Ecological Indicators最新文献_第6页

Advancing urban blue space monitoring and management: A review of remote sensing applications and interdisciplinary impact assessment 推进城市蓝色空间监测与管理：遥感应用与跨学科影响评估综述

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2026-01-01 DOI: 10.1016/j.ecolind.2025.114563

Yiyi Xu , Yingbao Yang , Qicheng Liu , Chunqiao Song

As urbanization accelerates and climate change intensifies, urban blue space (UBS) has attracted widespread research attention for its crucial role in improving ecological quality and promoting sustainable urban development. This review synthesizes 1369 studies published from 2000 to 2024. It reveals an expansion of research topics from early monitoring of water quantity and quality to the multidimensional assessment of social and environmental impacts. This review compares remote sensing data sources and methods for UBS extraction and ecological parameter estimation. It highlights an evolution in extraction methods, from simple thresholding to complex deep learning models, with enhanced superpixel methods combined with deep learning achieving 99.14 % average overall accuracy. By synthesizing findings from multiple studies, we summarize the spatio-temporal dynamics of UBS across various nations and identify potential driving factors. The results indicate divergent trends shaped by diverse climate conditions, urbanization patterns, and economic development modes, primarily driven by different human activities and policies. Finally, the review examines the multifaceted benefits and impacts of UBS, including ecosystem services, resident health, and economic value. While remote sensing has been used to assess these impacts by calculating blue space exposure and proximity, research in this area remains limited. Furthermore, insufficient assessment of social benefits and an unbalanced regional focus impede a holistic understanding of the complex relationship between UBS and urban systems. Therefore, this paper underscores the future importance of integrating multi-source remote sensing with socioeconomic data, promoting larger-scale coupling of monitoring and assessment research, and building an interdisciplinary research framework to foster sustainable management of UBS.

随着城市化进程的加快和气候变化的加剧，城市蓝色空间因其在提高生态质量和促进城市可持续发展方面的重要作用而受到广泛关注。本综述综合了2000年至2024年发表的1369项研究。它揭示了研究主题从水量和水质的早期监测到社会和环境影响的多维评估的扩展。本文对UBS提取和生态参数估计的遥感数据源和方法进行了比较。它强调了提取方法的演变，从简单的阈值分割到复杂的深度学习模型，增强的超像素方法结合深度学习实现了99.14%的平均整体准确率。通过综合多项研究结果，总结了不同国家UBS的时空动态，并确定了潜在的驱动因素。结果表明，不同的气候条件、城市化模式和经济发展模式塑造了不同的趋势，主要是由不同的人类活动和政策驱动的。最后，本文考察了UBS的多方面效益和影响，包括生态系统服务、居民健康和经济价值。虽然遥感已被用于通过计算蓝色空间暴露和接近程度来评估这些影响，但这方面的研究仍然有限。此外，对社会效益的评估不足和不平衡的区域重点阻碍了对UBS和城市系统之间复杂关系的整体理解。因此，本文强调了整合多源遥感与社会经济数据，促进监测和评估研究的更大规模耦合，以及构建跨学科研究框架对促进UBS可持续管理的未来重要性。

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引用次数: 0

Mapping global future coastal inundation risk and demographic vulnerability using multi-sensor remote sensing data and socioeconomic scenarios 利用多传感器遥感数据和社会经济情景绘制全球未来沿海淹没风险和人口脆弱性图

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2026-01-01 DOI: 10.1016/j.ecolind.2025.114598

Hui Li , Le li , Shuangmei Tong , Fengkai Li

Sea level rise, land subsidence and increasing wave heights will exacerbate inundation risk in future in coastal areas, which are usually densely populated and economically developed. However, the specific distribution of populations at inundation risk and the contribution rates of inundation risk impact factors remain unclear. To address these gaps, we first estimated the numbers and distribution of inundated coastal populations in 2050 and 2100 under three Shared Socioeconomic Pathways (SSPs), including SSP126, SSP245 and SSP585, which represent low-, medium-, and high-emission trajectories, respectively. Second, we analyzed the impacts of inundation risk on coastal populations across different income groups under the three scenarios. Third, we quantified the contribution of inundation risk factors for three scenarios in 2050 and 2100 by using random forest model. Finally, we discuss the impact of inundation risk on the socioeconomic and ecosystem of coastal areas. Our findings indicate that the global population at risk of inundation will exceed 543.6 million by 2050 and 568.7 million by 2100. Inundation risk increases with rising temperatures, with East and Southeast Asia accounting for two-thirds of the affected population. The results show that the populations at inundation risk with lower middle or low income will reach 277.4 million, 284.5 million, and 300.4 million by 2050 under SSP126, SSP245 and SSP585, respectively. These numbers are projected increase to 328 million, 356.7 million and 407.3 million in 2100, amplifying climate-related inequalities. We find that people with lower middle or low income at inundation risk are primarily concentrated in Southeast Asia, South Asia and Africa. Additionally, we identified regional differences in the dominant drivers of inundation risk. Land subsidence plays a primary role in the low latitude countries of Southeast Asia and South Asia, including Vietnam, Bangladesh, the Philippines and Indonesia. In contrast, wave height is the dominant factor in countries like the Netherlands, Egypt, China, and the United States, with its influence increasing as temperatures rise. Inundation can trigger many factors that threaten social stability in a coastal country with low income and severely damage the ecological system of coastal areas. These results underscore the urgent need for targeted climate adaptation strategies, particularly in low-income coastal regions. These findings enhance understanding of inundation risk drivers and provide scientific support for hazard mitigation and coastal ecosystem protection.

