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Increased human disturbance poses a profound threat to ecosystem sustainability worldwide. The spatial heterogeneity of the relationship between environmental quality and human disturbance increases the complexity of this issue. Different regions experience varying degrees of human disturbances and environmental conditions, resulting in diverse ecological responses. However, the heterogeneous relationship between the mechanisms of human activity and environmental quality has not been fully investigated. Therefore, based on multi-source data, remote sensing ecological and human footprint indices were used to assess the environmental quality and the intensity of human disturbance in the Ili Valley during 2009-2021. After determining that environmental quality has strong spatial autocorrelation through Moran's I, the Getis-Ord Gi* was used to identify spatially heterogeneous units of environmental quality distribution. Furthermore, LISA maps were plotted to observe the spatial aggregation relationship between environmental quality and human disturbances. Finally, the Spatial Error Model (SEM) was identified as the most appropriate model for measuring the spatial dependence between environmental quality and human disturbance, and it was used to explain this dependence across heterogeneous units. The results showed that: (1) the environmental quality in the valley was good, whereas the slopes on both sides of the valley had poor environmental quality. Hills at middle altitudes had good environmental quality, whereas mountains at high altitudes had poor environmental quality; (2) The human disturbance intensity was higher in the valley and lower at higher elevation areas, and high-intensity areas also showed a distribution along traffic roads; (3) four different patterns of local correlations between the two variables at each location were visualized; (4) SEM was more appropriate for assessing spatial dependence of environmental quality on human disturbance among heterogeneous units because it considered the effects of spatial autocorrelation; and (5) SEM results indicated that the effects of spatial dependence between environmental quality and human disturbance were different across heterogeneous units. These findings highlight the complex relationship between environmental quality and human activity, and provide valuable insights into the spatial dependence effects of environmental quality on human disturbances and potential guidance for coordinating environmental and human activities.

Changes in Arctic tundra vegetation, driven by climate change, may be inducing major shifts in ecosystem services and the Arctic carbon budget, and altering high latitude feedbacks to the climate system. Field-based studies have documented warming-induced shrub expansion, and remote sensing has revealed heterogeneous, but primarily positive, trends in peak summer greenness across the Arctic. However, efforts to move beyond remotely sensed measures of spectral greening to quantify the spatial extent and rate of shrub expansion have been constrained by spectral similarities among tundra vegetation types, limited ground truth data, low revisit frequency of satellite observations, and sub-pixel heterogeneity of land cover at medium spatial resolution (30 m). To address these challenges, we developed a methodology that integrates high spatial resolution (2 m) commercial satellite imagery with Harmonized Landsat and Sentinel-2 observations in a machine learning framework, and used it to produce annual maps for 2016 to 2023 of sub-pixel land cover fractions at 30-m spatial resolution across three Arctic tundra ecoregions spanning 3.35 × 105 km2 between the Seward and Tuktoyaktuk Peninsulas. Uncertainty was quantified at each pixel via Monte Carlo resampling. Independent accuracy assessments yielded good accuracies (mean squared errors of 15.98% and 11.89% for low-stature vegetation and erect shrub cover, respectively), that were comparable to or exceeded previous mapping efforts. Further, repeat commercial satellite image pairs enabled the first assessment of mapped fractional cover change in Arctic tundra (R2 of 0.46 and 0.55, change direction accuracies of 77% and 78% for low-stature vegetation and erect shrub cover, respectively). This novel, scalable, multi-sensor approach to fractional land cover mapping produced the first annual maps of land cover fractions in the Arctic tundra, which support more accurate representation of vegetation dynamics and their linkages to climate change and disturbance processes.

The ecological environment plays a crucial role in maintaining the balance and sustainability of ecosystems, particularly in the context of rapid urbanization. This study investigates the impact of urban growth on ecological environment quality in the Jalpaiguri Planning Area, a rapidly urbanizing area that has been overlooked in previous research. Using remote sensing and geographic information system, this study employed the Remote Sensing Ecological Index (RSEI) to quantitatively assess ecological environment quality by integrating key biophysical properties such as greenness, wetness, dryness, and surface temperature using multi-temporal Landsat data from 1991 to 2021. The results revealed a significant deterioration in eco-environment quality with a decline in mean RSEI values from 0.70 to 0.45. Areas with moderate to excellent ecological quality declined over time, while poor and fair quality zones increased, especially in the urban core and surrounding areas. Moran's I increased from 0.332 to 0.389, suggesting an increasing spatial dependence and clustering of ecological conditions, indicative of growing environmental polarization. Local indicators of spatial association highlighted a decreasing trend in High-High clusters and an expansion of Low-Low clusters, indicating degradation in greenness and wetness due to intensified built-up development. The outcomes of regression analysis revealed a strong and consistent negative correlation between growing built-up areas and RSEI, with correlation coefficients ranging from -0.76 to -0.86 over the study period. The results support targeted planning interventions, including protection of green and blue spaces, control of unplanned built-up expansion, and integration of RSEI-based ecological monitoring into urban planning for informed decision-making.

