Regional Scale Research Articles

Fusion of spectral and topographic features for land use mapping using a machine learning framework for a regional scale application.

This study investigated the dynamics of land use and land cover (LULC) modelling, mapping, and assessment in the Kegalle District ofSri Lanka, where policy decision-making is crucial in agricultural development where LULC temporal datasets are not readily available.Employing remotely sensed datasets and machine learning algorithms, the work presented here aims to compare the accuracy of three classification approaches in mapping LULC categories across the time in the study area primarily using the Google Earth Engine (GEE). Three classifiers namely random forest (RF), support vector machines (SVM), and classification and regression trees (CART) were used in LULC modelling, mapping, and change analysis. Different combinations of input features were investigated to improve classification performance. Developed models were optimised using the grid search cross-validation (CV) hyperparameter optimisation approach. It was revealed that the RF classifier constantly outstrips SVM and CART in terms of accuracy measures, highlighting its reliability in classifying the LULC. Land cover changes were examined for two periods, from 2001 to 2013 and 2013 to 2022, implying major alterations such as the conversion of rubber and coconut areas to built-up areas and barren lands. For suitable classification with higher accuracy, the study suggests utilising high spatial resolution satellite data, advanced feature selection approaches, and a combination of several spatial and spatial-temporal data sources. The study demonstrated practical applications of derived temporal LULC datasets for land management practices in agricultural development activities in developing nations.

Exploring the Dimensions of Religious Freedom: A Global and Comparative Analysis

Religion constitutes a fundamental aspect of the cultural and spiritual lives of individuals globally, encompassing an array of traditions, rituals, and practices. Within the context of liberal democracy's evolution, religion emerges as a critical element of political liberty and personal conscience. On an international scale, the right to religious freedom has been established as a core human right, essential for the sustenance of democratic governance. Despite its recognition, the right to religious liberty remains a highly contentious issue. Even in democracies that provide constitutional protections for religious freedom, debates persist regarding the extensive interpretation and implementation of this right, particularly concerning the rights of religious minorities within these nations. This study explores the concept of religious freedom as a globally recognized right, examining its representation and interpretation across various international and regional legal frameworks. Through this analysis, the paper endeavors to provide a comprehensive overview of religious freedom on both a global and regional scale. Furthermore, it delves into the state of religious freedom in various political systems, ranging from democracies such as United States to theocracies and monarchies like the United Kingdom and countries characterized by a strict demarcation between religion and state affairs, exemplified by France's secularist approach.

Spatio‐temporal variability in seed production of tree species: implications for restoration in the Cerrado–Amazonia transition zone

The use of propagules collected in ecotonal regions for restoration purposes is challenging because of the mix of species from different vegetation types and reproductive phenological variability. We used a database of the Xingu Seed Network in Brazil, which contains data on 139 native tree species for 8 years (2011–2018) in the Cerrado–Amazonia transition zone, to answer three questions: (1) What is the spatio‐temporal variability in the seed dispersal of tree species, and how does it relate to the species' preferential habitat? (2) Is there a relationship between the seed dispersal period and climate variables? (3) Is the frequency of tree species with different dispersal syndromes equally distributed among biomes and seed‐size classes? Independent of their preferred ecosystem (Amazonia or Cerrado), some species showed substantial spatio asynchrony in fruit dispersal, extending the dispersal period at a regional scale. The seed dispersal period was strongly correlated (rs > 0.6) with precipitation and minimum or maximum air temperature in 41% (n = 57) of the studied species. We showed that three‐quarters of the species (n = 105) dispersed seeds in the late dry and early rainy periods, with little variation among dispersal syndromes. Our findings will enhance the effectiveness of restoration initiatives by increasing the accuracy of predictions of the location and time for collecting seeds in the extensive Cerrado–Amazonia transition zone.

