{"title":"孟加拉国热带山林叶片功能性状如何影响地上树木碳?","authors":"Ariful Khan , Md Rezaul Karim , Mohammed A.S. Arfin-Khan , Md. Shamim Reza Saimun , Fahmida Sultana , Sharif A. Mukul","doi":"10.1016/j.ecolind.2025.113131","DOIUrl":null,"url":null,"abstract":"<div><div>Plant leaf functional traits significantly influence carbon cycling in tropical forests, though the relationships between these traits and carbon stocks are complex. The present study investigates the role of leaf functional traits, i.e., specific leaf area (SLA), leaf dry matter content (LDMC), leaf width, and leaf thickness—on above-ground tree carbon (AGTC) stocks in two forest protected areas (PA) in northeast Bangladesh: Khadimnagar National Park (KNP) and Rema Kalenga Wildlife Sanctuary (RKWS). Data were collected from 110 plots, comprising 60 in RKWS and 50 in KNP. We observed that the community-weighted mean (CWM) leaf trait values were predominantly higher in the southwestern regions of KNP, while in RKWS, they were primarily distributed in the northern or southern regions. The results revealed that, at the landscape level, CWM-leaf width (R<sup>2</sup> = 0.10, P < 0.01) had a significant effect on AGTC. In site-specific analyses, CWM-leaf thickness (R<sup>2</sup> = 0.25), CWM-leaf width (R<sup>2</sup> = 0.10), and CWM-SLA (R<sup>2</sup> = 0.17) had significant (p < 0.05) negative effects on AGTC in KNP. However, in RKWS, only CWM-leaf width (R<sup>2</sup> = 0.015, P < 0.01) significantly affected AGTC, while other CWM-leaf traits showed no significant impact. Additionally, the effects of two common environmental variables—solar radiation and mean annual temperature (MAT)—were significant (p < 0.05) predictors of AGTC at the landscape level but not at the site level. The total carbon stock in RKWS was 1.98 % higher than in KNP per hectare, with species-specific carbon content varying across the landscape. Notably, <em>Chukrasia tabularis</em> showed the highest carbon content (31.57 t ha<sup>−1</sup>). These findings highlight significant spatial variability in leaf functional traits and AGTC distribution across the two forests. This study enhances our understanding of how leaf functional traits influence AGTC stocks, underscoring the importance of localized investigations for global climate change mitigation efforts and supporting sustainable forest management in Bangladesh.</div></div>","PeriodicalId":11459,"journal":{"name":"Ecological Indicators","volume":"171 ","pages":"Article 113131"},"PeriodicalIF":7.0000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"How do leaf functional traits influence above-ground tree carbon in tropical hill forests of Bangladesh?\",\"authors\":\"Ariful Khan , Md Rezaul Karim , Mohammed A.S. Arfin-Khan , Md. Shamim Reza Saimun , Fahmida Sultana , Sharif A. Mukul\",\"doi\":\"10.1016/j.ecolind.2025.113131\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Plant leaf functional traits significantly influence carbon cycling in tropical forests, though the relationships between these traits and carbon stocks are complex. The present study investigates the role of leaf functional traits, i.e., specific leaf area (SLA), leaf dry matter content (LDMC), leaf width, and leaf thickness—on above-ground tree carbon (AGTC) stocks in two forest protected areas (PA) in northeast Bangladesh: Khadimnagar National Park (KNP) and Rema Kalenga Wildlife Sanctuary (RKWS). Data were collected from 110 plots, comprising 60 in RKWS and 50 in KNP. We observed that the community-weighted mean (CWM) leaf trait values were predominantly higher in the southwestern regions of KNP, while in RKWS, they were primarily distributed in the northern or southern regions. The results revealed that, at the landscape level, CWM-leaf width (R<sup>2</sup> = 0.10, P < 0.01) had a significant effect on AGTC. In site-specific analyses, CWM-leaf thickness (R<sup>2</sup> = 0.25), CWM-leaf width (R<sup>2</sup> = 0.10), and CWM-SLA (R<sup>2</sup> = 0.17) had significant (p < 0.05) negative effects on AGTC in KNP. However, in RKWS, only CWM-leaf width (R<sup>2</sup> = 0.015, P < 0.01) significantly affected AGTC, while other CWM-leaf traits showed no significant impact. Additionally, the effects of two common environmental variables—solar radiation and mean annual temperature (MAT)—were significant (p < 0.05) predictors of AGTC at the landscape level but not at the site level. The total carbon stock in RKWS was 1.98 % higher than in KNP per hectare, with species-specific carbon content varying across the landscape. Notably, <em>Chukrasia tabularis</em> showed the highest carbon content (31.57 t ha<sup>−1</sup>). 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This study enhances our understanding of how leaf functional traits influence AGTC stocks, underscoring the importance of localized investigations for global climate change mitigation efforts and supporting sustainable forest management in Bangladesh.</div></div>\",\"PeriodicalId\":11459,\"journal\":{\"name\":\"Ecological Indicators\",\"volume\":\"171 \",\"pages\":\"Article 113131\"},\"PeriodicalIF\":7.0000,\"publicationDate\":\"2025-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ecological Indicators\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1470160X25000603\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/28 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecological Indicators","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1470160X25000603","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/28 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