海平面上升、地面沉降和浪高增加将加剧沿海地区未来的淹没风险，这些地区通常是人口稠密和经济发达的地区。然而，淹没风险人口的具体分布和淹没风险影响因子的贡献率尚不清楚。为了解决这些差距，我们首先估算了2050年和2100年三个共享社会经济路径（ssp）下沿海淹没人口的数量和分布，包括SSP126、SSP245和SSP585，分别代表低、中、高排放轨迹。其次，分析了三种情景下淹没风险对不同收入群体沿海人口的影响。第三，利用随机森林模型量化了2050年和2100年三种情景下洪涝风险因子的贡献。最后，讨论了淹没风险对沿海地区社会经济和生态系统的影响。我们的研究结果表明，到2050年，全球面临淹没风险的人口将超过5.436亿，到2100年将超过5.687亿。随着气温上升，洪水泛滥的风险也在增加，东亚和东南亚占受影响人口的三分之二。结果表明：到2050年，在SSP126、SSP245和SSP585条件下，面临淹没风险的中低收入人口将分别达到2.774亿、2.845亿和3.004亿；预计到2100年，这一数字将分别增加到3.28亿、3.567亿和4.073亿，加剧与气候相关的不平等。我们发现，面临洪水风险的中低收入人群主要集中在东南亚、南亚和非洲。此外，我们还确定了洪水风险的主要驱动因素的区域差异。在东南亚和南亚的低纬度国家，包括越南、孟加拉国、菲律宾和印度尼西亚，地面沉降起着主要作用。相比之下，在荷兰、埃及、中国和美国等国家，浪高是主要因素，其影响随着气温的升高而增加。在一个低收入的沿海国家，洪水会引发许多威胁社会稳定的因素，并严重破坏沿海地区的生态系统。这些结果强调了制定有针对性的气候适应战略的迫切需要，特别是在低收入沿海地区。这些发现增强了对洪水风险驱动因素的认识，并为减灾和沿海生态系统保护提供了科学支持。

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引用次数: 0

Fishery knowledge-guided machine learning for spatial prediction of catch-per-unit-effort 渔业知识引导机器学习用于单位努力渔获量的空间预测

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2026-01-01 DOI: 10.1016/j.ecolind.2025.114586

Runze Shi , Mingyang Xie , Bin Liu , Xinjun Chen , Wei Yu , Jintao Wang

Catch per unit effort (CPUE) is a key indicator of fish stock abundance. However, CPUE estimates derived from fishery logbooks are highly susceptible to noise and missing entries, leading to systematic bias in abundance estimation. To address this issue, we developed a comprehensive knowledge-guided machine learning (KGML) framework that incorporates both data preprocessing and model refinement, designed to enhance the accuracy and ecological consistency of spatial CPUE predictions. We applied this framework to neon flying squid (Ommastrephes bartramii) data collected in the Northwest Pacific during 2002–2019, using ocean environmental variables, including sea surface temperature, salinity, height, and chlorophyll-a, as factors for modeling and prediction. Guided by fishery expertise, we first constructed a refined dataset by removing implausible outliers and likely false-zero records. Initial experimental results confirmed that knowledge-guided data cleaning substantially improved model performance. However, subsequent Shapley additive explanations (SHAP) feature contribution analysis revealed spatial information dominated the feature importance rankings to an unreasonable degree, suggesting the model primarily memorized locations. To mitigate this effect, we further introduced a cost-aware loss function in model refinement, assigning a greater weight to the loss incurred by non-zero CPUE samples. The final SHAP analysis validated this refinement strategy, confirming a successful shift in the model's predictive focus from spatial memorization towards environmental drivers. In conclusion, this two-stage KGML approach not only maximized predictive accuracy and robustness but also significantly strengthened species distribution models by ensuring theoretical consistency in feature contributions. This provides a practical and robust framework for improving ecological indicators and supporting ecosystem-based fishery management, particularly in data-limited contexts.