A Groundwater Quality Index Proposed for Assessment of Groundwater Quality: A Case Study of Quang Tri Province, Central Vietnam

Background: Water quality assessment is a crucial task to manage water sources and control water pollution. The purpose of this study is to develop a Groundwater Quality Index (GWQI) to assess groundwater quality. Methods: The GWQI was developed and functioned by weight (wi) and sub-index (qi) of ten selected water quality parameters. Principal Component Analysis (PCA) was applied to a water quality monitoring data set (year 2016 to 2019, 163 samples) at 27 locations in Quang Tri province, aiming to identify the weight of each parameter. Sub-index linear function was established based on the allowable limits for each parameter defined in the National Technical Regulation on drinking water quality. Multiplicate (GWQIM), additive (GWQIA), and reference (GWQIRef) formulas were used to compute the final GWQI indices. The sensitivity and suitability of the proposed GWQI in water quality evaluation were also tested. Results: Among the three GWQI calculation methods, GWQIM was found to be the most suitable in reflecting groundwater quality in the study area. From 2019 to 2022 (152 samples), in the province, 63.2% of GWQIM values were classified as EXCELLENT or GOOD, 17.1% as POOR, and 19.7% as VERY POOR, or UNFIT FOR DRINKING. Conclusion: This improved GWQI can be applied to assess groundwater quality at local or regional scales.

A “Region-Specific Model Adaptation (RSMA)”-Based Training Data Method for Large-Scale Land Cover Mapping

An accurate and historical land cover monitoring dataset for Alaska could provide fundamental information for a range of studies, such as conservation habitats, biogeochemical cycles, and climate systems, in this distinctive region. This research addresses challenges associated with the extraction of training data for timely and accurate land cover classifications in Alaska over longer time periods (e.g., greater than 10 years). Specifically, we designed the “Region-Specific Model Adaptation (RSMA)” method for training data. The method integrates land cover information from the National Land Cover Database (NLCD), LANDFIRE’s Existing Vegetation Type (EVT), and the National Wetlands Inventory (NWI) and machine learning techniques to generate robust training samples based on the Anderson Level II classification legend. The assumption of the method is that spectral signatures vary across regions because of diverse land surface compositions; however, despite these variations, there are consistent, collective land cover characteristics that span the entire region. Building upon this assumption, this research utilized the classification power of deep learning algorithms and the generalization ability of RSMA to construct a model for the RSMA method. Additionally, we interpreted existing vegetation plot information for land cover labels as validation data to reduce inconsistency in the human interpretation. Our validation results indicate that the RSMA method improved the quality of the training data derived solely from the NLCD by approximately 30% for the overall accuracy. The validation assessment also demonstrates that the RSMA method can generate reliable training data on large scales in regions that lack sufficient reliable data.

Exploring the Effect of Sampling Density on Spatial Prediction with Spatial Interpolation of Multiple Soil Nutrients at a Regional Scale

Exploring the Effect of Sampling Density on Spatial Prediction with Spatial Interpolation of Multiple Soil Nutrients at a Regional Scale

Adaptive copy number variations of ancient prokaryotic genes in marine fish colonization

During extensive evolution, higher eukaryotes have acquired numerous prokaryotic genes, some of which have experienced adaptive copy number variations (CNVs) in response to changing environments. The specific contribution of ancient prokaryotic genes to the evolutionary dynamics of marine fish remains unclear. In this study, we investigated ancient prokaryotic genes in 33 freshwater and 14 non-freshwater fish species. Non-freshwater fish exhibited a higher proportion of these genes (25.84 %) compared to freshwater fish (17.66 %), though functional categories showed similarity, suggesting genetic conservation. Comprehensive CNV analysis identified 24 genes with significantly higher copy numbers in non-freshwater fish, enriched in glycerolipid metabolism, immune regulation, and catechol O-methyltransferase activity. Eight of these genes displayed increased copy numbers in marine populations compared to Asian freshwater populations. These findings indicate that CNVs in genes related to glycerolipid metabolism, immune regulation, and methyltransferase activity may play crucial roles in marine adaptation at global and regional evolutionary scales.

Compound extreme climate events intensify yield anomalies of winter wheat in France

Abstract Compound extreme climate events (ECEs) are increasingly recognized for their potential to exacerbate food insecurity risks beyond those posed by isolated events. The notion of ‘compound event’ encompasses not only co-occurring ECEs but also multiple ECEs across (different) growth stages (mECEs). The additional effects of these mECEs on crop yield, particularly considering various types of ECEs and regional scales, remain poorly understood. To close this knowledge gap, we consider droughts, pluvials, heatwaves, and coldwaves, and further identify which types of compound events have additional effects on winter wheat yield in France, using statistical methods and datasets encompassing 94 counties over a 68-year period. Our results indicate co-occurring drought heatwaves in summer and spring, along with co-occurring pluvial heatwaves and pluvial coldwaves in winter, have negative additional effects on yield compared with single ECEs. We further identify the types of mECEs that have intensified effects, with the majority showing negative effects on yield. Key interactions leading to intensified yield loss include droughts in winter or spring combined with summer co-occurring drought heatwaves, pluvials across multiple growth stages, pluvials combined with coldwaves, and the transition between droughts and pluvials, with the most severe anomaly attaining −17.2%. Coldwaves are the main ECE related to intensified yield increases, while their frequency is decreasing. Overall, this study stresses the interactions among ECEs on crop yield, and the identified types of mECEs could serve as foundational information for designing control experiments and improving process-based crop models.

