Ecological Indicators最新文献_第3页

Spatial and temporal variation of water stress in China and its driving factors: A multi-scale analysis 中国水资源压力的时空变化及其驱动因素：多尺度分析

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

Ecological Indicators

Pub Date : 2024-11-14 DOI: 10.1016/j.ecolind.2024.112820

Jianmin Qiao , Qin Zhang , Jing Shao , Qian Cao , Haimeng Liu , Furong Lv

Water resources are fundamental for sustaining natural ecosystems and human activities, playing a critical role in the sustainable development of the regional environment. Under the dual pressures of human activities and climate change, however, the stress on water resources has become increasingly evident, emerging as one of the greatest global risks for the next decade. In this study, by applying the water stress index, Lorenz curve, and Theil index, we explored the spatiotemporal patterns and inequality distribution characteristics of water resource stress across two scales: catchment and basin. Additionally, we used partial least squares regression to identify the key factors influencing water resource stress. The results indicated significant regional variations in water stress across China during 2002 to 2020. At the catchment scale, areas with a water stress index greater than 0.4 were distributed in the eastern, northeastern and northwestern regions. While at the basin scale, a north–south pattern emerged with lower stress in the south and higher stress in the north. The Haihe and Huaihe river basins exhibited the highest water stress. The Lorenz curve deviated significantly from the line of absolute equality, indicating a high degree of inequality in regional water resource stress. The Theil index increased from 1.26 to 1.50, showing a slight upward trend in inequality. Analysis of the driving factors revealed that the Yellow River Basin was primarily influenced by GDP and population, the Songhua River Basin was affected by population and urban land use, and the Southwest River Basin is driven mainly by vegetation cover. Overall, precipitation was the most critical driver affecting water stress, predominantly exerting a negative influence. This study provides a theoretical basis for alleviating regional water stress and offers valuable insights for the scientific planning and management of water resources.

水资源是维持自然生态系统和人类活动的基础，在区域环境的可持续发展中发挥着至关重要的作用。然而，在人类活动和气候变化的双重压力下，水资源的压力日益明显，已成为未来十年全球最大的风险之一。在本研究中，我们运用水资源压力指数、洛伦兹曲线和泰尔指数，探讨了流域和盆地两个尺度上水资源压力的时空模式和不平等分布特征。此外，我们还利用偏最小二乘法回归找出了影响水资源压力的关键因素。结果表明，2002 年至 2020 年期间，中国水资源压力存在明显的区域差异。在流域尺度上，水资源紧张指数大于 0.4 的地区主要分布在东部、东北部和西北部地区。而在流域尺度上，则出现了南低北高的格局。海河和淮河流域的水压力最大。洛伦兹曲线明显偏离绝对平等线，表明区域水资源压力的不平等程度较高。Theil 指数从 1.26 上升到 1.50，表明不平等程度略有上升趋势。对驱动因素的分析表明，黄河流域主要受 GDP 和人口的影响，松花江流域受人口和城市土地利用的影响，西南河流域主要受植被覆盖的驱动。总体而言，降水是影响水资源压力的最关键驱动因素，主要产生负面影响。该研究为缓解区域水资源压力提供了理论依据，并为水资源的科学规划和管理提供了宝贵的启示。

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引用次数: 0

Invasion of Pine Wilt Disease: A threat to forest carbon storage in China 松材线虫病的入侵：对中国森林碳储存的威胁

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

Ecological Indicators

Pub Date : 2024-11-14 DOI: 10.1016/j.ecolind.2024.112819

Bohai Hu , Wenjiang Huang , Zhuoqing Hao , Jing Guo , Yanru Huang , Xiangzhe Cheng , Jing Zhao , Quanjun Jiao , Biyao Zhang

