Spatial Heterogeneity Research Articles

Estimating soil moisture content in citrus orchards using multi-temporal sentinel-1A data-based LSTM and PSO-LSTM models

Soil moisture content is a vital variable in agricultural, hydrological, ecological and climatological processes. However, susceptible to soil type, soil structure, topography, vegetation and human activities, soil moisture content exhibits strong spatial heterogeneity in spatial distribution, which makes it difficult to accurately estimate the soil moisture content distribution information at a large scale using conventional methods. To solve the problem, this study proposed a novel hybrid model (PSO-LSTM) based on the particle swarm optimization (PSO) and long short-term memory (LSTM) network model to accurately predict soil moisture content at a large scale. Five different input combinations were constructed based on the vertical polarization (VV) and cross-polarization (VH) of multi-phase Sentinel-1A data, and the soil moisture content at depths of 5 cm, 10 cm, 20 cm and 40 cm in citrus orchards were estimated using the standalone LSTM and hybrid PSO-LSTM models. The results showed that the estimation accuracy of the hybrid PSO-LSTM model was greater than that of the standalone LSTM model at different depths, with the normalized root mean square error (NRMSE) of 4.568–11.023 % and 18.056–30.156 %, respectively. With the VV polarization as the only inputs, the PSO-LSTM model obtained high prediction accuracy, with the normalized root mean square error (NRMSE) of 5.458–10.125 %, respectively. Therefore, the PSO-LSTM model with VV polarization input was recommended to estimate the soil moisture content at different depths in citrus orchards, which provides important data for decision-making on distributed precision irrigation at a large scale.

Integrated machine learning reveals aquatic biological integrity patterns in semi-arid watersheds

Semi-arid regions present unique challenges for maintaining aquatic biological integrity due to their complex evolutionary mechanisms. Uncovering the spatial patterns of aquatic biological integrity in these areas is a challenging research task, especially under the compound environmental stress. Our goal is to address this issue with a scientifically rigorous approach. This study aims to explore the spatial analysis and diagnosis method of aquatic biological based on the combination of machine learning and statistical analysis, so as to reveal the spatial differentiation patterns and causes of changes of aquatic biological integrity in semi-arid regions. To this end, we have introduced an innovative approach that combines XGBoost-SHAP and Fuzzy C-means clustering (FCM), we successfully identified and diagnosed the spatial variations of aquatic biological integrity in the Wei River Basin (WRB). The study reveals significant spatial variations in species number, diversity, and aquatic biological integrity of phytoplankton, serving as a testament to the multifaceted responses of biological communities under the intricate tapestry of environmental gradients. Delving into the depths of the XGBoost-SHAP algorithm, we discerned that Annual average Temperature (AT) stands as the pivotal driver steering the spatial divergence of the Phytoplankton Integrity Index (P-IBI), casting a positive influence on P-IBI when AT is below 11.8 °C. The intricate interactions between hydrological variables (VF and RW) and AT, as well as between water quality parameters (WT, NO3–N, TP, COD) and AT, collectively sculpt the spatial distribution of P-IBI. The fusion of XGBoost-SHAP with FCM unveils pronounced north-south gradient disparities in aquatic biological integrity across the watershed, segmenting the region into four distinct zones. This establishes scientific boundary conditions for the conservation strategies and management practices of aquatic ecosystems in the region, and its flexibility is applicable to the analysis of spatial heterogeneity in other complex environmental contexts.

Integrated modeling and simulation of recruitment of myocardial perfusion and oxygen delivery in exercise

