Pub Date : 2024-11-20DOI: 10.1016/j.ecolind.2024.112848
Xiangrui Xu , Lu Chen , Xiaoyun Du , Qiaojing Chen , Renpeng Yuan
Global economic growth has led to substantial carbon dioxide emissions, positioning urban low-carbon transformation as a crucial strategy for addressing climate change. A scientific evaluation of low-carbon city (LCC) performance is vital for effective implementation. However, existing studies predominantly focus on assessing LCCs from a singular perspective of either effectiveness or efficiency, often neglecting a comprehensive consideration of both. To address this gap, this study employs Back Propagation (BP) Neural Network and three-stage Data Envelopment Analysis (DEA) models to conduct an empirical assessment of LCC performance in 35 mega-cities in China from both effectiveness and efficiency dimensions. The findings reveal that: (1) The dual-perspective evaluation method effectively reflects LCC performance from both process and outcome aspects; (2) In some regions, effectiveness and efficiency yield consistent results, indicating both are either high or low; conversely, in other regions, they exhibit complementarity, with instances of high effectiveness coupled with low efficiency, or vice versa; (3) Temporal analysis indicates a continuous improvement in LCC effectiveness over the study period, while efficiency demonstrates considerable fluctuations; (4) Spatial analysis highlights that cities like Shanghai, Guangzhou, and Shenzhen excel, whereas Taiyuan, Hefei, and Zhengzhou lag behind. This research offers essential policy insights for the construction of LCCs.
{"title":"Development pathways for low carbon cities in China: A dual perspective of effectiveness and efficiency","authors":"Xiangrui Xu , Lu Chen , Xiaoyun Du , Qiaojing Chen , Renpeng Yuan","doi":"10.1016/j.ecolind.2024.112848","DOIUrl":"10.1016/j.ecolind.2024.112848","url":null,"abstract":"<div><div>Global economic growth has led to substantial carbon dioxide emissions, positioning urban low-carbon transformation as a crucial strategy for addressing climate change. A scientific evaluation of low-carbon city (LCC) performance is vital for effective implementation. However, existing studies predominantly focus on assessing LCCs from a singular perspective of either effectiveness or efficiency, often neglecting a comprehensive consideration of both. To address this gap, this study employs Back Propagation (BP) Neural Network and three-stage Data Envelopment Analysis (DEA) models to conduct an empirical assessment of LCC performance in 35 mega-cities in China from both effectiveness and efficiency dimensions. The findings reveal that: (1) The dual-perspective evaluation method effectively reflects LCC performance from both process and outcome aspects; (2) In some regions, effectiveness and efficiency yield consistent results, indicating both are either high or low; conversely, in other regions, they exhibit complementarity, with instances of high effectiveness coupled with low efficiency, or vice versa; (3) Temporal analysis indicates a continuous improvement in LCC effectiveness over the study period, while efficiency demonstrates considerable fluctuations; (4) Spatial analysis highlights that cities like Shanghai, Guangzhou, and Shenzhen excel, whereas Taiyuan, Hefei, and Zhengzhou lag behind. This research offers essential policy insights for the construction of LCCs.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"169 ","pages":"Article 112848"},"PeriodicalIF":7.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-20DOI: 10.1016/j.ecolind.2024.112860
Chen Xu , Juanle Wang , Yamin Sun , Meng Liu , Jingxuan Liu , Meer Muhammad Sajjad
The coastal regions of the China–Pakistan Economic Corridor (CPEC) are crucial links for the “21st Century Maritime Silk Road”. Nonetheless, this region is facing significant ecological challenges due to natural disasters and intensive human activity. To effectively monitor and assess the ecological health of these critical coastal zones, this study employed integrated labels and a deep learning model to obtain land cover data spanning from 2000 to 2023. It then constructed a vigour-organisation-resilience (VOR) model with 12 assessment indicators to evaluate the landscape ecological health of this region. The evaluation results showed distinct spatial patterns. Gwadar and Ormara’s “Bare land” areas remained “Sick,” while Karachi and Lower Indus’ “Impervious surfaces” were “Unhealthy” with minimal fluctuations. The Lower Indus region saw “Sub-healthy” expansion with increased “Crops” areas. Lasbela was “Healthy,” dominated by shrub-based “Other vegetation,” and the Indus Delta’s mangroves maintained a “Very healthy” state. Overall, the CPEC coastal regions were rated “Unhealthy,” with signs of moderate improvement. We recommend that the CPEC coastal areas focus on restoring “Sick” areas, promoting sustainable agriculture in “Sub-healthy” regions, and conserving “Healthy” and “Very healthy” areas. This study demonstrates the efficacy of deep learning and VOR model in assessing long-term ecological health, providing a valuable framework that can be applied in other coastal regions facing similar challenges.
