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This study aims to explore the spatial heterogeneity of influential factors of integrated use of ride‐hailing service with the metro. Using the operation data of ride‐hailing services in Chengdu, China, first, an identification method of integrated ride‐hailing trips is proposed. Then, the ordinary least squares (OLS) and geographically weighted regression (GWR) models are established to discern the factors that affect access‐integrated ride‐hailing use and egress‐integrated ride‐hailing use on weekdays and weekends. The model results demonstrate that the fitting effect of GWR models is superior to that of OLS models, and the coefficient estimates of each explanatory variable vary across regions. Accommodation facilities promote the access‐integrated trips in the eastern area, and this positive impact for egress‐integrated trips extends to the northeastern area. Tourist attractions have a positive impact on the integrated trips in the central and western regions, while they have a negative impact in the northwest and southeast regions. The research results can provide the theoretical support for the seamless connection and coordinated development of these two services. [ABSTRACT FROM AUTHOR]

Both the amount and spatial arrangement (configurational heterogeneity) of resources can affect population abundance and community diversity via influence on the growth, survival, reproduction, recruitment and movement of species. However, in most cases, it is difficult to separate the effects of resource amount from arrangement because these two attributes are often naturally correlated. In this study, we examined the configurational heterogeneity of resources (oviposition habitat—emergent rocks, ER) within rivers and decoupled the effects of resource amount from those due to the spatial arrangement on oviposition by eight species of aquatic insects (seven caddisflies and one mayfly).To capture the configurational heterogeneity of resources in 28 sites (riffles) across multiple streams in Australia and Scotland, we calculated fractal dimensions (DB) using the box‐counting technique. We then used simulated riffles to explore how numbers of ER, edginess (the proportion of ER along river margins) and patchiness (clustering of emergent rocks in the middle) separately and together affected the values of DB using asymptotic regression models. Finally, we used multiple regression to test whether the numbers of egg masses laid in natural riffles of each of the eight species were explained by the number of ER, fractal dimension or both.The distributions of ER in natural riffles were scale‐independent, self‐repeating patterns (i.e. they were fractal), and values of DB varied significantly among riffles. Variations in fractal dimensions among simulated riffles were significantly related to the number of ER, edginess and patchiness. However, in natural riffles, only the number of ER and patchiness affected DB. Egg mass abundances were related to the fractal dimensions of ER distributions in riffles in three species and to the number of ER in five species.The fractal dimensions of riffles are unlikely to be driven by large‐scale processes but instead may result from within‐riffle variability that influences rock movement, arrangement and emergence. Increased oviposition by aquatic insects in riffles with greater numbers of ER suggests that these species may be limited by the amount of oviposition resources. Of the species responding to fractal dimension, two species favoured tightly clustered ER whereas the third favoured ER in much looser clusters. It is feasible that aquatic insects can detect ER clusters from the air by responding to changes in the reflectivity of water (albedo) caused by turbulence around ER.That fractals can capture configurational heterogeneity of resources in streams suggests that this technique is useful to test for general ecological patterns across diverse stream systems. Whether configurational heterogeneity influences adults directly or indirectly, these results showed that the amount and arrangement of suitable oviposition habitat plays a role in determining egg mass densities, with potential consequences for larval densities. These patterns may have important community‐level and functional consequences and hence spatial arrangements should be considered when humans manipulate these resources in rivers. [ABSTRACT FROM AUTHOR]

LiDAR data acquired from airplanes and helicopters – known as airborne laser scanning (ALS) – are widely regarded as the gold standard for characterizing the 3D structure of forests at scale. But in the last decade, advances in unoccupied aerial vehicle (UAV) technologies have led to a rapid rise in the use of UAV laser scanning (ULS) for mapping forest structure. As both ALS and ULS data become increasingly available, they are being used to derive an ever‐growing number of metrics designed to measure different facets of canopy structure. However, which metrics can be robustly retrieved from both ALS and ULS platforms remains unclear. To address this question, we acquired coincident, high‐density ALS and ULS scans covering 115 plots (4‐ha in size) in an open‐canopy temperate ecosystem in Western Australia. Using this unique dataset, we quantified 32 canopy structural metrics related to canopy height, openness and heterogeneity, including metrics calculated directly from the point clouds and ones measured from derived canopy height models (CHM). Overall, we found that ALS and ULS‐derived metrics were strongly correlated (r2 = 0.90). However, this high degree of correlation masked considerable systematic differences between platforms. Specifically, point cloud metrics were less strongly (r2 = 0.87) correlated and had higher bias (10.7%) compared to CHM‐derived ones (r2 = 0.98; bias = 2.5%). Similarly, metrics of canopy openness and heterogeneity were less strongly correlated (r2 = 0.84 and 0.87) and exhibited greater bias (14.4 and 7.9%) than ones relating to canopy height (r2 = 0.96; bias = 3.8%). Our results indicate that only a small subset of the 32 metrics we tested were directly comparable between ALS and ULS platforms. Consequently, future efforts to combine laser scanning data across platforms and instruments should think carefully about which metrics are most appropriate, especially when working with point cloud data. [ABSTRACT FROM AUTHOR]

The oceans play a pivotal role in mitigating climate change by sequestering approximately 25% of annually emitted carbon dioxide (CO2). High‐latitude oceans, especially the Arctic continental shelves, emerge as crucial CO2 sinks due to their cold, low saline, and highly productive ecosystems. However, these heterogeneous regions remain inadequately understood, hindering accurate assessments of their carbon dynamics. This study investigates variation in pCO2 levels during peak ice sheet melt, in the Greenland coastal ocean and estimates rates of air‐sea exchange across 6° of latitude. The East and West coast of Greenland displayed distinct regions with unique controlling factors. Though, both coasts represent CO2 sinks in summer. Geographical variation in pCO2 and air‐sea exchange was linked intricately to freshwater export from the Greenland ice sheet and levels of primary production in these ecosystems. Air‐sea exchange of CO2 ranged from 0.23 to −64 mmol m−2 day−1. However, we found that flux estimation faces substantial uncertainties (up to 672%) due to wind product averaging and gas exchange formula selection. Upscaling only heightens this uncertainty leading to wide ranging estimates of Greenland coastal CO2 uptake between −16 and −26 Tg C year−1 (This study, Dai et al., 2022, https://doi.org/10.1146/annurev‐earth‐032320‐090746; Laruelle et al., 2014, https://doi.org/10.1002/2014gb004832). Obtaining a reliable assessment of air‐sea CO2 exchange necessitates data collection across seasons, and, even more so, refinement of the gas transfer velocity estimations in the Arctic coastal zone. Plain Language Summary: The oceans help to limit climate change by absorbing large amounts (1/4) of carbon dioxide (CO2) that is annually emitted to the atmosphere. The majority of oceanic CO2 uptake occurs near the poles due to the special conditions that occur in these regions: namely that there is cold, less salty water, with high quantities of photosynthetic algae in the surface ocean. However, scientists do not completely understand the regional variability of oceanic CO2 uptake, which limits our ability to accurately predict future climate scenarios. This study measured the partial pressure of CO2 in the ocean along East and West Greenland to calculate rates of air‐sea transfer. Air‐sea exchange of CO2 ranged from 0.23 to −64 mmol m−2 day−1 and the controlling factors behind this flux of carbon were wind speed, amount of glacial runoff, and the balance between biologic producers/consumers. However, several wide‐ranging estimates of uptake in the Greenland coastal ocean have been proposed, indicating the need to better understand carbon sequestration variability in this region. Key Points: The Greenland coastal ocean takes up large quantities of carbon dioxide yet displays considerable spatial heterogeneityBiology and freshwater runoff control pCO2 levels to varying degrees in different regionsEstimation of the true flux magnitude is beset by large uncertainties, particularly in polar coastal regions [ABSTRACT FROM AUTHOR]

The spatial heterogeneity of ecological vulnerability (EV) is a key indicator of regional ecosystem differentiation. However, identifying the factors that contribute to this heterogeneity remains a challenge in current research. This study proposed the "Ecological vulnerability‐spatial heterogeneity‐zone" (ESHZ) framework as a solution to overcome the difficulties of monitoring spatial heterogeneity. Then, based on the typical river valley city Baoji in Northwest China as the research object, this framework was used to analyze and predict the evolution law of heterogeneity of EV from 2000 to 2030. The findings indicate the following: (1) Synthesized ecological vulnerability index (SEVI) in 2000 and 2020 was 2.47 and 2.49, respectively, with less than 5% of the total area transitioning to areas of higher EV, indicating a relatively stable ecological environment. (2) The clustering characteristics of EV remained stable, primarily showing non‐significant, high–high, and low–low clustering, with varying degrees of heterogeneity across different regions. (3) Factors analysis revealed that DEM and LUCC had been the dominant factors of EV, and that interactions between factors were stronger than interactions within them, suggesting that its spatial heterogeneity was the result of a combination of factors. (4) The CA‐Markov model predicted a gradual improvement in the ecological environment by 2030, with a Kappa coefficient test value of 0.7733. The framework constructed in this study proposes a perspective for improved analyses of spatial heterogeneity of ecosystems, providing a viable approach to the management of regional ecological vulnerability. [ABSTRACT FROM AUTHOR]