Studying the ecological impacts of urbanization is crucial for advancing regional sustainable development. This study, based on panel data of ecological environment and new urbanization in Shandong Province (2000-2022), employed Global Principal Component Analysis to calculate improved Remote Sensing Ecological Index (GRSEI) and ecological environment index, and applied spatial panel regression models to analyze the spatiotemporal impacts of new urbanization on the ecological environment quality (EEQ). The results showed that: (1) From 2000 to 2022, the GRSEI of cities in Shandong Province exhibited a U-shaped evolution trend, first declining and then rising. The trend of environmental pressure in central Shandong was largely consistent with that of the GRSEI. Environmental protection across the province steadily improved, resulting in a gradual enhancement of EEQ, with coastal cities outperforming inland ones. (2) There was a significant positive correlation between EEQ and various indicators of new urbanization. Among them, indicators such as per capita retail sales of consumer goods and per capita education expenditure showed the strongest correlation with EEQ, reaching the 0.001 significance level. (3) The spatial panel regression results indicated that variables including the proportion of urban population, green coverage rate of built-up areas, and social urbanization score had significant positive effects on EEQ. Per capita GDP and economic urbanization exhibited negative impacts before 2018 but shifted to positive effects after 2019. This indicates that Shandong Province's urbanization shifted from a "pollution-intensive growth" model to a "green development" model, promoting the continuous improvement of EEQ.

Analyzing the spatiotemporal patterns and evolutionary dynamics within the ecological environment is vital for the sustainability of the Yellow River Basin. However, previous studies have not adequately addressed the scale effects on ecological environment patterns, which hampers the effective implementation of regional management strategies. This study aims to investigate the dynamic changes in eco-environmental quality within the Yellow River Basin of Henan Province since 1990, identifying patterns of variation in eco-environmental quality and its driving mechanisms across different spatial scales. Specifically, the Remote Sensing Ecological Index (RSEI) and Moran's index were employed to quantify the spatiotemporal evolution of eco-environmental quality from 1990 to 2021. Spearman's correlation analysis and hierarchical partitioning analysis were then applied to reveal the driving mechanisms underlying these variations. The results indicate significant differences in the eco-environmental quality distribution patterns between the western and eastern parts of the Yellow River Basin (Henan section). From 1990 to 2021, the driving mechanisms of eco-environmental quality showed increasing scale dependency over time, with different driving factors exhibiting varying responses to scale changes. The outcomes of this study provide a scientific foundation for the sustainable development and ecological environment conservation of the Yellow River Basin, thereby providing guidance for policymakers to implement targeted conservation strategies.

Shanxi Province is located in the eastern part of the Loess Plateau in China, characterized by natural geographic spatial heterogeneity and fragile ecological environments. Understanding the spatiotemporal distribution variations of ecological environment quality and their driving factors is crucial for promoting ecological conservation and coordinated socio-economic development in Shanxi. We constructed a remote sensing ecological index (RSEI) on the Google Earth Engine platform and analyzed the spatiotemporal variations in ecological environment quality in Shanxi from 2000 to 2020. Methods such as Theil-Sen median trend analysis, Mann-Kendall test, and Hurst index were employed to assess the trends and sustainability of RSEI changes. Additionally, geographic detectors, partial least squares structural equation modeling (PLS-SEM), and mediation analysis were introduced to explore interactions among factors and their direct and indirect effects on RSEI. The results showed that the mean RSEI fluctuated between 0.44 and 0.68 from 2000 to 2020, showing an overall upward trend with spatial distribution patterns of mountainous areas outperforming basins and southeastern regions outperforming northwestern ones. The ecological environment quality exhibited a significant improvement trend, with 81.6% of the area showing enhancement. The Hurst index suggested uncertainties and potential reversal risks in future trends. Geographic detector analysis revealed that elevation, potential evapotranspiration, and land use intensity were the primary factors driving spatial differentiation in RSEI, with the explanatory power of multi-factor interactions significantly exceeding that of single-factor. PLS-SEM and mediation analysis demonstrated that potential evapotranspiration weakened the positive effect of precipitation on RSEI by intensifying climatic stress, while the nighttime light index amplified the negative combined effects of urbanization and resource exploitation on RSEI. Our results deepened the understanding of ecological environment quality evolution mechanisms and could provide methodological support and policy insights for ecological governance and sustainable transformation in resource-dependent regions. 山西省位于中国黄土高原东部,自然环境空间异质性显著,生态环境脆弱。了解山西省生态环境质量的时空分布格局及其驱动因素,对促进生态保护与经济社会协调发展至关重要。本研究基于Google Earth Engine平台构建遥感生态指数(RSEI),分析了2000—2020年间山西省生态环境质量的时空变化格局;采用Theil-Sen中位数趋势分析、Mann-Kendall检验与Hurst指数等方法评估RSEI的变化趋势和可持续性,并引入地理探测器和偏最小二乘法结构方程模型(PLS-SEM)及中介分析,探讨各因子间的相互作用及其对RSEI的直接和间接效应。结果表明:2000—2020年间,研究区RSEI均值波动在0.44~0.68,整体呈上升趋势,空间分布上表现为山区优于盆地、东南优于西北;研究区生态环境质量呈现显著改善趋势,改善区域占比达81.6%,但Hurst指数显示未来变化趋势存在一定的不确定性和潜在的逆转风险。地理探测器分析表明,海拔、潜在蒸散发和土地利用强度是RSEI空间分异的主要因素,且多因子交互作用的解释力显著强于单因子;PLS-SEM与中介分析表明,潜在蒸散发通过加剧气候胁迫削弱了降水对RSEI的正向作用,夜间灯光指数强化了城镇化和资源开发对RSEI的负向复合作用。本研究深化了生态环境质量演变机制的理解,可为资源型地区的生态治理和可持续转型提供方法支撑和政策借鉴。.