{"title":"Deep learning-driven land cover monitoring and landscape ecological health assessment: A dynamic study in coastal regions of the China–Pakistan Economic Corridor from 2000 to 2023","authors":"Chen Xu , Juanle Wang , Yamin Sun , Meng Liu , Jingxuan Liu , Meer Muhammad Sajjad","doi":"10.1016/j.ecolind.2024.112860","DOIUrl":"10.1016/j.ecolind.2024.112860","url":null,"abstract":"<div><div>The coastal regions of the China–Pakistan Economic Corridor (CPEC) are crucial links for the “21st Century Maritime Silk Road”. Nonetheless, this region is facing significant ecological challenges due to natural disasters and intensive human activity. To effectively monitor and assess the ecological health of these critical coastal zones, this study employed integrated labels and a deep learning model to obtain land cover data spanning from 2000 to 2023. It then constructed a vigour-organisation-resilience (VOR) model with 12 assessment indicators to evaluate the landscape ecological health of this region. The evaluation results showed distinct spatial patterns. Gwadar and Ormara’s “Bare land” areas remained “Sick,” while Karachi and Lower Indus’ “Impervious surfaces” were “Unhealthy” with minimal fluctuations. The Lower Indus region saw “Sub-healthy” expansion with increased “Crops” areas. Lasbela was “Healthy,” dominated by shrub-based “Other vegetation,” and the Indus Delta’s mangroves maintained a “Very healthy” state. Overall, the CPEC coastal regions were rated “Unhealthy,” with signs of moderate improvement. We recommend that the CPEC coastal areas focus on restoring “Sick” areas, promoting sustainable agriculture in “Sub-healthy” regions, and conserving “Healthy” and “Very healthy” areas. This study demonstrates the efficacy of deep learning and VOR model in assessing long-term ecological health, providing a valuable framework that can be applied in other coastal regions facing similar challenges.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"169 ","pages":"Article 112860"},"PeriodicalIF":7.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-20DOI: 10.1016/j.ecolind.2024.112859
Ivana Svitková , Marek Svitok , Tomáš Čejka , Pavel Širka , Dobromil Galvánek , Dušan Gömöry , Erika Gömöryová , Judita Kochjarová , Dušan Senko , Katarína Skokanová , Michal Slezák , Barbora Šingliarová , Stanislav Španiel , Richard Hrivnák
River corridors are among the most important natural pathways for invasive species to spread into landscapes. Nevertheless, the ecological processes underlying invasions of riparian habitats are poorly understood for many taxonomic groups. We sampled bryophytes, vascular plants, and molluscs along three West Carpathian rivers (Central Europe) to identify spatial trends and drivers of native and alien species diversity across multiple taxa. Generalised additive models revealed decreasing downstream diversity patterns across all studied rivers and taxonomic groups. In contrast, alien diversity showed the opposite trend, displaying a high degree of idiosyncrasy among the rivers. Random forest analysis revealed that climate-induced variables (altitude and related temperature) played a more pronounced role in the diversity of alien species than in the diversity of native species. The diversity of native species was more influenced by local land use and habitat alternations (molluscs) or by source-to-mouth river interactions along the longitudinal gradient (plants). Dispersal limitation and temperature constrain alien species distributions along river corridors, while a multitude of natural and anthropic influences drive native species diversity. The climate-driven distribution of alien plants and molluscs suggests future altitudinal and longitudinal shifts in non-native species along river corridors, which will be exacerbated by ongoing climate warming and associated environmental changes.
{"title":"Contrasting diversity patterns of native and alien species across multiple taxa in Central European river corridors","authors":"Ivana Svitková , Marek Svitok , Tomáš Čejka , Pavel Širka , Dobromil Galvánek , Dušan Gömöry , Erika Gömöryová , Judita Kochjarová , Dušan Senko , Katarína Skokanová , Michal Slezák , Barbora Šingliarová , Stanislav Španiel , Richard Hrivnák","doi":"10.1016/j.ecolind.2024.112859","DOIUrl":"10.1016/j.ecolind.2024.112859","url":null,"abstract":"<div><div>River corridors<!--> <!-->are among<!--> <!-->the most important natural pathways for invasive species to spread into landscapes. Nevertheless, the ecological processes underlying invasions of riparian habitats are poorly understood for many taxonomic groups. We sampled bryophytes, vascular plants, and molluscs along three West Carpathian rivers (Central Europe) to identify spatial trends and drivers of native and alien species diversity across multiple taxa. Generalised additive models revealed decreasing downstream diversity patterns across all studied rivers and taxonomic groups. In contrast, alien diversity showed the opposite trend, displaying a high degree of idiosyncrasy among the rivers. Random forest analysis revealed that climate-induced variables (altitude and related temperature) played a more pronounced role in the diversity of alien species than in the diversity of native species. The diversity of native species was more influenced by local land use and habitat alternations (molluscs) or by source-to-mouth river interactions along the longitudinal gradient (plants). Dispersal limitation and temperature constrain alien species distributions along river corridors, while a multitude of natural and anthropic influences drive native species diversity. The climate-driven distribution of alien plants and molluscs suggests future altitudinal and longitudinal shifts in non-native species along river corridors, which will be exacerbated by ongoing climate warming and associated environmental changes.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"169 ","pages":"Article 112859"},"PeriodicalIF":7.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-20DOI: 10.1016/j.ecolind.2024.112862
Md. Ayenuddin Haque , Bithy Khatun , Md. Abu Sayed Jewel , Jesmin Ara , Md.Shahidul Islam Kazal , Jakia Hasan