One strand of modern coexistence theory (MCT) partitions invader growth rates (IGR) to quantify how different mechanisms contribute to species coexistence, highlighting fluctuation‐dependent mechanisms. A general conclusion from the classical analytic MCT theory is that coexistence mechanisms relying on temporal variation (such as the temporal storage effect) are generally less effective at promoting coexistence than mechanisms relying on spatial or spatiotemporal variation (primarily growth‐density covariance). However, the analytic theory assumes continuous population density, and IGRs are calculated for infinitesimally rare invaders that have infinite time to find their preferred habitat and regrow, without ever experiencing intraspecific competition. Here we ask if the disparity between spatial and temporal mechanisms persists when individuals are, instead, discrete and occupy finite amounts of space. We present a simulation‐based approach to quantifying IGRs in this situation, building on our previous approach for spatially non‐varying habitats. As expected, we found that spatial mechanisms are weakened; unexpectedly, the contribution to IGR from growth‐density covariance could even become negative, opposing coexistence. We also found shifts in which demographic parameters had the largest effect on the strength of spatial coexistence mechanisms. Our substantive conclusions are statements about one model, across parameter ranges that we subjectively considered realistic. Using the methods developed here, effects of individual discreteness should be explored theoretically across a broader range of conditions, and in models parameterized from empirical data on real communities. [ABSTRACT FROM AUTHOR]

With the rapid urbanization in China, urban land resources gradually become the core of urban development. This study spatially evaluated the urban land resource carrying capacity (LRCC) with a case study of the built‐up area in Wuhan from 2015 to 2020. Following an evaluation index system, five critical LRCC indicators, including population density, GDP per land area, plot ratio, building density, and road network density, were selected by an analytical hierarchical process. The synthesis of indicators, however, is usually challengeable due to homogeneous assumptions of traditional techniques. In this study, we adopted a local technique, geographically weighted principal component analysis, to calculate a comprehensive carrying pressure (CCP) concerning spatially varying contributions of each indicator on their synthesis across different geographic locations. On mapping these spatial outputs of the built‐up area in Wuhan, the highest CCP was found in the central areas, where population size tends to be influential and the dominant variable in 62.69% of subdistricts. Furthermore, increased construction over the 5 years has led to an increased CCP in some of the peripheries of the built‐up area, and 55.22% of subdistricts show rising changes. With the GWPCA technique, this framework works well in evaluating and analyzing urban LRCC from a new local perspective. [ABSTRACT FROM AUTHOR]

Direct comparison with in situ measurements serves as the primary method for validating TROPOMI SIF products. However, due to geometric errors in satellite data, the exact spatial extent of the nominal validation pixel may not align with the in situ site perfectly. In addressing this challenge, this study proposed, for the first time, a method to precisely identify the validation pixels matching with in situ sites. Moreover, the accuracy of the TROPOMI SIF product was reevaluated with the improved geolocation match method between the satellite pixel and the corresponding in situ site. The results indicate that ignoring the geometric errors of TROPOMI pixels can result in a 49% probability of mismatch between the validation pixel and the in situ site. The errors caused by geolocation mismatch mainly come from two aspects. One is the incorrect extraction of the validation pixel, with a maximum error of 1.385 mWm−2 sr−1 nm−1. The other is the pixel‐scale reference "truth," which resulted from the improper upscaling function of in situ measurements, and the maximum of this kind of error was 0.445 mWm−2 sr−1 nm−1. With this improved geolocation match method, the TROPOMI SIF product showed a RMSE of 0.58 mWm−2 sr−1 nm−1, a bias of 0.19 mWm−2 sr−1 nm−1, and a R2 of 0.51, which indicate a better performance than without considering geometric location matching errors. Plain Language Summary: To rigorously validate TROPOMI SIF products, it is crucial to identify the validation pixel that aligns perfectly with in situ site measurements. However, the geolocation shifts of TROPOMI pixels widely exist, and the magnitude and direction of shifts are random. Consequently, there is a possibility that the nominal validation pixel is mismatched with the in situ site. Currently, there is no effective method to accurately identify the exact validation pixels corresponding to in situ sites. In response to this challenge, this study proposed a novel method to precisely identify the exact validation pixel matched with in situ sites. With this method, the accuracy of the TROPOMI SIF product was reevaluated. The results indicate that the uncertainty of TROPOMI SIF products is overestimated when neglecting the geometric errors of TROPOMI. Key Points: An improved geolocation match method between TROPOMI pixels and in situ sites was proposedThere is a 49% probability that the TROPOMI pixel is mismatched with the in situ sites when its geometric errors are ignoredThe geolocation mismatch between TROPOMI pixels and in situ sites leads to the overestimation of the uncertainty of TROPOMI SIF products [ABSTRACT FROM AUTHOR]

Cross‐border e‐commerce (CBEC) significantly stimulates foreign trade development and the flow of goods and services. This study, using Chinese city‐level data and a multiperiod difference‐in‐differences model based on CBEC comprehensive pilot zones, explores the impact of CBEC reform on urban service industry entrepreneurship. The results show that CBEC reform increased entrepreneurial activity, and this is confirmed by robustness tests. The mechanisms driving this effect include the attraction of foreign direct investment, improved information infrastructure, and talent development. Regional analysis reveals stronger effects in central, western, and noncoastal cities, and industry analysis highlights public and consumer services. Spatial heterogeneity shows that the reform's impact diminished with distance from pilot zones, with potential inhibitory effects in remote areas. This study demonstrates how CBEC reform can stimulate entrepreneurship and foster economic growth. [ABSTRACT FROM AUTHOR]

Summary: Understanding the drivers of assemblages of arbuscular mycorrhizal fungi (AMF) is essential to leverage the benefits of AMF for plant growth and health. Arbuscular mycorrhizal fungi are heterogeneously distributed in space even at small scale. We review the role of plant distribution in driving AMF assemblages (the passenger hypothesis), using a transposition of the conceptual framework of landscape ecology. Because rooting systems correspond to habitat patches with limited carrying capacity that differ in quality due to host‐preference effects, we suggest considering plant communities as mosaics of AMF microhabitats. We review how predictions from landscape ecology apply to plant community effects on AMF, and the existing evidence that tests these predictions. Although many studies have been conducted on the effect of plant compositional heterogeneity on AMF assemblages, they mostly focused on the effect of plant richness, while only a few investigated the effect of configurational heterogeneity, plant connectivity or plant community temporal dynamics. We propose key predictions and future prospects to fill these gaps. Considering plant communities as landscapes extends the passenger hypothesis by including a spatially explicit dimension and its associated ecological processes and may help understand and manipulate AMF assemblages at small spatial scales. [ABSTRACT FROM AUTHOR]

Accurately understanding the travel demand of urban rail transit (URT) systems is crucial for effective operational management. Despite the recognition that the diversity in human activity patterns results in different travel demands, few studies have thoroughly investigated the heterogeneity among passengers and its impact on URT ridership. This study utilizes smart card data collected from the Beijing Subway to categorize passengers into four groups: tourist passengers, flexible commuters, regular commuters, and life‐oriented passengers, based on their spatiotemporal travel patterns. Furthermore, a Multiscale Geographically Weighted Regression (MGWR) model is employed to examine the relationship between station‐level ridership of URT and its determinants, including the built environment and station properties, for each passenger group. The results indicate that the influence of these determinants on station‐level ridership varies across passenger groups and spatial scales. For instance, regular commuters exhibit lower sensitivity to accessibility on workdays, whereas those unfamiliar with the URT network are more concerned about the bus accessibility in pedestrian‐ or bicycle‐unfriendly areas. Notably, for tourist and life‐oriented passengers, the stations significantly affected by population density are concentrated in areas with a higher proportion of elderly individuals. Conversely, for flexible and regular commuters, these stations are predominantly situated in areas associated with internet technology and scientific research. These findings are valuable for policymakers in designing strategies tailored to different passenger groups to balance trip demand and capacity, thereby improving URT services and promoting a sustainable urban environment. [ABSTRACT FROM AUTHOR]

Background: The purpose of this study is to construct models for predicting platinum resistance in high‐grade serous ovarian cancer (HGSOC) derived from quantitative spatial heterogeneity indicators obtained from 18F‐FDG PET/CT images. Methods: A retrospective study was conducted on patients diagnosed with HGSOC. Quantitative indicators of spatial heterogeneity were generated using conventional features and Haralick texture features from both CT and PET images. Three groups of predictive models (conventional, heterogeneity, and integrated) were built. Each group's optimal model was the one with the highest area under curve (AUC). Postoperative immunohistochemical staining for Ki‐67 and p53 was conducted. The correlation between the heterogeneity indicators and scores for Ki‐67 and p53 was assessed by Spearman's correlation coefficient (ρ). Results: A total of 286 patients (54.6 ± 9.3 years) were enrolled. And 107 spatial heterogeneity indicators were extracted. The optimal models for each group were obtained using the Gradient Boosting Machine (GBM) algorithm. There was an AUC of 0.790 (95% CI: 0.696, 0.885) in the conventional model for the validation set, and an AUC of 0.904 (95% CI: 0.842, 0.966) in the heterogeneity model for the validation set. The integrated model achieved the highest predictive performance, with an AUC value of 0.928 (95% CI: 0.872, 0.984) for the validation set. Spearman's correlation showed that HU_Kurtosis had the strongest correlation with p53 scores with ρ = 0.718, while cluster site entropy had the strongest correlation with Ki‐67 scores with ρ = 0.753. Conclusions: Adding quantitative spatial heterogeneity indicators derived from PET/CT images can improve the prediction of platinum resistance in patients with HGSOC. Spatial heterogeneity indicators were related to Ki‐67 and p53 scores. [ABSTRACT FROM AUTHOR]