Against the backdrop of global climate change and the "dual carbon" goals&#xff0c; by integrating multi-source remote sensing data &#xff08;MOD17A3 and ChinaLand30&#xff09; and statistical models &#xff08;Theil-Sen trend analysis&#xff0c; ridge regression residual decomposition&#xff0c; and Hurst index&#xff09;&#xff0c; the spatio-temporal evolution patterns and driving mechanisms of the Net Ecosystem Productivity &#xff08;NEP&#xff09; in Henan Province from 2003 to 2023 were analyzed. The results showed that&#xff1a; &#x2460; The average annual growth of NEP in Henan Province was 8.59 g&#xb7;&#xff08;m2&#xb7;a&#xff09;-1&#xff0c; and the proportion of carbon sink areas increased from 78.3% to 81.7%&#xff0c; but there was significant spatial heterogeneity. The areas with enhanced carbon sinks were concentrated in the Funiu Mountain ecological barrier &#xff08;44.5%&#xff09; and the plain farmland belt &#xff08;37.2%&#xff09;&#xff0c; while the Zhengzhou metropolitan area &#xff08;1.6%&#xff09; has become a hot spot of carbon sources due to the expansion of construction land. In 2019&#xff0c; the superposition of extreme climate and urbanization led to a sharp decline in NEP [-80.47 g&#xb7;&#xff08;m2&#xb7;a&#xff09;-1]. &#x2461; The land/NEP transfer matrix indicated that the conversion of cultivated land to shrubs [152.91 g&#xb7;&#xff08;m2&#xb7;a&#xff09;-1] and the transformation of wasteland into forest land [191.63 g&#xb7;&#xff08;m2&#xb7;a&#xff09;-1] significantly increased the carbon sink&#xff0c; while the conversion of cultivated land to impervious surfaces [-102.36 g&#xb7;&#xff08;m2&#xb7;a&#xff09;-1] caused carbon loss&#xff0c; highlighting the contradiction between ecological restoration and urbanization. &#x2462; Climate change extended the vegetation growth period through warming [0.05-1.14 &#x2103;&#xb7;&#xff08;10 a&#xff09;-1]&#xff0c; with a contribution rate of 91.83%. Human activities jointly increased the carbon sink through the return of farmland to forest and farmland intensification&#xff0c; with a contribution rate of 90.79%. The areas driven jointly by the two factors accounted for 78.73%&#xff0c; but the Zhengzhou metropolitan area was under the dual inhibition of "climate-human" factors due to the urban heat island effect. &#x2463; The Hurst index &#xff08;H&#xff09; predicted that the carbon sink in 49.06% of the plain agricultural belt will continue to be enhanced &#xff08;H>0.5&#xff09;&#xff0c; and 45.49% of the ecologically fragile areas &#xff08;such as the former course of the Yellow River&#xff09; may degrade &#xff08;H<0.5&#xff09;&#xff1b; thus&#xff0c; differential ecological management is required. The above results provide scientific support for the formulation of policies to coordinate food security and ecological security in Henan Province. It is recommended to strengthen the ecological compensation mechanism in the Funiu Mountain area and promote the "agroforestry composite" model in the plain agricultural area to enhance the potential of sustainable carbon sinks.

Does swidden agriculture, a prototypical coupled human and natural system, exhibit a process of adaptive self-organization in which cultural practices balance environmental constraints through adaptive feedback? Here, we investigate whether quantitative signatures of adaptive self-organization can be detected in a dataset consisting of 18,000+ contiguous swidden patches in 18 remote sensing images of swidden mosaics from tropical and subtropical regions globally. We find that the distributions of patch sizes in 16 of 18 swidden areas exhibit power law patterns with scaling exponents &#x2248;1, and correlation distances of &#x2248;548 m. To account for these patterns, we develop a plausible ethnographically informed agent-based model of labor exchange, land use, and swidden site selection in which both sustainable and unsustainable resource uses can emerge out of interactions among individuals or households. By analyzing the model, we identify spatial synchronization of swidden sites as the driver of power law formation, while social norms of swidden labor can guide the system to an intermediate level of landscape disturbance. Both mechanisms are required to maintain harvests and ecosystem productivity at high levels. Our model advances theoretical understanding of the socioecological dynamics of swidden agriculture, and supports the hypothesis that adaptive self-organization may be a general characteristic of coupled human and natural systems.