We investigated the spatial–temporal variability, contamination sources, and risk of heavy metals (HMs) on environment and public health in water of the Barnoi River, Bangladesh from July 2019 to June 2020. Surface waters were sampled from five distinct sampling sites and measured for physico-chemical parameters (temperature, pH, dissolved oxygen, total alkalinity, total hardness, total dissolved solids, electrical conductivity, biological oxygen demand, nitrate-nitrogen and phosphate-phosphorus) and HMs (As, Cd, Cr, Pb, Ni, Cu and Zn) content. Physico-chemical parameters were analyzed following standard procedure, and HMs concentration was analyzed by an atomic absorption spectrophotometer (AAS). We assessed the surface water quality for drinking and aquatic life standard, metal pollution indices (HPI and HEI) and non– cancer and cancer risks. Furthermore, we employed correlation analysis (CoA), principal component analysis (PCA), and cluster analysis (CA), to define the inter-metal relationship, distribution and potential origin. Our results indicated that all physico-chemical parameters satisfied drinking and aquatic life standards (WHO, USEPA). However, among the analyzed HMs, Cr, As, Pb and Cd surpassed the maximum recommended value. Principle component analysis (PCA) revealed distribution of the studied metals, possibly anthropogenic sources (industrial, agricultural, household, and municipal wastewater). Based on the indices, the Barnoi River water was ‘undrinkable’ (DWQI = 158.66) and the index value was higher at Site-1 and Site-2. However, the CCME-WQI index for aquatic life or fisheries indicated that the surface water was in a ‘fair’ condition (CCME-WQI = 70.65). The mean values of HPI (257.41) and HEI (17.47) indicate ‘unsuitable for drinking’ and ‘medium pollution’ conditions of the surface water, respectively. Health risk evaluation revealed that children are more susceptible to both non-carcinogenic and carcinogenic risks by Cr, As and Ni than the adults. Regular monitoring is recommended by the responsible authorities and policymakers.
{"title":"Assessment of water quality and heavy metal indices in a tropical freshwater river for aquatic life and public health standard","authors":"Md. Ayenuddin Haque , Bithy Khatun , Md. Abu Sayed Jewel , Jesmin Ara , Md.Shahidul Islam Kazal , Jakia Hasan","doi":"10.1016/j.ecolind.2024.112862","DOIUrl":"10.1016/j.ecolind.2024.112862","url":null,"abstract":"<div><div>We investigated the spatial–temporal variability, contamination sources, and risk of heavy metals (HMs) on environment and public health in water of the Barnoi River, Bangladesh from July 2019 to June 2020. Surface waters were sampled from five distinct sampling sites and measured for physico-chemical parameters (temperature, pH, dissolved oxygen, total alkalinity, total hardness, total dissolved solids, electrical conductivity, biological oxygen demand, nitrate-nitrogen and phosphate-phosphorus) and HMs (As, Cd, Cr, Pb, Ni, Cu and Zn) content. Physico-chemical parameters were analyzed following standard procedure, and HMs concentration was analyzed by an atomic absorption spectrophotometer (AAS). We assessed the surface water quality for drinking and aquatic life standard, metal pollution indices (HPI and HEI) and non– cancer and cancer risks. Furthermore, we employed correlation analysis (CoA), principal component analysis (PCA), and cluster analysis (CA), to define the inter-metal relationship, distribution and potential origin. Our results indicated that all physico-chemical parameters satisfied drinking and aquatic life standards (WHO, USEPA). However, among the analyzed HMs, Cr, As, Pb and Cd surpassed the maximum recommended value. Principle component analysis (PCA) revealed distribution of the studied metals, possibly anthropogenic sources (industrial, agricultural, household, and municipal wastewater). Based on the indices, the Barnoi River water was ‘undrinkable’ (DWQI = 158.66) and the index value was higher at Site-1 and Site-2. However, the CCME-WQI index for aquatic life or fisheries indicated that the surface water was in a ‘fair’ condition (CCME-WQI = 70.65). The mean values of HPI (257.41) and HEI (17.47) indicate ‘unsuitable for drinking’ and ‘medium pollution’ conditions of the surface water, respectively. Health risk evaluation revealed that children are more susceptible to both non-carcinogenic and carcinogenic risks by Cr, As and Ni than the adults. Regular monitoring is recommended by the responsible authorities and policymakers.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"169 ","pages":"Article 112862"},"PeriodicalIF":7.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706963","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1016/j.ecolind.2024.112852
Xianqi Zhang , Peng Chen , Shengnan Dai
This study delves into the hydrological cycle and drought characteristics of the Yi-Luo River Basin. By quantitatively assessing the impact of climate change on historical runoff variations in the basin, it provides a scientific basis for the rational planning of water and soil resources and the formulation of drought response strategies in the Yi-Luo River Basin. It offers more in-depth guidance for water resource management and ecological protection in the basin. Utilizing the SWAT model, a comprehensive assessment of the drought situation in the Yi-Luo River Basin from 1983 to 2023 was conducted. The study constructed a distributed hydrological model and a drought evaluation model for the basin, combined with GIS technology, to deeply analyze hydrological processes and drought characteristics. It was found that the drought phenomenon in the Yi-Luo River Basin has distinct seasonal and cyclical characteristics, with higher drought rates in summer and autumn due to insufficient precipitation, and a cyclical trend of increasing then decreasing and then increasing again since the 20th century. The spatial distribution characteristics of drought show that extreme and severe droughts are mainly concentrated in the middle reaches of the basin, especially in the Ruyang area, while the upper mountainous areas and downstream plains are generally characterized by mild drought. The distribution characteristics of drought duration indicate that the average duration of drought in the Yi-Luo River Basin is about 32 months, with severe and extreme droughts lasting about 16 months. Drought is more persistent in the middle reaches, while the upper and lower reaches have shorter drought periods. The spatial distribution characteristics of drought intensity show that the average drought intensity in the basin is 25, with higher drought intensity in the central and eastern parts facing more severe droughts. The impact of extreme droughts is relatively small in the upper and middle reaches and the northern part of the basin.
