Land-use intensification exerts a greater influence on soil microbial communities than seasonal variations in the Taihu Lake region, China.

The Taihu Lake region has undergone intensive land-use conversions from natural wetlands (NW) to conventional rice-wheat rotation fields (RW) and further to greenhouse vegetable fields (GH). Nevertheless, the effects of these conversions on soil microbes, particularly in wetland ecosystem, are not well explicit. To explore the impact of land-use intensification on soil microbial communities, monthly soil samples were obtained from replicate plots representing three land-use types (NW, RW, and GH) in subtropical wetlands and then subjected to amplicon sequencing. Land-use intensification had direct effects on bacterial and fungal community composition, with a more pronounced impact on bacteria than on fungi. These changes in bacterial communities were closely correlated with variations in soil environmental variables, such as NO3--N, pH, and electrical conductivity. Land-use intensification led to a decrease in bacterial deterministic processes, with an opposing trend observed in the fungal community. In addition, arable lands (RW and GH), which are affected by anthropogenic activities, exhibited more complex networks. Potential metabolic functional groups in GH had higher absolute abundance. Seasonal variations significantly influenced microbial diversity, composition, and potential metabolic functional groups within each land-use type, particularly in summer, although the magnitude of this impact was much smaller than the impact of land-use intensification. Our findings emphasize the importance of comprehending the ecological consequences of land-use intensification in wetlands for sustainable resource management and biodiversity conservation.

Spatial targeting of irrigation development and water resource management to mitigate vector-borne disease.

Scaling irrigated agriculture is a global strategy to mitigate food insecurity concerns. While expanding irrigated agriculture is critical to meeting food production demands, it is important to consider how these land use and land cover changes (LULCC) may alter the water resources of landscapes and impact the spatiotemporal epidemiology of disease. Here, a generalizable method is presented to inform irrigation development decision-making aimed at increasing crop production through irrigation while simultaneously mitigating malaria risk to surrounding communities. Changes to the spatiotemporal patterns of malaria vector (Anopheles gambiae s.s.) suitability, driven by irrigated agricultural expansion, are presented for Malawi's rainy and dry seasons. The methods presented may be applied to other geographical areas where sufficient irrigation and malaria prevalence data are available. Results show that approximately 8.60% and 1.78% of Malawi is maximally suitable for An. gambiae s.s. breeding in the rainy and dry seasons, respectively. However, the proposed LULCC from irrigated agriculture increases the maximally suitable land area in both seasons: 15.16% (rainy) and 2.17% (dry). Proposed irrigation development sites are analyzed and ranked according to their likelihood of increasing malaria risk for those closest to the schemes. Results illustrate how geospatial information on the anticipated change to the malaria landscape driven by increasing irrigated agricultural extent can assist in altering development plans, amending policies, or reassessing water resource management strategies to mitigate expected changes in malaria risk.

Enhancing machine learning models for total organic carbon prediction by integrating geospatial parameters in river watersheds.

This study utilizes machine learning (ML) algorithms to develop a robust total organic carbon (TOC) prediction model for river waters in the Geumho River sub-basins, South Korea, considering both non-rain and rain events. The model incorporates geospatial parameters such as land use, slope, flow rate, and basic water quality metrics including biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN), total phosphorus (TP), and suspended solids (SS). A key aspect of this research is examining how land use information enhances the model's predictive accuracy. We compared two ML algorithms-extreme gradient boosting (XGBoost) and deep neural networks (DNN)-with a traditional multiple linear regression (MLR) approach. XGBoost outperformed the others, achieving an R2 value between 0.61 and 0.68 in the test dataset and demonstrating significant improvement during rain events with an R2 of 0.77 when including land use data. In contrast, this enhancement was not observed with the MLR model. Feature importance analysis using Shapley values highlighted COD as the primary predictor for non-rain events, while during rain events, COD, TP, TN, SS and agricultural land collectively influenced TOC levels. This study significantly advances understanding of TOC variability across different land use scenarios in river systems and underscores the importance of integrating geospatial and water quality parameters to enhance TOC prediction, particularly during rain events. This methodology provides a valuable framework for developing river management strategies and monitoring long-term TOC trends, especially in scenarios with gaps in essential monitoring data.

Urbanization significantly increases greenhouse gas emissions from a subtropical headwater stream in Southeast China.