Accurate estimation of dynamic environmental phenomena through intelligent sensing systems plays a critical role in enabling reliable monitoring and decision-making in complex real-world scenarios. With the rapid development of artificial intelligence-driven sensing technologies and Internet of Things systems, modern agricultural monitoring is evolving from isolated data acquisition toward intelligent, multimodal perception and decision-making. However, traditional approaches predominantly rely on single data sources, making it difficult to simultaneously capture plant phenotypic variations and environment-driven mechanisms, thereby limiting model applicability in complex field scenarios. To address this issue, a multimodal pest density estimation framework, namely the Pest Density Estimation Framework (PDEF), is proposed, which integrates UAV-based imagery, trap monitoring data, and environmental sensor measurements. In this framework, crop canopy damage features are extracted using convolutional neural networks, while temporal encoding is employed to model dynamic environmental variations. Cross-modal feature alignment and environment-aware enhancement mechanisms are further introduced to achieve deep integration of multi-source information, enabling the construction of a unified feature representation space and improving estimation accuracy. Extensive experiments conducted on a constructed multimodal agricultural dataset demonstrate that the proposed method achieves MAE, RMSE, and MAPE values of 5.47, 7.62, and 14.9%, respectively, significantly outperforming the Transformer-based fusion model (MAE 6.01, RMSE 8.16). Meanwhile, the coefficient of determination reaches R2=0.84, indicating superior fitting capability and stability. In multimodal combination experiments, the three-modality fusion reduces error metrics by more than 20% on average compared with single-modality models, validating the effectiveness of multi-source collaborative modeling. From the perspective of integrating plant phenotypic analysis and environmental perception, this study provides a novel AI-driven intelligent sensing framework for pest monitoring and crop management, contributing to improved pest prediction capability and enhanced intelligence in agricultural production systems. This study further provides practical implications for agricultural economics and supply chain optimization by enabling data-driven decision-making through intelligent sensing systems.

With the acceleration of global urbanization, high-quality dark sky resources are becoming increasingly scarce. As the intersection of the front line of light pollution diffusion and ecological buffer zones, the urban fringe of megacities faces dark sky protection challenges arising from complex baseline light environments and intensified human activities. Taking Guangzhou as a case study, this paper proposes a dark sky park site selection and light environment optimization framework based on multi-source data integration. By integrating high-resolution nighttime light remote sensing data from SDGSAT-1, ground-based SQM measurements, all-sky fish-eye imagery, and POI-based socio-perceptual data, a multi-dimensional site suitability evaluation model is constructed, incorporating natural baseline conditions, transportation accessibility, and socio-economic factors. The results indicate that (1) distinct "dark sky islands" are present in northeastern Guangzhou, and a total of 13 natural protected areas with potential for dark sky park development were identified, among which Conghua Lianxi Municipal Forest Park exhibits the highest overall suitability with a score of 0.89; (2) Ground-based measurements confirm that under moonless conditions the zenith night sky brightness in this area reaches up to 21.28 mag/arcsec2, meeting the Dark-Sky International (IDA) Silver Tier standard (21.00&#x2264;SQM&#x2264;21.74), with the optimal observation window occurring between 02:00 and 05:00; (3) POI-based source attribution analysis reveals that near-horizontal light pollution is primarily generated by the adjacent Shantou-Zhanjiang Expressway as a linear source, while public service facilities within the protected areas exhibit characteristics of high-intensity point light sources. Based on these findings, this study proposes an integrated optimization pathway that combines top-down and bottom-up approaches, providing quantitative evidence and a practical framework for constructing dark sky ecological networks in the urban fringe of megacities.

As the core area of the alpine ecological sensitive zone&#xff0c; Xizang's ecological barrier function is of strategic significance. Based on the remote sensing data of the normalized difference vegetation index &#xff08;NDVI&#xff09; from 2000 to 2022&#xff0c; the corresponding vegetation coverage &#xff08;FVC&#xff09; was inverted by using the pixel dichotomy method. Sen+Mann-Kendall trend analysis and multiple regression residual analysis were used to study the spatial and temporal variation characteristics of FVC in Xizang Autonomous Region from 2000 to 2022 and the corresponding contributions of its main driving factors &#xff08;i.e.&#xff0c; climate change and human activities&#xff09;. The results show that&#xff1a; &#x2460; From 2000 to 2022&#xff0c; the overall FVC in Xizang showed a fluctuating upward trend&#xff0c; with an upward rate of 0.009&#xff08;10 a&#xff09;-1&#xff0c; showing a gradient decreasing pattern of high in southeast and low in northwest. The overall improvement area &#xff08;58.38%&#xff09; was greater than the degradation area &#xff08;23.81%&#xff09;. &#x2461; The area with very stable FVC change accounted for 18.81%&#xff0c; mainly concentrated in the area with high forest coverage&#xff0c; and the area with severe variation accounted for 38.71%&#xff0c; mainly located in the ecological environment sensitive area with low vegetation coverage. &#x2462; The impacts of climate change and human activities on FVC change in Tibet had obvious spatial differentiation&#xff0c; but both of them were mainly positive. On the whole&#xff0c; the driving factors of FVC spatial change in the study area were as follows&#xff1a; the combined effect of climate change and human activities &#xff08;63.31%&#xff09; > human activities &#xff08;23.79%&#xff09; > climate change &#xff08;12.91%&#xff09;. &#x2463; Climate change and human activities contributed 54.92% and 62.26% to the increase in FVC in the study area&#xff0c; respectively&#xff0c; and the positive effect of human activities on FVC change was more obvious. The research results are of great significance to promote the integrated protection and systematic management of mountains&#xff0c; rivers&#xff0c; forests&#xff0c; fields&#xff0c; lakes&#xff0c; and sands in Xizang.