China’s forests, which balance atmospheric carbon (C) levels through photosynthesis, play a crucial role in combating global climate change. The emergence of Pine wilt disease (PWD), caused by the pine wood nematode (PWN, Bursaphelenchus xylophilus), has challenged the stability of these forests, leading to significant tree mortality and disrupting the original ecological balance. However, the impact of PWD on carbon storage and recovery in Chinese forests remains unclear. In this study, we integrated multiple data sources, including forest surveys, remote sensing, and meteorological observations, and applied a method of finely partitioning the resistance of host pine trees across China. Using the MaxEnt model, a live carbon risk model, and a C recovery REGIME model that incorporates disturbance mechanisms, we predicted the forest C risk loss caused by the comprehensive invasion of PWD and assessed the C recovery time for affected forests. We estimate that the total risk of C loss due to PWD invasion under current climate conditions in Chinese forests is 483.23 Tg C, with an average C recovery time of 13.95 years. The main risk areas for PWD are concentrated in the southern coastal regions of China and adjacent provinces, presenting a risk spillover pattern that radiates from focal areas outward. The six provinces with the highest forest risk degree (risk C/total regional C) are, in order, Fujian (13.69%), Zhejiang (9.42%), Hunan (7.49%), Guangxi (7.40%), Jiangxi (7.35%), and Guangdong (7.05%). Our findings indicate that the severe consequences of PWD invasion have transformed affected forests from C sinks to sources. This underscores the urgency of implementing effective measures to block its introduction and spread, thereby promoting the recovery and sustainable development of forest ecosystems.

中国的森林通过光合作用平衡大气中的碳含量，在应对全球气候变化方面发挥着至关重要的作用。由松材线虫（PWN，Bursaphelenchus xylophilus）引起的松材线虫病（PWD）的出现对这些森林的稳定性提出了挑战，导致树木大量死亡，破坏了原有的生态平衡。然而，PWD对中国森林碳储存和碳恢复的影响仍不清楚。在这项研究中，我们整合了森林调查、遥感和气象观测等多种数据源，并应用一种方法对中国各地寄主松树的抗性进行了精细划分。利用 MaxEnt 模型、活碳风险模型和包含干扰机制的碳恢复 REGIME 模型，我们预测了 PWD 全面入侵造成的森林碳风险损失，并评估了受影响森林的碳恢复时间。我们估计，在当前气候条件下，中国森林因有害生物入侵造成的碳损失总风险为 483.23 Tg C，平均碳恢复时间为 13.95 年。破坏性干旱的主要风险区集中在中国南部沿海地区及邻近省份，呈现出从重点地区向外辐射的风险溢出模式。森林风险度（风险 C/区域总 C）最高的六个省份依次为福建（13.69%）、浙江（9.42%）、湖南（7.49%）、广西（7.40%）、江西（7.35%）和广东（7.05%）。我们的研究结果表明，PWD 入侵的严重后果已经使受影响的森林从碳汇转变为碳源。这凸显了采取有效措施阻止其引入和扩散，从而促进森林生态系统恢复和可持续发展的紧迫性。

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引用次数: 0

Developing cyanobacterial bloom indicators from spatiotemporal differences in productivity and water quality across a lake-stream network 根据湖泊-溪流网络中生产力和水质的时空差异制定蓝藻水华指标

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

Ecological Indicators

Pub Date : 2024-11-12 DOI: 10.1016/j.ecolind.2024.112838

Jordyn Brown , Aaron Krivchenia , Matt J. Pierce , Courtney E. Richmond , Nathan Ruhl

Cyanobacterial Harmful Algal Blooms (cHABs) are an increasingly common occurrence in inland waters and carry ecological, economic, and public health consequences. It is difficult to predict when a cHAB will occur and there is a need to develop methods (indicators) to accurately predict the development of cHABs Here, we studied planktonic primary production (chlorophyll and phycocyanin) in a lake-stream network that is prone to cHABs in southern New Jersey, during bloom and non-bloom years. Primary productivity was lake-dependent, with productivity patterns interacting across sampling locations and years (p < 0.001 for both chlorophyll and phycocyanin). The lake with recurrent cHABs had higher productivity readings in both years, but the sampling location within this lake had a large influence on the observed primary productivity patterns. Productivity differences among lakes were greater in the bloom year compared to the non-bloom year. The bloom year was characterized by a strong correlation between conductivity and nitrate readings, suggesting that cHABs in our study system are associated with nutrient-laden runoff. The linear progression of primary productivity readings was a better indicator for the onset of cHAB conditions than temporal autocorrelation using weekly samples.