单位渔获量（CPUE）是鱼类资源丰度的重要指标。然而，从渔业日志中得出的CPUE估计非常容易受到噪声和缺失条目的影响，导致丰度估计的系统性偏差。为了解决这个问题，我们开发了一个综合的知识引导机器学习（KGML）框架，该框架结合了数据预处理和模型改进，旨在提高空间CPUE预测的准确性和生态一致性。我们将这一框架应用于2002-2019年在西北太平洋收集的霓虹灯飞乌贼（Ommastrephes bartramii）数据，使用海洋环境变量，包括海面温度、盐度、高度和叶绿素-a，作为建模和预测的因素。在渔业专业知识的指导下，我们首先通过删除不可信的异常值和可能的假零记录构建了一个精炼的数据集。初步的实验结果证实，知识引导的数据清洗大大提高了模型的性能。然而，随后的Shapley加性解释（SHAP）特征贡献分析表明，空间信息在特征重要性排名中的主导地位不合理，表明模型主要记忆位置。为了减轻这种影响，我们在模型改进中进一步引入了成本感知损失函数，为非零CPUE样本造成的损失分配了更大的权重。最终的SHAP分析验证了这一改进策略，证实了模型预测重点从空间记忆向环境驱动因素的成功转变。总之，这种两阶段KGML方法不仅最大限度地提高了预测精度和鲁棒性，而且通过确保特征贡献的理论一致性，显著增强了物种分布模型。这为改善生态指标和支持基于生态系统的渔业管理，特别是在数据有限的情况下，提供了一个切实可行和强有力的框架。

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引用次数: 0

A global assessment of BirdNET performance: Differences among continents, biomes, and species BirdNET绩效的全球评估：大陆、生物群落和物种之间的差异

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2026-01-01 DOI: 10.1016/j.ecolind.2025.114550

David Funosas , Esther Sebastián-González , Jon Morant , Oscar H. Marín Gómez , Irene Mendoza , Miguel A. Mohedano-Muñoz , Eduardo Santamaría , Giulia Bastianelli , Alba Márquez-Rodríguez , Michał Budka , Gerard Bota , Cristina D. Alonso-Moya , José M. de la Peña-Rubio , Eladio L. García de la Morena , Manu Santa-Cruz , Pablo de la Nava , Mario Fernández-Tizón , Hugo Sánchez-Mateos , Adrián Barrero , Juan Traba , Cristian Pérez-Granados

Recent advances in machine learning have accelerated automated species detection across diverse ecological domains, enabling large-scale, non-invasive monitoring of biodiversity. In ornithological research, the combination of passive acoustic monitoring (PAM) and rapidly-developing novel identification tools such as BirdNET—a deep learning–based sound recognition algorithm—offers new opportunities for surveying vocally active bird communities. Here, we present the first worldwide evaluation of BirdNET using 4224 one-minute recordings from 67 sites across all continents annotated by local experts. More specifically, we assessed the capacity of BirdNET to accurately identify individual vocalizations and characterize bird communities based on the automated analysis of passively collected soundscapes. We further analyzed how its performance varies across continents, biomes, species, and minimum confidence thresholds. The proportion of correct BirdNET predictions (precision) was generally high and consistent across continents (range: 0.57–0.71) and biomes (range: 0.55–0.76). In contrast, the proportion of vocalizations successfully detected (recall) was generally lower and more heterogeneous across continents (range: 0.24–0.52) and biomes (range: 0.34–0.72), reflecting differences in species coverage and local ecological context. BirdNET predictive power, as measured by the Precision-Recall Area Under the Curve (PR AUC; higher values indicating better performance), was highest in North America, Oceania, and Europe (range: 0.16–0.23), moderate in Central/South America (0.13), and lowest in Africa and Asia (range: 0.03–0.04). Species-specific analyses revealed substantial heterogeneity in detection accuracy, with optimal confidence thresholds varying widely by species and analytical goal. Our results establish a global reference point for BirdNET reliability and highlight where algorithmic refinement and expanded acoustic sampling are most needed.

