical shear in a thin layer. The observed multidimensional physics sheds light on the complex interactions between outer entrainment and near-wall self-sustaining mechanisms with possible repercussions for theories.

Mapping ecosystem service temporal trends: a case study of European wood potential, supply and demand between 2008 and 2018

Wood is one of the key forest Ecosystem Services (ES) of growing ecological, social and economic importance; therefore, we need more precise information about its long-term usage. To achieve this, it is necessary to examine the spatio-temporal aspects of wood ES potential, supply and demand. In this study, we analyse spatio-temporal patterns of wood ES supply and demand at continental, national and regional scales to identify areas of increasing and decreasing supply and demand levels in Europe. In addition, we present background information about the potential of European forests to provide wood ES and its relationship to supply and demand. Our results showed that the overall European wood supply and demand as well as the wood ES potential were characterised by increasing trends. Furthermore, this increase was also regional, particularly in central and northern Europe. This study demonstrates not only the significance of spatio-temporal data in ES mapping, but also the importance of considering a broader range of components of the ES cascade model when assessing change. Our research has shown that potential, supply and demand can all increase in the same area, but also that low supply and demand do not guarantee wood potential growth. In addition, we found that a broad scale assessment helps to identify more general patterns and trends, but analysing data at a more accurate scale provides more comprehensive insights for identifying areas that may require targeted action for sustainable forest management.

Wind-urban structure interplay in PM2.5 variation: Insights from multi-seasonal mobile air quality campaign

The health risks associated with particulate matter pollution (PM2.5) highlight the importance of comprehending key drivers of its spatial and temporal variability. While explanatory modeling is widely used for statistical inference in such applications, the role of wind variables (speed, direction) and their interplay with built environment are often overlooked. This study addresses this gap through a mobile air quality campaign in a suburban area over 10 days for each of three distinct seasons. We developed four innovative wind-based buffers to account for the wind factors and assess the impact of LiDAR-derived 3D built environment structure on PM2.5 variability at local and regional scales. Our second objective is to assess the predictive capabilities of wind-based variables. Results indicate that in our low-elevation, low-rise building study area, the built environment does not emerge as a significant factor, contrary to findings in larger, denser urban areas. Wind direction proves more effective in capturing concentration fluctuations than wind speed, and its interaction with pollution source orientation can change pollution dynamics. This study introduces new insights and methodologies for incorporating wind-related factors into the analysis of air pollution dynamics, offering opportunities for further investigation in various urban settings.

Estimating Forest Stock Volume Based on Airborne Lidar Data

Abstract. Forest stock volume (FSV) stands as an important indicator in evaluating the potential for carbon sequestration. It is crucial for forest resource management at local, regional, and national scales. In order to achieve an accurate estimation of FSV, this article takes Mengyin County, Shandong Province, China as the research area, builds a random forest (RF) model for four tree species based on airborne Lidar data, and forms a monitoring system of "individual tree - grid - county" granularities. The results demonstrated that all four models exhibited excellent generalization capabilities, with no signs of overfitting. In the test phase, the R2 of the poplar and pine models exceeded 0.9, while the R2 of the cypress model was 0.81, and the rRMSE was controlled within 20%, indicating that the fitting effect of the three tree species models was better; the accuracy of the robinia pseudoacacia model was relatively poor, with R2 of 0.60 and rRMSE of 20.60%. This study provides a feasible method for estimating forest stock volume within the county, which provides strong technical support for forest resource management and planning, and helps promote sustainable forestry development.

Escalating rainstorm-induced flood risks in the Yellow River Basin, China

The warming climate-induced intensification of hydrological cycle is amplifying extreme precipitation and increasing flood risk at regional and global scales. The evaluation of flood risk, which depends on assessment indicators, weights, as well as data quality, is the first step toward mitigation flood disasters. In this study, we accepted ten risk assessment indicators concerning hazard of disaster-causing factors, sensitivity of hazard-forming environments, and vulnerability of disaster-bearing bodies. We used a combined weighting method based on the analytic hierarchy process and entropy weight (AHP-EW) technique to evaluate rainstorm-induced flood risks across the Yellow River Basin (YRB) from 2000 to 2018. We observed flood hazards are intensifying across the YRB. Specifically, areas with medium flood hazards expanded from the lower to the middle and upper YRB. The sensitivity to floods exhibited a spatial pattern of increasing from southeast to northwest (lower to upper YRB). The increase in vegetation coverage in the middle and upper reaches of the YRB reduces the sensitivity to flood disasters. Flood vulnerability shows an increasing trend, with higher vulnerability mainly observed in the middle and lower YRB. The overall flood risk in the YRB shows an increasing trend, with a 9-fold increase in flood risk from 2000 to 2018. Medium to high flood risk and vulnerability can mainly be identified in the middle and lower YRB, where population and gross domestic product are concentrated. The intensifying rainstorm-induced flood risks over urban areas in these regions should arouse public concern.