蓝藻有害藻华（cHABs）在内陆水域越来越常见，并对生态、经济和公共健康造成了影响。很难预测何时会发生 cHAB，因此有必要开发能准确预测 cHAB 发展的方法（指标）。在此，我们研究了新泽西州南部一个易发生 cHAB 的湖流网络在藻华和非藻华年的浮游初级生产力（叶绿素和藻蓝蛋白）。初级生产力取决于湖泊，不同取样地点和年份的生产力模式相互影响（叶绿素和藻蓝蛋白的 p 均为 0.001）。经常发生 cHABs 的湖泊在这两年的生产力读数都较高，但该湖泊的取样地点对观察到的初级生产力模式有很大影响。与非藻华年相比，藻华年湖泊之间的生产力差异更大。水华年的特点是电导率和硝酸盐读数之间有很强的相关性，这表明我们研究系统中的 cHABs 与富含营养物质的径流有关。初级生产力读数的线性递增比使用每周样本的时间自相关性更能说明 cHAB 的开始。

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引用次数: 0

Evaluating the trade-offs between nutrient and cadmium levels in soils in northeastern China: Accounting for variations in soil factors 评估中国东北地区土壤养分和镉含量之间的权衡：考虑土壤因子的变化

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

Ecological Indicators

Pub Date : 2024-11-12 DOI: 10.1016/j.ecolind.2024.112795

Guohao Xie , Yang Yang , Ying Hou , Bo Wang , Weiping Chen

Agricultural soils have relied on the application of fertilizers to enhance soil fertility and yields in response to increasing food demands. However, the potentially hazardous trace elements that accumulate in soils have been largely overlooked. In this study, we set out to determine the soil factor indicators in croplands using Exploratory Factor Analysis to illuminate the trade-off between surplus soil nutrients and cadmium (Cd) accumulation as a result of fertilizer application. The research in northeastern China highlights the fact that studies tend to ignore the accumulation and distribution of hazardous heavy metals in production fields in favor of an over-emphasis on soil fertility indicators; an ultimately unsustainable approach. The model showed that soil nutrient could be identified based on three soil factors: soil organic matter, soil available nutrients, and soil nutrient buffer structures. Fertilization enhanced the level of available nutrients and significantly increased both soil organic matter and available phosphorus by 0.71 % and 11 mg kg⁻¹, respectively. However, the long-term application of phosphorus (P) leads to a P-surplus and leaves soils more susceptible to Cd accumulation. The 90th percentile estimate of soil Cd concentration was 1.4 times higher than the P-optimal level. Scenario analyses of long-term fertilizer management indicated that, over a 50-year simulation period, the impact of Cd accumulation in soils in traditional agriculture was insignificant. However, prolonged application of excess P-fertilizer would lead to a continuous increase in the concentration of accumulated Cd from 0.17 mg kg⁻¹ to 0.40 mg kg⁻¹. Trade-off and scenario analyses guide agricultural fertilization practices to preserve soil quality while sustaining productivity.

