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Development of soil quality assessment framework: A comprehensive review of indicators, functions, and approaches
IF 7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2025-02-22 DOI: 10.1016/j.ecolind.2025.113272
Ya’nan Fan , Chao Zhang , Wenyou Hu , Khalid Saifullah Khan , Yongcun Zhao , Biao Huang
Soil quality assessment research has progressively developed over four decades through sustained methodological advancements. However, the multifaceted nature of soil functions and complexity of assessment objectives and scales continue to pose significant challenges. In this study, bibliometric analysis was applied to systematically present the development of soil quality assessment research, elucidating the complex interrelationships among soil functions, assessment indicators, and approaches. The results indicated that while the volume of publications on soil quality assessment has steadily increased, with a notable surge between 2018 and 2022, the primary research topics remain focused on soil fertility and productivity, soil environment and safety, soil health and conservation, as well as soil assessment and management. The evolution of assessment objectives demonstrated a transition from singular functional focuses (fertility, environment, health) towards integrated multi-functional assessment approaches. Soil organic matter/ soil organic carbon (SOM/SOC) has been the most popular indicator for the assessment of soil quality due to its significant impact on various soil functions. However, the application of biological indicators in soil quality assessment remained limited, highlighting a gap between understanding and practice in soil quality research. Methodologically, assessment approaches exhibited progression from qualitative to quantitative paradigms. The rapid development of digital technologies presents both challenges and opportunities. Integrating artificial intelligence with multi-source data offers potential for rapid, real-time, large-scale monitoring and assessment of soil quality. This review retrospect the development trends and hotspots, more importantly, forecast the future work directions of soil quality assessment research.
{"title":"Development of soil quality assessment framework: A comprehensive review of indicators, functions, and approaches","authors":"Ya’nan Fan ,&nbsp;Chao Zhang ,&nbsp;Wenyou Hu ,&nbsp;Khalid Saifullah Khan ,&nbsp;Yongcun Zhao ,&nbsp;Biao Huang","doi":"10.1016/j.ecolind.2025.113272","DOIUrl":"10.1016/j.ecolind.2025.113272","url":null,"abstract":"<div><div>Soil quality assessment research has progressively developed over four decades through sustained methodological advancements. However, the multifaceted nature of soil functions and complexity of assessment objectives and scales continue to pose significant challenges. In this study, bibliometric analysis was applied to systematically present the development of soil quality assessment research, elucidating the complex interrelationships among soil functions, assessment indicators, and approaches. The results indicated that while the volume of publications on soil quality assessment has steadily increased, with a notable surge between 2018 and 2022, the primary research topics remain focused on soil fertility and productivity, soil environment and safety, soil health and conservation, as well as soil assessment and management. The evolution of assessment objectives demonstrated a transition from singular functional focuses (fertility, environment, health) towards integrated multi-functional assessment approaches. Soil organic matter/ soil organic carbon (SOM/SOC) has been the most popular indicator for the assessment of soil quality due to its significant impact on various soil functions. However, the application of biological indicators in soil quality assessment remained limited, highlighting a gap between understanding and practice in soil quality research. Methodologically, assessment approaches exhibited progression from qualitative to quantitative paradigms. The rapid development of digital technologies presents both challenges and opportunities. Integrating artificial intelligence with multi-source data offers potential for rapid, real-time, large-scale monitoring and assessment of soil quality. This review retrospect the development trends and hotspots, more importantly, forecast the future work directions of soil quality assessment research.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113272"},"PeriodicalIF":7.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465347","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}
引用次数: 0
On mature reflection: Ozone damage can be detected in oak trees by hyperspectral reflectance
IF 7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2025-02-22 DOI: 10.1016/j.ecolind.2025.113263
Anna Lee Jones , Christian Pfrang , Felicity Hayes , Elizabeth S. Jeffers
At the near-surface, ozone (O3) is a toxic pollutant which has reached dangerously high concentrations across the world and is predicted to continue to rise. O3 reduces the growth, productivity and resilience of trees but the extent of O3 damage to forests is uncertain. To develop a high throughput method of monitoring O3 damage to forests, we pioneer hyperspectral monitoring of O3 damage in adult oak trees across a range of naturally occurring O3 concentrations. Using a machine learning approach, we demonstrate accurate prediction of O3 exposure of trees from hyperspectral leaf reflectance alone. This method could be used for forest level assessments of O3 damage. Vegetation indices characterising green reflectance and red-edge track O3 induced changes in leaf reflectance. Vegetation indices have the potential to scale up O3 damage monitoring across spatial scales. As O3 concentrations continue to rise globally, understanding the extent of O3 damage to forests is crucial to effectively harness the carbon sequestration potential of forests. We demonstrate the exciting potential of spectral monitoring of O3 damage in mature trees under natural conditions.