Biogeographic shifts in the microbial co-occurrence network features of three domains across complex environmental gradients in subtropical coastal waters

BackgroundBacteria, Archaea, and Microeukaryotes comprise taxonomic domains that interact in mediating biogeochemical cycles in coastal waters. Many studies have revealed contrasting biogeographic patterns of community structure and assembly mechanisms in microbial communities from different domains in coastal ecosystems; however, knowledge of specific biogeographic patterns on microbial co-occurrence relationships across complex coastal environmental gradients remains limited. Using a dense sampling scheme at the regional scale, SSU rRNA gene amplicon sequencing, and network analysis, we investigated intra- and inter-domain co-occurrence relationships and network topology-based biogeographic patterns from three microbial domains in coastal waters that show environmental gradients across the inshore-nearshore-offshore continuum in the East China Sea.ResultsOverall, we found the highest complexity and connectivity in the bacterial network, the highest modularity in the archaeal network, and the lowest complexity, connectivity, and modularity in the microeukaryotic network. Although microbial co-occurrence networks from the three domains showed distinct topological features, they exhibited a consistent biogeographic pattern across the inshore-nearshore-offshore continuum. Specifically, the nearshore zones with intermediate levels of terrestrial impacts reflected by multiple environmental factors (including water temperature, salinity, pH, dissolved oxygen, and nutrient-related parameters) had a higher intensity of microbial co-occurrence for all three domains. In contrast, the intensity of microbial co-occurrence was weaker in both the inshore and the offshore zones at the two ends of the environmental gradients. Archaea occupied a central position in the microbial inter-domain co-occurrence network. In particular, members of the Thaumarchaeota Marine Group I (MGI, now placed within the Family Nitrosopumilaceae of the Phylum Thermoproteota) appeared to be the hubs in the biogeographic shift between inter-domain network modules across environmental gradients.ConclusionsOur work offers new insights into microbial biogeography by integrating network features into biogeographic patterns, towards a better understanding of the potential of microbial interactions in shaping biogeographic patterns of coastal marine microbiota.

Quantifying the economy-wide employment effects of coal-fired power plants: Two different cases China and the United States

Ensuring a swift transition from coal to clean energy is essential for capping global temperature increases at 1.5 °C, but it poses significant economic and social challenges in coal-dependent regions. This paper examines the economy-wide employment impacts resulting from the activities of coal-fired power plants in China and the United States, the world's largest coal consumers. By analyzing observational data on the different stages of coal power, including both expansion and reduction in the two countries, we aim to derive insights that can inform a more effective and just transition during the phase-out of coal power. We leveraged unique periods post-2010 when China was predominantly opening coal plants while the US was closing them, identifying the economy-wide employment impacts at the county level. By using both two-way fixed effects and spatial econometric models for over 10 years of panel data, covering 2875 in China and 3233 counties in the US, we addressed spillover effects and ensured robust and comprehensive results. Our findings revealed that in China, coal plant deployment increased employment both locally and in the neighboring counties. In the U.S., our findings indicate that before 2015, closing coal plants generally had a negative impact on jobs. However, after 2015, when the U.S. introduced new federal and state-level transition policies, the impact on employment became positive. While it is hard to prove these policies directly facilitate the just transition, our findings demonstrate that they do not hinder the employment aspect of a just transition focused on coal power generation at a regional scale.