Streams are disproportionately significant contributors to increases in greenhouse gas (GHG) effluxes in river networks. In the context of global urbanization, a growing number of streams are affected by urbanization, which has been suggested to stimulate the water-air GHG emissions from fluvial systems. This study investigated the seasonal and longitudinal profiles of GHG (N2O, CH4, and CO2) concentrations of Jiuxianghe Stream, a headwater stream undergoing urbanization, and estimated its GHG diffusive fluxes and global warming potentials (GWPs) using the boundary layer method. The results showed that N2O, CH4, and CO2 concentrations in Jiuxianghe Stream were 0.45-7.19 µg L-1, 0.31-586.85 µg L-1, and 0.16-11.60 mg L-1, respectively. N2O, CH4, and CO2 concentrations in the stream showed 4.55-, 23.70-, and 7.68-fold increases from headwaters to downstream, respectively, corresponding to the forest-urban transition within the watershed. Multiple linear regression indicated that NO3--N, NH4+-N, and DOC:NO3--N accurately predicted N2O and CO2 concentrations, indicating that N nutrients were the driving factors. The Jiuxianghe Stream was a source of atmospheric GHGs with a daily GWP of 7.31 g CO2-eq m-2 d-1 on average and was significantly positively correlated with the ratio of construction land and forest in the sub-watershed. This study highlights the critical role of urbanization in amplifying GHG emissions from streams, thereby augmenting our understanding of GHG emissions from river networks. With global urbanization on the rise, streams experiencing urbanization are expected to make an unprecedentedly significant contribution to riverine GHG budgets in the future.

Applying a deep learning pipeline to classify land cover from low-quality historical RGB imagery.

Land use and land cover (LULC) classification is becoming faster and more accurate thanks to new deep learning algorithms. Moreover, new high spectral- and spatial-resolution datasets offer opportunities to classify land cover with greater accuracy and class specificity. However, deploying deep learning algorithms to characterize present-day, modern land cover based on state-of-the-art data is insufficient for understanding trends in land cover change and identifying changes in and drivers of ecological and social variables of interest. These identifications require characterizing past land cover, for which imagery is often lower-quality. We applied a deep learning pipeline to classify land cover from historical, low-quality RGB aerial imagery, using a case study of Vancouver, Canada. We deployed an atrous convolutional neural network from DeepLabv3+ (which has previously shown to outperform other networks) and trained it on modern Maxar satellite imagery using a modern land cover classification. We fine-tuned the resultant model using a small dataset of manually annotated and augmented historical imagery. This final model accurately predicted historical land cover classification at rates similar to other studies that used high-quality imagery. These predictions indicate that Vancouver has lost vegetative cover from 1995-2021, including a decrease in conifer cover, an increase in pavement cover, and an overall decrease in tree and grass cover. Our workflow may be harnessed to understand historical land cover and identify land cover change in other regions and at other times.

Differential impacts of land-use change on multiple components of common milkweed (Asclepias syriaca) pollination success.

Land-use change is one the greatest threats to biodiversity and is projected to increase in magnitude in the coming years, stressing the importance of better understanding how land-use change may affect vital ecosystem services, such as pollination. Past studies on the impact of land-use change have largely focused on only one aspect of the pollination process (e.g., pollinator composition, pollinator visitation, and pollen transfer), potentially misrepresenting the full complexity of land-use effects on pollination services. Evaluating the impacts across multiple components of the pollination process can also help pinpoint the underlying mechanisms driving land-use change effects. This study evaluates how land-use change affects multiple aspects of the pollination process in common milkweed populations, including pollinator community composition, pollinator visitation rate, pollen removal, and pollen deposition. Overall, land-use change altered floral visitor composition, with small bees having a larger presence in developed areas. Insect visitation rate and pollen removal were also higher in more developed areas, perhaps suggesting a positive impact of land-use change. However, pollen deposition did not differ between developed and undeveloped sites. Our findings highlight the complexity evaluating land-use change effects on pollination, as these likely depend on the specific aspect of pollination evaluated and on the of the intensity of disturbance. Our study stresses the importance of evaluating multiple components of the pollination process in order to fully understand overall effects and mechanisms underlying land-use change effects on this vital ecosystem service.

[Soil microorganisms and physicochemical properties in marshlands along the Yangtze River basin at different types of land use and their effects on distribution of Oncomelania hupensis snails].