农业土壤一直依赖施肥来提高土壤肥力和产量，以满足日益增长的粮食需求。然而，土壤中积累的潜在有害微量元素却在很大程度上被忽视了。在这项研究中，我们利用探索性因子分析法确定了耕地的土壤因子指标，以揭示施肥导致的土壤养分过剩与镉（Cd）积累之间的权衡关系。在中国东北地区进行的研究突出表明，研究往往忽视有害重金属在生产田中的积累和分布，而过分强调土壤肥力指标，这种做法最终是不可持续的。该模型表明，土壤养分可根据土壤有机质、土壤可利用养分和土壤养分缓冲结构这三个土壤因子进行识别。施肥提高了可利用养分的水平，并使土壤有机质和可利用磷分别显著增加了 0.71 % 和 11 mg kg-1。然而，长期施磷会导致磷过剩，使土壤更容易积累镉。土壤中镉浓度的第 90 百分位数估计值比最佳磷含量高 1.4 倍。对长期肥料管理的情景分析表明，在 50 年的模拟期内，传统农业土壤中镉积累的影响不大。然而，长期施用过量的钾肥会导致累积镉浓度从 0.17 毫克/千克-1 持续上升到 0.40 毫克/千克-1。权衡和情景分析可指导农业施肥实践，在保持生产力的同时保护土壤质量。

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引用次数: 0

Modelling and spatial prediction of earthworms ecological-categories distribution reveal their habitat and environmental preferences 蚯蚓生态类别分布建模和空间预测揭示其生境和环境偏好

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

Ecological Indicators

Pub Date : 2024-11-12 DOI: 10.1016/j.ecolind.2024.112832

Gabriel Salako , Andrey Zaitsev , Bibiana Betancur-Corredor , David J. Russell

Earthworms are one of the important soil animals and have been generally described as soil engineers. Knowledge on environmental conditions driving the distribution and population of this soil animal and the habitat which support these conditions especially at the ecological level is required to understand their responses to these environmental conditions at different habitats so as to guide its usage as bio indicator of soil quality and health. In this study we use RandomForest (RF), a machine learning algorithm to model species distribution, density/abundance based (SDM/SAM) and predict the biodiversity distribution (richness and density, ind.m⁻²) of three basic earthworms ecological categories: epigeic, endogeic and anecic (including the epi-anecic subcategory) across soil and climate variables at multiple habitat type/land uses in Germany. Our study shows there are spatial/ geographic variation in the distribution of the species richness and density among the three earthworms’ ecological categories. Also their environmental and habitat preferences are equally different, while epigeic species are predicted to be climate driven mostly in forests, endogeics are predicted to be the most diverse (in richness and density), but are mostly driven by soil textural contents (clay and silt) and found primarily in arable and grassland. Vineyard and crop flood plain are predicted to be suitable and the preferred habitat for anecics/epi-anecics. This study also identify optimum environmental gradient at which the species density is at the peak in each of the earthworm’s ecological category which would not only provide guide on soil biodiversity monitoring but also the soil health status.

蚯蚓是重要的土壤动物之一，通常被称为土壤工程师。为了了解蚯蚓在不同栖息地对这些环境条件的反应，我们需要了解驱动这种土壤动物分布和数量的环境条件以及支持这些条件的栖息地，尤其是生态层面的条件，从而指导将其用作土壤质量和健康的生物指标。在这项研究中，我们使用机器学习算法 RandomForest（RF）来模拟物种分布、密度/丰度（SDM/SAM），并预测德国多种栖息地类型/土地利用中三种基本蚯蚓生态类别（epigeic、endogeic 和 anecic，包括 epi-anecic 子类别）在土壤和气候变量中的生物多样性分布（丰富度和密度，ind.m-2）。我们的研究表明，三种蚯蚓生态类别的物种丰富度和密度分布存在空间/地理差异。此外，它们对环境和栖息地的偏好也同样不同，表生蚯蚓主要分布在森林中，受气候影响较大；内生性蚯蚓的种类（丰富度和密度）最多，但主要受土壤质地（粘土和淤泥）的影响，主要分布在耕地和草地中。据预测，葡萄园和农田冲积平原将是茴芹/茴芹的适宜栖息地和首选栖息地。这项研究还确定了蚯蚓各生态类别中物种密度最高的最佳环境梯度，这不仅能为土壤生物多样性监测提供指导，还能反映土壤健康状况。

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引用次数: 0

Spatio-temporal variation in arthropod-plant interactions: A direct comparison of eDNA metabarcoding of tree crop flowers and digital video recordings 节肢动物与植物相互作用的时空变化：作物花卉 eDNA 代谢编码与数字视频记录的直接比较