{"title":"On mature reflection: Ozone damage can be detected in oak trees by hyperspectral reflectance","authors":"Anna Lee Jones ,&nbsp;Christian Pfrang ,&nbsp;Felicity Hayes ,&nbsp;Elizabeth S. Jeffers","doi":"10.1016/j.ecolind.2025.113263","DOIUrl":"10.1016/j.ecolind.2025.113263","url":null,"abstract":"<div><div>At the near-surface, ozone (O<sub>3</sub>) is a toxic pollutant which has reached dangerously high concentrations across the world and is predicted to continue to rise. O<sub>3</sub> reduces the growth, productivity and resilience of trees but the extent of O<sub>3</sub> damage to forests is uncertain. To develop a high throughput method of monitoring O<sub>3</sub> damage to forests, we pioneer hyperspectral monitoring of O<sub>3</sub> damage in adult oak trees across a range of naturally occurring O<sub>3</sub> concentrations. Using a machine learning approach, we demonstrate accurate prediction of O<sub>3</sub> exposure of trees from hyperspectral leaf reflectance alone. This method could be used for forest level assessments of O<sub>3</sub> damage. Vegetation indices characterising green reflectance and red-edge track O<sub>3</sub> induced changes in leaf reflectance. Vegetation indices have the potential to scale up O<sub>3</sub> damage monitoring across spatial scales. As O<sub>3</sub> concentrations continue to rise globally, understanding the extent of O<sub>3</sub> damage to forests is crucial to effectively harness the carbon sequestration potential of forests. We demonstrate the exciting potential of spectral monitoring of O<sub>3</sub> damage in mature trees under natural conditions.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113263"},"PeriodicalIF":7.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465272","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}
引用次数: 0
Spatiotemporal dynamics and influencing factors of soil quality in aeolian desertified lands of the Qinghai-Tibet Plateau
IF 7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2025-02-22 DOI: 10.1016/j.ecolind.2025.113264
Mengzhen Huang , Ruijie Lu , Yongqiu Wu , Tianjun Zhao , Jin Zhao , Luo Ma
Aeolian desertified land (ADL) is widespread across the Qinghai-Tibet Plateau (QTP), posing a significant threat to regional ecological stability. Accurately assessing the spatiotemporal dynamics of soil quality and influencing factors is crucial for ecological restoration and sustainable development. This study analyzed soil quality changes in ADL of the QTP between the 1980s and 2020s and identified key drivers by considering both natural factors and human activities. The results indicate that the soils are characterized by high sand content, alkalinity, and compaction. Nitrogen, potassium, and calcium carbonate (CaCO3) levels are relatively high, whereas organic matter, phosphorus and cation exchange capacity are low. The minimum dataset constructed for soil quality assessment includes organic matter, bulk density, clay, CaCO3, and available phosphorus. Over 90 % of sample points are rated between grade III and V on the Soil Quality Index (SQI), indicating generally poor quality and a decreasing trend from the southeast to the northwest. Over the past 40 years, the SQI declined by 4.21 %, with increased spatial variability. Declines are concentrated in the southwestern plateau, especially in the central and eastern Brahmaputra River Basin and southeastern Inner Plateau. The primary factors influencing SQI, by explanatory power, are vegetation cover, precipitation, wind erosion, and land use. Additionally, interactions between these factors have a greater impact on soil quality than individual factors. Soil quality improves significantly when vegetation cover exceeds 60 %, annual precipitation exceeds 53 mm, wind erosion intensity remains below mild, and the dominant land use is grassland or cropland.