While exercise-mediated vasoregulation in the myocardium is understood to be governed by autonomic, myogenic, and metabolic-mediated mechanisms, we do not yet understand the spatial heterogeneity of vasodilation or its effects on microvascular flow patterns and oxygen delivery. This study uses a simulation and modeling approach to explore the mechanisms underlying the recruitment of myocardial perfusion and oxygen delivery in exercise. The simulation approach integrates model components representing: whole-body cardiovascular hemodynamics, cardiac mechanics and myocardial work; myocardial perfusion; and myocardial oxygen transport. Integrating these systems together, model simulations reveal: (1.) To match expected flow and transmural flow ratios at increasing levels of exercise, a greater degree of vasodilation must occur in the subendocardium compared to the subepicardium. (2.) Oxygen extraction and venous oxygenation are predicted to substantially decrease with increasing exercise level preferentially in the subendocardium, suggesting that an oxygen-dependent error signal driving metabolic mediated recruitment of flow would be operative only in the subendocardium. (3.) Under baseline physiological conditions approximately 4% of the oxygen delivered to the subendocardium may be supplied via retrograde flow from coronary veins.

Prediction of soil organic carbon stock combining Sentinel-1 and Sentinel-2 images in the Zoige Plateau, the northeastern Qinghai-Tibet Plateau

BackgroundSoil organic carbon (SOC) is a critical component of the global carbon cycle, and an accurate estimate of regional SOC stock (SOCS) would significantly improve our understanding of SOC sequestration and cycles. Zoige Plateau, locating in the northeastern Qinghai-Tibet Plateau, has the largest alpine marsh wetland worldwide and exhibits a high sensitivity to climate fluctuations. Despite an increasing use of optical remote sensing in predicting regional SOCS, optical remote sensing has obvious limitations in the Zoige Plateau due to highly cloudy weather, and knowledge of on the spatial patterns of SOCS is limited. Therefore, in the current study, the spatial distributions of SOCS within 100 cm were predicted using an XGBoost model—a machine learning approach, by integrating Sentinel-1, Sentinel-2 and field observations in the Zoige Plateau.ResultsThe results showed that SOC content exhibited vertical distribution patterns within 100 cm, with the highest SOC content in topsoil. The tenfold cross-validation approach showed that XGBoost model satisfactorily predicted the spatial patterns of SOCS with a model efficiency of 0.59 and a root mean standard error of 95.2 Mg ha−1. Predicted SOCS showed a distinct spatial heterogeneity in the Zoige Plateau, with an average of 355.7 ± 123.1 Mg ha−1 within 100 cm and totaled 0.27 × 109 Mg carbon.ConclusionsHigh SOC content in topsoil highlights the high risks of significant carbon loss from topsoil due to human activities in the Zoige Plateau. Combining Sentinel-1 and Sentinel-2 satisfactorily predicted SOCS using the XGBoost model, which demonstrates the importance of selecting modeling approaches and satellite images to improve efficiency in predicting SOCS distribution at a fine spatial resolution of 10 m. Furthermore, the study emphasizes the potential of radar (Sentinel-1) in developing SOCS mapping, with the newly developed fine-resolution mapping having important applications in land management, ecological restoration, and protection efforts in the Zoige Plateau.

What are the dominant drivers and optimal thresholds for a healthy ecosystem in the Yellow River Basin, China? from a perspective of nonlinear nexus

Identifying the dominant factors influencing ecosystem health and their optimal thresholds is essential for the management and restoration of ecosystems. Most of the works, however, have less detected the importance level of individual factors, especially their changes over time and scale, and rarely determined their threshold effects on ecosystem health (EH), which seriously hinders the formulation of targeted policies for sustainable ecosystem management. To address these research gaps, we present an innovative visualization approach to reveal importance changes in natural and anthropogenic variables and their nonlinear coupling process with EH in the China’s Yellow River Basin (YRB) in the last two decades. Results show that (i) Although the EH in the YRB has improved slightly in the past 20 years, it is still not optimistic and shows distinct spatial heterogeneity. (ii) The impact of human activities on the ecosystem health of the YRB has increased and increasingly plays a decisive role, while that of the natural aspect has decreased recently. Specifically, environmental factors (slope, NDVI, and annual temperature) are the dominant factors affecting the upstream EH. Midstream EH is significantly affected by the synergistic effect of natural and anthropogenic factors, but the importance of the proportion of built-up land is intensified (ranked from 8th to 4th). Anthropogenic factors exert the greatest influence on downstream EH variation, especially for population density whose importance order rising from 7th to 2nd. (iii) By illustrating the nonlinear response curves of EH at multi-scale, the tipping point caused ecosystem deterioration in the YRB is explored and the optimal thresholds of leading factors oriented towards the most effective ecosystem conservation goal are obtained. These results help precisely identify the current health status of YRB's ecosystem, as well as providing a valuable reference for moderate restoration standards and sustainable ecosystem management.