{"title":"A study on drought assessment in the Yi Luo River Basin based on SWAT","authors":"Xianqi Zhang , Peng Chen , Shengnan Dai","doi":"10.1016/j.ecolind.2024.112852","DOIUrl":"10.1016/j.ecolind.2024.112852","url":null,"abstract":"<div><div>This study delves into the hydrological cycle and drought characteristics of the Yi-Luo River Basin. By quantitatively assessing the impact of climate change on historical runoff variations in the basin, it provides a scientific basis for the rational planning of water and soil resources and the formulation of drought response strategies in the Yi-Luo River Basin. It offers more in-depth guidance for water resource management and ecological protection in the basin. Utilizing the SWAT model, a comprehensive assessment of the drought situation in the Yi-Luo River Basin from 1983 to 2023 was conducted. The study constructed a distributed hydrological model and a drought evaluation model for the basin, combined with GIS technology, to deeply analyze hydrological processes and drought characteristics. It was found that the drought phenomenon in the Yi-Luo River Basin has distinct seasonal and cyclical characteristics, with higher drought rates in summer and autumn due to insufficient precipitation, and a cyclical trend of increasing then decreasing and then increasing again since the 20th century. The spatial distribution characteristics of drought show that extreme and severe droughts are mainly concentrated in the middle reaches of the basin, especially in the Ruyang area, while the upper mountainous areas and downstream plains are generally characterized by mild drought. The distribution characteristics of drought duration indicate that the average duration of drought in the Yi-Luo River Basin is about 32 months, with severe and extreme droughts lasting about 16 months. Drought is more persistent in the middle reaches, while the upper and lower reaches have shorter drought periods. The spatial distribution characteristics of drought intensity show that the average drought intensity in the basin is 25, with higher drought intensity in the central and eastern parts facing more severe droughts. The impact of extreme droughts is relatively small in the upper and middle reaches and the northern part of the basin.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"169 ","pages":"Article 112852"},"PeriodicalIF":7.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1016/j.ecolind.2024.112846
Valentin H. Klaus , Franziska J. Richter , Nina Buchmann , Martin Hartmann , Andreas Lüscher , Olivier Huguenin-Elie
Grasslands are highly multifunctional ecosystems, providing forage to livestock and many regulating and cultural ecosystem services (ES). Agri-environmental schemes (AES) often aim at sustaining and increasing especially non-production ES, i.e., those services not primarily relevant for production but for society as a whole. An open question restricting the implementation of such AES for grassland ES multifunctionality is how to effectively measure and monitor multifunctionality without separately accounting for all single ES.
To address this question, we measured 30 plot-level ES indicators, including plant species richness, in 88 permanent grasslands along a fertilization intensity gradient in Switzerland. We explored the correlative structure among all ES indicators and the potential of each indicator to approximate non-production ES multifunctionality. We finally discuss potentially suitable ES-multifunctionality indicators for future result-based AES.
The analyses revealed two distinct bundles within the comprehensive list of ES indicators considered in the study. The first bundle consisted of ten ES indicators, including aesthetic appreciation, fungal richness, plant richness, and several ES indicators for reduced adverse environmental impacts (e.g., lower nutrient leaching risk). This bundle was strongly negatively related to the second bundle, composed of twelve ES indicators that were mostly directly related to intensive forage production (e.g., nutrient supply, yield quantity and yield quality). Plant species richness (positive) and fertilization intensity (negative) were the two measures most closely related to non-production multifunctionality, highlighting their potential to be put to use as multifunctionality indicators.
We argue that due to the policy relevance of biodiversity conservation, plant species richness could find application as indicator for AES designed to increase and monitor grassland non-production multifunctionality. While plant species richness is rather stable over time, considering changes (reductions) in fertilization intensity could be an option for a more responsive indicator to be used to facilitate ES-positive grassland management on the short term. Integrating our findings in future agricultural policies could be a significant step towards rewarding land users for the non-production benefits provided by their agroecosystems.