机器学习的最新进展加速了跨不同生态域的自动物种检测，使大规模、非侵入性的生物多样性监测成为可能。在鸟类学研究中，被动声学监测（PAM）和快速发展的新型识别工具（如birdnet）的结合为调查发声活跃的鸟类群落提供了新的机会。在这里，我们首次对BirdNET进行全球评估，使用4224段来自各大洲67个地点的一分钟录音，由当地专家注释。更具体地说，我们评估了BirdNET准确识别个体发声的能力，并基于被动收集的声景的自动分析来表征鸟类群落。我们进一步分析了其性能在大陆、生物群落、物种和最小置信阈值之间的差异。正确的BirdNET预测比例（精度）在各大洲（范围：0.57-0.71）和生物群系（范围：0.55-0.76）之间普遍较高且一致。相比之下，不同大陆（0.24-0.52）和生物群系（0.34-0.72）的成功发声比例普遍较低且异质性更强，这反映了物种覆盖和当地生态背景的差异。BirdNET的预测能力，通过曲线下的精确召回面积（PR AUC，更高的值表明更好的性能）来衡量，在北美、大洋洲和欧洲最高（范围：0.16-0.23），在中南美洲中等（0.13），在非洲和亚洲最低（范围：0.03-0.04）。物种特异性分析揭示了检测精度的实质性异质性，最佳置信阈值因物种和分析目标而异。我们的研究结果为BirdNET的可靠性建立了一个全球参考点，并突出了最需要算法改进和扩展声学采样的地方。

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引用次数: 0

The ecological and social benefits of desert photovoltaics: A case study of the Kubuqi Desert 沙漠光伏发电的生态效益和社会效益——以库布其沙漠为例

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2026-01-01 DOI: 10.1016/j.ecolind.2025.114602

Lu Li , Weici Quan , Kun Lyu , Bo Jiang , Bin Chen , Hongguang Cheng , Cuncun Duan

This study examines the correlation between desert photovoltaic (PV) development and ecological sustainability by establishing an integrated ecological-social assessment framework. We combined Markov-PLUS model, InVEST model and the social media-weighted travel cost method to quantify the eco-system services(ESV), which includes water yield, soil conservation, and carbon storage, and the social services (SSV), which includes cultural services social, economic services and technical services of Desert Photovoltaics in the Kubuqi Desert in 2020, 2023, and 2030. At the early construction stage, the system exhibits a trade-off in which improvements in carbon storage and soil conservation are accompanied by a suppression of annual water yield, which leads to a temporary decline of ESV. Under the ecological protection scenario in 2030, as the vegetation structure remains stable and PV arrays cumulatively decrease surface evaporation and promote soil water infiltration, the aggregate ESV and SSV both rebound. The aggregate benefits increase from approximately $344 million in 2020 to $462 million in 2030, indicating a steady upward trend. This study provides a new perspective to assess the benefits of the Desert Photovoltaics, and enhance replicability of the “PV + ecological restoration” approach in arid regions, and also support the integrated renewable-energy siting and ecological management.

本研究通过建立生态-社会综合评价框架，探讨了沙漠光伏发展与生态可持续性的关系。结合Markov-PLUS模型、InVEST模型和社会媒体加权旅行成本法，对库布其沙漠光伏发电在2020年、2023年和2030年的生态系统服务（ESV）和社会服务（SSV）进行了量化，其中生态系统服务包括产水、水土保持和碳储量，社会服务包括文化服务、社会服务、经济服务和技术服务。在早期建设阶段，该系统表现出一种权衡，即碳储量和土壤保持的改善伴随着年产水量的抑制，这导致ESV暂时下降。在2030年生态保护情景下，由于植被结构保持稳定，光伏阵列累计减少地表蒸发，促进土壤水分入渗，总ESV和SSV均出现反弹。总收益从2020年的约3.44亿美元增加到2030年的4.62亿美元，显示出稳步上升的趋势。该研究为沙漠光伏效益评估、增强“光伏+生态修复”模式在干旱区的可复制性、可再生能源综合选址和生态管理提供了新的视角。

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引用次数: 0

Scale-dependent mechanisms of karst ecosystem service drivers revealed by landscape pattern indices 景观格局指数揭示喀斯特生态系统服务驱动力的尺度依赖机制

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2026-01-01 DOI: 10.1016/j.ecolind.2025.114566

Yibo Zhang , Shaodong Qu , Fengxian Huang , Jiangbo Gao

Identifying driving factors is fundamental for ecosystem service management, yet these relationships exhibit strong scale dependence. While previous studies have recognized this scale effect, the underlying mechanisms governing how driving factors respond to scale variations remain poorly understood. To address this knowledge gap, we conducted a comprehensive multi-scale analysis of dominant factors affecting soil conservation and water yield services across 11 distinct grid sizes (ranging from 30 m to 1000 m) and three hierarchical spatial extents (provincial, municipal, and county levels) in Guizhou Province, a typical karst region. By incorporating landscape pattern indices, particularly the Interspersion and Juxtaposition Index (IJI), we elucidated the fundamental mechanisms behind differential scale responses. Our results reveal that IJI serves as a robust predictor of scale sensitivity: (1) Slope (high IJI > 70) exhibited strong grid-size dependence as the primary driver of soil conservation, with its explanatory power decreasing dramatically from 0.24 (30 m) to 0.05 (1000 m); (2) Precipitation (low IJI < 55) dominated water yield but showed greater sensitivity to extent changes, particularly in the transition from municipal to county-level analysis. This is due to the diminishing spatial heterogeneity of precipitation as the extent decreases. This study establishes IJI as a powerful diagnostic indicator for predicting scale-dependent behaviors of ecosystem service drivers, providing a scientific basis for multi-scale ecosystem management and conservation planning.