Supply and demand assessment of physical activity services provided by urban green spaces: A case study of Chongqing, China

Urban green spaces (UGSs) serve as preferred venues for residents' outdoor physical activities (PA). However, limited research explores the supply and demand match of physical activity services. This study aims to introduce a novel multi-dimensional framework to map the supply and demand of PA services in UGSs. This framework involves evaluating UGS supply from capacity, quality, and accessibility dimensions, mapping the demand for PA services from visiting frequency and duration based on massive trajectories, and then assessing supply-demand matches of PA services by spatial clustering analysis. The findings revealed distinct spatial imbalances in the supply and demand of PA services in the case of Chongqing, as indicated by 45.34% of surveyed blocks with mismatches. PA services exhibited spatial heterogeneity, with undersupply in the urban center and oversupply in the suburbs. The demand for jogging and walking peaked highest in certain periods, such as morning and evening peaks, intensifying the undersupply issue. This study provides insights for exploring the PA services of UGSs and offers valuable implications for UGS planning and management.

A novel framework for the spatiotemporal assessment of urban flood vulnerability

Considering the pressing Urban Flood (UF) challenges faced by developing countries, the assessment of Urban Flood Vulnerability (UFV) assumes paramount importance in comprehending the detrimental impacts of UF on urban environments, thereby facilitating effective UF management. Addressing a notable gap in previous UFV research, which predominantly focused on spatial characteristics while overlooking temporal dynamics, we introduce a novel framework for spatiotemporal UFV assessment, by presenting a multiplicative equation to elucidate the interaction between Urban Flood Susceptibility (UFS) and Vulnerable Entities (VE). Employing this framework in the Great Bay Area (GBA), by considering the composition of the UFV database, we leveraged Machine Learnings and the Urban Vitality Index for quantifying the UFS and VE, respectively. SHapley Additive exPlanations was employed to quantify the local contributions of various factors to UFV. We found (1) a discernible upward trajectory in UFV across the GBA from 2012 to 2020, coupled with an expansion in spatial scope, indicative of persistent challenges in the future, and (2) the impervious surface percentage emerges as the primary contributor to UFV, exhibiting spatial heterogeneity reflective of regional environments. We believe this study has the potential to significantly contribute to addressing UF challenges and fostering the development of sustainable cities.

Influence of climate and land use on watershed anthropogenic phosphorus inputs and riverine phosphorus export dynamics: A global analysis

Many studies have found predictive relationships between riverine phosphorus (P) export and net anthropogenic P inputs (NAPI) at the watershed scale, but the global or regional extent of these relationships has not been empirically quantified. Herein, we present a data-driven global assessment of the response of riverine total P (TP) fluxes to NAPI based on 358 watersheds. NAPI exhibited high spatial heterogeneity (2–12,085 kg P km−2 yr−1) and was well correlated with riverine TP fluxes. Riverine TP export fractions of NAPI were primarily regulated by NAPI components, hydroclimate factors, and land-use as determined through a random-forest meta-analysis. In watersheds dominated by disturbed land-use (e.g., agricultural and developed lands), runoff emerged as pivotal climate-related factors influencing riverine export fractions of NAPI. In watersheds dominated by natural land-use, runoff, precipitation and temperature were identified as the most critical factors. We developed a mixed-effects meta-regression model (R2 = 0.63–0.70, RMSE = 19–78 %, n = 87–202) to examine the quantitative relationship between riverine TP fluxes and NAPI, which avoids subjectivity in selecting influencing factors and regression forms. The model estimated that legacy P contributed 14–17 % of annual riverine TP fluxes in Chinese watersheds, 25 % in North American watersheds and 11–27 % in European watersheds. Annual NAPI contributions to annual riverine TP flux were 83–86 % in China, 75 % in North America and 73–89 % in Europe. The model forecasted 52–67 %, 69–71 % and 74–77 % reductions in riverine TP fluxes across Chinese, North American, and European watersheds by 2050 under five shared socio-economic pathway scenarios compared to 2010 baseline conditions, respectively. This study provides a straightforward and reliable method for quantifying anthropogenic P input and riverine P export dynamics within an acceptable error range. It provides guidance for developing phosphorus pollution control strategies to counter potential increases in phosphorus inputs due to expected changes in climate and land use.

