Evaluation of the impact of ecological factors on the habitat suitability and bioactive components accumulation of the medicinal holoparasitic plant Cynomorium songaricum using machine learning models
The efficacy of traditional Chinese medicine is determined by its bioactive components, which exhibit variability depending on environmental conditions and hereditary influences. In this study, we focus on Cynomorium songaricum Rupr., a medicinally significant species facing sustainability challenge...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1586682/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849318327281778688 |
|---|---|
| author | Jiacheng Ji Jiacheng Ji Xinxin Wei Xinxin Wei Huan Guan Huan Guan Zikang Jin Zikang Jin Xin Yue Zhuoran Jiang Youla Su Youla Su Shuying Sun Shuying Sun Guilin Chen Guilin Chen |
| author_facet | Jiacheng Ji Jiacheng Ji Xinxin Wei Xinxin Wei Huan Guan Huan Guan Zikang Jin Zikang Jin Xin Yue Zhuoran Jiang Youla Su Youla Su Shuying Sun Shuying Sun Guilin Chen Guilin Chen |
| author_sort | Jiacheng Ji |
| collection | DOAJ |
| description | The efficacy of traditional Chinese medicine is determined by its bioactive components, which exhibit variability depending on environmental conditions and hereditary influences. In this study, we focus on Cynomorium songaricum Rupr., a medicinally significant species facing sustainability challenges. However, the ecological drivers governing its distribution, as well as the relationship between environmental factors and bioactive components, remain unclear. Thus, we sampled 28 representative distribution areas of C. songaricum in China. Employing Maximum Entropy (MaxEnt) modeling, we projected current and future (2050s-2090s) habitat suitability under four emission scenarios. Notably, species distribution exhibited expansion (8.03%-29.06% range increase across scenarios) with precipitation of the wettest month (BIO13) and soil pH emerging as key drivers (combined contribution >49%). Ultra-performance liquid chromatography (UPLC) fingerprinting combined with machine learning regression was applied to quantify six key bioactive components in C. songaricum, 3,4-dihydroxybenzaldehyde, catechin, epicatechin, ursolic acid, total phenolics, and crude polysaccharides—revealing significant concentration variations among geographically distinct populations. Slope gradient (slope), min temperature of coldest month (BIO6), precipitation of coldest quarter (BIO19), sunshine duration in growing season(hsdgs), and isothermality (BIO3) were identified as key regulatory factors influencing the accumulation of multiple components. Specifically, slope acted as a key shared negative regulator for 3,4-dihydroxybenzaldehyde, catechin, and crude polysaccharides. BIO6 served as a key shared positive regulator for catechin and total phenolics, while functioning as a key negative regulator for ursolic acid. BIO19 was identified as a key shared negative regulator for catechin and epicatechin. Hsdgs acted as a key positive regulator for ursolic acid while negatively regulating crude polysaccharides. Additionally, BIO3 served as a key shared positive regulator for both ursolic acid and total phenolics. This study provides the scientific basis for enabling targeted cultivation zones that balance therapeutic compound yield with arid ecosystem conservation. |
| format | Article |
| id | doaj-art-313b6a7fb72742daa22a8938eb9d26a2 |
| institution | Kabale University |
| issn | 1664-462X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-313b6a7fb72742daa22a8938eb9d26a22025-08-20T03:50:53ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2025-07-011610.3389/fpls.2025.15866821586682Evaluation of the impact of ecological factors on the habitat suitability and bioactive components accumulation of the medicinal holoparasitic plant Cynomorium songaricum using machine learning modelsJiacheng Ji0Jiacheng Ji1Xinxin Wei2Xinxin Wei3Huan Guan4Huan Guan5Zikang Jin6Zikang Jin7Xin Yue8Zhuoran Jiang9Youla Su10Youla Su11Shuying Sun12Shuying Sun13Guilin Chen14Guilin Chen15Key Laboratory of Herbage and Endemic Crop Biology, Ministry of Education, School of Life Sciences, Inner Mongolia University, Hohhot, ChinaThe Good Agriculture Practice Engineering Technology Research Center of Chinese and Mongolian Medicine in Inner Mongolia, Inner Mongolia University, Hohhot, ChinaKey Laboratory of Herbage and Endemic Crop Biology, Ministry of Education, School of Life Sciences, Inner Mongolia University, Hohhot, ChinaThe Good Agriculture Practice Engineering Technology Research Center of Chinese and Mongolian Medicine in Inner Mongolia, Inner Mongolia University, Hohhot, ChinaKey Laboratory of Herbage and Endemic Crop Biology, Ministry of Education, School of Life Sciences, Inner Mongolia University, Hohhot, ChinaThe Good Agriculture Practice Engineering Technology Research Center of Chinese and Mongolian Medicine in Inner Mongolia, Inner Mongolia University, Hohhot, ChinaKey Laboratory of Herbage and Endemic Crop Biology, Ministry of Education, School of