识别驱动因素是生态系统服务管理的基础，但这些关系表现出强烈的规模依赖性。虽然以前的研究已经认识到这种规模效应，但控制驱动因素如何响应规模变化的潜在机制仍然知之甚少。为了解决这一知识空白，我们在典型喀斯特地区贵州省的11个不同网格尺寸（从30 m到1000 m）和3个分层空间范围（省、市、县）上对影响水土保持和产水服务的主导因素进行了综合多尺度分析。通过结合景观格局指数，特别是穿插和并列指数（IJI），我们阐明了差异尺度响应背后的基本机制。结果表明：①坡度（高IJI >； 70）是土壤保持的主要驱动因素，其解释能力从0.24 （30 m）急剧下降到0.05 (1000 m)；(2)降水（低IJI <； 55）主导水量，但对程度变化表现出更大的敏感性，特别是在从市级到县级的过渡分析中。这是由于降水的空间异质性随着程度的减小而减小。本研究确立了IJI作为预测生态系统服务驱动力尺度依赖行为的有力诊断指标，为多尺度生态系统管理和保护规划提供科学依据。

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引用次数: 0

Species' habitat change over twenty years in Colombia's tropical dry forests 在哥伦比亚的热带干燥森林中，物种的栖息地在20年内发生了变化

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2026-01-01 DOI: 10.1016/j.ecolind.2025.114562

M.I. Arce-Plata , N. Norden , J. Burbano-Girón , G. Larocque , M.C. Díaz , S. Rodriguez-Buriticá , G. Corzo , T. Poisot

<div><div>Countries worldwide are working together under the Convention on Biological Diversity to tackle biodiversity loss. As part of this effort, the monitoring framework of the Kunming-Montreal Global Biodiversity Framework includes a set of indicators to evaluate progress toward its goals and targets. One of them is the Species Habitat Index (SHI), a component indicator supporting Goal A, which measures changes in habitat extent and connectivity for multiple species. Here, we used the SHI to assess the state of species' habitats in Colombia's Tropical Dry Forests (TDF) from 2000 to 2020. This ecosystem has undergone extensive degradation and fragmentation, being reduced to less than 7–8 % of their original extent, dropping to as much as 2 % for certain regions. Overall, we found that Colombia's TDF have lost nearly one-third of its cover since 1990, despite a modest gain between 2010 and 2018. Most losses resulted from conversion to pastureland, although some forest regrowth was observed in most regions. We calculated the SHI values for 755 species (237 birds, 68 mammals and 450 plants) using official land cover data and examined habitat connectivity using both GISFrag and Omniscape. Across the potential TDF area, habitat and connectivity declined by approximately 20 % between 2000 and 2020, leaving only ∼860,000 ha of habitat for these 755 species. Species associated with natural habitats showed lower SHI values than those adapted to artificial environments; and mammals, many of which are threatened, had the lowest scores. About 12 % of the remaining habitat lies within protected areas. The increasing extent of successional forests, over 1000,000 ha, indicates a high potential of natural regeneration and provides insights for guiding restoration. Our results underscore the urgency of implementing nature based solutions. Regionally-tailored strategies will be critical to maintaining connectivity in this highly fragmented ecosystem.</div></div><div><h3>Resumen</h3><div>Países de todo el mundo trabajan juntos en el marco del Convenio sobre la Diversidad Biológica para hacer frente a la pérdida de biodiversidad. Como parte de este esfuerzo, el marco de monitoreo del Marco Global de Biodiversidad Kunming-Montreal incluye una serie de indicadores para hacer seguimiento a los avances hacia sus objetivos y metas. Uno de ellos es el Índice de Hábitat de Especies (SHI, por sus siglas en inglés), un indicador de componente que da soporte a los indicadores del Objetivo A y que mide los cambios en la extensión del hábitat y la conectividad de múltiples especies. En este estudio, aplicamos el SHI para evaluar el estado de los hábitats de las especies en los Bosques Secos Tropicales (BST) de Colombia entre 2000 y 2020. Este ecosistema ha sufrido una extensa degradación y fragmentación, que lo ha reducido a menos del 7–8 % de su extensión original, llegando hasta incluso un 2 % en algunas regiones. En general, encontramos que los BST de Colombia han perdid