草地是高度多功能的生态系统，为牲畜提供饲料，并提供许多调节和文化生态系统服务 (ES)。农业环境计划 (AES) 通常旨在维持和增加非生产性生态系统服务，即主要与生产无关但对整个社会有益的服务。为了解决这个问题，我们测量了瑞士施肥强度梯度上 88 块永久性草地的 30 个地块级 ES 指标，包括植物物种丰富度。针对这一问题，我们在瑞士施肥强度梯度的 88 块永久性草地上测量了 30 个地块级 ES 指标，包括植物物种丰富度。我们探讨了所有 ES 指标之间的相关结构，以及每个指标近似非生产 ES 多功能性的潜力。最后，我们讨论了可能适用于未来基于结果的农业生态学研究的ES多功能性指标。分析表明，在本研究考虑的ES指标综合清单中有两个不同的指标组。第一组由 10 个 ES 指标组成，包括美学鉴赏力、真菌丰富度、植物丰富度，以及若干降低不利环境影响的 ES 指标（如降低营养物质沥滤风险）。这一组指标与第二组指标呈强烈负相关，第二组指标由 12 个 ES 指标组成，大多与集约化饲草生产直接相关（如养分供应、产量和质量）。植物物种丰富度（正相关）和施肥强度（负相关）是与非生产多功能性关系最密切的两个指标，突出了它们作为多功能性指标的应用潜力。我们认为，由于生物多样性保护的政策相关性，植物物种丰富度可以作为旨在提高和监测草地非生产多功能性的农业生态服务的指标。虽然植物物种丰富度随着时间的推移相当稳定，但考虑施肥强度的变化（减少）也不失为一种更灵敏的指标选择，可用于促进短期内积极的ES草原管理。将我们的研究结果纳入未来的农业政策中，将是对土地使用者因其农业生态系统提供的非生产性效益而给予奖励的重要一步。
{"title":"Indicators for assessing the multifunctionality of agriculturally used grasslands","authors":"Valentin H. Klaus , Franziska J. Richter , Nina Buchmann , Martin Hartmann , Andreas Lüscher , Olivier Huguenin-Elie","doi":"10.1016/j.ecolind.2024.112846","DOIUrl":"10.1016/j.ecolind.2024.112846","url":null,"abstract":"<div><div>Grasslands are highly multifunctional ecosystems, providing forage to livestock and many regulating and cultural ecosystem services (ES). Agri-environmental schemes (AES) often aim at sustaining and increasing especially non-production ES, i.e., those services not primarily relevant for production but for society as a whole. An open question restricting the implementation of such AES for grassland ES multifunctionality is how to effectively measure and monitor multifunctionality without separately accounting for all single ES.</div><div>To address this question, we measured 30 plot-level ES indicators, including plant species richness, in 88 permanent grasslands along a fertilization intensity gradient in Switzerland. We explored the correlative structure among all ES indicators and the potential of each indicator to approximate non-production ES multifunctionality. We finally discuss potentially suitable ES-multifunctionality indicators for future result-based AES.</div><div>The analyses revealed two distinct bundles within the comprehensive list of ES indicators considered in the study. The first bundle consisted of ten ES indicators, including aesthetic appreciation, fungal richness, plant richness, and several ES indicators for reduced adverse environmental impacts (e.g., lower nutrient leaching risk). This bundle was strongly negatively related to the second bundle, composed of twelve ES indicators that were mostly directly related to intensive forage production (e.g., nutrient supply, yield quantity and yield quality). Plant species richness (positive) and fertilization intensity (negative) were the two measures most closely related to non-production multifunctionality, highlighting their potential to be put to use as multifunctionality indicators.</div><div>We argue that due to the policy relevance of biodiversity conservation, plant species richness could find application as indicator for AES designed to increase and monitor grassland non-production multifunctionality. While plant species richness is rather stable over time, considering changes (reductions) in fertilization intensity could be an option for a more responsive indicator to be used to facilitate ES-positive grassland management on the short term. Integrating our findings in future agricultural policies could be a significant step towards rewarding land users for the non-production benefits provided by their agroecosystems.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"169 ","pages":"Article 112846"},"PeriodicalIF":7.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-19DOI: 10.1016/j.ecolind.2024.112851
Wenwen Gao , Zuoyong Huang , Xiuzhong Li , Baocun Ji , Na Li , Senyang Li , Xingyu Liu , Qingwei Zeng , Guangnian Sun , Dan Zhao
Wuliangsuhai watershed is a large freshwater lake in Hulunbuir City, Inner Mongolia Autonomous Region, with important ecological, cultural and economic values. Based on the Landsat 8 OLI remote sensing images, this study used the object-oriented CART decision tree to map the land cover, the pixel dichotomy model to estimate the FVC and Albedo-NDVI formula to estimate DDI in the Wuliangsuhai watershed in 2020, 2021 and 2022. Besides, Geo-informatic tupu and geographical detector model were also used to analysis the changing patterns and drivers of desertification. The results indicated a decline in desertification in the Wuliangsuhai watershed from 2020 to 2022. The area of barren lands exhibited a 17.41% reduction with a 37.78% decline in the FVC in the no vegetation class. The area of the DDI in the no desertification level increased, while that of the extremely severe desertification level decreased. Furthermore, the geo-informatic tupu indicated that the desertification improvement area showed consistent growth during the study period. However, a considerable proportion of the grasslands (32.73%) remained degraded, and the area of FVC in the moderately dense vegetation class exhibited a notable decline (21.32%). And the area affected by severe desertification level of DDI increased. This can be attributed to a combination of climatic, ecosystem conditions and human impacts. In particular, the reduction in precipitation, coupled with the steep slopes and negative human activities, has accelerated the desertification process. Consequently, this study provides recommendations for policy makers to prioritize negative human activities and soil erosion in the Wuliangsuhai watershed.