{"title":"Spatiotemporal dynamics and influencing factors of soil quality in aeolian desertified lands of the Qinghai-Tibet Plateau","authors":"Mengzhen Huang ,&nbsp;Ruijie Lu ,&nbsp;Yongqiu Wu ,&nbsp;Tianjun Zhao ,&nbsp;Jin Zhao ,&nbsp;Luo Ma","doi":"10.1016/j.ecolind.2025.113264","DOIUrl":"10.1016/j.ecolind.2025.113264","url":null,"abstract":"<div><div>Aeolian desertified land (ADL) is widespread across the Qinghai-Tibet Plateau (QTP), posing a significant threat to regional ecological stability. Accurately assessing the spatiotemporal dynamics of soil quality and influencing factors is crucial for ecological restoration and sustainable development. This study analyzed soil quality changes in ADL of the QTP between the 1980s and 2020s and identified key drivers by considering both natural factors and human activities. The results indicate that the soils are characterized by high sand content, alkalinity, and compaction. Nitrogen, potassium, and calcium carbonate (CaCO<sub>3</sub>) levels are relatively high, whereas organic matter, phosphorus and cation exchange capacity are low. The minimum dataset constructed for soil quality assessment includes organic matter, bulk density, clay, CaCO<sub>3</sub>, and available phosphorus. Over 90 % of sample points are rated between grade III and V on the Soil Quality Index (SQI), indicating generally poor quality and a decreasing trend from the southeast to the northwest. Over the past 40 years, the SQI declined by 4.21 %, with increased spatial variability. Declines are concentrated in the southwestern plateau, especially in the central and eastern Brahmaputra River Basin and southeastern Inner Plateau. The primary factors influencing SQI, by explanatory power, are vegetation cover, precipitation, wind erosion, and land use. Additionally, interactions between these factors have a greater impact on soil quality than individual factors. Soil quality improves significantly when vegetation cover exceeds 60 %, annual precipitation exceeds 53 mm, wind erosion intensity remains below mild, and the dominant land use is grassland or cropland.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113264"},"PeriodicalIF":7.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465348","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}
引用次数: 0
Adapting the concept of functionally dominant species for observational data
IF 7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2025-02-22 DOI: 10.1016/j.ecolind.2025.113271
Audréanne Loiselle , Raphaël Proulx , Stéphanie Pellerin
Conservation ecologists often rely on surrogate species to identify biodiversity hotspots due to the high cost of monitoring programs. While the keystone species approach is an appealing framework for that purpose, it has been criticized for its lack of a clear threshold to identify functionally important species and for its limited ability to handle observational data variability. Here, we propose a modified version of the functionally dominant species (FDS) framework using a bootstrapping random sampling method implemented with either strict or flexible parameters to identify species that disproportionately contribute to the increase or the decrease of biodiversity. We tested our approach on plant, bird, and fish communities of 37 lake-edge wetlands. We identified eight FDS using a 95% confidence interval, of which two displayed a positive contribution to diversity while six had a negative contribution. Using a 99% confidence interval, we found four FDS, all displaying a negative contribution to biodiversity. Most of the identified FDS had ecological or biological traits that support their disproportionate impact on biodiversity. By addressing the limitations of the keystone species framework and providing a statistical framework for analyzing observational data, our method represents a promising tool for conservation ecology.
{"title":"Adapting the concept of functionally dominant species for observational data","authors":"Audréanne Loiselle ,&nbsp;Raphaël Proulx ,&nbsp;Stéphanie Pellerin","doi":"10.1016/j.ecolind.2025.113271","DOIUrl":"10.1016/j.ecolind.2025.113271","url":null,"abstract":"<div><div>Conservation ecologists often rely on surrogate species to identify biodiversity hotspots due to the high cost of monitoring programs. While the keystone species approach is an appealing framework for that purpose, it has been criticized for its lack of a clear threshold to identify functionally important species and for its limited ability to handle observational data variability. Here, we propose a modified version of the functionally dominant species (FDS) framework using a bootstrapping random sampling method implemented with either strict or flexible parameters to identify species that disproportionately contribute to the increase or the decrease of biodiversity. We tested our approach on plant, bird, and fish communities of 37 lake-edge wetlands. We identified eight FDS using a 95% confidence interval, of which two displayed a positive contribution to diversity while six had a negative contribution. Using a 99% confidence interval, we found four FDS, all displaying a negative contribution to biodiversity. Most of the identified FDS had ecological or biological traits that support their disproportionate impact on biodiversity. By addressing the limitations of the keystone species framework and providing a statistical framework for analyzing observational data, our method represents a promising tool for conservation ecology.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113271"},"PeriodicalIF":7.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465349","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}
引用次数: 0
Regional ecological risk assessment and transfer mechanism based on improved gravity and social network analysis model: A case study of Northwest China
IF 7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2025-02-22 DOI: 10.1016/j.ecolind.2025.113243
Ruiyang Li , Zhaocai Wang , Yanyu Li , Tunhua Wu