OBJECTIVE: To investigating the microbial communities and physicochemical properties of soil and distribution of Oncomelania hupensis snails in marshlands along the Yangtze River basin at different types of land use, and to examine the effects of soil microorganisms and physicochemical properties on snail distribution, so as to provide insights into snail control and schistosomiasis prevention in marshland along the Yangtze River basin. METHODS: Marshlands with four types of land use were selected along the Yangtze River basin on April 2021, including poplar forest-crops integrated planting, reed areas, agricultural cultivation lands and ditches. The distribution of snails and physicochemical properties of soil were investigated in marshlands with different types of land use, and the V3 to V4 regions of the bacterial 16S ribosomal RNA (16S rRNA) gene, fungal internal transcribed spacer-1 (ITS1) gene and algal ribulose-bisphosphate carboxylase (rbcL) gene in soils were subjected to high-throughput sequencing. The occurrence of frames with living snails and density of living snails were compared in marshland with different types of land use. The associations of soil microorganisms and physicochemical properties with the density of living snails were examined using Pearson correlation analysis, and the contributions of soil microorganisms and physicochemical properties to the density of living snails were evaluated using variance partitioning analysis. RESULTS: In marshlands with four types of land use, the greatest occurrence of frames with living snails [(4.94 ± 2.14)%] and density of living snails [(0.070 ± 0.026) snails/0.1 m2] were seen in ditches, and the lowest were found in [(1.23 ± 1.23)%] agricultural cultivation lands [(0.016 ± 0.019) snails/0.1 m2]. A total of 2 phyla, 5 classes, 8 orders, 9 families and 11 genera of algae were detected in soils at four types of land use, with Chlorophyta as the dominant phylum and Pseudoneochloris as the dominant genus. A total of 44 phyla, 134 classes, 281 orders, 338 families and 516 genera of bacteria were detected in soils at four types of land use, with Proteobacteria and Acidobacteriota as the dominant phyla and uncultured Acidobacterium, MND1, Mitrospira, Haliangium and Sphingomonas as dominant genera. A total of 11 phyla, 41 classes, 108 orders, 223 families and 408 genera of fungi were detected in soils at four types of land use, with phyla Ascomycota, Basidiomycota and Mortierellomycota presenting high relative abundances and genera Cladorrhinum, Mortierella and Humicola presenting high relative abundances. Pearson correlation analysis revealed that the density of living snails correlated negatively with the relative abundance of Proteobacteria (r = -0.965, P < 0.05) and soil electronic conductivity (r = -0.962, P < 0.05) and positively with soil moisture (r = 0.951, P < 0.05). Variance partitioning analysis demonstrated that the physicochemical properties and microorganisms of soil contributed 69% and 10% to the density of living snails, respectively. CONCLUSIONS: The diversity of microbial communities varies in soils at different types of land use in marshland along the Yangtze River basin, and the physicochemical properties and microorganisms of soils may affect the distribution of O. hupensis snails.

The impact of vegetation reconstruction on soil erosion in the Loess plateau.

Vegetation restoration not only extensively reshapes spatial land use patterns but also profoundly affects the dynamics of runoff and sediment loss. However, the influence of vegetation restoration on runoff and sediment yield from a regional perspective are scarce. This study therefore focused on 85 sites within the "Grain for Green" Project (GGP) region on the Loess Plateau, to investigate the impacts of the GGP on soil erosion. The results revealed a notable reduction in sediment loss and runoff due to vegetation restoration. Since the inception of the GGP in 1999, approximately 4.1 × 106 ha of degraded lands have been converted into forestlands, shrublands, and grasslands, resulting in an average annual reduction of 1.4 × 109 m3 in runoff and a decrease of 3.6 × 108 t in annual sediment loss on the whole Loess Plateau, with the GGP contributing approximately 26.7% of the sediment reduction in the Yellow River basin. The reduced soil erosion has mainly been regulated by vegetation cover, soil properties (clay, silt, and sand), slope, and precipitation on the Loess Plateau. The insights gained offer valuable contributions to large-scale assessments of changes in soil erosion in response to vegetation reconstruction and enhance our understanding of the spatial configurations associated with soil erosion control measures.

Study on trade-offs and synergies of rural ecosystem services in the Tacheng-Emin Basin, Xinjiang, China: Implications for zoning management of rural ecological functions.

