Important Medicinal and Food Taxa (Orders and Families) in Kenya, Based on Three Quantitative Approaches
Globally, food and medicinal plants have been documented, but their use patterns are poorly understood. Useful plants are non-random subsets of flora, prioritizing certain taxa. This study evaluates orders and families prioritized for medicine and food in Kenya, using three statistical models: Regre...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/12/5/1145 |
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| author | Fredrick Munyao Mutie Yuvenalis Morara Mbuni Peninah Cheptoo Rono Elijah Mbandi Mkala John Mulinge Nzei Methee Phumthum Guang-Wan Hu Qing-Feng Wang |
| author_facet | Fredrick Munyao Mutie Yuvenalis Morara Mbuni Peninah Cheptoo Rono Elijah Mbandi Mkala John Mulinge Nzei Methee Phumthum Guang-Wan Hu Qing-Feng Wang |
| author_sort | Fredrick Munyao Mutie |
| collection | DOAJ |
| description | Globally, food and medicinal plants have been documented, but their use patterns are poorly understood. Useful plants are non-random subsets of flora, prioritizing certain taxa. This study evaluates orders and families prioritized for medicine and food in Kenya, using three statistical models: Regression, Binomial, and Bayesian approaches. An extensive literature search was conducted to gather information on indigenous flora, medicinal and food plants. Regression residuals, obtained using LlNEST linear regression function, were used to quantify if taxa had unexpectedly high number of useful species relative to the overall proportion in the flora. Bayesian analysis, performed using BETA.INV function, was used to obtain superior and inferior 95% probability credible intervals for the whole flora and for all taxa. To test for the significance of individual taxa departure from the expected number, binomial analysis using BINOMDIST function was performed to obtain <i>p</i>-values for all taxa. The three models identified 14 positive outlier medicinal orders, all with significant values (<i>p</i> < 0.05). Fabales had the highest (66.16) regression residuals, while Sapindales had the highest (1.1605) R-value. Thirty-eight positive outlier medicinal families were identified; 34 were significant outliers (<i>p</i> < 0.05). Rutaceae (1.6808) had the highest R-value, while Fabaceae had the highest regression residuals (63.2). Sixteen positive outlier food orders were recovered; 13 were significant outliers (<i>p</i> < 0.05). Gentianales (45.27) had the highest regression residuals, while Sapindales (2.3654) had the highest R-value. Forty-two positive outlier food families were recovered by the three models; 30 were significant outliers (<i>p</i> < 0.05). Anacardiaceae (5.163) had the highest R-value, while Fabaceae had the highest (28.72) regression residuals. This study presents important medicinal and food taxa in Kenya, and adds useful data for global comparisons. |
| first_indexed | 2024-03-11T07:14:09Z |
| format | Article |
| id | doaj.art-fbbf5cea534044d29f5eabd83d8969aa |
| institution | Directory Open Access Journal |
| issn | 2223-7747 |
| language | English |
| last_indexed | 2024-03-11T07:14:09Z |
| publishDate | 2023-03-01 |
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| record_format | Article |
| series | Plants |
| spelling | doaj.art-fbbf5cea534044d29f5eabd83d8969aa2023-11-17T08:24:45ZengMDPI AGPlants2223-77472023-03-01125114510.3390/plants12051145Important Medicinal and Food Taxa (Orders and Families) in Kenya, Based on Three Quantitative ApproachesFredrick Munyao Mutie0Yuvenalis Morara Mbuni1Peninah Cheptoo Rono2Elijah Mbandi Mkala3John Mulinge Nzei4Methee Phumthum5Guang-Wan Hu6Qing-Feng Wang7CAS Key Laboratory of Plant Germplasm and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, ChinaEast African Herbarium, Nairobi National Museums, P.O. Box 45166, Nairobi 00100, KenyaCAS Key Laboratory of Plant Germplasm and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, ChinaCAS Key Laboratory of Plant Germplasm and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, ChinaSino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan 430074, ChinaDepartment of Pharmaceutical Botany, Faculty of Pharmacy, Mahidol University, Bangkok 10400, ThailandSino-Africa Joint Research Center, Chinese Academy of Sciences, Wuhan 430074, ChinaCAS Key Laboratory of Plant Germplasm and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074, ChinaGlobally, food and medicinal plants have been documented, but their use patterns are poorly understood. Useful plants are non-random subsets of flora, prioritizing certain taxa. This study evaluates orders and families prioritized for medicine and food in Kenya, using three statistical models: Regression, Binomial, and Bayesian approaches. An extensive literature search was conducted to gather information on indigenous flora, medicinal and food plants. Regression residuals, obtained using LlNEST linear regression function, were used to quantify if taxa had unexpectedly high number of useful species relative to the overall proportion in the flora. Bayesian analysis, performed using BETA.INV function, was used to obtain superior and inferior 95% probability credible intervals for the whole flora and for all taxa. To test for the significance of individual taxa departure from the expected number, binomial analysis using BINOMDIST function was performed to obtain <i>p</i>-values for all taxa. The three models identified 14 positive outlier medicinal orders, all with significant values (<i>p</i> < 0.05). Fabales had the highest (66.16) regression residuals, while Sapindales had the highest (1.1605) R-value. Thirty-eight positive outlier medicinal families were identified; 34 were significant outliers (<i>p</i> < 0.05). Rutaceae (1.6808) had the highest R-value, while Fabaceae had the highest regression residuals (63.2). Sixteen positive outlier food orders were recovered; 13 were significant outliers (<i>p</i> < 0.05). Gentianales (45.27) had the highest regression residuals, while Sapindales (2.3654) had the highest R-value. Forty-two positive outlier food families were recovered by the three models; 30 were significant outliers (<i>p</i> < 0.05). Anacardiaceae (5.163) had the highest R-value, while Fabaceae had the highest (28.72) regression residuals. This study presents important medicinal and food taxa in Kenya, and adds useful data for global comparisons.https://www.mdpi.com/2223-7747/12/5/1145Bayesian analysisbinomial analysislinear regressionoutlier |
| spellingShingle | Fredrick Munyao Mutie Yuvenalis Morara Mbuni Peninah Cheptoo Rono Elijah Mbandi Mkala John Mulinge Nzei Methee Phumthum Guang-Wan Hu Qing-Feng Wang Important Medicinal and Food Taxa (Orders and Families) in Kenya, Based on Three Quantitative Approaches Plants Bayesian analysis binomial analysis linear regression outlier |
| title | Important Medicinal and Food Taxa (Orders and Families) in Kenya, Based on Three Quantitative Approaches |
| title_full | Important Medicinal and Food Taxa (Orders and Families) in Kenya, Based on Three Quantitative Approaches |
| title_fullStr | Important Medicinal and Food Taxa (Orders and Families) in Kenya, Based on Three Quantitative Approaches |
| title_full_unstemmed | Important Medicinal and Food Taxa (Orders and Families) in Kenya, Based on Three Quantitative Approaches |
| title_short | Important Medicinal and Food Taxa (Orders and Families) in Kenya, Based on Three Quantitative Approaches |
| title_sort | important medicinal and food taxa orders and families in kenya based on three quantitative approaches |
| topic | Bayesian analysis binomial analysis linear regression outlier |
| url | https://www.mdpi.com/2223-7747/12/5/1145 |
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