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

Ecological Indicators

Pub Date : 2024-11-12 DOI: 10.1016/j.ecolind.2024.112827

Joshua H. Kestel , Philip W. Bateman , David L. Field , Nicole E. White , Ben L. Phillips , Paul Nevill

Collating data about natural capital and the ecosystem services that underpin agricultural productivity, such as the activity of beneficial (e.g., pollinators) and antagonistic (e.g., plant pests) native and introduced arthropod taxa, is critical for timely management strategies. To date, these monitoring efforts have largely relied upon conventional survey and monitoring methods (e.g., sweep netting and morphological identifications), which are difficult to implement at the large scale of agriculture. Environmental DNA (eDNA) metabarcoding is a molecular method that amplifies trace amounts of DNA deposited by organisms from diverse substrates including soil, plant tissue and even air. In this study, we used eDNA metabarcoding of tree flowers, complemented with digital video recording (DVR) devices, to detect temporal, fine- and large-scale arthropod community changes across two Persea americana (‘Hass’ avocado) orchards. In total, we detected 42 arthropod families with eDNA metabarcoding. This molecular method detected five times the number of unique taxa (N = 50) compared to the DVRs (N = 10), nearly all of which are unmanaged native species. The number of arthropod eDNA detections increased by 14 % during peak flowering and included species from different functional groups including known arthropod pollinators, pests, parasites and predators. At fine-spatial scales, inflorescence samples collected in the upper and lower canopy show that Hymenoptera taxa were 13 % more likely to be detected in the upper canopy. While at large-spatial scales, eDNA metabarcoding showed that the arthropod communities in both orchards shared less than 50 % similarity at low flowering and became more similar towards peak flowering. With occupancy modelling, we determined that arthropod length did not correlate with eDNA detection probability. Our findings highlight the value of eDNA-based monitoring and illustrate that agroecosystem management requires a growing awareness that the production boundary has expanded, and that the goods and services that unmanaged arthropod species provide need to be included on the balance sheet.

整理有关支撑农业生产力的自然资本和生态系统服务的数据，如有益（如传粉昆虫）和有害（如植物害虫）的本地和引进节肢动物类群的活动，对于及时制定管理战略至关重要。迄今为止，这些监测工作主要依赖于传统的调查和监测方法（如扫网和形态鉴定），而这些方法在大规模农业中很难实施。环境 DNA（eDNA）元编码是一种分子方法，可从土壤、植物组织甚至空气等不同基质中扩增生物沉积的微量 DNA。在这项研究中，我们利用树花的 eDNA 代谢标定法，辅以数字视频记录（DVR）设备，检测了两个 Persea americana（"哈斯 "鳄梨）果园中节肢动物群落的时间、精细和大规模变化。通过 eDNA 代谢编码，我们总共检测到 42 个节肢动物家族。这种分子方法检测到的独特类群数量（N = 50）是 DVRs（N = 10）的五倍，其中几乎所有类群都是未经管理的本地物种。在开花高峰期，节肢动物 eDNA 的检测数量增加了 14%，其中包括不同功能类群的物种，包括已知的节肢动物授粉者、害虫、寄生虫和捕食者。在精细空间尺度上，在树冠上部和下部采集的花序样本显示，膜翅目类群在树冠上部的检出率高出 13%。而在大空间尺度上，eDNA 代谢编码显示，两个果园的节肢动物群落在低花期的相似度低于 50%，而在盛花期则变得更加相似。通过占据模型，我们确定节肢动物的长度与 eDNA 检测概率无关。我们的研究结果凸显了基于 eDNA 的监测的价值，并说明农业生态系统管理需要日益认识到生产边界已经扩大，需要将未管理的节肢动物物种提供的产品和服务纳入资产负债表。

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引用次数: 0

The impact of multidimensional urbanization on sustainable development goals (SDGs): A long-term analysis of the 31 provinces in China 多维城市化对可持续发展目标（SDGs）的影响：对中国 31 个省份的长期分析