The fluorescence intensities of the cellular respiratory cofactors NADH (reduced nicotinamide adenine dinucleotide) and FAD++ (oxidized flavin adenine dinucleotide) reflect energy metabolism in skin and other tissues and can be quantified in vivo by fluorescence spectroscopy (FS). However, the variability of physiological parameters largely determines the reproducibility of measurement results and the reliability of the diagnostic test. In this prospective study, we evaluated the interday reproducibility of NADH and FAD++ fluorescence intensity measurements in the skin of 51 healthy volunteers assessed by the FS at baseline, after local cooling (10°C) and heating of the skin (35°C). Results showed that the fluorescence amplitude of NADH (AFNADH) in forearm skin was the most reproducible of the FS parameters studied. Assessment of AFNADH in the dorsal forearm in combination with a thermal functional test is the most promising method for clinical use for assessing energy metabolism in the skin. [ABSTRACT FROM AUTHOR]

Spatio‐temporal data are often analysed by means of spatial dynamic panel data (SDPD) models. In the last decade, several versions of these models have been proposed, generally based on specific assumptions and estimator properties. We focus on an SDPD model with heterogeneous coefficients both in the spatial and exogeneous regression components. We propose a strategy to identify the specific structure of the SDPD model through a multiple testing procedure that allows to choose between a general version of the model and a nested version derived from the general one by imposing restrictions on the parameters. Our proposal can be used to test the homogeneity of the model parameters as well as the absence of specific components, such as spatial effects, dynamic effects or exogenous regressors. It is also possible to use the proposed testing procedure for the identification of relevant locations. The theoretical results highlight the consistency of the testing procedure in the high‐dimensional setup, when the number of spatial units goes to infinity and exceeds the number of time‐observations per spatial unit. Further, we also conduct a Monte Carlo simulation study, which gives empirical evidence of the good performance of the testing procedure in finite samples. [ABSTRACT FROM AUTHOR]

2D Ruddlesden‐Popper perovskites (RPP) with formamidinium‐cesium (FAC) alloyed cations possess powerful thermal‐moisture stability. However, their photovoltaic performance is hindered by the elusive spatial heterogeneity at multiscale lengths, highly associated with their coupled charge selective contacts. Herein, it reports on how the rational formation of self‐assembly molecules (SAMs) govern the orderliness of the spatial heterogeneity and crystal growth model in FACs‐RPP films, which is proposed to dictate the charge‐carrier dynamics. Unlike the disordered RPP film driven by the amorphous polymeric contacts, the laterally ordered crystallized SAM contacts facilitate a spatially ordered 2D/3D heterogeneity of FACs‐RPP film in long range, where distinct minor 2D phases are tightly coupled in short range. Such ordered heterogeneity can improve the energy transfer efficiency, facilitating the charge‐carrier dynamics in a well‐aligned 2D/3D landscape is discovered. Finally, the champion solar cells using selective SAM‐based FACs‐RPP films (n = 5) yield an efficiency of 18.85% (certified 18.19%) and excellent damp‐heat/operational stability, ranking in the top league of reported 2D RPP solar cells. More importantly, this SAM‐based principle can be adapted to diverse 2D structures, active areas, and substrate types. This work provides design directions for leveraging bottom‐selective contacts to tune spatial heterogeneity in state‐of‐the‐art 2D RPP optoelectronics. [ABSTRACT FROM AUTHOR]

The impacts of large‐scale disturbance events on the species diversity of rocky intertidal sessile assemblages across multiple spatial scales are not well understood. To evaluate the influence of the 2011 Tohoku Earthquake on alpha and beta diversities of rocky intertidal sessile assemblages, we surveyed sessile assemblages in the mid‐shore zone from 2011 to 2019 and compared the data with those collected from 2003 to 2010 before the earthquake at the same region. The census was conducted across 22 study plots on five rocky shores along 30 km of the Sanriku Coast of Japan, which is located 150–160 km north–northwest of the earthquake epicenter. Alpha diversity was measured with three Hill numbers (H0, H1, and H2), which represent the number of equally common species that would exist in a community with the same diversity as the sampled community, with higher values of the subscript indicating more weight placed on abundant species. Beta diversity was measured with two metrics (BDtotal at two spatial scales). Values were compared between the post‐earthquake period (2011–2019) and the pre‐earthquake period (2003–2010). The results show that the Tohoku Earthquake significantly altered the species diversity of intertidal sessile assemblages across multiple spatial scales. All diversity metrics obtained at multiple spatial scales (i.e., alpha diversities: H0, H1, and H2; beta diversities: BDtotal at the shore and regional scales) decreased immediately after the earthquake and then increased in subsequent years. At 2 years after the earthquake, H0 recovered to within the range of pre‐earthquake values and H1 and H2 became significantly higher than pre‐earthquake values. Most metrics of alpha and beta diversities recovered to pre‐earthquake levels after several years, but regional BDtotal remained low for a longer period. [ABSTRACT FROM AUTHOR]

Objective: To investigate the role of protein kinase C (PKC) in action potential duration (APD) restitution and ventricular tachyarrhythmias (VAs). Methods and results: Rabbits hearts were isolated and prepared for Langendorff perfusion technique. The stimuli–extra‐stimulus (S1–S2) method and dynamic S1 pacing protocol were performed to construct APD restitution and to induce APD alternans or VA, respectively, at 10 sites throughout the ventricular chamber. Administration of phorbol‐12‐myristate‐13‐acetate (PMA) (100 nM) (n = 15) greatly steepened the restitution curves (Smax > 1) (p <.01) at each site compared to the control group (n = 15). Furthermore, treatment with PMA also induced larger spatial dispersions of Smax (p <.05) and decreased the thresholds of the VA and APD alternans (p <.01). However, perfused with the PKC inhibitor, bisindolylmaleimide (BIM) (500 nM) (n = 10), reversibly flattened the APD restitution curves at each site (Smax < 1), decreased the spatial dispersions of Smax, and increased the thresholds of APD alternans and VA. According to the results of patch‐clamp, peak amplitude of L‐type Ca2+ current was significantly increased by addition of PMA compared with control (CTL) group (p <.05). Antagonize this current with verapamil (n = 10) can fully inhibited the PMA induced increasing of Smax and inducibility of VA and alternans. Conclusion: PKC activation increased the dispersion of APD restitution and thus led to occurrence of VA, which possibly related to the increased Ca2+ influx. [ABSTRACT FROM AUTHOR]

The suddenness and destructive power of earthquake disasters pose significant threats to ecosystems. Understanding the changes in post‐earthquake ecosystem services and the complex relationships of their trade‐offs and synergistic effects is crucial for post‐disaster recovery and achieving a holistic and sustainable balance of human well‐being. However, quantifying post‐disaster ecosystem services and these intricate relationships, as well as probing the mechanisms behind their formation from a spatial perspective, remain challenging. This study presents a comprehensive framework that integrates spatial statistical methods, the InVEST model, geodetectors, and the Multiscale Geographically Weighted Regression model (MGWR) to quantify trade‐offs and synergistic effects among ecosystem services before and after earthquakes, and to investigate the spatial heterogeneity of these relationship effects with various driving factors. Results indicate (1) Typical ecosystem services, that is, water yield, soil conservation, carbon storage, and habitat quality, have more weak trade‐off effects and fewer synergistic effects. These relationship intensities vary considerably spatially, demonstrating some agglomerative distribution, suggesting that the ecosystem has not yet recovered to a good state after the quake; (2) The impact of different factors on the trade‐offs and synergies between ecosystem services varies. Interaction effects of driving factors are stronger than singular effects. Relationships between soil conservation and other ecosystem services are primarily influenced by topography and climate. Relationships involving carbon storage are mainly affected by vegetation and climate, while those concerning habitat quality are attributed mostly to human and climatic factors; (3) In the modeling process, considering the different spatial scales of driving factors, MGWR outperforms the Geographically Weighted Regression in explaining the relationship between ecosystem services and driving factors. Moreover, several spatially feasible and targeted ecological measures are proposed to enhance the synergy among ecosystem services. By focusing on the changes in ecosystem services, this study reveals the intrinsic mechanisms of trade‐offs and synergies between ecosystem services, offering insights for related studies in other regions. [ABSTRACT FROM AUTHOR]

This paper concerns with the global threshold dynamics of a Tuberculosis (TB) model incorporating age‐space structure, treatment, and relapse. The original model is converted into a hybrid system comprising two Volterra integral equations and two partial differential equations by integrating along the characteristic line. The well‐posedness for the model is demonstrated by using the fixed point theory in conjunction with the induction method. In order to discuss whether a disease is persistent or extinct, we provide the explicit formulation of the basic reproduction number. By analyzing the distribution of the characteristic roots of the characteristic equations and constructing the proper Lyapunov functionals, the local and global stability for the steady states are addressed. Numerical simulations are conducted to confirm the conclusions of our analytical results and reveal that reduction of the TB transmission coefficient, reduction of infectiousness of treated individuals infected with TB, and increasing the treatment rate of infectious class are three feasible measures to control the transmission of TB. [ABSTRACT FROM AUTHOR]