With the acceleration of industrialization and urbanization, regional ecological risk issues have become increasingly prominent. Scientifically assessing and analyzing ecological risks is crucial for implementing effective ecological protection measures. Currently, most studies primarily focus on the isolated ecological risk conditions of individual regions, while the interactive transfer relationships of ecological risks among these regions have received little attention. This limitation hinders a comprehensive and scientific reflection of the overall trends in regional ecological risks. Therefore, it is necessary to construct relevant models for an in-depth investigation of this issue. This study employs an improved gravity model to simulate the interactive transfer of ecological risks between different regions within the ecosystem framework, considering soil pollution, air pollution, and economic transmission pathways. Additionally, a social network analysis (SNA) model is applied to further dissect the mechanisms of ecological risk transfer among regions. Furthermore, by integrating ecological risk indices based on land use types and landscape patterns, a comprehensive ecological risk assessment method is proposed. Finally, a comprehensive assessment and analysis of ecological risks in five provinces of Northwest China from 2003 to 2022 is conducted. The results indicate that during the period from 2003 to 2022, the comprehensive ecological risk index of the region exhibited a fluctuating trend, remaining within an alert range overall. The spatial relative differences in ecological risk first increased and then decreased, showing a peak pattern. The transfer of pollution among regions through different pathways significantly increased the share of regional comprehensive risks, particularly in Gansu (88.87%), Qinghai (79.35%), and Shaanxi (70.91%). Moreover, the findings also indicate that Gansu is a significant transmitter of regional comprehensive ecological risks, warranting substantial attention. This research provides a scientific basis for risk assessment and governance in ecologically fragile areas.
{"title":"Regional ecological risk assessment and transfer mechanism based on improved gravity and social network analysis model: A case study of Northwest China","authors":"Ruiyang Li ,&nbsp;Zhaocai Wang ,&nbsp;Yanyu Li ,&nbsp;Tunhua Wu","doi":"10.1016/j.ecolind.2025.113243","DOIUrl":"10.1016/j.ecolind.2025.113243","url":null,"abstract":"<div><div>With the acceleration of industrialization and urbanization, regional ecological risk issues have become increasingly prominent. Scientifically assessing and analyzing ecological risks is crucial for implementing effective ecological protection measures. Currently, most studies primarily focus on the isolated ecological risk conditions of individual regions, while the interactive transfer relationships of ecological risks among these regions have received little attention. This limitation hinders a comprehensive and scientific reflection of the overall trends in regional ecological risks. Therefore, it is necessary to construct relevant models for an in-depth investigation of this issue. This study employs an improved gravity model to simulate the interactive transfer of ecological risks between different regions within the ecosystem framework, considering soil pollution, air pollution, and economic transmission pathways. Additionally, a social network analysis (SNA) model is applied to further dissect the mechanisms of ecological risk transfer among regions. Furthermore, by integrating ecological risk indices based on land use types and landscape patterns, a comprehensive ecological risk assessment method is proposed. Finally, a comprehensive assessment and analysis of ecological risks in five provinces of Northwest China from 2003 to 2022 is conducted. The results indicate that during the period from 2003 to 2022, the comprehensive ecological risk index of the region exhibited a fluctuating trend, remaining within an alert range overall. The spatial relative differences in ecological risk first increased and then decreased, showing a peak pattern. The transfer of pollution among regions through different pathways significantly increased the share of regional comprehensive risks, particularly in Gansu (88.87%), Qinghai (79.35%), and Shaanxi (70.91%). Moreover, the findings also indicate that Gansu is a significant transmitter of regional comprehensive ecological risks, warranting substantial attention. This research provides a scientific basis for risk assessment and governance in ecologically fragile areas.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113243"},"PeriodicalIF":7.0,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143471748","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}
引用次数: 0
Effects of stand structure, individual dominant species and environment on herb diversity in a temperate forest region
IF 7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2025-02-21 DOI: 10.1016/j.ecolind.2025.113262
Jie Li , Zheng Yin , Fushan Cheng , Klaus von Gadow , Minhui Hao , Chunyu Fan , Xiuhai Zhao , Chunyu Zhang
Herbaceous plants are a vital component of biodiversity in forest ecosystems. Herbs are, however, often excluded from forest diversity experiments, which limits our understanding of the structure and dynamics of entire ecosystems. We measured the diversity of understory herbaceous communities and stand structural attributes. The herb dominance index (HDI) was used to quantify herb dominance, incorporating both the cover and height of individual dominant species. Using linear mixed-effects models and structural equation modelling, we examined the direct effects of stand structure (stand density and variation of tree heights), dominant herb species and environmental factors on herb diversity, and the indirect effects via HDI. The diversity of herbaceous communities is reduced by the presence of a few dominant herbs. Increasing forest density reduced the HDI and thus indirectly enhanced herb diversity. The diversity of herbaceous communities was negatively correlated with an increase in the variation of tree heights. Environmental factors directly and indirectly influenced herb diversity by affecting the variation of tree heights and the HDI. Significant interactions between stand density–soil nutrients and the variation of tree heights–HDI suggest that stand structural attributes have significant effects on the soil–herbaceous plant and interspecific relationships. Stand structure, local environment, and individual herb dominance jointly affected the diversity of herbaceous plant communities in our temperate forests. Unraveling the complex interactions between forest density and structure and environmental factors improves our understanding of understory herb community assembly.