Rural areas are the main source of ecosystem services in arid and semi-arid areas, and ecosystem services are the background conditions for rural revitalization. In this study, the spatial pattern of key ecosystem services in the countryside was assessed, and the trade-offs and synergistic relationships among ecosystem services were investigated, using the Tacheng-Emin Basin in China as the study area. Finally, the types of ecological function zoning and development strategies for the countryside are proposed. The results showed that: (1) the area of ecological land was large, and the average land use intensity was 2.48, which belonged to the medium intensity. (2) The mean values of the six ecosystem services are all in the middle and lower classes, and the spatial distribution of the five ecosystem services is similar, except for food production. (3) Except for grain production, the other five ecosystem services showed positive feedback to elevation. The other five ecosystem services are synergistic, and there are trade-offs between grain production and other ecosystem services. In the nonlinear interaction mechanism of ecosystem services, the fluctuation constraint occupies the largest proportion. (4) At smaller spatial scales, there are more types of ecosystem service clusters. Combining the results of the study, the villages in the study area can be categorized into five types. This study formulates five priority levels of rural ecological revitalization and proposes different development recommendations for the sustainable development of each type of village. This study is helpful for the fine management of land resources and the revitalization of rural ecology and provides a reference for the sustainable development of ecosystem services in arid and semi-arid areas.

Synergy level of urban resilience and urban land use efficiency in the Yellow River Basin: Spatial-temporal evolution characteristics and driving factors.

The complex global context, including globalization, rapid urbanization, and climate change, poses significant challenges to urban stability and development. Balancing urban land use efficiency and resilience is crucial for sustainable progress. Focusing on the vulnerable Yellow River Basin (YRB), this study examines the interplay between urban resilience and land use efficiency. Panel data from 2013 to 2020 for 54 cities in the YRB were used, it quantifies the Coupling Coordination Degree of Urban Resilience and Urban Land Use Efficiency (CCDUU), explores its spatiotemporal evolution and influencing factors. Key findings include: The CCDUU exhibits a sustained and discernible growth trend. Notably, CCDUU is higher in downstream areas in comparison to the middle reaches, reaching its lowest point in the upstream areas; however, the increase in CCDUU in the upstream areas surpasses that observed in other regions. Concurrently, regional disparities in CCDUU are diminishing. Despite the presence of a notable positive spatial correlation in CCDUU within the YRB, the strength of this spatial association is not sufficiently robust. Of paramount importance factor is the role of regional innovation, which significantly influences the enhancement of CCDUU. Following closely is the degree of openness, whereas the positive effects of government support and population density are concentrated predominantly in the upper YRB region. In contrast, urban-rural disparity exerts an adverse impact on CCDUU in most regions. Policy recommendations for enhancing CCDUU in YRB cities include strengthening government support and planning control, particularly in upstream regions, to achieve efficient resource utilization and environmental protection. Implementing population density management policies, encouraging rational movement, and promoting population migration to upstream areas can alleviate pressure in downstream cities. Enhancing openness, attracting foreign investment, and promoting innovation and industrial upgrading will drive economic structural upgrades and improve CCDUU.

Impacts of future climate and land use/land cover change on urban runoff using fine-scale hydrologic modeling.

Future changes in land use/land cover (LULC) and climate (CC) affect watershed hydrology. Despite past research on estimating such changes, studies on the impacts of both these nonstationary stressors on urban watersheds have been limited. Urban watersheds have several important details such as hydraulic infrastructure that call for fine-scale models to predict the impacts of LULC and CC on watershed hydrology. In this paper, a fine-scale hydrologic model-Personal Computer Storm Water Management Model (PCSWMM)-was applied to predict the individual and joint impacts of LULC changes and CC on surface runoff attributes (peak and volume) in 3800 urban subwatersheds in Midwest Florida. The subwatersheds a range of characteristics in terms of drainage area, surface imperviousness, ground slope and LULC distribution. The PCSWMM also represented several hydraulic structures (e.g., ponds and pipes) across the subwatersheds. We analyzed changes in the runoff attributes to determine which stressor is most responsible for the changes and what subwatersheds are mostly sensitive to such changes. Six 24-h design rainfall events (5- to 200-year recurrence intervals) were studied under historical (2010) and future (year 2070) climate and LULC. We evaluated the response of the subwatersheds in terms of runoff peak and volume to the design rainfall events using the PCSWMM. The results indicated that, overall, CC has a greater impact on the runoff attributes than LULC change. We also found that LULC and climate induced changes in runoff are generally more pronounced in greater recurrence intervals and subwatersheds with smaller drainage areas and milder slopes. However, no relationship was found between the changes in runoff and original subwatershed imperviousness; this can be due to the small increase in urban land cover projected for the study area. This research helps urban planners and floodplain managers identify the required strategies to protect urban watersheds against future LULC change and CC.