Aims: Understanding the spatio‐temporal patterns of restoration outcomes is crucial to improve predictability of restoration. High beta diversity of species‐rich communities is sought because it increases overall biodiversity and improves ecosystem stability and multifunctionality. For predictive restoration, it is important to identify the significance of drivers like site characteristics but also uncontrolled factors such as spatial effects, historical factors, and year effects. Location: Dikes at river Danube, SE Germany. Methods: We studied dike grasslands 4–19 years after restoration over five years (2017–2021, 41 plots in 12 sites). We calculated beta diversity indices to describe spatial variation and temporal turnover, including their additive components 'replacement' and 'nestedness', or 'gains' and 'losses'. We analysed the main drivers of beta diversity like local site characteristics, landscape, and historical factors. Results: Spatial variation of the restored dike grasslands was dominated by the replacement component and showed no homogenisation despite a significant temporal turnover. The replacement drivers changed over time, although replacement was mainly affected by slope aspect and landscape factors. Historical factors were inconsistent over time, and no statistically clear drivers of nestedness were found. The dike grasslands exhibited a year‐to‐year turnover in species composition of 37 ± 11%. Gains and losses were balanced over time, although the ratio changed and was most pronounced on south‐facing slopes. Conclusions: The restored grasslands exhibited spatial variation by site characteristics but also by spatial factors which were not controlled by restorations. Moreover, high non‐directional temporal turnover occurred, caused most likely by weather fluctuations, slightly varying management, and stochastic biotic dynamics. Thus, flexible targets are recommended for restoration monitoring, by defining a set of desired states within a certain range. Furthermore, the dominance of the replacement component of spatial variation should move the focus from defining one precise restoration approach to defining a set of possible methods which together would foster beta diversity. [ABSTRACT FROM AUTHOR]

Geological formations with fractures are frequently encountered in various research fields. Accurately characterizing these fractured media is of paramount importance when it comes to tasks that demand precise predictions of liquid flow and solute transport within them. Since directly measuring fractured media poses inherent challenges, data assimilation (DA) techniques are typically employed to derive inverse estimates of media properties using observable state variables. Nonetheless, the considerable difficulties arising from the strong heterogeneity and non‐Gaussian nature of fractured media have diminished the effectiveness of existing DA methods. In this study, we formulate a novel DA approach known as parameter estimator with deep learning (PEDL) that harnesses the capabilities of DL to capture nonlinear relationships and extract non‐Gaussian features. To evaluate PEDL's performance, we conduct three case studies, comprising two numerical cases and one real‐world case. In these cases, we systematically compare PEDL with three widely used DA methods: ensemble smoother with multiple DA (ESMDA), iterative local updating ES (ILUES), and ES with DL‐based update (ESDL). Notably, in the problems characterized by highly non‐Gaussian features, ESMDA and ILUES produce significantly divergent results. Conversely, employing the DL‐based update, ESDL demonstrates improved performance. However, its estimation uncertainty remains high, potentially attributable to ESDL's updating mechanism. Comprehensive analyses confirm PEDL's validity and adaptability across various ensemble sizes and DL model architectures. Moreover, even in scenarios where structural difference exists between the accurate reference model and the simplified forecast model, PEDL adeptly identifies the primary characteristics of fracture networks. Key Points: A deep learning‐based data assimilation method, named parameter estimator with deep learning (PEDL), is proposed to characterize fractured media with highly non‐Gaussian featuresPEDL outperforms three popular data assimilation methods based on deep learning or the Kalman formulaComprehensive analyses confirm PEDL's validity and adaptability across various ensemble sizes and DL model architectures [ABSTRACT FROM AUTHOR]

Natural rivers are inherently dynamic. Spatial and temporal variations in water, sediment, and wood fluxes both cause and respond to an increase in geomorphic heterogeneity within the river corridor. We analyze 16 two‐km river corridor segments of the Swan River in Montana, USA to examine relationships between logjams (distribution density, count, and persistence), channel dynamism (total sinuosity and average channel migration), and geomorphic heterogeneity (patch density) in the river corridor. We hypothesize that (a) more dynamic river segments correlate with a greater presence, persistence, and distribution of logjams; (b) higher annual peak discharges correspond with greater channel dynamism and logjam presence and distribution; and (c) greater logjam distribution densities and channel dynamism are predictive of more spatially heterogeneous sections of the river corridor. Our results suggest that, first, decadal‐scale channel dynamism, as reflected in total sinuosity, corresponds to greater numbers of logjams and greater persistence of logjams through time. Second, higher peak discharges correspond to greater presence and distribution of logjams, but not to greater channel dynamism. Third, greater geomorphic heterogeneity in the river corridor, as reflected in the spatial distribution of landscape patch density, is explained by greater logjam distribution density, total sinuosity, and proportions of beaver meadows. Our results reflect the complex interactions of water, sediment, and wood in river corridors; the difficulties of interpreting causal relationships among these variables through time; and the importance of spatial and temporal analyses of past and present river processes to understand future river conditions. Plain Language Summary: Natural rivers inherently experience changes in their shape, channel locations, and floodplain compositions across space and time due to variations in water, sediment, and wood fluxes. We analyze 16 two‐km segments of the Swan River in Montana, USA to examine relationships between logjams (the total count, persistence over a decade, and distribution density), how much the main and secondary channels move with time, and how patchy the river corridor is. We hypothesize that (a) where there is more channel movement, there will be more logjams; (b) years with higher flow correspond with more logjams and channel movement; and (c) logjams and channel movement play a role in explaining the patchiness of the river corridor. Our results suggest that there is a positive relationship between channel movement and logjams. Second, higher flows correspond to greater values of logjams, but not to channel movement. Third, persistent values of river corridor patchiness are explained logjam density, beaver meadows, and channel movement. Our results reflect the complex interactions of water, sediment, and wood in rivers and the importance of spatial and temporal analyses of past and present river processes to understand future river conditions. Key Points: Decadal‐scale channel dynamism, as reflected in total sinuosity, corresponds to greater logjam presence and persistence through timeHigher peak discharges correspond with a greater number and distribution of logjams, but not with greater channel dynamismLogjams, channel dynamism, and beaver meadows increase spatial heterogeneity, as reflected in landscape patch density [ABSTRACT FROM AUTHOR]

Dissolved organic matter (DOM) is one of the active soil carbon fractions. The characteristics of DOM in the riparian zone are closely related to the occurrence of eutrophication. However, there is no scientific consensus on how flooding intensity on DOM distribution in the riparian zone of the Three Gorges Reservoir (TGR). In this study, 99 soil riparian samples were collected from three water level elevations (145–155 m asl, 155–165 m asl and 165–175 m asl) in both mainstream and tributaries of the Yangtze River within the TGR. DOM characteristics were investigated using ultraviolet–visible absorption spectra and three‐dimensional fluorescence excitation–emission matrix‐parallel factor analysis as well as principal component analysis (PCA). The result showed that dissolved organic carbon (DOC), absorption coefficient at 254 nm (a254), absorption coefficient at 350 nm, specific ultraviolet absorbance at 254 nm (SUVA254), and humification index (HIX) decreased while freshness index (β:α) and biological index (BIX) increased with the decline of the water level elevations. The trend in the DOM characteristics along the riparian zone was mainly attributed to the variation in the bioavailability of DOM and fluorescence components (C1, C3 and C4). Overall, our study highlights the newly formed DOM during the dry season, indicated by β:α and BIX, might be more accumulated at the lower elevation. Furthermore, because of the greater lability during the reservoir impoundment, the newly formed DOM can be easily utilized by algae and may raise water eutrophication under re‐flooding and longer flooding, thus enforced strategic mitigation measures could help to achieve the goal of riparian restoration in TGR. [ABSTRACT FROM AUTHOR]

Understanding heat‐related impacts is crucial to improving public awareness of addressing urban heat challenges and enabling decision makers to mainstream heat action plans. This study explores heat‐related physiological and psychological impacts, symptoms, and demographic determinants based on 4210 questionnaires across nine large Chinese cities, including Chengdu, Chongqing, Guangzhou, Hangzhou, Nanchang, Nanjing, Shanghai, Shenyang, and Xi'an in August 2020. The results indicate that heat‐related psychological impacts are equally severe as physiological impacts. Chongqing is under the most severe heat‐related health impact on both the physiological and psychological aspects. Skin heat damage (SKI), digestive system illnesses (DIG), respiratory illnesses (RES), and cardiovascular illnesses (CAR) are prominent physiological illnesses in all cities, while emotional irritability (EMO), easy to lose control temper (EAS), low mood (LOW), and insomnia (INS) are prominent psychological symptoms. Chongqing and Guangzhou are the most vulnerable cities in terms of physiological and psychological symptoms. Chengdu, Hangzhou, Nanjing, and Shanghai are more resilient in terms of physiological symptoms, whereas Chengdu, Hangzhou, and Shanghai are more resistant to psychological symptoms. Heat‐related health impacts, symptoms, and demographic determinants are spatially heterogeneous. The elderly and patients are more vulnerable, while such a conclusion is not always true. The spatial heterogeneity of heat‐related physiological and psychological impacts, symptoms, and drivers highlights the significance of developing city‐specific heat health action plans. [ABSTRACT FROM AUTHOR]