世界各国正根据《生物多样性公约》共同努力，解决生物多样性丧失问题。作为这一努力的一部分，《昆明-蒙特利尔全球生物多样性框架》的监测框架包括一套指标，用于评估其目标和具体目标的进展情况。其中之一是物种栖息地指数（SHI），这是支持目标a的一个组成指标，衡量多种物种的栖息地范围和连通性的变化。在这里，我们使用SHI来评估2000年至2020年哥伦比亚热带干燥森林（TDF）物种栖息地的状况。该生态系统经历了广泛的退化和破碎化，减少到不足其原始程度的7 - 8%，某些地区甚至下降到2%。总体而言，我们发现哥伦比亚的TDF自1990年以来损失了近三分之一的覆盖率，尽管在2010年至2018年期间略有增加。虽然在大多数地区观察到一些森林再生，但大部分损失是由于转向牧场造成的。我们利用官方土地覆盖数据计算了755种物种（237种鸟类、68种哺乳动物和450种植物）的SHI值，并使用GISFrag和Omniscape检查了栖息地的连通性。在整个潜在的TDF区域，2000年至2020年期间，栖息地和连通性下降了约20%，这755种物种的栖息地仅剩下约86万公顷。与自然生境相关的物种的SHI值低于适应人工环境的物种；哺乳动物的得分最低，其中许多是濒危动物。大约12%的现存栖息地位于保护区内。演替森林的面积不断增加，超过100万公顷，表明自然更新的潜力很大，并为指导恢复提供了见解。我们的研究结果强调了实施基于自然的解决方案的紧迫性。在这个高度分散的生态系统中，区域定制战略对于保持连通性至关重要。ResumenPaíses de de de el mundo trabajan juntos en el marco del concordia la Diversidad Biológica para hacer frente a la pcameddia de biodiversidad。国际生物多样性监测中心、全球生物多样性监测中心和昆明-蒙特利尔国际生物多样性监测中心包括一系列指标、生物多样性监测中心、生物多样性监测中心、生物多样性监测中心、生物多样性监测中心、生物多样性监测中心、生物多样性监测中心、生物多样性监测中心、生物多样性监测中心等。Uno de elelelÍndice de Hábitat de Especies (SHI, psus siglas en inglsamys), Uno de componente que que de sorsores（目标），Uno de componente que que los cambios (extensión del hábitat), Uno de conconvides （múltiples Especies）。在2000年至2020年期间，哥伦比亚哥伦比亚热带雨林（BST）的应用程序评估了哥伦比亚热带雨林（BST）的生态系统。Este ecosistema, sufrido una extensa degradación y fragmentación, que lo，减少了7 - 8%的菜单，减少了7 - 8%的菜单，减少了7 - 8%的菜单，减少了7 - 8%的菜单，减少了7 - 8%的菜单。总而言之，从1990年起，从2010年至2018年，从1990年起，从2010年至2018年，从1990年起，从2010年至2018年，从1990年起，从1990年起，从1990年起，从2010年至2018年，从1990年起，从2010年至2018年，在哥伦比亚建立了一个统一的BST。关于博斯克人的个人信息收集，关于博斯克人的个人信息收集，关于博斯克人的个人信息收集，关于observó博斯克人的个人信息收集，关于recuperación博斯克人的个人信息收集。Calculamos洛杉矶英勇德施帕拉755 especies(237鸟纲,68 mamiferos 450 y足底)使用的拿督oficiales de cobertura terrestre y examinamos la conectividad del栖息地usando GISFrag y Omniscape。到目前为止，el área潜在的损失BST， el hábitat从2000年到2020年，大约减少了20%，dejando sólo有860,000个，de hábitat有755个品种。两种合生种hábitats自然种，一种天然种，一种自然种，一种人工种，一种人工种。Y los mamíferos, much chos de los cuales están amenazados, obtuvieron los valores de índice más bajos。已经有12%的线虫线虫hábitat抗性线虫线虫线虫和áreas蛋白线虫。La creciente extensión de los bosques sucales， mayor a 100万公顷，indicque hay unbuen potential de regeneración natural y - proporciona información相关para - orientalmedidas de restauración。新结果表明，应用解决方案的迫切性是基于自然的。Las strategies as a cada región serán fundamentales para mantener la conectivida, en este ecosistema and fragmentado。

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引用次数: 0

Atmospheric heatwaves expand hypoxic zones in a deeply stratified mega-reservoir 大气热浪扩大了一个深分层巨型水库的缺氧区

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2026-01-01 DOI: 10.1016/j.ecolind.2025.114565