Effects of spatial variability in vegetation phenology, climate, landcover, biodiversity, topography, and soil property on soil respiration across a coastal ecosystem

Coastal terrestrial-aquatic interfaces (TAIs) are crucial contributors to global biogeochemical cycles and carbon exchange. The soil carbon dioxide (CO2) efflux in these transition zones is however poorly understood due to the high spatiotemporal dynamics of TAIs, as various sub-ecosystems in this region are compressed and expanded by complex influences of tides, changes in river levels, climate, and land use. We focus on the Chesapeake Bay region to (i) investigate the spatial heterogeneity of the coastal ecosystem and identify spatial zones with similar environmental characteristics based on the spatial data layers, including vegetation index (kNDVI), climate, landcover, diversity, topography, soil property, and relative tidal elevation; (ii) understand the primary driving factors affecting soil respiration within sub-ecosystems of the coastal ecosystem. Specifically, we employed hierarchical clustering analysis to identify spatial regions with distinct environmental characteristics, followed by the determination of main driving factors using Random Forest regression and SHapley Additive exPlanations. Maximum and minimum temperature are the main drivers common to all sub-ecosystems, while each region also has additional unique major drivers that differentiate them from one another. Precipitation exerts an influence on vegetated lands, while soil pH value holds importance specifically in forested lands. In croplands characterized by high clay content and low sand content, the significant role is attributed to bulk density. Wetlands demonstrate the importance of both elevation and sand content, with clay content being more relevant in non-inundated wetlands than in inundated wetlands. The topographic wetness index significantly contributes to the mixed vegetation areas, including shrub, grass, pasture, and forest. Additionally, our research reveals that dense vegetation land covers and urban/developed areas exhibit distinct soil property drivers. Overall, our research demonstrates an efficient method of employing various open-source remote sensing and GIS datasets to comprehend the spatial variability and soil respiration mechanisms in coastal TAI. There is no one-size-fits-all approach to modeling carbon fluxes released by soil respiration in coastal TAIs, and our study highlights the importance of further research and monitoring practices to improve our understanding of carbon dynamics and promote the sustainable management of coastal TAIs.

Evolution of rates, patterns, and driving forces of green eco-spaces in a subtropical hilly region

Monitoring ecological resource change in mountainous and hilly areas (MHAs) is vital for theoretical and practical advancements of ecological resource utilization and management in complex ecosystems. The factors driving structural and functional changes in green eco-spaces (GES) in these areas are complex and uncertain, with notable spatial scale effects. However, analyzing the multi-scale driving mechanisms of ecological and socioeconomic factors at a fine spatiotemporal scale presents significant challenges. To address these challenges, we analyzed dynamic changes in GES and eco-socio-economic development in Shanghang County, a typical mountainous region in southern China. We used multiple linear regression and multi-scale geographically weighted regression model to identify key factors driving GES changes and their multi-scale effects at both global and local levels. Over the past two decades, the GES area in the study area has exhibited a consistent pattern of decline, characterized by phases of gradual decline (2000–2005), sharp decline (2005–2009), slow decline (2009–2019). Key global factors driving GES changes included elevation (ELE), slope (SLOPE), population density (PD), distance to settlements (SETTLE), and distance to administrative centers (ADMIN). These factors exhibited significant spatial heterogeneity and multi-scale effects on GES changes. Specifically, SETTLE, PD, SLOPE, and ELE consistently drove GES changes at the local level, while ADMIN only showed significant localized effects during 2005–2009. The synergy between SETTLE and SLOPE had a considerable impact on GES changes, increasing over time, whereas ELE and PD demonstrated a consistent trade-off effect. These findings provide detailed spatiotemporal insights into the driving mechanisms of natural ecological resources, offering crucial guidance for environmental management, land source management, regional economic development, and biodiversity conservation in Shanghang and analogous subtropical hilly regions worldwide.