Wildfire smoke fine particles (PM2.5) are a growing public health threat as wildfire events become more common and intense under climate change, especially in the Western United States. Studies assessing the association between wildfire PM2.5 exposure and health typically summarize the effects over the study area. However, health responses to wildfire PM2.5 may vary spatially. We evaluated spatially‐varying respiratory acute care utilization risks associated with short‐term exposure to wildfire PM2.5 and explored community characteristics possibly driving spatial heterogeneity. Using ensemble‐modeled daily wildfire PM2.5, we defined a wildfire smoke day to have wildfire‐specific PM2.5 concentration ≥15 μg/m3. We included daily respiratory emergency department visits and unplanned hospitalizations in 1,396 California ZIP Code Tabulation Areas (ZCTAs) and 15 census‐derived community characteristics. Employing a case‐crossover design and conditional logistic regression, we observed increased odds of respiratory acute care utilization on wildfire smoke days at the state level (odds ratio [OR] = 1.06, 95% confidence interval [CI]: 1.05, 1.07). Across air basins, ORs ranged from 0.88 to 1.57, with the highest effect estimate in San Diego. A within‐community matching design and spatial Bayesian hierarchical model also revealed spatial heterogeneity in ZCTA‐level rate differences. For example, communities with a higher percentage of Black or Pacific Islander residents had stronger wildfire PM2.5‐outcome relationships, while more air conditioning and tree canopy attenuated associations. We found an important heterogeneity in wildfire smoke‐related health impacts across air basins, counties, and ZCTAs, and we identified characteristics of vulnerable communities, providing evidence to guide policy development and resource allocation. Plain Language Summary: Wildfire smoke is a growing public health threat, one becoming more pressing as climate change progresses. People are exposed to different levels of wildfire smoke. People also have different abilities to protect themselves from smoke exposure based on their job, housing quality, or other factors. In addition, people have different physiological responses to smoke. Therefore, the relationship between wildfire smoke and health could vary across the state of California. We conducted a study using modeled daily wildfire smoke fine particle concentrations and daily respiratory acute care utilizations 2006–2019 in California. We estimated area‐specific wildfire smoke and acute care utilization associations at state, air basin, county, and ZIP Code Tabulation Areas levels. We found different associations across the state, with the strongest association in San Diego air basin. San Francisco Bay air basin had the highest number of acute care utilizations attributable to wildfire smoke due to their large population. We identified several community characteristics that may have explained the observed spatial differences, including higher proportions of Black and Pacific Islander populations and less community affluence. Our findings support the allocation of scarce resources to areas and communities more vulnerable to wildfire smoke to improve population health in a changing climate. Key Points: Statewide, exposure to wildfire PM2.5 is associated with increased odds of respiratory acute care utilization in CaliforniaThe wildfire PM2.5‐health association varies spatially across air basins, counties, and ZIP Code Tabulation AreasAreas with higher proportions of Black and Pacific Islander populations and less affluence had worse wildfire PM2.5‐related outcomes [ABSTRACT FROM AUTHOR]

This study explores the impact of sea surface temperature (SST) spatial heterogeneity on tropical cyclone (TC) intensity through a combination of observations and simulations, aiming to provide a reference for further improving TC intensity forecasting skills. Two distinct patterns of SST spatial heterogeneity are identified based on a statistical analysis of observational data, when the SST at the TC center is above and below 29.3°C, respectively. One is a warm‐core pattern (WCP) with a warm peak SST at the TC center decreasing centrifugally which favors TC development, and the other one is a poleward‐decreasing pattern (PDP) with a warm SST at the south decreasing poleward which suppresses TC development. The numerical simulations confirm the opposite influence of the WCP and the PDP on TC intensity. The WCP intensifies TC intensity by strengthening TC secondary circulation, increasing the conversion from ocean heat energy to TC kinetic energy, and compacting TC structure. In contrast, the PDP weakens TC intensity by inducing opposing responses of these processes. The magnitude of TC intensity change caused by SST spatial heterogeneity is comparable to those caused by a 1°C change in SST at the TC center. These findings offer valuable insights into the role of SST spatial heterogeneity in TC development and provide a new perspective to improve TC intensity forecasting by incorporating SST spatial heterogeneity into statistical‐dynamic models. Plain Language Summary: Tropical cyclones pose a significant threat to human society, and improving their intensity forecasting is crucial to mitigate their potential damage. However, current forecasting models fail to accurately account for the influence of sea‐surface temperature spatial variations on cyclone intensity change. This study identified two types of sea‐surface temperature spatial heterogeneity that play opposing roles in tropical cyclone (TC) development: a warm‐core pattern positively influences cyclone intensity, while a polar‐decreasing pattern negatively affects it. These findings emphasize the critical role of sea‐surface temperature spatial heterogeneity in TC intensity and suggest a new perspective for improving intensity forecasting models, which could ultimately help reduce the damage caused by these destructive storms. Key Points: Two spatial patterns of sea surface temperature (SST) beneath tropical cyclones (TCs) are identified based on SST value at the TC centerSST exhibiting a warm‐core and poleward‐decreasing spatial pattern facilitate and inhibit the development of TC, respectivelySpatial heterogeneity in SST modulates the energy conversion from ocean to TC via secondary circulation, leading to changes in TC intensity [ABSTRACT FROM AUTHOR]

Aphids exploit different patches that offer a favourable habitat during their life cycle. Continuous colonization of new patches is critical for the survival of these insect pests. We developed a mesocosm experimental system to study the effects of stress regimes on the colonization of plants by aphids. In the first experiment, we treated Eruca sativa Mill (arugula) plants with either low ozone (O3) (ambient air with no O3 added), elevated O3 (ambient air with O3 addition) or low O3 + Myzus persicae aphid feeding, with subsequent observation of natural aphid infestation over time. The second experiment consisted of aphid sources and differently treated aphid sinks. The aphid sources were also exposed to either low or high O3 levels. The colonization of the sinks was then monitored over time and distance from source. The infestation rate was increased if the patches had been recently exposed to aphid herbivory. On the contrary, aphids did not colonize patches that experienced a recent O3 episode. The patches of E. sativa plants without prior biotic or abiotic stress were colonized by aphids at the beginning of the experiment, but infestation was poor and most of the plants remained aphid‐free throughout the experiment. Aphids from control patches had a better chance of successfully disseminate on hosts with previous herbivory, and aphid spread from O3‐stressed plants was generally poor. Hence, the colonization risk for a generalist aphid plague will depend on the space–time arrangement between crop patches with different stress levels. Ozone contamination could have a negative effect on the spread infestation of aphids in annual crops, because of habitat fragmentation. Here, we present important information to understand how physiological traits of individuals can be scaled up to predict changes within populations in a scenario of climate change. [ABSTRACT FROM AUTHOR]

To increase the effectiveness of conservation programs focused on reducing agricultural nutrient runoff and targeting management interventions, some have called for greater attention to the role of diversity in both management and physical context. To examine the independent and interactive effects of behavioral and physical heterogeneity on phosphorus loads, a sensitivity analysis was conducted using six different assumptions about distributions of phosphorus fertilizer application rates and soil test phosphorus (STP) levels for hydrologic response units in a SWAT model for the Maumee River Watershed. Results indicated that changing assumptions about behavior and STP levels can significantly affect estimated dissolved reactive phosphorus (DRP) loads and the level of disproportionality, which is a measure of the unequal distribution of pollutant loading. Placing the highest fertilizer application rates on fields with the most excessive STP produced 14% greater estimated DRP load and higher levels of disproportionality compared to a baseline model, where homogeneity in farmer fertilizer behavior and STP were assumed. In contrast, placing the lowest fertilizer application rates on the fields with the most excessive STP led to estimated DRP loads and level of disproportionality that were similar to the baseline model. Results from this analysis suggest that simplistic or uniform assumptions about behavior or STP levels may mask serious environmental risks in agricultural watershed models. [ABSTRACT FROM AUTHOR]

Cancer immunotherapy using anti‐programmed death‐ligand 1 (PD‐L1) antibodies has been used in various clinical applications and achieved certain results. However, such limitations as autoimmunity, tumor hyperprogression, and overall low patient response rate impede its further clinical application. Mounting evidence has revealed that PD‐L1 is not only present in tumor cell membrane but also in cytoplasm, exosome, or even nucleus. Among these, the dynamic and spatial heterogeneous expression of PD‐L1 in tumors is mainly responsible for the unsatisfactory efficacy of PD‐L1 antibodies. Hence, numerous studies focus on inhibiting or degrading PD‐L1 to improve immune response, while a comprehensive understanding of the molecular mechanisms underlying spatial heterogeneity of PD‐L1 can fundamentally transform the current status of PD‐L1 antibodies in clinical development. Herein, the concept of spatial heterogeneous expression of PD‐L1 is creatively introduced, encompassing the structure and biological functions of various kinds of PD‐L1 (including mPD‐L1, cPD‐L1, nPD‐L1, and exoPD‐L1). Then an in‐depth analysis of the regulatory mechanisms and potential therapeutic targets of PD‐L1 is provided, seeking to offer a solid basis for future investigation. Moreover, the current status of agents is summarized, especially small molecular modulators development directed at these new targets, offering a novel perspective on potential PD‐L1 therapeutics strategies. [ABSTRACT FROM AUTHOR]

This paper is concerned with a degenerate and time delayed virus infection model with spatial heterogeneity and general incidence. The well‐posedness of the system, including global existence, uniqueness, and ultimately boundedness of the solutions, as well as the existence of a global attractor, is discussed. The basic reproduction number R0$\mathcal {R}_0$ is defined and a characterization of R0$\mathcal {R}_0$ is presented. Without the compactness of the solution semiflow, we establish the global dynamics of the system based on R0$\mathcal {R}_0$. In addition, when the system is spatially homogeneous, the unique infection steady state is globally asymptotically stable. Simulations are presented to illustrate our theoretical results. [ABSTRACT FROM AUTHOR]