Existing evidence on the effect of urban forest management in carbon solutions and avian conservation: a systematic literature map

BackgroundUrgent solutions are needed in cities to mitigate twin crises of global climate change and biodiversity loss. Urban nature-based solutions (actions that protect, sustainably manage, and restore ecosystems while simultaneously providing human wellbeing and biodiversity benefits) are being advocated for as multi-functional tools capable of tackling these societal challenges. Urban forest management is a proposed nature-based solution with potential to address both climate change mitigation and biodiversity loss along with multiple other benefits. However, bodies of evidence measuring multiple outcomes (e.g., biodiversity conservation and nature-based climate solutions) remain siloed which limits conservation and management opportunities. In this article, we present a systematic map of the literature on urban forest management strategies that measure both biodiversity goals (through avian conservation) and climate change mitigation goals (through carbon storage and sequestration).MethodsFollowing a published protocol, we searched for evidence related to urban forest management strategies for (1) avian conservation and (2) carbon solutions within the global temperate region in academic and grey literature. In addition to Scopus, ProQuest and Web of Science Core Collection, we searched 21 specialist websites. We screened English language documents using predefined inclusion criteria on titles and abstracts, and then full texts. All qualifying literature items were coded, and metadata were extracted. No study validity appraisal was conducted. We identified knowledge clusters and gaps related to forest management strategies for both topics.Review findingsOur searches identified 19,073 articles published, of which 5445 were duplicates. The title and abstract screening removed a further 11,019 articles. After full-text screening (1762 and 1406), a total of 277 avian and 169 forest carbon literature items met the eligibility criteria and were included in the final database. We found a large knowledge base for broad-scale avian metrics: abundance, species richness. We similarly found that both avian and carbon solutions most often used broad-scale forest management components: land use type, composition, and forested area and least often considered fragmentation, connectivity, and diversity metrics (abundance, richness). The most understudied avian metrics were foraging, resources, and survival while the most understudied carbon solutions metrics were soil carbon, dead wood and organic matter and infrastructure. Avian literature most often used an experimental design (56% with comparator, 44% no comparator) while forest carbon solutions literature was dominated by observational studies (86%). In both topics, studies most often occurred over short timelines between 0 and 1 and 2–5 years. The body of evidence for both avian and carbon outcomes present a scale-mismatch between the scale of forest management strategy (e.g., land use type) and scale of application (e.g., patch). For example, the majority of studies considered forest strategies at broad scales, like land use type or composition, yet were conducted at a patch or multi-patch scale. Our systematic map also highlights that multi-city and regional urban scales are underrepresented in both carbon solutions and avian conservation and will require additional research efforts. Finally, we highlight gaps in the inclusion of recommendations in both bodies of literature. Roughly 30% of articles in each topic’s database did not include recommendations for practitioners or researchers.ConclusionsOur systematic map provides a database and identifies knowledge gaps and clusters of urban forest management strategies for (1) avian conservation and (2) carbon solutions. Overall, our map will allow researchers to fill existing gaps in literature through new research investigations, meta-analyses or systematic reviews while also pointing policymakers toward strong knowledge bases in addition to understudied or mismatched areas that require more funding.Graphical

Climate change impacts on hydrology and water resources in East Africa considering CMIP3, CMIP5, and CMIP6

Climate change impacts on the hydrology and water resources in East Africa require proper planning of regional adaptation measures. Past studies on the subject matter tended to focus on catchment instead of regional scale. To support actionable policy regarding regional planning of water resources management, this paper reviewed studies conducted in East Africa based on CMIP3, CMIP5, and CMIP6. Compared to the historical condition, temperature of East Africa over the mid- to late 21st century will increase by varying extents over the range 0.17–7°C with about 2.6°C on average. Future precipitation over East Africa will increase by 8.5% on average. Changes in discharge based on CMIP3, CMIP5, and CMIP6 will vary from −25.3 to +60.5%, −42.5 to 129%, and −23.4 to 69%, respectively. Ensemble means of the changes in discharge across the study area based on CMIP3, CMIP5, and CMIP6 will be by 25.2, 19.2, and 19.1%, respectively. Future peak river discharge across East Africa will increase by 8% on average. These results are important for planning regional climate change adaptation across East Africa. To support local or catchment adaptation, results of climate change impacts of temperature, precipitation and discharge of about 50 catchments across East Africa can be found summarized in this review paper. It is recommended that both regional and local policies or approaches should be considered for climate change adaptation to ensure a balanced, effective and sustainable framework for climate resilience in support of measures for tackling broad and specific needs cohesively.