{"title":"The spatial–temporal changes and driving factors of desertification in the Wuliangsuhai watershed based on remote sensing","authors":"Wenwen Gao , Zuoyong Huang , Xiuzhong Li , Baocun Ji , Na Li , Senyang Li , Xingyu Liu , Qingwei Zeng , Guangnian Sun , Dan Zhao","doi":"10.1016/j.ecolind.2024.112851","DOIUrl":"10.1016/j.ecolind.2024.112851","url":null,"abstract":"<div><div>Wuliangsuhai watershed is a large freshwater lake in Hulunbuir City, Inner Mongolia Autonomous Region, with important ecological, cultural and economic values. Based on the Landsat 8 OLI remote sensing images, this study used the object-oriented CART decision tree to map the land cover, the pixel dichotomy model to estimate the FVC and Albedo-NDVI formula to estimate DDI in the Wuliangsuhai watershed in 2020, 2021 and 2022. Besides, Geo-informatic tupu and geographical detector model were also used to analysis the changing patterns and drivers of desertification. The results indicated a decline in desertification in the Wuliangsuhai watershed from 2020 to 2022. The area of barren lands exhibited a 17.41% reduction with a 37.78% decline in the FVC in the no vegetation class. The area of the DDI in the no desertification level increased, while that of the extremely severe desertification level decreased. Furthermore, the geo-informatic tupu indicated that the desertification improvement area showed consistent growth during the study period. However, a considerable proportion of the grasslands (32.73%) remained degraded, and the area of FVC in the moderately dense vegetation class exhibited a notable decline (21.32%). And the area affected by severe desertification level of DDI increased. This can be attributed to a combination of climatic, ecosystem conditions and human impacts. In particular, the reduction in precipitation, coupled with the steep slopes and negative human activities, has accelerated the desertification process. Consequently, this study provides recommendations for policy makers to prioritize negative human activities and soil erosion in the Wuliangsuhai watershed.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"169 ","pages":"Article 112851"},"PeriodicalIF":7.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-18DOI: 10.1016/j.ecolind.2024.112857
Ying Wang , Yanan Chen , Jianguang Wen , Chaoyang Wu , Wei Zhou , Lei Han , Xuguang Tang
Droughts have posed, and continue to pose, severe risks to terrestrial ecosystems. Particularly against the backdrop of global climate change, the intensity and frequency of extreme droughts are expected to further aggravate. However, a significant gap persists in early drought warning for vegetation monitoring. Therefore, this study examined the spatial and temporal dynamics of two summer drought events happened in Southwest China in 2011 and 2022, and analyzed the early responses of four ecological indicators including global Orbiting Carbon Observatory-2 (OCO-2) SIF dataset (GOSIF), the leaf-scale fluorescence yield (), the near-infrared reflectance of vegetation (NIRv) and the normalized difference vegetation index (NDVI) to drought extremes. All these indicators successfully captured the drought-induced vegetation stress, but as a proxy for vegetation photosynthesis, GOSIF was the most sensitive. Specifically, during the 2022 drought, GOSIF fell below the baseline year as early as day of year (DOY) 193, whereas NIRv and NDVI began at DOY 201, and lagged severely. Similar behaviour was also found in the drought period of 2011. Overall, compared to the baseline year, GOSIF, , NIRv and NDVI decreased by 96.93 %, 54.11 %, 43.92 % and 17.03 % in 2011, and reduced by 70.00 %, 42.01 %, 48.74 % and 19.53 % in 2022, respectively. During the past two decades, GOSIF exhibited the strongest correlation with drought intensity (r = 0.880, p < 0.05), followed by NIRv (r = 0.875, p < 0.05) and NDVI (r = 0.871, p < 0.05), and was the weakest (r = 0.432, p > 0.05). Spatially, the proportion of areas where the correlations exceeded 0.6 by GOSIF and NIRv were 42.39 % and 39.32 %, respectively. In summary, this study demonstrated that global re-constructed GOSIF possesses considerable potential as an early warning indicator for vegetation drought.