In savannas, the coexistence between trees and grasses is determined by complex mechanisms based on water partitioning and disturbances. But little is known about the contribution of other resources, such as soil nitrogen (N). In West African savannas, nitrification inhibition by grasses and nitrification stimulation by trees create spatial heterogeneity in nitrification fluxes and N stocks. Savanna trees can also extend part of their roots in the surrounding open area to absorb N.To investigate the role of the spatial heterogeneity of nitrification in tree–grass coexistence, we used a two‐patch model that simulates N dynamics between an open patch (without trees) and a tree clump patch (trees with grasses under their canopy). The open patch was characterized by a low nitrification rate, while the tree clump patch was characterized by a high nitrification rate. Both patches were connected through horizontal fluxes due to soil horizontal exploration by tree roots. We tested coexistence for different spatial tree distributions, as they are known to strongly influence savanna dynamics.Our results show that the spatial heterogeneity of nitrification induces spatial partitioning between ammonium (NH4+) and nitrate (NO3−) promoting tree–grass coexistence. As nitrification inhibition by grasses leads to high NH4+ availability in the open, the possibilities of coexistence are optimized when trees have different preferences in the open versus under their canopy. Thus, tree–grass coexistence is observed when grasses prefer NH4+, while trees prefer NH4+ in the open and NO3− under their canopy.Contrary to random tree distribution, tree clumping enhances tree–grass coexistence. Intraspecific aggregation strengthens the effect of spatial heterogeneity, which decreases interspecific competition and favours tree–grass coexistence. On the contrary, increasing the surface explored by tree roots in the open tends to increase tree–grass competition. This enhances the competitive ability of trees for N acquisition and consequently favours tree invasion.Synthesis. This study shows that this new coexistence mechanism based on mineral N partitioning into NH4+ and NO3− can be determinant in the functioning of West African humid savannas. This mechanism likely interacts with mechanisms based on disturbances, but such interactions should be studied using new models. [ABSTRACT FROM AUTHOR]

Understanding the spatial distribution of soil organic carbon (SOC) and soil nutrients is crucial for optimizing land management in watersheds. This study explored the spatial distribution of SOC, total nitrogen (TN) and total phosphorus (TP) in topsoil (0-20 cm) at a hilly small watershed site in Northeast China, and identified the controlling factors. The Gaussian mixture model optimally described SOC and TN with the ratio of nugget to sill variance (C0/[C + C0]) indicating moderate spatial dependence for SOC (63.7%) and TN (59.3%). TP was best modelled by the spherical model and demonstrated weak spatial autocorrelation and strong human influence, with a nugget to sill variance of 0.841. Land use significantly affected SOC, TN and TP contents, with the highest values recorded in woodland, followed by farmland and shrubland. Soil properties including bulk density (BD), gravel content (GC), moisture content (MC), soil porosity (SP) and the mean weight diameter (MWD) of water-stable aggregates significantly correlated with SOC and TN content, but not with TP. Redundancy analysis revealed that soil properties (BD, GC and SP), topography (slope and aspect) and gully erosion contributed 55.7%, 24.7% and 4.3% of the spatial variation in SOC, TN and TP, respectively. Hence, these are the dominant factors shaping the spatial variation of SOC and soil nutrients at the site. These results can aid the further development of optimized land management strategies. [ABSTRACT FROM AUTHOR]

Total housing real returns in large agglomerations in Kazakhstan are approximately 1% per year lower than in the rest of the country. Our results are also supportive of emerging literature, that housing real rent returns, adjusted for population, are generally lower in large agglomerations compared to the rest of the country. We indicate that within‐country heterogeneity of housing returns mainly result from the spatial concentration of economic activity in urban centers. Our findings also suggest that residential real estate in a developing economy produces higher returns compared to financial capital over the long‐run. At the same time, the volatility of holding housing investment is substantially higher than its "safe" alternative (i.e. deposit rate paid for savings deposits). As such, "Yield Curves for Main Street" concept refers to the general positive spread between total housing returns and financial capital returns in a developing economy. [ABSTRACT FROM AUTHOR]

Poyang Lake is an essential natural wetland in the Yangtze River basin and plays a vital role in maintaining the ecosystem function and ecological security in the middle and lower reaches of the Yangtze River. However, the relative importance and spatial heterogeneity of the impacts of human activities and land use changes on ecological security needs to be further explored. Here, we analyzed the habitat quality level around Poyang Lake in 2022 and explored the factors of habitat quality change from a geographical perspective. The land use structure changes around the Poyang Lake basin from 2000 to 2022 were quantitatively analyzed, and then the relative importance and spatial heterogeneity of each factor on ecological security changes were investigated using geographic probes. The results show that (1) The worst quality habitat (0–0.1) consists mainly of construction land (1624.9 km2) with an area of 1634.64 km2; (2) Construction land continues to increase with the most significant change, and the dynamic land use attitude is 0.47. Grassland and mudflats have the greatest decrease. The increase in cultivated land in different periods is mainly due to the shift of water surface and forest land; (3) The drivers of habitat quality in Poyang Lake were significantly influenced by the interaction of socioeconomic factors. The explanatory power of population density interacting with the total year‐end population and population density interacting with administrative area exceeded 0.84. These values were higher than the explanatory power of each individual factor, indicating that habitat quality was primarily associated with population density, total year‐end population, and administrative area. These results suggest that human activities contribute to the degradation of wetlands around Poyang Lake. This study has significant reference value for coordinating human–land relationships in Poyang Lake, optimizing land management policy, and improving the sustainable development of cities. [ABSTRACT FROM AUTHOR]

While it is well‐accepted that predation plays a key role in shaping both size structure and species composition in natural ecosystems, fine‐scale variation in predation risk within ecosystems has been largely neglected. The objective of this study was to use normalised size spectra (NSS) to investigate whether small‐scale spatial variation in predation risk (by fish) can modulate zooplankton community structure at the within‐lake level in a small temperate lake.We tested the difference in the NSS between two hydrologically well‐connected basins of a lake exhibiting contrasting thermal regimes. The first is a well‐mixed shallow basin (max. depth 2.5 m) that is inaccessible to brook charr (Salvelinus fontinalis) during summer because the temperature is too warm. The second is a deeper basin (max. depth 18 m) offering a thermal refuge during summer because of lake stratification. As a consequence of these differences in fish accessibility, the fishless basin should contain larger and more abundant zooplankters, inducing a change in NSS slope and height.Our results showed that both the zooplankton size structure and taxonomic composition were distinct in the two basins, with a higher proportion of small individuals and a higher abundance of zooplankton in the shallow basin compared to the deep one. Furthermore, following cooling of the epilimnion in late summer, the zooplankton communities of both basins exhibited a clear shift in size structure and species composition: in autumn, there were smaller individuals and larger proportions of Bosminidae and Daphniidae compared to colonial rotifers than in summer. These results suggest a potential effect of the change in the spatial distribution of brook charr which feed preferentially on large individuals, reducing the average body size of the zooplankton community.Our results show that predation risk by fish can induce a spatial and temporal variation in zooplankton community structure within small temperate lake ecosystems exhibiting contrasted thermal regimes. Other potential predators also influenced the zooplankton community structure, creating a complex interplay that has to be taken into account to disentangle ecological processes and predict their future changes. [ABSTRACT FROM AUTHOR]

Autumn phenology plays a critical role in regulating growing season length and matter and energy exchanges in terrestrial ecosystems. The climate‐driven mechanism of the grassland autumn phenology process and its spatial distribution patterns at the hemispheric scale are, however, still poorly understood. In this study, we employed 17 existing and modified models to simulate the satellite‐derived end‐of‐season (EOS) over northern mid‐latitude grasslands (25° N–55° N), aiming to determine how grassland autumn phenology responds to individual and combined changes in low temperature, photoperiod, and water supply. The results showed that the prediction accuracy of EOS based on models driven by daily minimum temperature is slightly superior to that of the counterparts forced by daily average temperature. The model modified with water content as a regulatory factor of leaf senescence rate determined by low temperature and photoperiod performed best (root‐mean‐square error, 10.4 days; correlation coefficient, 0.38) on average. The autumn phenology process in 68.4% of grassland areas was better simulated by the models that incorporated water conditions. Spatially, optimal models jointly forced by low temperature, photoperiod, and water supply occupied 61.4% of the North American grasslands, 58.5% of the Central‐West Asian grasslands, and 42.1% of the Mongolian grasslands. Grasslands on the Tibetan Plateau were primarily forced by the associated effects of low temperature and photoperiod. Further analysis revealed that low temperature‐photoperiod driven models and low temperature‐photoperiod‐moisture driven models were mostly distributed in relatively hotter and wetter grasslands, while optimal models forced solely by low temperature were largely concentrated in those grassland areas with cooler and drier autumn. Overall, this study highlights the complex patterns and spatial heterogeneity of environmental control on autumn phenology in the Northern Hemisphere grasslands, which could shed light on global grassland development investigation. [ABSTRACT FROM AUTHOR]