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

Ecological Indicators

Pub Date : 2024-11-11 DOI: 10.1016/j.ecolind.2024.112822

Xiaoxuan Li , Wen Song , Shisong Cao , You Mo , Mingyi Du , Ziyue He

Sustainable development, intimately linked to the survival of the global human population, has garnered immense attention. The rapid pace of urbanization has exerted a profound influence on the achievement and progress towards the United Nations’ Sustainable Development Goals (SDGs). Nevertheless, a knowledge gap persists regarding the comprehensive impact of urbanization on these goals. The present study delved into the multifaceted impacts of urbanization on SDGs through a comprehensive analysis of four distinct urbanization dimensions: land urbanization (LURB), economic urbanization (EURB), population urbanization (PURB), and social urbanization (SURB). We analyzed the spatiotemporal characteristics of the four-dimensional urbanizations in 31 provinces of China from 1995 to 2015 using impervious surface and statistical data. We employed the Spearman coefficient to investigate the interaction between urbanizations and 17 SDGs. Furthermore, we delved into how economic zone settings influenced these interactions. The results reveal that land expansion, GDP per capita, and the degree of social consumption exhibited stronger synergies with SDGs, whereas the share of the secondary sector and the urban population rate demonstrated more trade-off effects. This underscores the importance of considering the multifaceted nature of urbanization when striving to achieve the SDGs. Additionally, the diverse impact of urbanization patterns on SDG implementation across various economic zones emphasizes the need for tailored and region-specific strategies to maximize the positive outcomes of urbanization and promote sustainable development.

可持续发展与全球人类的生存息息相关，已引起人们的极大关注。城市化的快速发展对联合国可持续发展目标（SDGs）的实现和进展产生了深远的影响。然而，在城市化对这些目标的全面影响方面仍然存在知识空白。本研究通过全面分析四个不同的城市化维度：土地城市化（LURB）、经济城市化（EURB）、人口城市化（PURB）和社会城市化（SURB），深入探讨了城市化对可持续发展目标的多方面影响。我们利用不透水面积和统计数据，分析了 1995 年至 2015 年中国 31 个省份四维城市化的时空特征。我们利用斯皮尔曼系数研究了城市化与 17 个可持续发展目标之间的相互作用。此外，我们还深入研究了经济区设置如何影响这些相互作用。结果显示，土地扩张、人均 GDP 和社会消费程度与可持续发展目标之间的协同作用更强，而第二产业的比重和城市人口比率则表现出更多的权衡效应。这强调了在努力实现可持续发展目标时考虑城市化多面性的重要性。此外，城市化模式对不同经济区实现可持续发展目标的影响各不相同，这强调了需要制定有针对性的、针对具体地区的战略，以最大限度地发挥城市化的积极成果，促进可持续发展。

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引用次数: 0

Phosphorus transport process and driving mechanism in the sediment of farm ponds in small watersheds of three Gorges Reservoir area 三峡库区小流域养殖池塘沉积物中磷迁移过程及驱动机制

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

Ecological Indicators

Pub Date : 2024-11-11 DOI: 10.1016/j.ecolind.2024.112787

Yifan Zhao, Wei Zhang, Weihua Zhang

The water quality health of the farm ponds in the small watersheds of the Three Gorges Reservoir Area is critical to maintaining agricultural productivity. The main challenge in managing the water quality is predicting and controlling the release of total phosphorus (TP) from endogenous pollution in the substrate. Numerous studies have shown that endogenous pollution release from large water bodies like lakes is influenced by factors such as temperature and pH. However, knowledge about the response mechanisms in smaller water bodies, such as farm ponds, is still lacking. This study focuses on TP, using indoor simulation tests and orthogonal tests to investigate the transport and transformation of TP in four representative farm ponds located in Ruxi Town, at the heart of the Three Gorges Reservoir Area. Results showed that seasonal variations led to temperature changes thereby significantly affect TP release, with the highest release rates occurring in summer when the temperature was highest. The farm ponds demonstrated a significant annual cycle in phosphorus source-sink dynamics. Furthermore, factors including pH and water depth influenced the release rates; acidic conditions promoted phosphorus release from the substrate more effectively than alkaline conditions. Additionally, disturbances at lower intensities were observed to inhibit TP release. Building on these findings, this study further explored the advantages and limitations of using multiple regression analysis and BP Neural Network models for modeling phosphorus release and predicting annual TP release. Ultimately, the study proposes measures to reduce and control endogenous pollution, laying a foundation for managing eutrophication and protecting aquatic health in farm ponds.