ELIMS: Ensemble Learning-Based Spatial Segmentation of Mass Spectrometry Imaging to Explore Metabolic Heterogeneity.

Spatial segmentation is an essential processing method for image analysis aiming to identify the characteristic suborgans or microregions from mass spectrometry imaging (MSI) data, which is critical for understanding the spatial heterogeneity of biological information and function and the underlying molecular signatures. Due to the intrinsic characteristics of MSI data including spectral nonlinearity, high-dimensionality, and large data size, the common segmentation methods lack the capability for capturing the accurate microregions associated with biological functions. Here we proposed an ensemble learning-based spatial segmentation strategy, named eLIMS, that combines a randomized unified manifold approximation and projection (r-UMAP) dimensionality reduction module for extracting significant features and an ensemble pixel clustering module for aggregating the clustering maps from r-UMAP. Three MSI datasets are used to evaluate the performance of eLIMS, including mouse fetus, human adenocarcinoma, and mouse brain. Experimental results demonstrate that the proposed method has potential in partitioning the heterogeneous tissues into several subregions associated with anatomical structure, i.e., the suborgans of the brain region in mouse fetus data are identified as dorsal pallium, midbrain, and brainstem. Furthermore, it effectively discovers critical microregions related to physiological and pathological variations offering new insight into metabolic heterogeneity.

[18F]FMISO-PET imaging reveals the role of hypoxia severity in checkpoint blockade response

ContextHypoxia within the tumor microenvironment is a critical factor influencing the efficacy of immunotherapy, including immune checkpoint inhibition. Insufficient oxygen supply, characteristic of hypoxia, has been recognized as a central determinant in the progression of various cancers. The reemergence of evofosfamide, a hypoxia-activated prodrug, as a potential treatment strategy has sparked interest in addressing the role of hypoxia in immunotherapy response. This investigation sought to understand the kinetics and heterogeneity of tumor hypoxia and their implications in affecting responses to immunotherapeutic interventions with and without evofosfamide. PurposeThis study aimed to investigate the influence of hypoxia on immune checkpoint inhibition, evofosfamide monotherapy, and their combination on colorectal cancer (CRC). Employing positron emission tomography (PET) imaging, we developed novel analytical methods to quantify and characterize tumor hypoxia severity and distribution. ProceduresMurine CRC models were longitudinally imaged with [18F]-fluoromisonidazole (FMISO)-PET to quantify tumor hypoxia during checkpoint blockade (anti-CTLA-4 + and anti-PD1 +/− evofosfamide). Metrics including maximum tumor [18F]FMISO uptake (FMISOmax) and mean tumor [18F]FMISO uptake (FMISOmean) were quantified and compared with normal muscle tissue (average muscle FMISO uptake (mAvg) and muscle standard deviation (mSD)). Histogram distributions were used to evaluate heterogeneity of tumor hypoxia. FindingsSevere hypoxia significantly impeded immunotherapy effectiveness consistent with an immunosuppressive microenvironment. Hypoxia-specific PET imaging revealed a striking degree of spatial heterogeneity in tumor hypoxia, with some regions exhibiting significantly more severe hypoxia than others. The study identified FMISOmax as a robust predictor of immunotherapy response, emphasizing the impact of localized severe hypoxia on tumor volume control during therapy. Interestingly, evofosfamide did not directly reduce hypoxia but markedly improved the response to immunotherapy, uncovering an alternative mechanism for its efficacy. ConclusionsThese results enhance our comprehension of the interplay between hypoxia and immune checkpoint inhibition within the tumor microenvironment, offering crucial insights for the development of personalized cancer treatment strategies. Non-invasive hypoxia quantification through molecular imaging evaluating hypoxia severity may be an effective tool in guiding treatment planning, predicting therapy response, and ultimately improving patient outcomes across diverse cancer types and tumor microenvironments. It sets the stage for the translation of these findings into clinical practice, facilitating the optimization of immunotherapy regimens by addressing tumor hypoxia and thereby enhancing the efficacy of cancer treatments.