"Eastern Economic Corridor" is a development plan under the Thailand 4.0 scheme to revitalize the economy. However, it has influenced the environment through numerous infrastructural and industrial projects. The ecological environment is the most fragile component since it is susceptible to land use, land cover changes (LULCC) and rapid development. Meanwhile, there is currently a lack of comprehensive studies on spatial heterogeneity and future simulation of ecological environment quality (EEQ). Therefore, this study aims to analyze changes in EEQ and controlling factors and to predict near‐future EEQ by adopting an integrated approach of remote sensing and spatial analyses. It revealed that the regional EEQ has considerably fluctuated in the period 2013–2021. The dynamic areas, accounting for nearly half of the area, include ecologically degraded areas in coastal urban and surrounding areas and ecologically improved areas in the eastern forest and perennial plantations. Chonburi is the most vulnerable province due to its long coast with extensive urban infrastructures and industrial estates. These changes are an integrated consequence of natural and socioeconomic impacts. Particularly, LULCC, infrastructural projects, and climate are the most crucial contributors since they directly regulate the functions of the ecological environment, which will amplify the future changes of EEQ reflected via the near‐future prediction. This research identified key controlling factors and spatial heterogeneities that are useful for policymakers and local authorities to have appropriate local‐oriented plans for each area relevant to their uniqueness. Besides, regional plans should be also considered along with regional concerns such as LULCC, crop structures, water management, and green infrastructure, as well as legal interventions to promote sustainable development in this special economic zone. [ABSTRACT FROM AUTHOR]

Advances in the digital pathology field have facilitated the characterization of histology samples for both clinical and preclinical research. However, uncovering subtle correlations between bioimaging, clinical and molecular parameters requires extensive statistical analysis. As a user‐friendly software, Hourglass, simplifies multiparametric dataset analysis through intuitive data visualization and statistical tools. Systemic analysis of interleukin‐6 (IL‐6)/pStat3 signaling pathway through Hourglass revealed differences in regional immune cell composition within tumors. Moreover, these regional disparities were partially mediated by sex. Overall, Hourglass simplifies information extraction from complex datasets, resolving overlooked regional and global spatial tumor differences. © 2024 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

Urban blight is a complex problem that has been challenging for cities in the United States "Rust Belt" region for many decades. However, in the wake of the real estate and financial crisis, it is also a growing challenge for urban communities in many states such as California, Arizona, Nevada, and Texas. Detroit was particularly hit hard, where more than 40,000 blighted structures were identified in 2014. To curb blight, the city mobilized the most extensive demolition program in the country. Funded through the Federal Hardest Hit Fund (HHF), the Detroit Demolition Program began in 2014 and has demolished more than 20,800 properties at the cost of over $250 million. Furthermore, during 2009–2015, the city demolished 11,400 structures with hazardous materials such as asbestos. In this article, we assess the impacts of demolitions on the value of neighboring properties using a Repeat Sales (RS) regression approach. Specifically, we use housing sales price information from 2009 through 2019 to construct real estate price indices. We also rely on information from the 2009 Detroit Residential Survey to differentiate by dilapidation and blight levels before the start of the program. On average, blight removal through the demolition program does not appear to have been capitalized into the residential property prices. However, when considering the effect of ex‐ante program property characteristics, we find a modest positive effect of demolitions on property prices in areas with a low level of blight before the demolitions. Given the magnitude of the observed effect and the high costs associated with the demolition program, our results highlight the need for further research on alternative blight removal strategies that might provide a more cost‐effective solution to this urban challenge. [ABSTRACT FROM AUTHOR]

Plant species richness is an important property of ecosystems that is altered by grazing. In a semiarid environment, we tested the hypotheses that (1) small‐scale herbaceous plant species richness declines linearly with increasing grazing intensity by large ungulates, (2) precipitation and percent sand interact with grazing intensity, and (3) response of herbaceous plant species richness to increasing intensity of ungulate grazing varies with patch productivity. During January–March 2012, we randomly allocated 50, 1.5‐m × 1.5‐m grazing exclosures within each of six 2500 ha study sites across South Texas, USA. We counted the number of herbaceous plant species and harvested vegetation in 0.25‐m2 plots within exclosures (ungrazed control plots) and in the grazed area outside the exclosures (grazed treatment plots) during October–November 2012–2019. We estimated percent use (grazing intensity) based on the difference in herbaceous plant standing crop between control plots and treatment plots. We selected the negative binomial regression model that best explained the relationship between grazing intensity and herbaceous plant species richness using the Schwarz‐Bayesian information criterion. After accounting for the positive effect of precipitation and percent sand on herbaceous plant species richness, species richness/0.25 m2 increased slightly from 0% to 30% grazing intensity and then declined with increasing grazing intensity. Linear and quadratic responses of herbaceous plant species richness to increasing grazing intensity were greater for the least productive patches (<15.7 g/0.25 m2) than for productive patches (≥15.7 g/0.25 m2). Our results followed the pattern predicted by the intermediate disturbance hypothesis model for the effect of grazing intensity on small‐scale herbaceous plant species richness. [ABSTRACT FROM AUTHOR]

Reproductive strategies are defined by a combination of behavioural, morphological, and life‐history traits. Reproductive investment and offspring propagule size are two key traits defining reproductive strategies. While a substantial amount of work has been devoted to understanding the independent fitness effects of each of these traits, it remains unclear how coevolution between them ultimately affects the evolution of reproductive strategies, and how this might influence the relationship between dispersal and environmental factors. In this study we explore how the evolution of reproductive strategies defined by these two coevolving traits is influenced by resource availability and spatial structuring of the environment using a simulation model. We find three possible equilibrium strategies across all scenarios: a competitor strategy with high reproductive investment (producing large propagules which disperse short distances), and two coloniser strategies differing in reproductive investment (both producing small propagules which disperse long distances). The possible equilibrium strategies for each scenario depended on starting conditions, spatial structure and resource availability. Evolutionary transitions between these equilibrium strategies were more likely in heterogeneous than homogeneous landscapes and at higher resource levels. Transition from coloniser strategy to competitor strategy was usually a two‐step process, with changes in propagule size following initial evolution in investment. This highlights how the interaction between the two trait axes affects the evolution of reproductive strategies, particularly where fitness valleys preclude the simultaneous evolution of traits. Our results highlight the need to incorporate trait coevolution into evolutionary models to help develop a more integrative understanding of the structure of natural populations and how the interaction between traits constrains or hinders evolutionary processes. [ABSTRACT FROM AUTHOR]

An improved understanding of the potential controls on vegetation carbon sequestration (VCS) is essential for the prediction of VCS in response to global change. Ecosystem restoration can provide remarkable contributions to VCS. However, attention to the impact of soil conservation (SC) on VCS is lacking. Therefore, the Yellow River Basin, a typical area of soil erosion in the world, was chosen as the study area. The VCS and SC trends from 2000 to 2020 were analyzed and the potential response of VCS to SC was explored by adopting correlation analysis, elastic coefficient method, geographically weighted regression model, and geographical detector model. The influence of SC drivers on the spatial heterogeneity of VCS was also revealed. Results showed the following: (1) VCS and SC had a significant upward trend, especially in the eastern monsoonal ecoregion. (2) The area with a significant positive correlation between VCS and SC accounted for 31.74% of the total, mainly concentrated in the key areas of SC, wherein a 100% increase in SC would lead to a 25%–100% increase in VCS. (3) Among the SC drivers, vegetation cover and management (C) factor and rainfall erosion force (R) factor mainly influenced spatial heterogeneity in VCS, and their interaction considerably enhances this effect. Therefore, the interaction between precipitation and vegetation should be considered when evaluating the impact of water erosion management on VCS. The results of the study have important implications for the enhancement of VCS capacity of degraded land in the ecological restoration process. [ABSTRACT FROM AUTHOR]

Modern histologic imaging platforms coupled with machine learning methods have provided new opportunities to map the spatial distribution of immune cells in the tumor microenvironment. However, there exists no standardized method for describing or analyzing spatial immune cell data, and most reported spatial analyses are rudimentary. In this review, we provide an overview of two approaches for reporting and analyzing spatial data (raster versus vector‐based). We then provide a compendium of spatial immune cell metrics that have been reported in the literature, summarizing prognostic associations in the context of a variety of cancers. We conclude by discussing two well‐described clinical biomarkers, the breast cancer stromal tumor infiltrating lymphocytes score and the colon cancer Immunoscore, and describe investigative opportunities to improve clinical utility of these spatial biomarkers. © 2023 The Pathological Society of Great Britain and Ireland. [ABSTRACT FROM AUTHOR]