The Gansu-Qinghai contiguous region of the upper Yellow River occupies a strategic position in China's ecological security framework. However, comprehensive long-term assessment of ecological quality changes and their mechanisms in this ecologically fragile zone remains limited. Systematically evaluating ecological quality dynamics is necessary for supporting high-quality development strategies in the Yellow River Basin. This study utilizes MODIS series remote sensing imagery from 2000 to 2022, accessed through the Google Earth Engine platform. The Remote Sensing Ecological Index (RSEI) was constructed via principal component analysis (PCA). Theil-Sen median trend analysis, Mann-Kendall tests, and coefficient of variation methods were applied to examine spatiotemporal patterns and stability. Pearson correlation analysis and random forest modeling were employed to quantify the contributions of ten driving factors. Results indicate that the ecological quality of the study area showed an overall improving trend with local fluctuations from 2000 to 2022. Spatially, it exhibited a west-high and east-low pattern, with "good" and "excellent" areas continuously expanding. About 43% of the region experienced ecological improvement, 20% showed degradation, and over 86% remained highly stable. Vegetation greenness was the dominant positive driver, while land surface temperature and dryness index had significant negative impacts. Precipitation and humidity displayed threshold responses, and socioeconomic factors such as GDP and population density mainly influenced local ecology through land-use intensity. Overall, ecological quality was jointly regulated by vegetation dynamics, hydrothermal conditions, and human activities. This study establishes baseline data for systematic ecological monitoring in high-altitude ecologically sensitive regions. The findings demonstrate that targeted ecological restoration projects have achieved measurable effectiveness, while emphasizing the necessity of integrating climate change considerations into future conservation management strategies for the upper Yellow River.

Land use changes reshape the generation and transport patterns of nitrogen (N) entering the water environment from both natural and anthropogenic activities, by altering the type and intensity of N-emitting activities and the retention effect on N in surface runoff that traverses the land. This study develops an integrated methodological framework that combines N flow analysis, geospatial analysis, land use change prediction, and nutrient transport simulation to analyze the spatial patterns of waterborne N emissions under various land use scenarios, considering different future land structures and the implementation of riparian buffers as artificial interventions. Using the Guangdong-Hong Kong-Macao Greater Bay Area as a case, we find that all land use scenarios in 2030 and 2040 involve the expansion of impervious land and the reduction of forest and water, with only the Ecological Conservation scenario resulting in a minimal loss of forest. Direct N emissions to water account for >85 % of the total waterborne N emissions, while indirect N emissions from diffuse sources exhibit an export rate of around 16 %. By 2040, the Ecological Conservation scenario preserves 511 km&#xb2; more arable land compared to the Economic Development scenario, while also achieving a reduction of 870 t of N export. This benefit is particularly significant for highly urbanized cities. Riparian buffers are critical areas for reforestation with an estimated reduction of approximately 6.9 t N for every additional km&#xb2; of riparian forest. The findings offer land management strategies for mitigating waterborne N emissions in fast-urbanizing city clusters.

Clarifying the trade-offs and synergies among ecosystem services and their spatiotemporal dynamics is a critical scientific issue for constructing ecological security patterns and optimizing multifunctional land use in arid regions. This study&#xff0c; focusing on the Weibei dry plateau region of Shaanxi Province&#xff0c; integrates the InVEST model&#xff0c; geographic detectors&#xff0c; geographically and temporally weighted regression &#xff08;GTWR&#xff09; models&#xff0c; XGBoost-SHAP explanation frameworks&#xff0c; and spatial partial correlation analyses&#xff0c; based on five phases of remote sensing and socioeconomic statistical data from 2002 to 2022. It systematically elucidates the dynamic evolution and driving mechanisms of three ecosystem services&#xff1a; water yield &#xff08;WY&#xff09;&#xff0c; habitat quality &#xff08;HQ&#xff09;&#xff0c; and soil conservation &#xff08;SC&#xff09;. The primary findings include&#xff1a; &#x2460; Temporal evolution characteristics revealed that WY&#xff0c; HQ&#xff0c; and SC significantly increased during the study period&#xff0c; with increments of 521.87 mm&#xff0c; 0.78&#xff0c; and 275.83 t&#xb7;km-2&#xff0c; respectively. Spatial differentiation identifies southern Yanan and the hilly areas of Tongchuan as core zones of ecosystem service enhancement. &#x2461; Driver mechanism analysis indicated spatial variation in explanatory power among different service drivers. WY was mainly influenced by land use &#xff08;LULC&#xff09;&#xff0c; slope &#xff08;SLOPE&#xff09;&#xff0c; and annual average precipitation&#xff08;PRE&#xff09;. HQ was significantly affected by TMP and normalized difference vegetation index &#xff08;NDVI&#xff09;&#xff0c; though this influence was declining. SC consistently responded to slope&#xff0c; displaying a terrain-dominated structure. Furthermore&#xff0c; interactions between driving factors enhanced service responses&#xff0c; notably increasing explanatory power through nonlinear enhancement and temporal shifts. &#x2462; Spatial heterogeneity verification via GTWR modeling demonstrated that the influence of each factor on ecosystem services exhibited spatial heterogeneity and dynamics. &#x2463; Machine learning interpretation using the XGBoost-SHAP model revealed distinct importance rankings of factors across ecosystem services&#xff0c; though no significant temporal differentiation in importance ranking was observed during the study period. &#x2464; Trade-offs and synergies showed binary differentiation among ecosystem services. WY and HQ predominantly displayed trade-offs&#xff0c; WY and SC exhibited synergistic relationships&#xff0c; while HQ and SC maintained a balanced spatial pattern of both synergy and trade-off interactions. By coupling multiple models&#xff0c; this study highlights the nonlinear and spatially heterogeneous interactions among ecosystem services in arid regions&#xff0c; providing a quantitative decision-making basis for regional ecological security barriers and precise territorial governance.

