Above-ground carbon stocks and its functional relationship with tree species diversity: the case of Kakamega and North Nandi Forests, Kenya
Abstract Estimating aboveground carbon (AGC) dynamics and tree diversity functionality relationships is critical in understanding the role of vegetation in implementing climate change mitigation strategies and promoting sustainable forest management. This study aimed to evaluate AGC stocks and their...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-11-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-47871-6 |
| _version_ | 1797415441859084288 |
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| author | Ouko Amose Obonyo Humphrey Agevi Mugatsia Harrison Tsingalia |
| author_facet | Ouko Amose Obonyo Humphrey Agevi Mugatsia Harrison Tsingalia |
| author_sort | Ouko Amose Obonyo |
| collection | DOAJ |
| description | Abstract Estimating aboveground carbon (AGC) dynamics and tree diversity functionality relationships is critical in understanding the role of vegetation in implementing climate change mitigation strategies and promoting sustainable forest management. This study aimed to evaluate AGC stocks and their functional relationship with tree species diversity in Kakamega and North Nandi Forests, Kenya. A nested approach was adopted in sampling aboveground vegetation for biomass estimation in least disturbed, transformed, and disturbed sites. Tree biomass was estimated using an allometric equation based on tree diameter, tree height, and wood density. The biomass was then converted to carbon stocks using the carbon conversion factor. One-way ANOVA was used to determine the variation in carbon and tree diversity between forests and forest types. The correlation between tree diversity and AGC was evaluated. It was established that Kakamega Forest had the highest AGC (157.93 ± 26.91tha −1). The least disturbed areas had the highest AGC (65.96 ± 8.56tha −1). Additionally, Shannon diversity revealed a higher tree species diversity in Kakamega Forest (H′ = 1.82 ± 0.95). There was a significant positive correlation between AGC and tree species diversity (r = 0.62, p < 0.05). Kakamega and North Nandi forests vary in their AGC, and that tree species diversity positively influences the AGC of the two forests. |
| first_indexed | 2024-03-09T05:48:42Z |
| format | Article |
| id | doaj.art-f71f506e5ddb4b33bdc941ca4190b873 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-09T05:48:42Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-f71f506e5ddb4b33bdc941ca4190b8732023-12-03T12:19:28ZengNature PortfolioScientific Reports2045-23222023-11-0113111010.1038/s41598-023-47871-6Above-ground carbon stocks and its functional relationship with tree species diversity: the case of Kakamega and North Nandi Forests, KenyaOuko Amose Obonyo0Humphrey Agevi1Mugatsia Harrison Tsingalia2Department of Biological Sciences, Masinde Muliro University of Science and Technology (MMUST)Department of Biological Sciences, Masinde Muliro University of Science and Technology (MMUST)Department of Biological Sciences, Masinde Muliro University of Science and Technology (MMUST)Abstract Estimating aboveground carbon (AGC) dynamics and tree diversity functionality relationships is critical in understanding the role of vegetation in implementing climate change mitigation strategies and promoting sustainable forest management. This study aimed to evaluate AGC stocks and their functional relationship with tree species diversity in Kakamega and North Nandi Forests, Kenya. A nested approach was adopted in sampling aboveground vegetation for biomass estimation in least disturbed, transformed, and disturbed sites. Tree biomass was estimated using an allometric equation based on tree diameter, tree height, and wood density. The biomass was then converted to carbon stocks using the carbon conversion factor. One-way ANOVA was used to determine the variation in carbon and tree diversity between forests and forest types. The correlation between tree diversity and AGC was evaluated. It was established that Kakamega Forest had the highest AGC (157.93 ± 26.91tha −1). The least disturbed areas had the highest AGC (65.96 ± 8.56tha −1). Additionally, Shannon diversity revealed a higher tree species diversity in Kakamega Forest (H′ = 1.82 ± 0.95). There was a significant positive correlation between AGC and tree species diversity (r = 0.62, p < 0.05). Kakamega and North Nandi forests vary in their AGC, and that tree species diversity positively influences the AGC of the two forests.https://doi.org/10.1038/s41598-023-47871-6 |
| spellingShingle | Ouko Amose Obonyo Humphrey Agevi Mugatsia Harrison Tsingalia Above-ground carbon stocks and its functional relationship with tree species diversity: the case of Kakamega and North Nandi Forests, Kenya Scientific Reports |
| title | Above-ground carbon stocks and its functional relationship with tree species diversity: the case of Kakamega and North Nandi Forests, Kenya |
| title_full | Above-ground carbon stocks and its functional relationship with tree species diversity: the case of Kakamega and North Nandi Forests, Kenya |
| title_fullStr | Above-ground carbon stocks and its functional relationship with tree species diversity: the case of Kakamega and North Nandi Forests, Kenya |
| title_full_unstemmed | Above-ground carbon stocks and its functional relationship with tree species diversity: the case of Kakamega and North Nandi Forests, Kenya |
| title_short | Above-ground carbon stocks and its functional relationship with tree species diversity: the case of Kakamega and North Nandi Forests, Kenya |
| title_sort | above ground carbon stocks and its functional relationship with tree species diversity the case of kakamega and north nandi forests kenya |
| url | https://doi.org/10.1038/s41598-023-47871-6 |
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