三峡库区小流域内农田池塘的水质健康对维持农业生产力至关重要。水质管理的主要挑战是预测和控制底质中内源污染释放的总磷（TP）。大量研究表明，湖泊等大型水体的内源污染释放受温度和 pH 值等因素的影响。然而，人们对养殖池塘等小型水体的反应机制仍缺乏了解。本研究以可溶性固形物（TP）为研究对象，采用室内模拟试验和正交试验的方法，研究了位于三峡库区中心地带汝溪镇的四个代表性养殖池塘中可溶性固形物的迁移和转化。结果表明，季节变化导致温度变化，从而显著影响 TP 的释放，夏季温度最高时释放率最高。养殖池塘的磷源-汇动态表现出明显的年周期性。此外，pH 值和水深等因素也会影响释放率；酸性条件比碱性条件更有效地促进磷从底质中释放。此外，还观察到较低强度的干扰会抑制 TP 的释放。在这些发现的基础上，本研究进一步探讨了使用多元回归分析和 BP 神经网络模型来模拟磷释放和预测每年 TP 释放量的优势和局限性。最终，该研究提出了减少和控制内源污染的措施，为治理养殖池塘富营养化和保护水生健康奠定了基础。

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引用次数: 0

How can the natural background and ecological & environment promote the green and sustainable development of Chinese tourist attractions? 自然背景和生态环境如何促进中国旅游景点的绿色可持续发展？

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

Ecological Indicators

Pub Date : 2024-11-11 DOI: 10.1016/j.ecolind.2024.112813

Ning Yan , Jing Zhang , Bing Xia , Shihua Li , Wen Yang

In the context of the global carbon peak and carbon neutrality initiatives and post-pandemic, studying the green and sustainable development of tourist attractions is of great significance for the sustainable utilization of tourism resources. This study focuses on tourist attractions in 30 provinces in China from 2001 to 2019, establishes an input–output indicator system for economic efficiency and eco-efficiency, and uses the Super-SBM model in Data Envelopment Analysis to calculate the economic efficiency and eco-efficiency of tourist attractions in China. To analyze the natural background and environmental driving factors that affect eco-efficiency, as well as the interaction between these factors, using a geographic detector model, and propose a green and sustainable development path for tourist attractions. The research results indicate that the eco-efficiency of Chinese tourist attractions was higher than the economic efficiency, and both showed a downward trend. The proportion of altitude and nature reserve area to the area under the jurisdiction, as well as the total investment in environmental pollution control, have a significant impact on eco-efficiency; The interaction between temperature, precipitation, and normalized difference vegetation index (NDVI), and the proportion of nature reserves in the jurisdiction and the total investment in environmental pollution control, is significantly enhanced, indirectly affecting the eco-efficiency of Chinese tourist attractions. Among the natural factors, temperature, precipitation, and NDVI all could interact with altitude to significantly the impacts on the eco-efficiency of Chinese tourist attractions. The research aims to provide a Chinese solution for developing tourist attractions in developing countries similar to China.