European forests are increasingly managed to harmonize production goals with biodiversity conservation, through practices such as retention and close-to-nature forestry. Forest birds may benefit from these practices, but it remains unclear how the effects of different management practices compare, and whether responses to management are driven by changes in the availability of invertebrates, a crucial element of bird diets during the breeding season. To answer these questions, we carried out bird point counts on 135 1-ha plots in southwestern Germany from 2017 to 2022, and measured the abundance of invertebrate groups in the lower forest strata using flight interception traps and pitfall traps. We used N-mixture models and Bayesian generalized linear models (GLMs) to estimate, respectively, how abundances of 32 bird species and 20 invertebrate groups respond to predictors representing forest management, structure, composition, and the abiotic environment. We then compared the responses of birds and invertebrates, and employed piecewise structural equation models (SEMs) to disentangle the causal links between forest structure and abundances of bird guilds and invertebrate groups. Bird abundances responded to predictors representing retention and close-to-nature forestry practices, but the direction of effects varied across species and facets of management. Moreover, the effects of retention practices were weaker than those of close-to-nature practices, especially those of admixing broadleaf trees. Hence, these management practices likely need to be applied in tandem with others (e.g., gap creation) to secure a diverse forest bird assemblage. Invertebrate abundances responded to both management types, but responses did not clearly align with those of bird species, and SEMs did not support direct links between bird and invertebrate abundances. Still, we revealed parallel positive responses of birds and invertebrate groups to the same habitat features, such as broadleaf share, suggesting that these may function as cues for high food availability during habitat selection by birds. Therefore, forest management that aims at increasing bird populations should address other potential limiting factors, such as nest site availability, in addition to fostering high invertebrate abundances, which may safeguard habitat quality for birds.