Soil microbial community variation among different land use types in the agro-pastoral ecotone of northern China is likely to be caused by anthropogenic activities.

There are various types of land use in the agricultural and pastoral areas of northern China, including natural grassland and artificial grassland, scrub land, forest land and farmland, may change the soil microbial community However, the soil microbial communities in these different land use types remain poorly understood. In this study, we compared soil microbial communities in these five land use types within the agro-pastoral ecotone of northern China. Our results showed that land use has had a considerable impact on soil bacterial and fungal community structures. Bacterial diversity was highest in shrubland and lowest in natural grassland; fungal diversity was highest in woodland. Microbial network structural complexity also differed significantly among land use types. The lower complexity of artificial grassland and farmland may be a result of the high intensity of anthropogenic activities in these two land-use types, while the higher structural complexity of the shrubland and woodland networks characterised by low-intensity management may be a result of low anthropogenic disturbance. Correlation analysis of soil properties (e.g., soil physicochemical properties, soil nutrients, and microbiomass carbon and nitrogen levels) and soil microbial communities demonstrated that although microbial taxa were correlated to some extent with soil environmental factors, these factors did not sufficiently explain the microbial community differences among land use types. Understanding variability among soil microbial communities within agro-pastoral areas of northern China is critical for determining the most effective land management strategies and conserving microbial diversity at the regional level.

Machine learning-based surrogate modelling of a robust, sustainable development goal (SDG)-compliant land-use future for Australia at high spatial resolution.

We developed a high-resolution machine learning based surrogate model to identify a robust land-use future for Australia which meets multiple UN Sustainable Development Goals. We compared machine learning models with different architectures to pick the best performing model considering the data type, accuracy metrics, ability to handle uncertainty and computational overhead requirement. The surrogate model, called ML-LUTO Spatial, was trained on the Land-Use Trade-Offs (version 1.0) model of Australian agricultural land system sustainability. Using the surrogate model, we generated projections of land-use futures at 1.1 km resolution with 95% classification accuracy, and which far surpassed the computational benchmarks of the original model. This efficiency enabled the generation of numerous SDG-compliant (SDGs 2, 6, 7, 13, 15) future land-use maps on a standard laptop, a task previously dependent upon high-performance computing clusters. Combining these projections, we derived a single, robust land-use future and quantified the uncertainty. Our findings indicate that while agricultural land-use remains dominant in all Australian regions, extensive carbon plantings were identified in Queensland and environmental plantings played a role across the study area, reflecting a growing urgency for offsetting greenhouse gas emissions and the restoration of ecosystems to support biodiversity across Australia to meet the 2050 Sustainable Development Goals.

Impact of urban space on PM2.5 distribution: A multiscale and seasonal study in the Yangtze River Delta urban agglomeration.

Despite concerted efforts in emission control, air pollution control remains challenging. Urban planning has emerged as a crucial strategy for mitigating PM2.5 pollution. What remains unclear is the impact of urban form and their interactions with seasonal changes. In this study, base on the air quality monitoring stations in the Yangtze River Delta urban agglomeration, the relationship between urban spatial indicators (building morphology and land use) and PM2.5 concentrations was investigated using full subset regression and variance partitioning analysis, and seasonal differences were further analysed. Our findings reveal that PM2.5 pollution exhibits different sensitivities to spatial scales, with higher sensitivity to the local microclimate formed by the three-dimensional structure of buildings at the local scale, while land use exerts greater influence at larger scales. Specifically, land use indicators contributed sustantially more to the PM2.5 prediction model as buffer zone expand (from an average of 2.41% at 100 m range to 47.30% at 5000 m range), whereas building morphology indicators display an inverse trend (from an average of 13.84% at 100 m range to 1.88% at 5000 m range). These results enderscore the importance of considering building morphology in local-scale urban planning, where the increasing building height can significantly enhance the disperion of PM2.5 pollution. Conversely, large-scale urban planning should prioritize the mixed use of green spaces and construction lands to mitigate PM2.5 pollution. Moreover, the significant seasonal differences in the ralationship between urban spatical indicatiors and PM2.5 pollution were observed. Particularly moteworthy is the heightened association between forest, water indicators and PM2.5 concentrations in summer, indicating the urban forests may facilitate the formation of volatile compunds, exacerbating the PM2.5 pollution. Our study provides a theoretical basis for addressing scale-related challenges in urban spatial planning, thereby forstering the sustainable development of cities.