{"title":"Effects of stand structure, individual dominant species and environment on herb diversity in a temperate forest region","authors":"Jie Li ,&nbsp;Zheng Yin ,&nbsp;Fushan Cheng ,&nbsp;Klaus von Gadow ,&nbsp;Minhui Hao ,&nbsp;Chunyu Fan ,&nbsp;Xiuhai Zhao ,&nbsp;Chunyu Zhang","doi":"10.1016/j.ecolind.2025.113262","DOIUrl":"10.1016/j.ecolind.2025.113262","url":null,"abstract":"<div><div>Herbaceous plants are a vital component of biodiversity in forest ecosystems. Herbs are, however, often excluded from forest diversity experiments, which limits our understanding of the structure and dynamics of entire ecosystems. We measured the diversity of understory herbaceous communities and stand structural attributes. The herb dominance index (<em>HDI</em>) was used to quantify herb dominance, incorporating both the cover and height of individual dominant species. Using linear mixed-effects models and structural equation modelling, we examined the direct effects of stand structure (stand density and variation of tree heights), dominant herb species and environmental factors on herb diversity, and the indirect effects via <em>HDI</em>. The diversity of herbaceous communities is reduced by the presence of a few dominant herbs. Increasing forest density reduced the <em>HDI</em> and thus indirectly enhanced herb diversity. The diversity of herbaceous communities was negatively correlated with an increase in the variation of tree heights. Environmental factors directly and indirectly influenced herb diversity by affecting the variation of tree heights and the <em>HDI</em>. Significant interactions between stand density–soil nutrients and the variation of tree heights–<em>HDI</em> suggest that stand structural attributes have significant effects on the soil–herbaceous plant and interspecific relationships. Stand structure, local environment, and individual herb dominance jointly affected the diversity of herbaceous plant communities in our temperate forests. Unraveling the complex interactions between forest density and structure and environmental factors improves our understanding of understory herb community assembly.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113262"},"PeriodicalIF":7.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465270","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}
引用次数: 0
Exploring the spatiotemporal variation of carbon storage on Hainan Island and its driving factors: Insights from InVEST, FLUS models, and machine learning
IF 7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2025-02-21 DOI: 10.1016/j.ecolind.2025.113236
Jinlin Lai , Shi Qi , Jiadong Chen , Jianchao Guo , Hui Wu , Yizhuang Chen
Land use/cover changes (LUCCs) significantly influence the carbon cycle. However, as an important conservation area in China, the impact of the LUCCs on carbon storage (CS) in Hainan Island has not been systematically studied. This study integrates the InVEST and FLUS models to analyze the historical and future changes in CS, and uses machine learning algorithms to explore the driving forces behind the spatial heterogeneity of CS. The main results are as follows: (1) The CS distribution on Hainan Island shows a spatial pattern of higher levels in the central mountainous areas and lower levels along the coast. From 1990 to 2020, CS decreased by 2.28 × 106 t, primarily in coastal regions. (2) Natural factors, such as elevation and normalized difference vegetation index, play a decisive role in the spatial heterogeneity of CS, while anthropogenic factors, such as population density and gross domestic product, also have a significant impact on CS. (3) According to predictions for three scenarios (natural development, rapid development, and ecological protection), CS is expected to decrease by 3.11 × 106 t and 4.06 × 106 t in the natural development and rapid development scenarios, respectively, by 2050. However, under the ecological protection scenario, the decline in CS is effectively controlled, with a decrease of only 0.27 × 106 t. This study combines the InVEST, FLUS, and CatBoost models for an in-depth analysis of the spatiotemporal variations in CS and the underlying driving mechanisms. “It offers a novel framework for carbon management in tropical islands and other similar regions. Based on these findings, we recommend strengthening ecological protection on Hainan Island, limiting the unchecked expansion of urban areas, and striking a balance between economic development and ecological conservation to support the achievement of carbon neutrality goals.