The Mu Us Sandy Land is a crucial component of China's northern ecological barrier. It is of paramount importance to clarify the spatiotemporal variations of the impacts of climate change and human activities on the normalized difference vegetation index (NDVI). Based on remote sensing imagery and meteorological data from 1990 to 2023, we analyzed the variations of NDVI and its response to meteorological factors in Uxin Banner, Inner Mongolia, and quantitatively delineated the spatiotemporal patterns for the impacts of climate change and human activities on NDVI. The results showed that the NDVI in Uxin Banner showed a overall significant increasing trend [0.03&#xb7;(10 a)-1]. The vegetation showed a predominance of moderate improvement (71.6%), but areas with low vegetation cover dominated (90.5%). Pixels with slight decline accounted for 28.4%, highly overlapping with the extremely low NDVI zones (<0.1) in the northwestern central region. Annual precipitation and mean annual temperature in Uxin Banner increased with rates of 49.40 mm&#xb7;(10 a)-1 and 0.37 &#x2103;&#xb7;(10 a)-1, respectively, both showing significant positive correlation with NDVI. Relative importance analysis showed that mean annual temperature was the dominant climatic factor affecting NDVI, followed by annual precipitation, with relative importance contribution of 74.9% and 18.4%. Both climate change and human activities drove NDVI increases in the vast majority of the region (98.9%), with their relative contribution rates exhibiting distinct spatially differentiated and complementary characteristics. Climate change contributed 60%-80% to NDVI increases in localized areas of south-central Uxin Banner, while human activities contributed 60%-80% in the northern part. This study could provide scientific evidence for the ecological restoration and sustainable management of the core area in the central Mu Us Sandy Land. &#x6bdb;&#x4e4c;&#x7d20;&#x6c99;&#x5730;&#x662f;&#x4e2d;&#x56fd;&#x5317;&#x65b9;&#x751f;&#x6001;&#x5c4f;&#x969c;&#x533a;&#x7684;&#x91cd;&#x8981;&#x7ec4;&#x6210;&#x90e8;&#x5206;,&#x660e;&#x786e;&#x6c14;&#x5019;&#x53d8;&#x5316;&#x548c;&#x4eba;&#x7c7b;&#x6d3b;&#x52a8;&#x5bf9;&#x8be5;&#x533a;&#x5f52;&#x4e00;&#x5316;&#x690d;&#x88ab;&#x6307;&#x6570;(NDVI)&#x5f71;&#x54cd;&#x7684;&#x65f6;&#x7a7a;&#x5206;&#x5f02;&#x7279;&#x5f81;&#x81f3;&#x5173;&#x91cd;&#x8981;&#x3002;&#x57fa;&#x4e8e;1990&#x2014;2023&#x5e74;&#x9065;&#x611f;&#x5f71;&#x50cf;&#x548c;&#x6c14;&#x8c61;&#x6570;&#x636e;,&#x7cfb;&#x7edf;&#x5206;&#x6790;&#x4e86;&#x5185;&#x8499;&#x53e4;&#x4e4c;&#x5ba1;&#x65d7;NDVI&#x52a8;&#x6001;&#x6f14;&#x53d8;&#x53ca;&#x4e0e;&#x6c14;&#x8c61;&#x8981;&#x7d20;&#x7684;&#x54cd;&#x5e94;&#x5173;&#x7cfb;,&#x660e;&#x786e;&#x91cf;&#x5316;&#x4e86;&#x6c14;&#x5019;&#x53d8;&#x5316;&#x548c;&#x4eba;&#x7c7b;&#x6d3b;&#x52a8;&#x5bf9;NDVI&#x9a71;&#x52a8;&#x529b;&#x7684;&#x65f6;&#x7a7a;&#x683c;&#x5c40;&#x3002;&#x7ed3;&#x679c;&#x8868;&#x660e;:1990&#x2014;2023&#x5e74;&#x95f4;,&#x4e4c;&#x5ba1;&#x65d7;NDVI&#x6574;&#x4f53;&#x5448;&#x6781;&#x663e;&#x8457;&#x4e0a;&#x5347;&#x8d8b;&#x52bf;[0.03&#xb7;(10 a)-1],&#x690d;&#x88ab;&#x4ee5;&#x8f7b;&#x4e2d;&#x5ea6;&#x6539;&#x5584;&#x4e3a;&#x4e3b;(71.6%),&#x4f46;&#x4f4e;&#x690d;&#x88ab;&#x8986;&#x76d6;&#x533a;&#x57df;&#x5360;&#x4e3b;&#x5bfc;(90.5%),&#x8f7b;&#x5ea6;&#x4e0b;&#x964d;&#x50cf;&#x5143;&#x5360;&#x6bd4;&#x4e3a;28.4%,&#x4e14;&#x4e0e;NDVI&#x6781;&#x4f4e;&#x503c;&#x533a;(<0.1)&#x9ad8;&#x5ea6;&#x91cd;&#x5408;&#x5728;&#x4e2d;&#x90e8;&#x504f;&#x897f;&#x5317;&#x90e8;&#x3002;&#x4e4c;&#x5ba1;&#x65d7;&#x5e74;&#x964d;&#x6c34;&#x91cf;&#x548c;&#x5e74;&#x5747;&#x6c14;&#x6e29;&#x5206;&#x522b;&#x4ee5;49.40 mm&#xb7;(10 a)-1&#x548c;0.37 &#x2103;&#xb7;(10 a)-1&#x53d8;&#x5316;&#x503e;&#x5411;&#x7387;&#x6781;&#x663e;&#x8457;&#x589e;&#x52a0;,&#x5e76;&#x4e0e;NDVI&#x5448;&#x73b0;&#x6781;&#x663e;&#x8457;&#x6b63;&#x76f8;&#x5173;;&#x76f8;&#x5bf9;&#x91cd;&#x8981;&#x6027;&#x5206;&#x6790;&#x7ed3;&#x679c;&#x8868;&#x660e;,&#x5e74;&#x5747;&#x6c14;&#x6e29;&#x662f;&#x5f71;&#x54cd;NDVI&#x53d8;&#x5316;&#x7684;&#x4e3b;&#x5bfc;&#x6c14;&#x5019;&#x56e0;&#x5b50;&#x800c;&#x5e74;&#x964d;&#x6c34;&#x91cf;&#x6b21;&#x4e4b;,&#x76f8;&#x5bf9;&#x91cd;&#x8981;&#x6027;&#x5360;&#x6bd4;&#x5206;&#x522b;&#x4e3a;74.9%&#x548c;18.4%&#x3002;&#x6c14;&#x5019;&#x53d8;&#x5316;&#x548c;&#x4eba;&#x7c7b;&#x6d3b;&#x52a8;&#x5171;&#x540c;&#x9a71;&#x52a8;&#x4e4c;&#x5ba1;&#x65d7;&#x7edd;&#x5927;&#x90e8;&#x5206;&#x533a;&#x57df;NDVI&#x589e;&#x52a0;(98.9%),&#x4e8c;&#x8005;&#x76f8;&#x5bf9;&#x8d21;&#x732e;&#x7387;&#x5177;&#x6709;&#x660e;&#x663e;&#x7a7a;&#x95f4;&#x5206;&#x5f02;&#x7279;&#x5f81;:&#x6c14;&#x5019;&#x53d8;&#x5316;&#x5bf9;&#x4e4c;&#x5ba1;&#x65d7;&#x4e2d;&#x90e8;&#x504f;&#x5357;&#x5c40;&#x57df;NDVI&#x76f8;&#x5bf9;&#x8d21;&#x732e;&#x7387;&#x4e3a;60%&#xff5e;80%,&#x4eba;&#x7c7b;&#x6d3b;&#x52a8;&#x5bf9;&#x4e4c;&#x5ba1;&#x65d7;&#x5317;&#x90e8;NDVI&#x76f8;&#x5bf9;&#x8d21;&#x732e;&#x7387;&#x4e3a;60%&#xff5e;80%&#x3002;&#x672c;&#x7814;&#x7a76;&#x7ed3;&#x679c;&#x53ef;&#x4e3a;&#x6bdb;&#x4e4c;&#x7d20;&#x6c99;&#x5730;&#x4e2d;&#x90e8;&#x6838;&#x5fc3;&#x533a;&#x7684;&#x751f;&#x6001;&#x6062;&#x590d;&#x548c;&#x53ef;&#x6301;&#x7eed;&#x7ba1;&#x7406;&#x63d0;&#x4f9b;&#x79d1;&#x5b66;&#x4f9d;&#x636e;&#x3002;.