Challenges for sustainable development goal of land degradation neutrality in drylands: Evidence from the Northern Slope of the Tianshan Mountains, China

The SDG 15.3.1 target of Land Degradation Neutrality (LDN) only has 15 years from conception (in 2015) to realization (in 2030). Therefore, investigating the effectiveness and challenges of LDN has become a priority, especially in drylands, where fragile ecosystems intersect with multiple disturbances. In this study, solutions are proposed and validated based on the challenges of LDN. We chose the Northern Slope of the Tianshan Mountains as a case study and set baselines in 2005 and 2010. The region and degree of land change (including degraded, stable, and improved) were depicted at the pixel scale (100 × 100 m), and LDN realization was assessed at the regional scale (including administrative districts and 5000 × 5000 m grids). The results showed a significant disparity between the two baselines. The number of areas that realized the LDN target was rare, regardless of the scale of the administrative districts or grids. Chord plots, Spearman's correlation, and curve estimation were employed to reveal the relationship between LDN and seven natural or socioeconomic factors. We found that substantial degradation was closely related to the expansion of unused, urban, and mining land and reduction in water, glaciers, and forests. Further evidence suggests that agricultural development both positively and negatively affects LDN, whereas urbanization and mining activities are undesirable for LDN. Notably, the adverse effects of glacier melting require additional attention. Therefore, we consider the easy-to-achieve and hard-to-achieve baselines as the mandatory and desirable targets of LDN, respectively, and focus further efforts in three aspects: preventing agricultural exploitation from occupying ecological resources, defining reasonable zones for urbanization and mining, and reducing greenhouse gas emissions to mitigate warming. Overall, this study is expected to be a beneficial addition to existing LDN theoretical systems and serve as a case validation of the challenges of LDN in drylands.

Moving towards an ecological management of overabundant ungulates: insights from wildlife-vehicle collisions and hunting bag data

Increasing abundance of large ungulates is raising human-wildlife impacts and the effectiveness of recreational hunting to reduce their population growth is increasingly questioned. We report on long-term trends (> 15 years) in wildlife-vehicle collisions (WVC) and hunting bags, and on associations between the annual growth rate of WVC and that of hunting bags for three ungulates – the wild boar, the red deer, and the roe deer – and the grey wolf in northwest Spain to evaluate the regulating capacity of recreational hunting at large spatial scale. Wildlife-vehicle collisions increased by 332% in 16 years and 91% of all traffic accidents were caused by collisions with these three ungulates. All ungulate species showed significant positive trends in WVC and hunting bags, but we did not observe a negative association between annual growth rate of hunting bags and that of WVC except for the wild boar. Results suggest that recreational hunting was unable to reduce ungulate population growth at the regional scale. There was no upward trend of vehicle collisions with wolves over the study period, possibly reflecting stable wolf populations. Natural mortality due to predation could be promoted through the protection of apex predators, but the lethal management of apex predators, often based on sociopolitical pressures rather than damage levels, can conflict with the strategy for mitigating ungulate impacts. Ungulate management needs to be reconsidered from an ecological perspective that integrates human management measures, including recreational hunting, based on the population dynamics and the recovery of predator–prey interactions by favoring the expansion of apex predators.

Monitoring saltwater intrusion to estuaries based on UAV and satellite imagery with machine learning models

Saltwater intrusion is a natural mixture process between watershed freshwater and seawater that frequently occurs in estuaries. Station-based monitoring of saltwater intrusion is time-consuming and labor-intensive. To enable quick monitoring of saltwater intrusion, this study developed new remote sensing algorithms for water surface salinity measurement using four decision tree-based machine learning models. These models were built based on simultaneously collected in-situ salinity data from a waterway in the Pearl River Delta and Unmanned Aerial Vehicle (UAV) hyperspectral images. A 10-fold cross-validation was applied to assess the performance of the models, with XGBoost outperforming the other three models (R2=0.93, RMSE = 0.88 psu). Then the developed model was employed for Sentinel-2 multispectral satellite images to invert the estuarine salinity distribution at a larger spatial scale. Results displayed the high performance of the machine learning models proposed in this study for mapping the salinity distribution in river channels, making it an efficient and practical technique for monitoring saltwater intrusion in river channels at a regional scale.