We use the five landscape ecology metrics of aggregation index, percentage of like adjacencies, interspersion and juxtaposition index, patch density, and Shannon's evenness index to assess spatial heterogeneity at 15 floodplains in the continental United States. Assessments are based on floodplain classes and patches delineated remotely using topography and vegetation. Floodplain reaches examined here represent diverse drainage areas, flow regimes, valley geometries, channel planforms, and biomes. We selected sites with minimal direct human alteration. Our objectives are to quantify floodplain spatial heterogeneity; evaluate whether statistically significant patterns are present; and interpret the statistical analyses with respect to the influence of lateral channel mobility and valley‐floor space available. We develop a conceptual model of the influences on lateral mobility and space available, and then test specific hypotheses derived from this conceptual model. These natural floodplains have a median aggregation index of 58.8%, median percentage of like adjacencies of 58.5%, median interspersion and juxtaposition index of 74.9%, median density of 1,241 patches/ha, and median Shannon's evenness index of 0.934 (n = 15). In other words, natural floodplains have moderate aggregation of classes, high evenness and intermixing of classes, and a wide range of patch densities. Drainage area, the ratio of floodplain/channel width, elevation, precipitation, total sinuosity, large wood volume, planform, and flow regime emerge as important variables to floodplain heterogeneity. These results highlight the influence of biotic‐abiotic interactions in shaping floodplain heterogeneity across diverse river corridors. Plain Language Summary: As river channels move across their valley floors, they create diverse topography including natural levees, cutoff meanders, secondary channels, and floodplain wetlands. These features increase the spatial variability of the floodplain. Many studies indicate that this variability strongly influences habitat abundance and diversity, storage of flood waters and excess nitrate or phosphate, and other floodplain functions. However, no one has systematically measured floodplain variability across multiple rivers and different geographic regions. We measured the variability of natural floodplains at 15 sites in the continental United States using metrics developed by landscape ecologists. We find that natural floodplains have distinctive variability signatures that relate to drainage area, the size of the floodplain, and other characteristics. Key Points: Natural floodplains have moderate aggregation of classes, high evenness and intermixing of classes, and a wide range of patch densitiesFloodplain heterogeneity reflects space available on the valley floor and lateral channel mobility [ABSTRACT FROM AUTHOR]

This paper investigates the impact of climate change on the spatial distribution of agricultural production in Turkey between 2004 and 2019. Our findings show that climate change has a pervasive impact on agricultural development. The empirical results show that the average temperature is negatively associated with agricultural land use, grain, and legume production. Moreover, regional variability analyses reveal the nonmonotonic relationships between climatic factors and agricultural output. Climate change hurts agricultural production in northern and central regions the most. This finding points out the ineffectiveness of one‐size‐fits‐all‐type policies for mitigating the adverse effects of climate change in topography with sizeable spatial dissimilarities. Overall, our results suggest that climate change will significantly threaten the evolution of agricultural activities that are critical for regional development. In addition, findings show that spatial spillovers and heterogeneity will be crucial for designing climate change policies for rural and agricultural development. [ABSTRACT FROM AUTHOR]

Natural disturbances change forest habitat quality for many species. As the extent and intensity of natural disturbances may increase under climate change, it is unclear how this increase can affect habitat quality on different spatial scales. To support management tools and policies aiming to prevent habitat loss, we studied how habitat quality develops in the long run depending on the disturbance severity using a space‐for‐time substitution approach.We explored the effects of time since disturbance (0–250 years) and disturbance severity (20%–100% canopy removal) on structure‐based habitat quality indicators in European primary Norway spruce Picea abies forests using 1000 m2 circular plots in hierarchical design (a total of 407 plots in 35 stands). Disturbance history was reconstructed from tree cores. Habitat quality indicators were modelled as a function of the severity of the most severe disturbance and the time since this disturbance. We hypothesised that high within‐stand habitat heterogeneity is formed by different successional stages after disturbances of various intensities.The results showed a U‐shaped response of habitat quality to post‐disturbance habitat succession on the plot scale. The decline deepened with disturbance severity. The U‐shape response occurred in: large tree occurrence, amount of standing and lying deadwood, diversity of understory and understory openness. The spatial diversity in disturbance parameters increased spatial diversity of habitat quality on a stand level as expected. This high within‐stand habitat heterogeneity also decreased with increasing age of the most recent disturbance. This suggests that the absence of young successional stages results in the absence of some important elements for biodiversity, for example sun‐exposed snags.Synthesis and applications. Our results demonstrate that currently intensifying natural disturbance regime can consequently result in a lower habitat heterogeneity. In managed spruce forests after natural disturbances, we recommend at least the partial retention of biological legacies to preserve habitat heterogeneity and to avoid uniform and dense plantations resulting in a greater homogenisation. To emulate the natural disturbances pattern, spruce forests should be managed with a wide range of harvested patches of the size limited by a local natural disturbance regime creating spatial heterogeneity. [ABSTRACT FROM AUTHOR]

Forest carbon is a large and uncertain component of the global carbon cycle. An important source of complexity is the spatial heterogeneity of vegetation vertical structure and extent, which results from variations in climate, soils, and disturbances and influences both contemporary carbon stocks and fluxes. Recent advances in remote sensing and ecosystem modeling have the potential to significantly improve the characterization of vegetation structure and its resulting influence on carbon. Here, we used novel remote sensing observations of tree canopy height collected by two NASA spaceborne lidar missions, Global Ecosystem Dynamics Investigation and ICE, Cloud, and Land Elevation Satellite 2, together with a newly developed global Ecosystem Demography model (v3.0) to characterize the spatial heterogeneity of global forest structure and quantify the corresponding implications for forest carbon stocks and fluxes. Multiple‐scale evaluations suggested favorable results relative to other estimates including field inventory, remote sensing‐based products, and national statistics. However, this approach utilized several orders of magnitude more data (3.77 billion lidar samples) on vegetation structure than used previously and enabled a qualitative increase in the spatial resolution of model estimates achievable (0.25° to 0.01°). At this resolution, process‐based models are now able to capture detailed spatial patterns of forest structure previously unattainable, including patterns of natural and anthropogenic disturbance and recovery. Through the novel integration of new remote sensing data and ecosystem modeling, this study bridges the gap between existing empirically based remote sensing approaches and process‐based modeling approaches. This study more generally demonstrates the promising value of spaceborne lidar observations for advancing carbon modeling at a global scale. [ABSTRACT FROM AUTHOR]

Spatiotemporal measurements observed through river networks have two distinct characteristics: a spatiotemporal correlation under the flow‐connected structure and the existence of heterogeneous covariances, which require a careful approach to implement principal component analysis (PCA). This paper focuses on developing a PCA method to reflect the unique characteristics of river networks. We propose a novel method combining flow‐directed PCA and geographically weighted PCA for the domain of river networks. The strengths of our approach are that it can (i) reduce dimensionality for streamflow data while effectively removing correlation among them and (ii) identify the group structure of data. It is possible to find essential patterns and sources of variation that may not be disclosed due to the attributes of flow‐connected networks. We apply the proposed method to the daily monitoring records of total organic carbon in the Geum River catchment area in South Korea. The results show that the proposed method successfully adjusts for the topological structure of the network and temporal correlation among observations while considering the spatial heterogeneity, enabling a more concrete understanding of monitoring networks. [ABSTRACT FROM AUTHOR]

Floodplains provide numerous ecosystem services that depend on the spatial heterogeneity, or patchiness, of the floodplain. Direct and indirect human alterations of rivers can reduce floodplain heterogeneity and function, but relatively little is known of patterns of floodplain heterogeneity in natural, fully functional floodplains. We quantify floodplain heterogeneity at four sites in the United States with the objectives of (i) developing a method of combining field measurements and remote sensing data products to calculate integrative landscape‐scale metrics of floodplain spatial heterogeneity and (ii) demonstrating which metrics from landscape ecology are likely to be useful for identifying qualities of natural floodplains, differentiating floodplains, and inferring processes, based on a case study of three prairie floodplains and one beaver‐modified floodplain in the continental United States. We developed a new unsupervised classification workflow that combines field data, topography, and Sentinel‐2A imagery to create classified floodplains for all four field sites that could be used to calculate heterogeneity metrics. We identified six heterogeneity metrics for characterizing natural floodplain heterogeneity: aggregation index, interspersion and juxtaposition index, largest patch index, patch density, percentage of like adjacencies, and Shannon's evenness index, and these metrics capture both intermetric (variation in spatial heterogeneity between the floodplains) and intrametric variation (variation in the patterns of the metrics). Results show that natural floodplains have high evenness and interspersion and juxtaposition of classes, and we attribute this to natural flow and sediment regimes driving channel migration, erosion, deposition, vegetation succession, and active beaver modifications. Colorado floodplains show higher aggregation and lower fragmentation than the Oklahoma floodplain. We attribute this to the greater incision and lower hydrologic variability at the Oklahoma site. [ABSTRACT FROM AUTHOR]

Aim: The global patterns of body size distributions are affected by global environmental changes (GEC), but our knowledge of the interaction effects between GEC and natural drivers is still limited. In this study, we aimed to test the effects of these interactions on fish community size spectra, that is, the variation in a community property across the body size range of individuals in the community. Location: One thousand and ninety‐five stream locations across France between 2015 and 2018. Methods: We fitted size spectrum slopes for each fish community based on individual biomass size and using binning and maximum‐likelihood methods through regression models across size classes and negative log‐likelihood functions, respectively. Interactions between natural drivers and GEC were tested using model‐averaging and the best models were selected with information criterion. Results: Our results demonstrated that size spectra were steeper (i.e. higher proportion of small‐bodied individuals) in warmer than colder locations, as expected by temperature‐size rules. However, eutrophication (total phosphorus concentration) and biological invasions (percentage of non‐native individuals) modulated the effect of temperature, with flatter size spectrum slopes in streams with more nutrients and higher proportion of non‐native individuals. In addition, fish size spectrum slopes were generally steeper in upstream than in downstream locations, and this effect was more pronounced in stream locations with more nutrients. Finally, size spectrum slopes were flatter in species‐rich communities when nutrient concentration was high. Main Conclusions: Our study highlights that accounting for GEC, such as eutrophication and biological invasions, can help understanding the complex patterns of fish body size distributions in ecological communities. [ABSTRACT FROM AUTHOR]