{"title":"Early warning of drought-induced vegetation stress using multiple satellite-based ecological indicators","authors":"Ying Wang , Yanan Chen , Jianguang Wen , Chaoyang Wu , Wei Zhou , Lei Han , Xuguang Tang","doi":"10.1016/j.ecolind.2024.112857","DOIUrl":"10.1016/j.ecolind.2024.112857","url":null,"abstract":"<div><div>Droughts have posed, and continue to pose, severe risks to terrestrial ecosystems. Particularly against the backdrop of global climate change, the intensity and frequency of extreme droughts are expected to further aggravate. However, a significant gap persists in early drought warning for vegetation monitoring. Therefore, this study examined the spatial and temporal dynamics of two summer drought events happened in Southwest China in 2011 and 2022, and analyzed the early responses of four ecological indicators including global Orbiting Carbon Observatory-2 (OCO-2) SIF dataset (GOSIF), the leaf-scale fluorescence yield (<span><math><msub><mi>Φ</mi><mi>f</mi></msub></math></span>), the near-infrared reflectance of vegetation (NIRv) and<!--> <!-->the normalized difference vegetation index (NDVI) to drought extremes. All these indicators successfully captured the drought-induced vegetation stress, but as a proxy for vegetation photosynthesis, GOSIF was the most sensitive. Specifically, during the 2022 drought, GOSIF fell below the baseline year as early as day of year (DOY) 193, whereas NIRv and NDVI began at DOY 201, and <span><math><msub><mi>Φ</mi><mi>f</mi></msub></math></span> lagged severely. Similar behaviour was also found in the drought period of 2011. Overall, compared to the baseline year, GOSIF, <span><math><msub><mi>Φ</mi><mi>f</mi></msub></math></span>, NIRv and NDVI decreased by 96.93 %, 54.11 %, 43.92 % and 17.03 % in 2011, and reduced by 70.00 %, 42.01 %, 48.74 % and 19.53 % in 2022, respectively. During the past two decades, GOSIF exhibited the strongest correlation with drought intensity (<em>r</em> = 0.880, <em>p</em> < 0.05), followed by NIRv (<em>r</em> = 0.875, <em>p</em> < 0.05) and NDVI (<em>r</em> = 0.871, <em>p</em> < 0.05), and <span><math><msub><mi>Φ</mi><mi>f</mi></msub></math></span> was the weakest (<em>r</em> = 0.432, <em>p</em> > 0.05). Spatially, the proportion of areas where the correlations exceeded 0.6 by GOSIF and NIRv were 42.39 % and 39.32 %, respectively. In summary, this study demonstrated that global re-constructed GOSIF possesses considerable potential as an early warning indicator for vegetation drought.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"169 ","pages":"Article 112857"},"PeriodicalIF":7.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-18DOI: 10.1016/j.ecolind.2024.112831
Shuhang Chang , Xiang Gao , Jie Li , Qiuran Li , Xiaojiao Song , An Yan , Kevin Lo
Fragile dryland ecosystems face increasing destabilization risks due to frequent hydrometeorological anomalies. This study assessed ecosystem stability in the arid region of Northwest China (ARNWC) under hydroclimatic disturbances using the Autoregression (ARx) model. The assessment was based on the Normalized Difference Vegetation Index (NDVI) from 2001 to 2022 and ecosystem responses to changes in precipitation, soil moisture, and potential evapotranspiration. Key findings include: 1) Precipitation anomaly is the most important controlling factor affecting the ecosystem stability of ARNWC. 2) Ecosystem resilience and resistance exhibited a trade-off relationship, with 26.2 % of the area showing significant clustering of “high resilience - low resistance” or “low resilience - high resistance”. 3) Steppe and desert vegetation demonstrated lower resilience but higher resistance, while shrubs and meadows showed the opposite pattern. 4) Resilience generally decreased with increasing aridity (R2 = 0.584, p < 0.001), while resistance increased (R2 = 0.656, p < 0.001). 5) Lower altitude regions were more resistant but less likely to recover from disturbances, while higher altitude regions were more sensitive to hydroclimatic anomalies but more resilient; In different mountains, a complex nonlinear relationship between stability and altitude was observed. In addition, using an “exposure-resistance-resilience” framework, ecosystems in Northern Xinjiang were found vulnerable to precipitation anomalies, while the Hexi Region faced greater pressure from soil moisture anomalies. This study highlights the crucial role of wet and dry conditions in dryland ecosystem stability, suggesting that long-term ecosystem stability equilibrium is achieved through species adaptation to environmental conditions. These insights provide valuable guidance for ecosystem management in ARNWC and similar drylands.