Satellite-based Spectral Indices for Extracting Village Water Bodies in Ludhiana District of Punjab, India

Ensuring water security is the foremost priority of every nation, and water bodies are the most precious life-sustaining resources on the earth. Availability of reliable and updated information of water bodies including knowledge about their locations is necessary for the sustainable planning and management of water resources at a regional scale. Remote sensing technique has revolutionized extraction of village water bodies as it provides fine spatial resolution, gives rapid and precise results and covers large areas, and hence, it has been widely used in the recent studies. In this study, three remote sensing-based spectral indices, i.e., normalized difference water index (NDWI), modified normalized difference water index (MNDWI), and normalized difference pond index (NDPI) were employed to demarcate the area of ponds in each village of Ludhiana district in Punjab. Accuracy of the spectral indices was evaluated by comparing the area of the ponds delineated by the indices with that of digitized manually. The results indicated that NDWI, MNDWI and NDPI could identify 370, 1263 and 1410 number of village ponds, respectively. On the other hand, a total of 1513 village ponds could be demarcated through manual digitization. Thus, the efficiency of NDWI in identification of correct number of village ponds was 24.5%, while the efficiency of MNDWI and NDPI was 83.5% and 93.2%, respectively. Furthermore, NDPI demonstrated an efficiency of 60-65% in demarcating the area of village ponds.

Differential response of multiple stream ecosystem processes to basin- and reach-scale drivers

Stream ecosystems are inherently dependent on their surroundings and, thus, highly vulnerable to anthropogenic impacts, which alter both their structure and functioning. Anchored in biologically-mediated processes, the response of stream ecosystem functioning to environmental conditions exhibits intricate patterns, reflecting both natural dynamics and human-induced changes. Our study aimed at determining the natural and anthropogenic drivers influencing multiple stream ecosystems processes (nutrient uptake, biomass accrual, decomposition, and ecosystem metabolism) at a regional scale. By examining 38 natural and anthropogenic variables across 63 stream reaches in Gipuzkoa (northern Iberian Peninsula), we used structural equation modeling to unravel the cascading effect of basin- and reach-scale drivers onto ecosystem process. The results reveal significant variability in ecosystem processes, with contrasting spatial patterns, suggesting that studied processes respond differently to environmental factors. Urban land-use emerged as a primary basin-scale driver, whereas reach-scale variables reflected both natural and anthropogenic influence. Nutrient uptake rates were primarily driven by nutrient concentrations in stream water, but models for biomass accrual, decomposition, and ecosystem metabolism exhibited more complex cause-effect relationships. Our findings highlight the impact of urban areas on multiple ecosystem processes and services, disproportionate when considering their small land cover. The present study emphasizes the convenience of measuring multiple ecosystem functions simultaneously to get a comprehensive diagnosis of the functional status of rivers.

Agricultural pesticides in feathers of the burrowing owl (Athene cunicularia) and its relationship to land-use in the argentinean pampas

The pampas of South America represent one of the most productive lands for agriculture in the world, and consequently this activity has expanded throughout the region, especially in Argentina. In this context, native fauna faces various risks associated with agriculture, with exposure to pesticides being one of the most dangerous and deadly. Assessing the impact of pesticides on wildlife becomes fundamental and the use of sentinel species emerges as an important tool to monitor environmental health. In this study, we determined pesticide levels in the burrowing owl Athene cunicularia nesting in both rural and urban areas of the argentinean pampas. We used a multiple scale approach to evaluate the influence of land-uses (urban, crops, and grazing fields) at the nesting site on the contaminant load of A. cunicularia individuals, and assessed the potential use of this owl as a sentinel species. From March 2018 to January 2020, A. cunicularia feathers were collected at their nest sites in one urban and two rural areas. These samples were analyzed for the presence of contaminants, including chlorpyrifos and 18 organochlorine pesticides. All the compounds analyzed were detected in A. cunicularia samples. The chlorpyrifos showed the highest frequency of occurrence and concentration, thus denoting the exposure of owls to current-use pesticides. ∑DDTs showed the highest occurrence among organochlorines, followed by ∑HCHs > ∑Endosulfans > ∑Heptachlors > ∑Drins = ∑Chlordanes > methoxychlor. Principal Component Analysis showed that most of the compounds found in A. cunicularia samples showed a positive association with grazing fields, suggesting that exposure to contaminants does not depend on specific habitat use at the local scale, but probably the regional scale. Our results evidenced the exposure of A. cunicularia individuals to past and current-use pesticides in the pampas of Argentina, and indicate that this owl would be useful as a sentinel species.