Biodiversity is declining in many parts of the world. Biological diversity measurement and monitoring are fundamental to the assessment of the causes and consequences of environmental changes, identification of key areas for the protection of biodiversity or ecosystem services, determining the effectiveness of actions, and the creation of decision-support tools critical to maintaining a sustainable planet. Biodiversity measurement is rapidly changing due to advances in citizen science, image recognition, acoustic monitoring, environmental DNA, genomics, remote sensing, and AI. In this perspective, we outline the exciting opportunities these developments offer but also consider the challenges. Our key recommendations are to 1) Capitalize on the ability of novel technology to integrate data sources 2) agree to standard methods for data collection 3) ensure new technologies are calibrated with existing data; 4) fill data gaps by using emerging technologies and increasing capacity, especially in the tropics; 5) create living safeguarded databases of trusted information to reduce the risk of poisoning by AI hallucinated, or false, information; 6) ensure data generation is valued; 7) ensure respectful incorporation of Indigenous Knowledge; 8) ensure measurements enable the quantification of effectiveness of actions, and 9) increase the resilience of global datasets to technical and societal change. Radical new collaborations are needed between computer scientists, engineers, molecular biologists, data scientists, field ecologists, citizen scientists, Indigenous peoples, policymakers, and local communities to create the rigorous, resilient, accessible biodiversity information systems required to underpin policies and practices that ensure the maintenance and restoration of ecological systems.

Human fire use is a key activity and process in many landscapes and ecosystems around the world, varying spatiotemporally depending on social, economic, and ecological factors. Recently, initiatives have begun to synthesise data on global fire use from across multiple disciplines and disparate sources into coherent databases. Here, we draw on information from one of these databases, the Livelihood Fire Database, which collates data on fire use practices worldwide from case studies in the literature. We examine data from 345 case study locations spanning 69 countries regarding return interval, area burned, and seasonality of anthropogenic fires set to meet small-scale rural livelihood objectives and/or for cultural reasons. We distinguish patterns in the spatiotemporal nature of fires associated with different fire-use purposes, such as clearing vegetation for agriculture, maintaining pasture for livestock, promoting certain plant species for gathering, or driving game when hunting. For many fire uses, especially those related to hunting, gathering, human wellbeing, and social signalling, there are very limited quantitative data available, but it is possible to draw qualitative insights from case studies. Case studies demonstrate that environmental and social conditions drive variation in fire use for the same purpose, reiterating that assumptions of uniform drivers of anthropogenic fire may be misleading. Nonetheless where quantitative data are available, we find some correspondence between the spatiotemporal nature of fires and fire-use purpose, suggesting that distinguishing between different fire-use purposes may be useful to understand and to better model their likely timing, size, and frequency relative to climate and other drivers. We recommend examples where the diagnosis of these broad relationships between fire-use purpose and fire properties could enable improved representation of anthropogenic fire in global land surface models, and aid interpretation of remote sensing data. Many of the smaller fires now being revealed in global burned area data by new fine-scale remote sensing products are likely human-set; continued collection, collation, and analyses of case study data on human fire use globally will be essential to help interpret this improved detection of small fires, and to ensure appropriate representation of the underlying drivers of human activity when modelling fire regimes.

Monitoring biodiversity in protected areas is essential to mitigate biodiversity loss and evaluate the effectiveness of conservation policies. Integrating satellite remote sensing technologies, ecological niche models, and time-series analyses of biodiversity trends offers a fast and robust approach for assessing habitat suitability changes and species vulnerability over time. In this study, we implemented a framework combining these tools to monitor biodiversity in the Montesinho/Nogueira Special Conservation Area (Northeast Portugal). Using the MaxEnt algorithm, we generated ecological niche models for 342 species based on a time series (2001-2023) of remote sensing data from the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. We analysed habitat suitability trends with the Mann-Kendall test to detect changes in habitat quality, as a metric of species vulnerability for individual species, five major taxonomic groups (vascular flora, amphibians, reptiles, birds, and mammals), functional groups (e.g. climate affinity, habitat type, diet, activity, reproduction), and conservation status (regional and European levels). Our study revealed a significant decline in habitat suitability over the past two decades, impacting all taxonomic groups and ecological functions. We observed a high variability in habitat suitability trends among species and taxonomic/functional groups, highlighting the complexity of biodiversity responses to environmental changes. Functional traits such as climatic affinity, trophic level or habitat specialisation were associated with variable rates of habitat decline, with species of Atlantic affinity, species associated with croplands and wetlands, and species specialised in insectivorous diets being at higher risk. Overall, these findings emphasise the need for comprehensive biodiversity monitoring programmes and demonstrate the utility of our approach to inform evidence-based conservation strategies in protected areas globally.