{"title":"Ecosystem stability assessment under hydroclimatic anomalies in the arid region of Northwest China","authors":"Shuhang Chang , Xiang Gao , Jie Li , Qiuran Li , Xiaojiao Song , An Yan , Kevin Lo","doi":"10.1016/j.ecolind.2024.112831","DOIUrl":"10.1016/j.ecolind.2024.112831","url":null,"abstract":"<div><div>Fragile dryland ecosystems face increasing destabilization risks due to frequent hydrometeorological anomalies. This study assessed ecosystem stability in the arid region of Northwest China (ARNWC) under hydroclimatic disturbances using the Autoregression (ARx) model. The assessment was based on the Normalized Difference Vegetation Index (NDVI) from 2001 to 2022 and ecosystem responses to changes in precipitation, soil moisture, and potential evapotranspiration. Key findings include: 1) Precipitation anomaly is the most important controlling factor affecting the ecosystem stability of ARNWC. 2) Ecosystem resilience and resistance exhibited a trade-off relationship, with 26.2 % of the area showing significant clustering of “high resilience - low resistance” or “low resilience - high resistance”. 3) Steppe and desert vegetation demonstrated lower resilience but higher resistance, while shrubs and meadows showed the opposite pattern. 4) Resilience generally decreased with increasing aridity (R<sup>2</sup> = 0.584, <em>p</em> < 0.001), while resistance increased (R<sup>2</sup> = 0.656, <em>p</em> < 0.001). 5) Lower altitude regions were more resistant but less likely to recover from disturbances, while higher altitude regions were more sensitive to hydroclimatic anomalies but more resilient; In different mountains, a complex nonlinear relationship between stability and altitude was observed. In addition, using an “exposure-resistance-resilience” framework, ecosystems in Northern Xinjiang were found vulnerable to precipitation anomalies, while the Hexi Region faced greater pressure from soil moisture anomalies. This study highlights the crucial role of wet and dry conditions in dryland ecosystem stability, suggesting that long-term ecosystem stability equilibrium is achieved through species adaptation to environmental conditions. These insights provide valuable guidance for ecosystem management in ARNWC and similar drylands.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"169 ","pages":"Article 112831"},"PeriodicalIF":7.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142705740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-18DOI: 10.1016/j.ecolind.2024.112769
Feng Lin , Jicheng Zhu , Aiju You , Lei Hua
The precise monitoring and quantification of fish migration are crucial for enhancing agricultural productivity and promoting environmental conservation. However, conducting these tasks in natural environments presents challenges due to the subtle characteristics of fish and the inherent complexities in detection. This study addresses these challenges by introducing DVE-YOLO (Dynamic Vision Enhanced YOLO), a novel framework based on the YOLOv8 architecture, complemented by a tailored sample allocation strategy and a dedicated loss function. Operating on dual-frame input, DVE-YOLO integrates deep features from consecutive images to create composite anchor boxes from adjacent frames. This design enables DVE-YOLO to capture dynamic object features, reveal correlations of detected objects across frames, and facilitate efficient tracking and detection. Furthermore, this research proposes an innovative method for identifying fish migration through fish counting, documenting both the migration area and the duration of fish presence for subsequent analysis. Evaluation on an extensive fish migration dataset demonstrates that DVE-YOLO outperforms YOLOv8 and other mainstream detection algorithms, showcasing superior detection accuracy with higher and metrics. In terms of counting accuracy, DVE-YOLO achieves a lower Mean Squared Error (MSE) compared to YOLOv8+BoTSORT and YOLOv8+ByteTrack, indicating improved counting performance. Additionally, DVE-YOLO exhibits enhanced precision in identifying fish migration in contrast to YOLOv8+BoTSORT and YOLOv8+ByteTrack. Ultimately, these machine learning methods holds promising prospects for ecological applications.
{"title":"Machine vision approach for monitoring and quantifying fish school migration","authors":"Feng Lin , Jicheng Zhu , Aiju You , Lei Hua","doi":"10.1016/j.ecolind.2024.112769","DOIUrl":"10.1016/j.ecolind.2024.112769","url":null,"abstract":"<div><div>The precise monitoring and quantification of fish migration are crucial for enhancing agricultural productivity and promoting environmental conservation. However, conducting these tasks in natural environments presents challenges due to the subtle characteristics of fish and the inherent complexities in detection. This study addresses these challenges by introducing DVE-YOLO (Dynamic Vision Enhanced YOLO), a novel framework based on the YOLOv8 architecture, complemented by a tailored sample allocation strategy and a dedicated loss function. Operating on dual-frame input, DVE-YOLO integrates deep features from consecutive images to create composite anchor boxes from adjacent frames. This design enables DVE-YOLO to capture dynamic object features, reveal correlations of detected objects across frames, and facilitate efficient tracking and detection. Furthermore, this research proposes an innovative method for identifying fish migration through fish counting, documenting both the migration area and the duration of fish presence for subsequent analysis. Evaluation on an extensive fish migration dataset demonstrates that DVE-YOLO outperforms YOLOv8 and other mainstream detection algorithms, showcasing superior detection accuracy with higher <span><math><msub><mrow><mtext>AP</mtext></mrow><mrow><mn>50</mn></mrow></msub></math></span> and <span><math><msub><mrow><mtext>AP</mtext></mrow><mrow><mn>50</mn><mo>−</mo><mn>95</mn></mrow></msub></math></span> metrics. In terms of counting accuracy, DVE-YOLO achieves a lower Mean Squared Error (MSE) compared to YOLOv8+BoTSORT and YOLOv8+ByteTrack, indicating improved counting performance. Additionally, DVE-YOLO exhibits enhanced precision in identifying fish migration in contrast to YOLOv8+BoTSORT and YOLOv8+ByteTrack. Ultimately, these machine learning methods holds promising prospects for ecological applications.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"169 ","pages":"Article 112769"},"PeriodicalIF":7.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142706422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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