Forecasting Road Freight and Passenger Transport Demands in Turkey Using the Exponential Smoothing

Passenger and freight transportation are the primary contributors to greenhouse gas emissions caused by energy use globally. These emissions are expected to increase in industrializing and rapidly developing countries. Failure to take action to reduce energy consumption and greenhouse gas emissions in freight and passenger transportation may render the rapidly globalizing world uninhabitable. Sustainable transportation is essential, and the continuous growth of the transportation sector and increased carbon emissions from transportation are highly relevant. This study analyzed ten years' worth of data on road freight and passenger transportation on a regional scale to make future predictions. Based on the findings, the study presents solution suggestions and evaluates the policies that should be implemented to address the issue.

High-resolution soil erosion mapping in croplands via Sentinel-2 bare soil imaging and a two-step classification approach

Erosion-induced lateral soil redistribution leads to spatially heterogenous soil composition, which can be captured through the distinctive spectral reflectance of soils under varying levels of erosion influence. This points to the potential of using remotely sensed soil spectra to detect severe erosion and deposition hotspots in exposed croplands and, importantly, further differentiate the intra-class spectral variability of moderate erosion that often occupies the largest proportion. Here, we aim to develop a two-step erosion classification and mapping approach based on multitemporal compositing of Sentinel-2 bare soil images of a typical agricultural region (11,500 km2) of northeast China. A random forest classifier was firstly trained against the ground-truth data derived from very high resolution (VHR) imagery in Google Earth, with an overall accuracy of 91 % that allowed for clear delineation of severe erosion and deposition areas based on their distinct topographic and spectral features particularly in the red and red-edge bands. In the second step, the remaining area of moderate erosion (60.30 %) was further differentiated using Iterative Self-Organizing cluster unsupervised classification to yield a five-class soil erosion map at 10 m spatial resolution. The accuracy of the predicted map was successfully validated by independent Caesium-137 (137Cs) and soil organic carbon observations at catchment and regional scales, as revealed by significant inter-class differences in soil redistribution rates estimated from 137Cs inventory. The severe erosion class had a soil loss rate of 5.5 mm yr−1, suggesting that previous assessments have underestimated erosion severity. The spatial accordance of crop growth with soil erosion intensity, particularly in localized settings, further highlighted the potential of bare soil imaging for mapping the spatiotemporal development of soil erosion and its response to targeted sustainable cropland management efforts.

What is the relative impact of nudging and online coupling on meteorological variables, pollutant concentrations and aerosol optical properties?

Abstract. Meteorological and chemical modelling at the regional scale often involve the nudging of the modelled meteorology towards reanalysis fields and meteo-chemical coupling to properly consider the interactions between aerosols, clouds and radiation. Both types of processes can change the meteorology, but not for the same reasons and not necessarily in the same way. To assess the possible interactions between nudging and online coupling, several simulations are carried out with the WRF–CHIMERE (Weather Research and Forecasting) model in its offline and online configurations. Through comparison with measurements, we show that the use of nudging significantly improves the model performances. We also show that coupling changes the results much less than nudging. Finally, we show that when nudging is used, it limits the variability in the results due to coupling.

Assessing microplastics-antibiotics coexistence induced ciprofloxacin-resistant Pseudomonas aeruginosa at a water region scale

Microplastics (MPs) waste is widespread globally in water systems. The opportunistic human pathogen Pseudomonas aeruginosa can cause serious acute and chronic infections that are notoriously difficult to treat. Ciprofloxacin (CIP) is broadly applied as an anti-P. aeruginosa drug. A growing evidence reveals that antibiotic-resistance genes-carrying Pseudomonas aeruginosa were detected on MPs forming plastisphere due to their adsorbability along with high occurrence of CIP in water environments. The MPs-niched CIP-resistant P. aeruginosa has been likely to emerge as an unignorable public health issue. Here, we offered a novel approach to assess the development of CIP-resistant P. aeruginosa under MPs-antibiotic coexistence at a water region scale. By combing the adsorption isotherm models used to estimate CIP condensation around MPs and a pharmacokinetic/pharmacodynamic-based microbial population dynamic model, we predicted the P. aeruginosa development on CIP-adsorbed MPs in waters. Our assessment revealed a high antibiotic resistance in the P. aeruginosa populations (∼50 %) with a wider range of waterborne total cell counts (∼10−2–104 cfu mL−1) among water regions in that the resistance proportion was primarily determined by CIP pollution level and relative abundance of various polymer type of MPs. We implicate that water region-specific MPs were highly likely to provide media for P. aeruginosa propagation. Our results highlight the importance of antibiotic-resistant pathogen colonization−emerging environmental medium interactions when addressing global threat from MPs pollution, in the context of MPs-antibiotics co-contamination assessment and for the continued provision of water system management.