High-resolution spatial and spatiotemporal modelling of air pollution using fixed site and mobile monitoring in a Canadian city.

The development of high-resolution spatial and spatiotemporal models of air pollutants is essential for exposure science and epidemiological applications. While fixed-site sampling has conventionally provided input data for statistical predictive models, the evolving mobile monitoring method offers improved spatial resolution, ideal for measuring pollutants with high spatial variability such as ultrafine particles (UFP). The Quebec Air Pollution Exposure and Epidemiology (QAPEE) study measured and modelled the spatial and spatiotemporal distributions of understudied pollutants, such as UFPs, black carbon (BC), and brown carbon (BrC), along with fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) in Quebec City, Canada. We conducted a combined fixed-site (NO2 and O3) and mobile monitoring (PM2.5, BC, BrC, and UFPs) campaign over 10-months. Mobile monitoring routes were monitored on a weekly basis between 8am - 10am and designed using location/allocation modelling. Seasonal fixed-site sampling campaigns captured continuous 24-hour measurements over two-week periods. Flexible Generalized Additive Models (GAMs), which combined data on pollution concentrations, spatial and spatial and temporal GIS predictors, and spatial and temporal terms, were used to model and predict concentration surfaces. Annual models for PM2.5, NO2, O3 as well as seven of the smallest size fractions in the UFP range, had high out of sample predictive accuracy (range r2: 0.54 - 0.86). Varying spatial patterns was observed across UFP size ranges measured as Particle Number Counts (PNC). The monthly spatiotemporal models for PM2.5 (r2 = 0.49), BC (r2 = 0.27), BrC (r2 = 0.29), and PNC (r2 = 0.49) had moderate or moderate-low out of sample predictive accuracy. We conducted a sensitivity analysis and found that the minimum number of 'n visits' (mobile monitoring sessions) required to model annually representative air pollution concentrations was between 24 and 32 visits dependent on the pollutant. This study provides a single source of predictive exposure models for a comprehensive set of air pollutants in Quebec City, Canada. These exposure models will feed into epidemiological research on the health impacts of ambient UFPs and other pollutants.

The impact of land use on eco-environment in the Dianchi Basin.

(1) Studying the dynamic correlation between land use and the eco-environment in the Dianchi Basin is important for improving the basin's spatial layout and enhancing ecological development and conservation; (2) Through dynamic analysis and comprehensive evaluation of land use, the introduction of ecological and environmental quality index, and the use of FLUS models, the impacts on eco-environments in the Dianchi Basin for the recent 20 years were analyzed; (3) The past two decades witnessed a constant increase in the construction land in the Dianchi Basin and a decline in the farmland at an average annual rate of 0.93 %; The utilization level of land in the Dianchi Basin presented a negative correlation with the quality of the area's eco-environment, which reduces first and then increases; When natural production becomes a priority, both the construction land and farmland have witnessed growth. However, when ecological protection becomes a priority, it is projected that by 2035, the Dianchi Basin will achieve its highest eco-environmental quality index; (4) Studying how the change of land use types affects eco-environment is crucial for optimizing the current allocation of land resources and promoting sustainable development in the basin.

Body-size-dependent effects of landscape-level resource energetics on pollinator abundance in woodland remnants.

Land use change alters floral resource availability, thereby contributing to declines in important pollinators. However, the severity of land use impact varies by species, influenced by factors such as dispersal ability and resource specialization, both of which can correlate with body size. Here. we test whether floral resource availability in the surrounding landscape (the 'matrix') influences bee species' abundance in isolated remnant woodlands, and whether this effect varies with body size. We sampled quantitative flower-visitation networks within woodland remnants and quantified floral energy resources (nectar and pollen calories) available to each bee species both within the woodland and the matrix. Bee abundance in woodland increased with floral energy resources in the surrounding matrix, with strongest effects on larger-bodied species. Our findings suggest important but size-dependent effects of declining matrix floral resources on the persistence of bees in remnant woodlands, highlighting the need to incorporate landscape-level floral resources in conservation planning for pollinators in threatened natural habitats.