摘要
热带森林植物叶片功能性状对碳循环有显著影响,但叶片功能性状与碳储量之间的关系较为复杂。本文研究了孟加拉国东北部两个森林保护区(Khadimnagar National Park, KNP)和Rema Kalenga野生动物保护区(RKWS)的叶片功能性状,即比叶面积(SLA)、叶干物质含量(LDMC)、叶宽和叶厚对地上树木碳(AGTC)储量的影响。从110个地块收集数据,其中60个在RKWS, 50个在KNP。结果表明,群落加权平均(CWM)叶片性状值以西南地区较高为主,而在东北地区主要分布在北部或南部地区。结果表明,在景观水平上,cwm叶片宽度(R2 = 0.10, P <;0.01)对AGTC有显著影响。在特定地点的分析中,cwm叶片厚度(R2 = 0.25), cwm叶片宽度(R2 = 0.10)和cwm sla (R2 = 0.17)具有显著的差异(p <;0.05)对KNP的AGTC有负面影响。而在RKWS中,只有cwm叶片宽度(R2 = 0.015, P <;0.01)对AGTC有显著影响,而其他性状无显著影响。此外,两个常见的环境变量——太阳辐射和年平均温度(MAT)——的影响也很显著(p <;0.05)在景观水平上对AGTC有预测作用,而在立地水平上没有。每公顷RKWS的总碳储量比KNP高1.98%,不同物种的碳含量在不同的景观中存在差异。其中,黑桃碳含量最高(31.57 t ha - 1)。这些发现突出了两种森林叶片功能性状和AGTC分布的显著空间变异性。这项研究增强了我们对叶片功能性状如何影响AGTC种群的理解,强调了本地化调查对全球气候变化缓解工作的重要性,并支持孟加拉国的可持续森林管理。
How do leaf functional traits influence above-ground tree carbon in tropical hill forests of Bangladesh?
Plant leaf functional traits significantly influence carbon cycling in tropical forests, though the relationships between these traits and carbon stocks are complex. The present study investigates the role of leaf functional traits, i.e., specific leaf area (SLA), leaf dry matter content (LDMC), leaf width, and leaf thickness—on above-ground tree carbon (AGTC) stocks in two forest protected areas (PA) in northeast Bangladesh: Khadimnagar National Park (KNP) and Rema Kalenga Wildlife Sanctuary (RKWS). Data were collected from 110 plots, comprising 60 in RKWS and 50 in KNP. We observed that the community-weighted mean (CWM) leaf trait values were predominantly higher in the southwestern regions of KNP, while in RKWS, they were primarily distributed in the northern or southern regions. The results revealed that, at the landscape level, CWM-leaf width (R2 = 0.10, P < 0.01) had a significant effect on AGTC. In site-specific analyses, CWM-leaf thickness (R2 = 0.25), CWM-leaf width (R2 = 0.10), and CWM-SLA (R2 = 0.17) had significant (p < 0.05) negative effects on AGTC in KNP. However, in RKWS, only CWM-leaf width (R2 = 0.015, P < 0.01) significantly affected AGTC, while other CWM-leaf traits showed no significant impact. Additionally, the effects of two common environmental variables—solar radiation and mean annual temperature (MAT)—were significant (p < 0.05) predictors of AGTC at the landscape level but not at the site level. The total carbon stock in RKWS was 1.98 % higher than in KNP per hectare, with species-specific carbon content varying across the landscape. Notably, Chukrasia tabularis showed the highest carbon content (31.57 t ha−1). These findings highlight significant spatial variability in leaf functional traits and AGTC distribution across the two forests. This study enhances our understanding of how leaf functional traits influence AGTC stocks, underscoring the importance of localized investigations for global climate change mitigation efforts and supporting sustainable forest management in Bangladesh.
期刊介绍:
The ultimate aim of Ecological Indicators is to integrate the monitoring and assessment of ecological and environmental indicators with management practices. The journal provides a forum for the discussion of the applied scientific development and review of traditional indicator approaches as well as for theoretical, modelling and quantitative applications such as index development. Research into the following areas will be published.
• All aspects of ecological and environmental indicators and indices.
• New indicators, and new approaches and methods for indicator development, testing and use.
• Development and modelling of indices, e.g. application of indicator suites across multiple scales and resources.
• Analysis and research of resource, system- and scale-specific indicators.
• Methods for integration of social and other valuation metrics for the production of scientifically rigorous and politically-relevant assessments using indicator-based monitoring and assessment programs.
• How research indicators can be transformed into direct application for management purposes.
• Broader assessment objectives and methods, e.g. biodiversity, biological integrity, and sustainability, through the use of indicators.
• Resource-specific indicators such as landscape, agroecosystems, forests, wetlands, etc.