{"title":"Exploring the spatiotemporal variation of carbon storage on Hainan Island and its driving factors: Insights from InVEST, FLUS models, and machine learning","authors":"Jinlin Lai ,&nbsp;Shi Qi ,&nbsp;Jiadong Chen ,&nbsp;Jianchao Guo ,&nbsp;Hui Wu ,&nbsp;Yizhuang Chen","doi":"10.1016/j.ecolind.2025.113236","DOIUrl":"10.1016/j.ecolind.2025.113236","url":null,"abstract":"<div><div>Land use/cover changes (LUCCs) significantly influence the carbon cycle. However, as an important conservation area in China, the impact of the LUCCs on carbon storage (CS) in Hainan Island has not been systematically studied. This study integrates the InVEST and FLUS models to analyze the historical and future changes in CS, and uses machine learning algorithms to explore the driving forces behind the spatial heterogeneity of CS. The main results are as follows: (1) The CS distribution on Hainan Island shows a spatial pattern of higher levels in the central mountainous areas and lower levels along the coast. From 1990 to 2020, CS decreased by 2.28 × 10<sup>6</sup> t, primarily in coastal regions. (2) Natural factors, such as elevation and normalized difference vegetation index, play a decisive role in the spatial heterogeneity of CS, while anthropogenic factors, such as population density and gross domestic product, also have a significant impact on CS. (3) According to predictions for three scenarios (natural development, rapid development, and ecological protection), CS is expected to decrease by 3.11 × 10<sup>6</sup> t and 4.06 × 10<sup>6</sup> t in the natural development and rapid development scenarios, respectively, by 2050. However, under the ecological protection scenario, the decline in CS is effectively controlled, with a decrease of only 0.27 × 10<sup>6</sup> t. This study combines the InVEST, FLUS, and CatBoost models for an in-depth analysis of the spatiotemporal variations in CS and the underlying driving mechanisms. “It offers a novel framework for carbon management in tropical islands and other similar regions. Based on these findings, we recommend strengthening ecological protection on Hainan Island, limiting the unchecked expansion of urban areas, and striking a balance between economic development and ecological conservation to support the achievement of carbon neutrality goals.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113236"},"PeriodicalIF":7.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452789","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}
引用次数: 0
The need for advancing algal bloom forecasting using remote sensing and modeling: Progress and future directions
IF 7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2025-02-21 DOI: 10.1016/j.ecolind.2025.113244
Cassia B. Caballero , Vitor S. Martins , Rejane S. Paulino , Elliott Butler , Eric Sparks , Thainara M. Lima , Evlyn M.L.M. Novo
Algal blooms are often major drivers of environmental and economic challenges. As these blooms increase in frequency and size, there is an increasing need for forecasting models to accurately predict their occurrence and progression. Such algal bloom forecast systems can provide early warnings to mitigate the harmful impacts on ecosystems and public health. This study presents an overview of the current progress for algal bloom forecasting (i.e., predicting the future occurrence, distribution, frequency, and intensity of algal blooms in water bodies) and emphasizes the need for research initiatives and future directions on this topic. Remote sensing, particularly ocean-color products, has emerged as a foundation for algal bloom monitoring and forecasting, providing critical spatial–temporal data to address the limitations of in situ measurements. Machine learning and deep learning models dominate recent developments, demonstrating their capabilities in capturing non-linear and complex dynamics and enhancing accuracy in forecasting. Forecast intervals used vary, ranging from daily forecasts to weeks, monthly, seasonal, and annual predictions. A relevant aspect of algal bloom forecasting is the input variables, and we identified the key inputs, including surface temperature, nitrogen and phosphorus concentrations, wind patterns, and previous/current bloom information. However, most studies are geographically concentrated in the Northern Hemisphere, specifically North America, Europe, and Asia, focusing on lakes and coastal waters, leaving tropical regions, rivers, reservoirs, and open oceans underexplored. Despite the advancement in this field, operational algal bloom forecasting systems are still scarce, particularly when compared to other environmental fields, such as meteorology and air quality forecasting. With new hyperspectral capabilities being developed, integrating these emerging technologies offers unprecedented opportunities to refine predictions, particularly for phytoplankton community composition and functional types. This study emphasizes the need to expand forecasting research to underrepresented regions and water body types, such as reservoirs and estuaries. Under current climate change scenarios, algal blooms may become more frequent and intense, and it is crucial to continuously develop and advance algal bloom research to support coastal and inland water management.