Reconstructing long-term natural flows by ensemble machine learning

Large-scale human activities have caused significant disruption to the natural flow regime of rivers worldwide, leading to substantial deviations from river flows’ natural state. The natural hydrological regime of rivers holds great significance for the conservation and management of riverine ecosystems, which relies on the accurate reconstruction of natural river flows. However, the application of existing high spatiotemporal resolution large-scale natural flow datasets at the regional scale has not been adequately assessed, thereby limiting their regional-scale utility. In this study, utilizing global reanalysis datasets and observation data, in combination with ensemble machine learning models, we constructed long-term natural flow time series for inflows into the Pearl River Delta and evaluated the performance of these reconstructed inflows based on observed flow records during undisturbed periods. The reconstruction shows that the ensemble model, the AutoGluon model, indicates the best performance when compared with other machine learning models and outperforms the existing publicly available large-scale datasets in terms of evaluation metrics for the corresponding natural flows. The results of Indicator of Hydrologic Alteration and Range of Variability Analysis (IHA-RVA) indicate that climate variability, reservoir regulation and human activities have significantly altered the natural flow regime of the Pearl River Delta. Specifically, human activities generally tend to smooth natural flow variability in the Pearl River Delta. This research is expected to provide an efficient and reliable method for reconstructing natural flows and offers guidance for water resource management and ecosystem conservation.

Reactivity and bioconcentration of stable cesium in a hyperturbid fluvial-estuarine continuum: A combination of field observations and geochemical modeling

Effective, post-accidental management needs an accurate understanding of the biogeochemical behavior of radionuclides in surface environments at a regional scale. Studies on stable isotopes (element homologs) can improve this knowledge. This work focuses on the biogeochemical behavior of stable cesium (Cs) along a major European fluvial-estuarine system, the Gironde Estuary (SW France). We present results obtained from (i) a long-term monitoring (2014–2017) of dissolved (Csd) and particulate (Csp) Cs concentrations at five sites along the freshwater continuum of the Garonne watershed, (ii) Csd and Csp concentrations during four oceanographic campaigns at contrasting hydrological conditions along longitudinal profiles of the estuarine system, (iii) a 24 h cycle of Csp at the estuary mouth, and (iv) a historical trend of Cs bioconcentration in wild oysters at the estuary mouth (RNO/ROCCH, 1984–2017). In addition, we model the partitioning of Cs within the estuarine environment for clay mineral interactions via PhreeqC. At fluvial sites, we observe a geogenic dependence of the Csp and a seasonal variability of Csd, with a downstream increase of the solid-liquid partitioning (log10 Kd values from 3.64 to 6.75 L kg−1) for suspended particulate matter (SPM) < 200 mg L−1. Along the estuarine salinity gradients, Cs shows a non-conservative behavior where fresh SPM (defined as Cs-depleted particles recently put in contact with Csd) act as a Cs sink during both flood and low discharge (drought) conditions. This sorption behavior was explained by the geochemical model, highlighting the relevance of ionic strength, water and SPM residence times. However, at high salinities, the overall log10 Kd value decreases from 6.02 to 5.20 for SPM ∼300–350 mg L−1 due to the Csd oceanic endmember. Despite wild oysters showing low bioconcentration factors (∼1220 L kg−1) at the estuary mouth, they are sensitive organisms to Cs fluxes.

The impact of habitat loss and fragmentation on biodiversity in global protected areas

Protected areas (PAs) serve as effective means for biodiversity conservation but face threats from habitat loss and fragmentation. Current research on the impact of habitat loss or habitat fragmentation on biodiversity in PAs mostly focuses on individual PA or regional scales. At the global scale, the extent of habitat loss and fragmentation in PAs and their effects on biodiversity remains unclear. Therefore, we investigated the degree of habitat loss and fragmentation in global PAs from 2000 to 2020, analyzed the impact of habitat loss and fragmentation on biodiversity in PAs, identified hotspot PAs of severe habitat loss or fragmentation, and highlighted critically endangered species within these PAs. Our study reveals that, between 2000 and 2020, 19 % of global PAs experienced habitat loss, and 34 % experienced habitat fragmentation, with large PAs and South American tropical PAs exhibiting the most severe levels of habitat loss and fragmentation. The impact of habitat loss and fragmentation on biodiversity was most significant in small PAs and African tropical PAs. There are 10 global hotspot PAs of habitat loss or fragmentation, posing a serious threat to the survival of endangered species within PAs. Biodiversity conservation remains a prominent research focus globally, and the issues of habitat loss and fragmentation in PAs may impact the achievement of the COP15 biodiversity conservation goals. Therefore, this study aims to provide data support and scientific guidance for the management and development of global PAs.