Transforming the feeding regime towards low-input increases the environmental impact of organic milk production on a case study farm in central germany

Abstract Purpose Despite the direct effect of the feeding regime on the environmental impacts of dairy farming systems, its level of intensity, particularly in organic systems, has rarely been investigated. This study compares the environmental impact of a high-input feeding regime with a grassland-based, low-input feeding regime scenario within an organic milk production system conducted on Gladbacherhof, the research farm of Justus Liebig University Giessen, in Central Germany. Methods An integrated Life Cycle Assessment (LCA) analysis was performed from a cradle-to-farm gate perspective to quantify five environmental impacts, namely Global Warming (GW), Non-Renewable Energy Use (NREU), Land Use (LU), Terrestrial Acidification (TA), and Freshwater Eutrophication (FE). All agronomic data of the Gladbacherhof research farm, averaged over the years 2010–2017, were included. When not directly measured on the farm, ecoinvent data were included. Results and discussion Contrary to our hypothesis, the results suggest that a grassland-based low-input system has a higher environmental impact as compared to a high-input system for each of the five impact categories when using fat and protein-corrected milk (FPCM) as the functional unit. A 50% reduction in concentrates and exclusion of maize silage from the feed ration in the modelled low-input production system lead to a 20% drop in milk yield. To balance the energy content in low-input feeding ration, longer grazing period and higher amount of hay, alfalfa, and grass silage are required. This in turn results in higher emissions from enteric fermentation, manure management, and feed production and hence in higher environmental impact, particularly for GW, TA, and FE. Conclusions This study is one of the few that directly explores the environmental impact of feeding intensity in an organic milk production system. Nevertheless, there is a lack of research on consolidated emission factors for several greenhouse gas (GHG) sources in organic livestock and cropping systems to perform more robust carbon footprint calculations that comply with the Intergovernmental Panel on Climate Change (IPCC) Tier 3 GHG reporting guidelines. To generalize the results at the regional or national scale, direct comparisons with a larger number of organic farms representative of high-input and low-input intensities are still essential.

Assessing comprehensive anthropogenic impacts at a regional scale using ecological integrity

Various anthropogenic disturbances, such as urbanization, agricultural production, and infrastructure development, are contributing to global and regional ecosystem degradation. However, comprehensive quantitative assessment methods for evaluating regional-scale anthropogenic impacts remain insufficient. This study introduces a comprehensive assessment framework based on ecological integrity (EI) derived from the landscape condition model (LCM), combined with commonality analysis and other quantitative methods. This framework was applied to the Beijing–Tianjin–Hebei (BTH) region in North China to assess the spatio-temporal variations and spatial aggregation of EI, identify the major drivers of EI degradation, and determine the formation mechanisms of resultant EI patterns. The anthropogenic impacts, as measured by EI, exhibit significant spatial and aggregation heterogeneity across functional zones. This heterogeneity, along with the observed temporal variations (2000–2022), is attributed to diverse anthropogenic disturbances within intricate biophysical and socioeconomic contexts. This framework can indirectly improve the R2 of multiple linear regression models related to EI by identifying grassland and cropland as suppressor variables to enhance the predictive capacity of other variables. Consequently, this framework enables decision-makers to accurately identify major drivers of EI degradation, such as road construction and urbanization, and understand the combined effects of major drivers and suppressor variables on the resultant EI patterns. Lastly, within specific policy contexts, this framework offers several management implications for mitigating anthropogenic threats to EI, including effectively regulating urbanization and restoring forest landscapes. This framework provides fundamental support for anthropogenic impact assessment and management strategy design at a regional scale.

Improving the simulation accuracy of summer maize growth and yield by pixel-based parameterization based on assimilating upscaled MODIS LAI