Heterogeneous PSMA ligand uptake inside parotid glands

The purpose of this investigation is to quantify the spatial heterogeneity of prostate-specific membrane antigen (PSMA) positron emission tomography (PET) uptake within parotid glands. We aim to quantify patterns in well-defined regions to facilitate further investigations. Furthermore, we investigate whether uptake is correlated with computed tomography (CT) texture features. Methods: Parotid glands from [18F]DCFPyL PSMA PET/CT images of 30 prostate cancer patients were analyzed. Uptake patterns were assessed with various segmentation schemes. Spearman’s rank correlation coefficient was calculated between PSMA PET uptake and feature values of a Grey Level Run Length Matrix using a long and short run length emphasis (GLRLML and GLRLMS) in subregions of the parotid gland. Results: PSMA PET uptake was significantly higher (p < 0.001) in lateral/posterior regions of the glands than anterior/medial regions. Maximum uptake was found in the lateral half of parotid glands in 50 out of 60 glands. The difference in SUVmean between parotid halves is greatest when parotids are divided by a plane separating the anterior/medial and posterior/lateral halves symmetrically (out of 120 bisections tested). PSMA PET uptake was significantly correlated with CT GLRLML (p < 0.001), and anti-correlated with CT GLRLMS (p < 0.001). Conclusion: Uptake of PSMA PET is heterogeneous within parotid glands, with uptake biased towards lateral/posterior regions. Uptake within parotid glands was strongly correlated with CT texture feature maps.

Modifications on the coastal atmospheric sulfur and cloud condensation nuclei along the Eastern China seas by shipping fuel transition

Marine fuel combustion from shipping releases SO2 and forms sulfate particles, which may alter low cloud characteristics. A series of strategies were implemented to control the sulfur content of ship fuel oil from 2018 to 2020, offering insights into the effects of the ship fuel oil transition on sulfur-related pollutants and the consequent cloud condensation nuclei (CCN) in the atmosphere. Compared to 2018 in the southeast China waters, shipping SO2 emission decreased by 78 % in 2020, resulting in a 76 % reduction in ship-related total sulfur concentration, and a decrease of 54 % in CCN number concentration under supersaturation 0.2 % (CCN0.2) contributed by shipping. The response of CCN0.2 to ship-related sulfate modification is more pronounced in relatively clean environments than polluted environments, highlighting the uneven changes in coastal CCN along the Eastern China Sea induced by the ship fuel policies. CCN can trigger the formation of cloud droplets, 2020 fuel regulation may have and will reduce the cooling radiative forcing effect with strong spatial heterogeneity. The study provides insights into the variations in coastal atmospheric sulfur-related pollutants and CCN in uneven response to changes in ship fuel oil, prompting the need for further comprehensive assessments of the climate effects resulting from potential shifts in ship fuel use in the future.

Threshold dynamics of a reaction–advection–diffusion schistosomiasis epidemic model with seasonality and spatial heterogeneity

Most water-borne disease models ignore the advection of water flows in order to simplify the mathematical analysis and numerical computation. However, advection can play an important role in determining the disease transmission dynamics. In this paper, we investigate the long-term dynamics of a periodic reaction–advection–diffusion schistosomiasis model and explore the joint impact of advection, seasonality and spatial heterogeneity on the transmission of the disease. We derive the basic reproduction number R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {R}}_0$$\\end{document} and show that the disease-free periodic solution is globally attractive when R0<1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {R}}_0<1$$\\end{document} whereas there is a positive endemic periodic solution and the system is uniformly persistent in a special case when R0>1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {R}}_0>1$$\\end{document}. Moreover, we find that R0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathcal {R}}_0$$\\end{document} is a decreasing function of the advection coefficients which offers insights into why schistosomiasis is more serious in regions with slow water flows.