{"title":"The need for advancing algal bloom forecasting using remote sensing and modeling: Progress and future directions","authors":"Cassia B. Caballero ,&nbsp;Vitor S. Martins ,&nbsp;Rejane S. Paulino ,&nbsp;Elliott Butler ,&nbsp;Eric Sparks ,&nbsp;Thainara M. Lima ,&nbsp;Evlyn M.L.M. Novo","doi":"10.1016/j.ecolind.2025.113244","DOIUrl":"10.1016/j.ecolind.2025.113244","url":null,"abstract":"<div><div>Algal blooms are often major drivers of environmental and economic challenges. As these blooms increase in frequency and size, there is an increasing need for forecasting models to accurately predict their occurrence and progression. Such algal bloom forecast systems can provide early warnings to mitigate the harmful impacts on ecosystems and public health. This study presents an overview of the current progress for algal bloom forecasting (i.e., predicting the future occurrence, distribution, frequency, and intensity of algal blooms in water bodies) and emphasizes the need for research initiatives and future directions on this topic. Remote sensing, particularly ocean-color products, has emerged as a foundation for algal bloom monitoring and forecasting, providing critical spatial–temporal data to address the limitations of in situ measurements. Machine learning and deep learning models dominate recent developments, demonstrating their capabilities in capturing non-linear and complex dynamics and enhancing accuracy in forecasting. Forecast intervals used vary, ranging from daily forecasts to weeks, monthly, seasonal, and annual predictions. A relevant aspect of algal bloom forecasting is the input variables, and we identified the key inputs, including surface temperature, nitrogen and phosphorus concentrations, wind patterns, and previous/current bloom information. However, most studies are geographically concentrated in the Northern Hemisphere, specifically North America, Europe, and Asia, focusing on lakes and coastal waters, leaving tropical regions, rivers, reservoirs, and open oceans underexplored. Despite the advancement in this field, operational algal bloom forecasting systems are still scarce, particularly when compared to other environmental fields, such as meteorology and air quality forecasting. With new hyperspectral capabilities being developed, integrating these emerging technologies offers unprecedented opportunities to refine predictions, particularly for phytoplankton community composition and functional types. This study emphasizes the need to expand forecasting research to underrepresented regions and water body types, such as reservoirs and estuaries. Under current climate change scenarios, algal blooms may become more frequent and intense, and it is crucial to continuously develop and advance algal bloom research to support coastal and inland water management.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113244"},"PeriodicalIF":7.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452790","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}
引用次数: 0
Cooling the land surface: Ecosystem health and water availability drive the landscape capacity to mitigate climate change
IF 7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2025-02-21 DOI: 10.1016/j.ecolind.2025.113265
Jana Müllerová, Erik Šiffel
Land surface temperature (LST) is profoundly interlinked with the landscape state, settings and functioning, with the connections being very complex. Although the role of wetlands and (semi)natural habitats, in mitigating climate extremes is generally understood, the mechanisms explaining the thermal patterns in complex landscapes remain unclear. We address this knowledge gap, investigating a link between the dynamics of LST and characteristics of a diverse sandstone landscape, focusing on the role of water features and forest health in alleviating temperature extremes. For our study, we used a model example of a sandstone protected area in the north of the Czech Republic that underwent significant changes during the last decade due to a bark beetle infestation and a consequent forest die-off and wildfire. LST data were obtained from MODIS and Landsat 8 sensors in a cloud-based Google Earth Engine platform. Machine learning regression model enabled us to assess complex multivariable relationships and increase the LST spatial resolution. The results suggest the significant effect of both water availability and ecosystem health on LST, with vegetation indices, land cover and elevation being the main factors. The correlation of the satellite-based LST and in situ measured temperature depended on the canopy cover. The study indicates that in complex landscapes, LST data of high spatial and temporal resolution is necessary to disentangle local patterns and environmental drivers. Satellite data can serve as a reliable means to understand the mechanisms and prepare adaptive management measures to make the landscape more resistant to climate change related threats.