在全球碳峰值和碳中和倡议以及后大流行的背景下，研究旅游景区的绿色可持续发展对旅游资源的可持续利用具有重要意义。本研究以2001-2019年中国30个省份的旅游景区为研究对象，建立经济效益和生态效益的投入产出指标体系，运用数据包络分析法中的Super-SBM模型计算中国旅游景区的经济效益和生态效益。利用地理检测器模型分析影响生态效益的自然背景和环境驱动因素，以及这些因素之间的相互作用，提出旅游景区绿色可持续发展路径。研究结果表明，中国旅游景区的生态效益高于经济效益，且均呈下降趋势。海拔高度、自然保护区面积占辖区面积的比例以及环境污染治理总投资对生态效益有显著影响；气温、降水、归一化差异植被指数（NDVI）与辖区自然保护区面积占辖区面积的比例以及环境污染治理总投资的交互作用显著增强，间接影响中国旅游景区的生态效益。在自然因素中，气温、降水和 NDVI 均可与海拔高度相互作用，对中国旅游景区的生态效益产生显著影响。该研究旨在为类似中国的发展中国家开发旅游景点提供中国解决方案。

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引用次数: 0

Study on spatiotemporal changes of wetlands based on PLS-SEM and PLUS model: The case of the Sanjiang Plain 基于 PLS-SEM 和 PLUS 模型的湿地时空变化研究：以三江平原为例

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

Ecological Indicators

Pub Date : 2024-11-09 DOI: 10.1016/j.ecolind.2024.112812

Jinhao Shi , Peng Zhang , Yang Liu , Le Tian , Yazhuo Cao , Yue Guo , Ji Li , Yunhan Wang , Junhan Huang , Ri Jin , Weihong Zhu

Wetlands are among the most productive ecosystems and play crucial roles in relation to biodiversity conservation and various ecosystem services. However, rapid urbanization and environmental changes have led to the loss of a significant number of wetlands, making it imperative to understand the driving forces behind wetland changes. This study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) and the Patch-generating Land Use Simulation (PLUS) model to investigate the influences of natural factors and urbanization on wetland distribution. Based on the driving factors, simulations were conducted for three scenarios—Natural Increase Scenario (NIS), Economic Development Scenario (EDS), and Wetland Protection Scenario (WPS)—projecting the wetland distribution in the Sanjiang Plain until 2050. Results indicate that from 1990 to 2020, the wetland area increased by 9,548.58 km², with paddy fields increasing by 12,995.73 km² and marsh wetlands decreasing by 1,031.9 km². The factors driving wetland distribution varied across different periods. Between 1990 and 2000, topography and urbanization significantly influenced wetland distribution, whereas climate factors became gradually more significant between 2010 and 2020. Furthermore, in addition to exerting direct impacts on wetland distribution, urbanization and climate factors can indirectly affect wetland distribution by influencing topography and soil. Future development scenarios indicate an inevitable increase in paddy field areas and decrease in wetland areas. This framework provides an effective approach for exploring regional wetland changes and supporting regional wetland conservation and future sustainable development.

湿地是最具生产力的生态系统之一，在生物多样性保护和各种生态系统服务方面发挥着至关重要的作用。然而，快速的城市化和环境变化导致大量湿地消失，因此了解湿地变化背后的驱动力势在必行。本研究采用偏最小二乘法结构方程模型（PLS-SEM）和斑块生成土地利用模拟（PLUS）模型来研究自然因素和城市化对湿地分布的影响。根据驱动因素，模拟了三种情景--自然增长情景（NIS）、经济发展情景（EDS）和湿地保护情景（WPS），预测了三江平原至2050年的湿地分布。结果表明，从 1990 年到 2020 年，湿地面积增加了 9548.58 平方公里，其中水田增加了 12995.73 平方公里，沼泽湿地减少了 1031.9 平方公里。不同时期湿地分布的驱动因素各不相同。1990 年至 2000 年期间，地形和城市化对湿地分布的影响较大，而 2010 年至 2020 年期间，气候因素对湿地分布的影响逐渐增大。此外，除了对湿地分布产生直接影响外，城市化和气候因素也会通过影响地形和土壤间接影响湿地分布。未来的发展情景表明，水田面积的增加和湿地面积的减少不可避免。该框架为探索区域湿地变化、支持区域湿地保护和未来可持续发展提供了有效方法。

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引用次数: 0