Integrating ecological feedbacks across scales and levels of organization

In ecosystems, species interact in various ways with other species, and with their local environment. In addition, ecosystems are coupled in space by diverse types of flows. From these links connecting different ecological entities can emerge circular pathways of indirect effects: feedback loops. This contributes to creating a nested set of ecological feedbacks operating at different organizational levels as well as spatial and temporal scales in ecological systems: organisms modifying and being affected by their local abiotic environment, demographic and behavioral feedbacks within populations and communities, and spatial feedbacks occurring at the landscape scale. Here, we review how ecological feedbacks vary in space and time, and discuss the emergent properties they generate such as species coexistence or the spatial heterogeneity and stability of ecological systems. With the aim of identifying similarities across scales, we identify the abiotic and biotic modulators that can change the sign and strength of feedback loops and show that these feedbacks can interact in space or time. Our review shows that despite acting at different scales and emerging from different processes, feedbacks generate similar macroscopic properties of ecological systems across levels of organization. Ultimately, our contribution emphasizes the need to integrate such feedbacks to improve our understanding of their joint effects on the dynamics, patterns, and stability of ecological systems.

Advancing Green TFP Calculation: A Novel Spatiotemporal Econometric Solow Residual Method and Its Application to China’s Urban Industrial Sectors

The Solow residual method, traditionally pivotal for calculating total factor productivity (TFP), is typically not applied to green TFP calculations due to its exclusion of undesired outputs. Diverging from traditional approaches and other frontier methodologies such as Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), this paper integrates undesired outputs and three types of spatial spillover effects into the conventional Solow framework, thereby creating a new spatiotemporal econometric Solow residual method (STE-SRM). Utilizing this novel method, the study computes the industrial green TFPs for 280 Chinese cities from 2003 to 2019, recalculates these TFPs using DEA-SBM and Bayesian SFA for the same cities and periods, and assesses the accuracy of the STE-SRM-derived TFPs through comparative analysis. Additionally, the paper explores the statistical properties of China’s urban industrial green TFPs as derived from the STE-SRM, employing Dagum’s Gini coefficient and spatial convergence analyses. The findings first indicate that by incorporating undesired outputs and spatial spillover into the Solow residual method, green TFPs are computable in alignment with the traditional Solow logic, although the allocation of per capita inputs and undesired outputs hinges on selecting the optimal empirical production function. Second, China’s urban industrial green TFPs, calculated using the STE-SRM with the spatial Durbin model with mixed effects as the optimal model, show that cities like Huangshan, Fangchenggang, and Sanya have notably higher TFPs, whereas Jincheng, Datong, and Taiyuan display lower TFPs. Third, comparisons of China’s urban industrial green TFP calculations reveal that those derived from the STE-SRM demonstrate broader but more concentrated results, while Bayesian SFA results are narrower and less concentrated, and DEA-SBM findings sit between these extremes. Fourth, the study highlights significant spatial heterogeneity in China’s urban industrial green TFPs across different regions—eastern, central, western, and northeast China—with evident sigma convergence across the urban landscape, though absolute beta convergence is significant only in a limited subset of cities and time periods.

A Complex of Management Tools to Overcome Differentiation of Region’s Spatial Development