Accurately simulating maize (Zea mays L.) yield at the regional scale is of paramount importance for making informed policy adjustments and for ensuring food security. Sobol sensitivity analysis was used in this study to screen sensitive parameters of a process-based crop model (Chinese Agricultural Meteorological Model, CAMM). An upscaling approach was utilized to reduce the intra-pixel heterogeneity error of MODIS LAI. Then upscaled MODIS LAI data were assimilated into the CAMM model through a 4DVar assimilation algorithm to optimize pixel-level sensitive parameters, thereby improving the simulation accuracy of the summer maize growth process and yield. Specific leaf area (SLATB_0, SLATB_1) during the period from emergence to tasseling of summer maize exhibited the strongest impact on summer maize yield. Additionally, the average LAI value within the 85–95 % range of ordered LAI values for small pixels within large pixels effectively reduced the intra-pixel heterogeneity error of MODIS LAI, and pixel-based parameterization of SLATB_0 and SLATB_1 at a larger pixel scale (0.0625°) was achieved. Based on yields recorded at agrometeorological stations from 2015 to 2020, assimilated yields in both data assimilation scheme 1 (DA1, optimization of only the sensitive parameter SLATB_0) and scheme 2 (DA2, simultaneous optimization of sensitive parameters SLATB_0 and SLATB_1) exhibited higher accuracy than schemes without data assimilation (with r values of 0.41–0.72 and NRMSE values of 19–30 %). Furthermore, DA2 showed greater simulation accuracy (r: 0.64–0.93, NRMSE: 9–21 %) than DA1 (r: 0.61–0.91, NRMSE: 12–23 %). Upscaling remotely sensed LAI products can significantly reduce the uncertainties of LAI data at a larger pixel scale, and assimilating these LAI data into crop models can effectively increase the simulation accuracy of crop growth and development processes at the regional scale.

Bacterial and fungal keystone taxa play different roles in maintaining community resistance and driving soil organic carbon dynamics in response to Solidago Canadensis invasion

The invasion of alien plants has significant implications for vegetation structure and diversity, which could lead to changes in the carbon (C) input from vegetation and change the transformation and decomposition processes of C, thereby altering the dynamics of soil organic carbon (SOC) within ecosystems. Whether alien plant invasion increases the SOC stock and changes SOC fractions consistently within regional scales, and the underlying mechanisms driving these SOC dynamics remain poorly understood. This study investigated SOC dynamics by comparing the plots that suffered invasion and non-invasion of Solidago Canadensis across five ecological function areas in Anhui Province, China, considering climate, edaphic factors, vegetation, and soil microbes. The results demonstrated that the impact of S. Canadensis invasion on SOC storage was not consistent at each site in the 0–20 cm soil layer, as indicated by the range of SOC content (5.94–12.45 g kg−1) observed at non-invaded plots. Stable SOC exhibited similar response patterns with SOC to plant invasion, whereas labile SOC did not. In addition, bacterial and fungal communities were shifted in structure at each site by plant invasion. Bacterial communities exhibited greater resistance to S. Canadensis invasion than did fungal communities, as evidenced by three aspects of the resistance indices—community resistance, phylogenetic conservation, and network complexity. The mechanisms driving SOC dynamics under S. Canadensis invasion were explored using structural equation models. This revealed that fungal keystone taxa responsible for community resistance controlled stable SOC fractions. In contrast, bacterial keystone taxa had the opposite effect on labile and stable SOC. Climatic and edaphic factors were also involved in the labile and stable SOC dynamics. Overall, this study provides novel insights into the dynamics of SOC under S. Canadensis invasion on a regional scale.

Regional and local patterns of soil arthropod diversity are explained by different processes in southwest China

Understanding the distribution patterns and underlying mechanisms of community assembly at different spatial scales is crucial for the ecology and conservation of organisms, yet little is known about soil arthropods. In this study, we tested the relative importance of the two hypotheses that could explain soil arthropod distribution patterns at local and regional scales: (1) the habitat heterogeneity hypothesis (spatial complexity and variability of a habitat allow the coexistence of many species), and (2) the more-individuals hypothesis (the amount of resources per se, regardless of its spatial heterogeneity, increases overall soil arthropod abundance and richness). We extensively collected soil arthropods (identified to families or subfamilies) and measured soil physicochemical properties in four forests distributed across latitudes in Yunnan Province, China. We found that soil arthropod abundance and taxon richness increased with increasing latitudes, contrasting with the generally known pattern for plants and aboveground animals. Litter biomass (resources) explained the taxon richness and abundance at the regional scale, which aligned with the more-individuals hypothesis. Other soil physicochemical properties (e.g., Ca, TP, Mg, Mn, and pH) were more important at the local scale, underpinning the habitat heterogeneity hypothesis. However, the relative importance of individual soil properties was highly location-specific, suggesting that the environmental factors operating in one location do not necessarily reflect those operating in others at different latitudes. These findings improve our understanding of the community assembly and ecological processes of soil arthropods at different spatial scales, which is vital for predicting the future of soil biodiversity.