Miao Liu , Qunfang Huang , Huiyun Li , Guangwei Zhu

Atmospheric heatwaves are emerging as a dominant stressor on freshwater ecosystems under global climate change. However, the impacts of atmospheric heatwaves on hypoxia formation and dissipation in deep lakes remain insufficiently understood. Here, we used the high-frequency buoy data and meteorological data (2017–2024) to examine the effects of extreme high air temperature events on thermal stratification and dissolved oxygen (DO) in Lake Qiandao, a deep subtropical mega-reservoir in China. On average, Lake Qiandao experienced four atmospheric heatwave events per year, with a total duration averaging 27 days per year and the 2022 event setting a record duration (55 days). Prolonged atmospheric heatwaves and consequent enhanced thermal stratification reduced subsurface DO by more than 32 % and expanded deep hypoxic zones vertically, covering up to 40 % of the water column during extreme events. Our findings reveal a 14-day lag between heatwave-induced stratification intensification and hypoxia development, highlighting a delayed but predictable ecosystem response. Future projections suggest that the duration of atmospheric heatwaves could increase fivefold by 2100 under high greenhouse gas emissions scenarios, substantially exacerbating hypoxia risks. These findings underscore atmospheric heatwaves as major drivers of hypoxia in lakes and call for urgent nutrient control and climate mitigation efforts to safeguard water quality and ecosystem resilience.

在全球气候变化的背景下，大气热浪正成为淡水生态系统的主要压力源。然而，大气热浪对深湖缺氧形成和消散的影响尚不清楚。利用高频浮标数据和2017-2024年气象数据，研究了极端高温事件对千岛湖热分层和溶解氧（DO）的影响。千岛湖平均每年经历4次大气热浪事件，平均每年持续27天，2022年的事件创造了55天的记录。长时间的大气热浪和随之而来的热分层增强使地下DO降低了32%以上，并在垂直方向上扩大了深层缺氧区，在极端事件期间覆盖了高达40%的水柱。我们的研究结果揭示了热浪引起的分层加剧和缺氧发展之间的14天滞后，突出了延迟但可预测的生态系统响应。未来的预测表明，在高温室气体排放情景下，到2100年，大气热浪的持续时间可能会增加5倍，大大加剧缺氧风险。这些发现强调了大气热浪是湖泊缺氧的主要驱动因素，并呼吁紧急控制营养物质和减缓气候变化，以保护水质和生态系统的恢复能力。

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引用次数: 0

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2026-01-01 DOI: 10.1016/j.ecolind.2025.114526

Xin Zhang , Yanxia Zhong , Tingqi Xiao

Rivers serve as a critical foundation for social and economic sustainable development, particularly in the arid and semi-arid regions of Northwest China. However, intensifying human activities severely threatens the health of rivers. Thus, this study aimed to systematically assess the health status of rivers in these areas. Specifically, the Diannong River and the Kushui River were selected as the study areas. A comprehensive indicator system that integrates ecological integrity (including physical habitat integrity, hydrological integrity, and biological integrity) and non-ecological performance (social service functions) was developed. The weights of the indicators were determined by a game-theoretic approach. A multidimensional similarity cloud model was established for river health assessment. Additionally, a novel similarity calculation method (SBIO) based on the inner and outer products of fuzzy subsets was proposed, demonstrating stronger noise resistance and stability. Comparative analyses with SES, ECM, and SDSL models suggest that the SBIO model provides a uniquely robust and inclusive similarity measurement under varying entropy and hyper-entropy conditions, yielding consistently higher similarity values and the flattest response curve. From the perspectives of both artificial and natural rivers, key issues affecting ecological health in Northwestern China were identified. The results reflect that both the Diannong and Kushui Rivers are in an overall “Unhealthy” state, with approximately 70 % of their indicators rated as “Unhealthy” or worse. Reduced river connectivity, driven by urbanization and water conservancy projects, emerges as the core issue, followed by non-compliance with chemical oxygen demand (COD_Cr) and total nitrogen (TN) levels. In the Diannong River, TN, COD_Cr, and permanganate index (COD_Mn) exceed standard limits by 1.02–1.60 times during non-water supplementation periods. In the Kushui River, TN and COD_Cr exceed standards by up to 3.93 and 5.14 times annually at most sites. Furthermore, significant alterations appeared in the structure of local aquatic biological communities. This study provides strategies and recommendations for improving river health and promoting sustainable water resource management in Northwest China.