{"title":"Cooling the land surface: Ecosystem health and water availability drive the landscape capacity to mitigate climate change","authors":"Jana Müllerová,&nbsp;Erik Šiffel","doi":"10.1016/j.ecolind.2025.113265","DOIUrl":"10.1016/j.ecolind.2025.113265","url":null,"abstract":"<div><div>Land surface temperature (LST) is profoundly interlinked with the landscape state, settings and functioning, with the connections being very complex. Although the role of wetlands and (semi)natural habitats, in mitigating climate extremes is generally understood, the mechanisms explaining the thermal patterns in complex landscapes remain unclear. We address this knowledge gap, investigating a link between the dynamics of LST and characteristics of a diverse sandstone landscape, focusing on the role of water features and forest health in alleviating temperature extremes. For our study, we used a model example of a sandstone protected area in the north of the Czech Republic that underwent significant changes during the last decade due to a bark beetle infestation and a consequent forest die-off and wildfire. LST data were obtained from MODIS and Landsat 8 sensors in a cloud-based Google Earth Engine platform. Machine learning regression model enabled us to assess complex multivariable relationships and increase the LST spatial resolution. The results suggest the significant effect of both water availability and ecosystem health on LST, with vegetation indices, land cover and elevation being the main factors. The correlation of the satellite-based LST and in situ measured temperature depended on the canopy cover. The study indicates that in complex landscapes, LST data of high spatial and temporal resolution is necessary to disentangle local patterns and environmental drivers. Satellite data can serve as a reliable means to understand the mechanisms and prepare adaptive management measures to make the landscape more resistant to climate change related threats.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113265"},"PeriodicalIF":7.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452792","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}
引用次数: 0
Spatial effects and influence mechanisms of urban land use green transition on urban carbon emissions
IF 7 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES Pub Date : 2025-02-21 DOI: 10.1016/j.ecolind.2025.113261
Kun Ge , Ying Wang , Xiaoyuan Liu , Longji Hu , Shangan Ke , Xu Jiang , Wenjuan Zhang
Reducing carbon emissions is essential for achieving the “dual carbon” targets. This study investigates the impact of Urban Land Use Green Transition (ULUGT) on Urban Carbon Emissions (UCE) and explores the underlying mediation mechanisms. Utilizing panel data from 107 cities in the Yangtze River Economic Belt (YREB) covering the period 2003–2021, we apply the Spatial Durbin Model and Mediation Effect Model to analyze the direct effects, spatial spillover effects, and mediation mechanisms of ULUGT on UCE. Our findings reveal that: (1) UCE in the YREB increased from 13.32 to 28.94 million tons, with an annual growth rate of 4.17 % between 2003 and 2021. Although UCE overall increased, significant spatial imbalances were observed, although these disparities have diminished over time. (2) ULUGT significantly reduces local UCE but generates positive spatial spillover effects on UCE in neighboring cities. (3) The impact of ULUGT on UCE varies according to resource endowment and geographic location. (4) Environmental regulations, industrial agglomeration, and green technological innovations act as mediation mechanisms, partially mediating the effect of ULUGT on UCE. This study highlights the need for enhanced regional cooperation, timely implementation of green technological innovations, and coordinated interventions to effectively mitigate UCE.
{"title":"Spatial effects and influence mechanisms of urban land use green transition on urban carbon emissions","authors":"Kun Ge ,&nbsp;Ying Wang ,&nbsp;Xiaoyuan Liu ,&nbsp;Longji Hu ,&nbsp;Shangan Ke ,&nbsp;Xu Jiang ,&nbsp;Wenjuan Zhang","doi":"10.1016/j.ecolind.2025.113261","DOIUrl":"10.1016/j.ecolind.2025.113261","url":null,"abstract":"<div><div>Reducing carbon emissions is essential for achieving the “dual carbon” targets. This study investigates the impact of Urban Land Use Green Transition (ULUGT) on Urban Carbon Emissions (UCE) and explores the underlying mediation mechanisms. Utilizing panel data from 107 cities in the Yangtze River Economic Belt (YREB) covering the period 2003–2021, we apply the Spatial Durbin Model and Mediation Effect Model to analyze the direct effects, spatial spillover effects, and mediation mechanisms of ULUGT on UCE. Our findings reveal that: (1) UCE in the YREB increased from 13.32 to 28.94 million tons, with an annual growth rate of 4.17 % between 2003 and 2021. Although UCE overall increased, significant spatial imbalances were observed, although these disparities have diminished over time. (2) ULUGT significantly reduces local UCE but generates positive spatial spillover effects on UCE in neighboring cities. (3) The impact of ULUGT on UCE varies according to resource endowment and geographic location. (4) Environmental regulations, industrial agglomeration, and green technological innovations act as mediation mechanisms, partially mediating the effect of ULUGT on UCE. This study highlights the need for enhanced regional cooperation, timely implementation of green technological innovations, and coordinated interventions to effectively mitigate UCE.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"172 ","pages":"Article 113261"},"PeriodicalIF":7.0,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143452793","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}
引用次数: 0
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Ecological Indicators
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