Coking exhaust contributes to airborne particulate matter in the Beijing-Tianjin-Hebei region.

Coking was regarded as a predominant source of air pollution. Despite the adoption of more environmentally friendly equipment, whether the coking enterprises in the Beijing-Tianjin-Hebei (BTH) region are still causing regional air pollution is worthy of study, which is essential for the control of coking enterprises in this area. To improve the prediction accuracy of large-scale air pollutant distribution, the air particle distribution in the BTH region was simulated via land use regression (LUR) combined with Bayesian maximum entropy (BME); then, the distribution was correlated with the exhaust gas emitted from coking enterprises. Results indicated that the R2 of the "LUR + BME" method reached 0.95, higher than 0.82 using LUR alone. The air quality distribution presented a pattern of "low in the northern mountains and high in the southern plains", similar to the distribution of coking enterprises in BTH region. A significant correlation was found between exhaust emissions from coking enterprises and air quality in the BTH region, confirming the contribution of coking emissions to air pollution in this region, and the necessity to continue the strict control on coking enterprises in BTH area.

Quantification of soil erosion and sediment yield using the RUSLE model in Boyo watershed, central Rift Valley Basin of Ethiopia.

Changes in land use and land cover (LULC) are becoming recognized as critical to sustainability research, particularly in the context of changing landscapes. Soil erosion is one of the most important environmental challenges today, particularly in developing countries like Ethiopia. The objective of this study was evaluating the dynamics of soil loss, quantifying sediment yield, and detecting soil erosion hotspot fields in the Boyo watershed. To quantify the soil erosion risks, the Revised Universal Soil Loss Equation (RUSLE) model was used combined with remote sensing (RS) and geographic information system (GIS) technology, with land use/land cover, rainfall, soil, and management approaches as input variables. The sediment yield was estimated using the sediment delivery ratio (SDR) method. In contrast to a loss in forest land (1.7 %), water bodies (3.0 %), wetlands (1.5 %), and grassland (1.7 %), the analysis of LULC change (1991-2020) showed a yearly increase in the area of cultivated land (1.4 %), built-up land (0.8 %), and bare land (3.5 %). In 1991, 2000, and 2020, respectively, the watershed's mean annual soil loss increases by 15.5, 35.9, and 38.3 t/ha/y. Approximately 36 cm of the watershed's economically productive topsoil was lost throughout the study's twenty-nine-year period (1991-2020). According to the degree of erosion, 16 % of the watershed was deemed seriously damaged, while 70 % was deemed slightly degraded. Additionally, it is estimated for the year 2020 that 74,147.25 t/y of sediment (8.52 % of the total annual soil loss of 870,763.12 t) reach the Boyo watershed outlet. SW4 and SW5 were the two sub-watersheds with the highest erosion rates, requiring immediate conservation intervention to restore the ecology of the Boyo watershed.

CONTEXTS.py (CS.py): A supervised contextual post-classification method to access multiple dimensions of complex geospatial objects.

The qualitative dimensions of visible features in space can be captured by connecting spatial configurations arranged in a variety of different ways to diverse conceptual spaces. By conceptual spaces, we intend mental concepts describing specific spatial configurations present in a geographical area, defined by the contextual relationships among their constitutive elements. This paper presents a new supervised post-classification method allowing the extraction of semantically complex spatial objects from a single image of the Earth as, for instance, diverse conceptual spaces referring to multiple dimensions of land use (temporal, cultural, social, etc.). Computationally, our method is operationalised by CONTEXTS.py (CS.py), a plugin written in Python for QGIS. CS.py relies on training areas, defined by the user at diverse scales, to identify and extract in the input image conceptual spaces whose spatial contexts have the same spatial features present in the training areas. Applied to a case study on the island of Sicily, where millennial land use dynamics have resulted in a mosaic landscape, CS.py could detect from an orthophoto diverse conceptual spaces of land use in an area ordinarily classified as one land cover, thus expanding the capabilities of geospatial analysis to reach additional qualitative dimensions of information from image data.•CS.py simplifies a supervised contextual post-classification routine in an easy-to-use, practical and accessible QGIS plugin;•CS.py joins a family of tools for supervised object-based classification (e.g. OTB, GRASS), providing, additionally, the possibility to include contextual information as spatial criteria to train the classification routine.•CS.py has broad applications in different disciplines investigating landscape from quantitative and qualitative perspectives, allowing both, as in multiple environments.