河流是社会和经济可持续发展的重要基础，特别是在中国西北干旱和半干旱地区。然而，人类活动的加剧严重威胁着河流的健康。因此，本研究旨在系统地评估这些地区河流的健康状况。具体而言，以滇农河和苦水河为研究区。建立了生态完整性（包括自然生境完整性、水文完整性和生物完整性）和非生态绩效（社会服务功能）相结合的综合指标体系。采用博弈论方法确定指标的权重。建立了河流健康评价的多维相似云模型。此外，提出了一种基于模糊子集内外积的相似度计算方法（SBIO），该方法具有较强的抗噪性和稳定性。与SES、ECM和SDSL模型的比较分析表明，SBIO模型在变熵和超熵条件下提供了独特的鲁棒性和包容性的相似性度量，得到的相似性值始终较高，响应曲线最平坦。从自然河流和人工河流两方面分析了影响西北地区生态健康的关键问题。结果表明，滇农河和苦水河总体处于“不健康”状态，约70%的指标被评为“不健康”或更差。城市化和水利工程驱动的河流连通性下降是核心问题，其次是化学需氧量（CODCr）和总氮（TN）水平不符合。在非补水期，滇农河TN、CODCr和高锰酸盐指数（CODMn）均超过标准值1.02 ~ 1.60倍。在库水河，大部分监测点的TN和CODCr年超标次数分别高达3.93和5.14次。此外，当地水生生物群落结构也发生了显著变化。本研究为改善西北地区河流健康和促进水资源可持续管理提供了策略和建议。

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引用次数: 0

From cascading drivers to precision management: Integrating causal inference and machine learning for lake trophic state analysis 从级联驱动到精确管理：湖泊营养状态分析的因果推理和机器学习集成

IF 7 2区环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES

Ecological Indicators

Pub Date : 2026-01-01 DOI: 10.1016/j.ecolind.2025.114585

Yong Fang, Jing Wang, Lingjiao Kong, Xianyang Shi

Understanding the cascading drivers of lake trophic states is critical for advancing sustainable water resource management. This study addresses a key research gap by analyzing large-scale patterns across 863 lakes in the United States, incorporating anthropogenic and natural drivers that are often neglected in single-lake assessments. Using generalized additive models and Spearman correlation analysis, we identified significant associations between trophic states ranging from oligotrophic to hypereutrophic and multiple quantitative variables, including population density, geographic coordinates, physicochemical properties, biological indices, and habitat complexity. Collectively, these drivers accounted for a high proportion of deviance (86.3 %) in trophic states. Strong positive correlations were observed between trophic state and turbidity, total nitrogen (TN), and total phosphorus (TP), while a significant negative correlation was found with Secchi depth (SD, |r| > 0.7, p < 0.05). Furthermore, categorical analyses indicated significant variations in the lake origin, surface area, and the integrity of riparian vegetation across different trophic states. Structural equation modeling (SEM) revealed a pivotal causal cascade wherein urbanization and artificial lake typologies intensified anthropogenic disturbances, leading to riparian habitat degradation, increased nutrient loading, elevated turbidity, reduced light penetration, and diminished zooplankton function, ultimately accelerating eutrophication. Complementary analysis using an eXtreme Gradient Boosting model confirmed turbidity, TN, SD, and TP as dominant nonlinear drivers, reflecting ecological threshold responses and multi-stable states. Zooplankton, as key biological mediators, exhibited complex indirect regulatory roles under multistressor conditions. This integrated framework, linking SEM-derived causal inference with machine learning interpretability, advances a scalable, mechanistically grounded approach to lake eutrophication management. The findings support precision restoration strategies that emphasize habitat complexity and zooplankton community dynamics, offering a compelling alternative to conventional mitigation models for conserving and restoring lakes under increasing anthropogenic pressures.

了解湖泊营养状态的级联驱动因素对于推进可持续水资源管理至关重要。本研究通过分析美国863个湖泊的大尺度模式，结合在单一湖泊评估中经常被忽视的人为和自然驱动因素，解决了一个关键的研究空白。利用广义加性模型和Spearman相关分析，我们发现从贫营养到超富营养的营养状态与种群密度、地理坐标、理化性质、生物指标和栖息地复杂性等多个定量变量之间存在显著关联。总的来说，这些驱动因素占营养状态偏差的比例很高（86.3%）。营养状态与浊度、总氮（TN）、总磷（TP）呈极显著正相关，与Secchi深度呈极显著负相关（SD, | > 0.7, p < 0.05）。此外，分类分析表明，不同营养状态的湖泊起源、表面积和河岸植被完整性存在显著差异。结构方程模型（SEM）揭示了一个关键的因果级联，其中城市化和人工湖类型加剧了人为干扰，导致河岸栖息地退化、营养负荷增加、浊度升高、光穿透减少和浮游动物功能减弱，最终加速了富营养化。利用极端梯度增强模型的互补分析证实，浊度、TN、SD和TP是主要的非线性驱动因素，反映了生态阈值响应和多稳定状态。浮游动物作为重要的生物介质，在多应激条件下表现出复杂的间接调控作用。这个集成框架将sem衍生的因果推理与机器学习的可解释性联系起来，为湖泊富营养化管理提出了一种可扩展的、基于机械的方法。研究结果支持强调栖息地复杂性和浮游动物群落动态的精确恢复策略，为在日益增加的人为压力下保护和恢复湖泊提供了一个令人信服的替代传统缓解模型。

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