A key direction of the state regional policy is sustainable socio-economic development of the regions of the Russian Federation. However, the solution of this issue is significantly complicated by a number of problems associated with the spatial heterogeneity of the country and further inequality in the level of socio-economic development of Russian regions. The relevance of this study is determined by the necessity to solve the problem of systematization of theoretical approaches to the description of mechanisms of socio-economic development of the subjects of the Russian Federation, aimed to overcome the spatial differentiation of territories by basic indicators. The object of the study is the regions of the Far Eastern and Siberian Federal Districts. The aim of the study is to develop the principles of systematization of tools for spatial development of the subjects of the Russian Federation depending on the level of differentiation of regions by basic principles. To achieve the aim, the authors used the following research methods: comparative analysis, empirical analysis, synthesis, statistical method, graphic research methods. Based on the comparative analysis of the existing causes of differentiation of spatial development of the subjects of the Siberian and Far Eastern Federal Districts, the objective and subjective factors of regional inequality were identified. The analysis of existing mechanisms of spatial development of the regions of the Siberian and Far Eastern Federal Districts revealed their insufficient efficiency in solving the problem of smoothing regional socio-economic inequality. The author’s approach to the formation of a set of management tools to overcome the differentiation of spatial development of regions on the example of the Siberian and Far Eastern Federal Districts on the basis of grouping the subjects according to the studied groups of basic indicators is proposed. The paper proposes the definition of individual characteristics of each subject of the Russian Federation as a basis for the formation of tools for spatial development of the region.

Analysis of the Coupling Coordination between the Ecosystem Service Value and Urbanization in the Circum-Bohai-Sea Region and Its Obstacle Factors

In light of the tremendous pressure for improving urbanization levels and expanding construction land on ecosystems, regional sustainable development is premised on the coordinated development of the ecological environment and urbanization. This paper focuses on the Circum-Bohai-Sea Region and assesses the ecosystem service value (ESV) and urbanization level using the equivalence factor and entropy-weighted TOPSIS methods. Based on these assessments, it examines the coupling coordination between the ESV and urbanization as well as the obstacles to this coordination, utilizing the coupling coordination degree model, spatial autocorrelation model, and obstacle model. The results revealed several key findings. First, the Circum-Bohai-Sea Region experienced a continuous improvement in the urbanization level from 2005 to 2020, which presented a “center–periphery” spatial pattern. Overall, the ESV exhibited an N-shaped trend, namely, initially increasing, then decreasing, and then rapidly increasing. The ESV per unit of area exhibited a spatial difference, with the boundary of Baoding and Cangzhou as the dividing line. The ESV per unit of area in the northern parts was higher than that in the southern parts. Second, the degree of the coupling coordination between the ESV and urbanization exhibited an inverted U-shaped trend, initially increasing and then slightly decreasing. It also exhibited significant spatial heterogeneity, with the degree of the coupling coordination in the northern parts being higher than that in the southern parts and that in the central parts being higher than those in the eastern and western parts. Additionally, the ESV showed a significant spatially positive correlation with urbanization. H-H clusters were predominantly found in central and southern Liaoning Province, while L-L clusters were primarily located in southern Hebei Province and western Shandong Province. Third, the obstacle factors remained stable during the study period. The top five obstacles in the ESV system were the water resource supply, nutrient cycling maintenance, raw material production, aesthetic landscape, and food production. Moreover, the top five obstacles in the urbanization system were the number of college students per 10,000 people, population density, number of health technical personnel per 10,000 people, per capita road area, and proportion of secondary and tertiary industry output values.

Analysis of Factors Influencing Housing Prices in Mountain Cities Based on Multiscale Geographically Weighted Regression—Demonstrated in the Central Urban Area of Chongqing

By leveraging a multiscale geographically weighted regression (MGWR) model, this paper delves into the intricate factors that influence housing prices in the prototypical mountainous cityscape of Chongqing’s central urban area. The key findings are as follows: Firstly, the distribution of housing prices in the study region exhibits pronounced spatial heterogeneity, with the core area exhibiting a distinct “high-high” clustering pattern and manifesting characteristics of a multicenter group distribution. Secondly, the MGWR model effectively assigns an individual bandwidth to each feature quantity, allowing for a more nuanced portrayal of the varying influence scales exerted by diverse variables. Lastly, the study reveals that factors such as property cost, greening rate, building age, and proximity to rivers have a notable negative impact on housing prices, whereas, educational facilities exert a marked positive influence. Elevation, floor area ratio, and distance from the Central Business District (CBD) exhibit a more complex influence on housing prices.