Life Sciences, Inner Mongolia University, Hohhot, ChinaThe Good Agriculture Practice Engineering Technology Research Center of Chinese and Mongolian Medicine in Inner Mongolia, Inner Mongolia University, Hohhot, ChinaSchool of Pharmacy, Inner Mongolia Medical University, Hohhot, ChinaKey Laboratory of Herbage and Endemic Crop Biology, Ministry of Education, School of Life Sciences, Inner Mongolia University, Hohhot, ChinaKey Laboratory of Herbage and Endemic Crop Biology, Ministry of Education, School of Life Sciences, Inner Mongolia University, Hohhot, ChinaThe Good Agriculture Practice Engineering Technology Research Center of Chinese and Mongolian Medicine in Inner Mongolia, Inner Mongolia University, Hohhot, ChinaKey Laboratory of Herbage and Endemic Crop Biology, Ministry of Education, School of Life Sciences, Inner Mongolia University, Hohhot, ChinaThe Good Agriculture Practice Engineering Technology Research Center of Chinese and Mongolian Medicine in Inner Mongolia, Inner Mongolia University, Hohhot, ChinaKey Laboratory of Herbage and Endemic Crop Biology, Ministry of Education, School of Life Sciences, Inner Mongolia University, Hohhot, ChinaThe Good Agriculture Practice Engineering Technology Research Center of Chinese and Mongolian Medicine in Inner Mongolia, Inner Mongolia University, Hohhot, ChinaThe efficacy of traditional Chinese medicine is determined by its bioactive components, which exhibit variability depending on environmental conditions and hereditary influences. In this study, we focus on Cynomorium songaricum Rupr., a medicinally significant species facing sustainability challenges. However, the ecological drivers governing its distribution, as well as the relationship between environmental factors and bioactive components, remain unclear. Thus, we sampled 28 representative distribution areas of C. songaricum in China. Employing Maximum Entropy (MaxEnt) modeling, we projected current and future (2050s-2090s) habitat suitability under four emission scenarios. Notably, species distribution exhibited expansion (8.03%-29.06% range increase across scenarios) with precipitation of the wettest month (BIO13) and soil pH emerging as key drivers (combined contribution >49%). Ultra-performance liquid chromatography (UPLC) fingerprinting combined with machine learning regression was applied to quantify six key bioactive components in C. songaricum, 3,4-dihydroxybenzaldehyde, catechin, epicatechin, ursolic acid, total phenolics, and crude polysaccharides—revealing significant concentration variations among geographically distinct populations. Slope gradient (slope), min temperature of coldest month (BIO6), precipitation of coldest quarter (BIO19), sunshine duration in growing season(hsdgs), and isothermality (BIO3) were identified as key regulatory factors influencing the accumulation of multiple components. Specifically, slope acted as a key shared negative regulator for 3,4-dihydroxybenzaldehyde, catechin, and crude polysaccharides. BIO6 served as a key shared positive regulator for catechin and total phenolics, while functioning as a key negative regulator for ursolic acid. BIO19 was identified as a key shared negative regulator for catechin and epicatechin. Hsdgs acted as a key positive regulator for ursolic acid while negatively regulating crude polysaccharides. Additionally, BIO3 served as a key shared positive regulator for both ursolic acid and total phenolics. This study provides the scientific basis for enabling targeted cultivation zones that balance therapeutic compound yield with arid ecosystem conservation.https://www.frontiersin.org/articles/10.3389/fpls.2025.1586682/fullCynomorium songaricum Rupr.environmental factorshabitat suitabilitymachine learning modelsbioactive componentshigh-quality growing zones |
| spellingShingle | Jiacheng Ji Jiacheng Ji Xinxin Wei Xinxin Wei Huan Guan Huan Guan Zikang Jin Zikang Jin Xin Yue Zhuoran Jiang Youla Su Youla Su Shuying Sun Shuying Sun Guilin Chen Guilin Chen Evaluation of the impact of ecological factors on the habitat suitability and bioactive components accumulation of the medicinal holoparasitic plant Cynomorium songaricum using machine learning models Frontiers in Plant Science Cynomorium songaricum Rupr. environmental factors habitat suitability machine learning models bioactive components high-quality growing zones |
| title | Evaluation of the impact of ecological factors on the habitat suitability and bioactive components accumulation of the medicinal holoparasitic plant Cynomorium songaricum using machine learning models |
| title_full | Evaluation of the impact of ecological factors on the habitat suitability and bioactive components accumulation of the medicinal holoparasitic plant Cynomorium songaricum using machine learning models |
| title_fullStr | Evaluation of the impact of ecological factors on the habitat suitability and bioactive components accumulation of the medicinal holoparasitic plant Cynomorium songaricum using machine learning models |
| title_full_unstemmed | Evaluation of the impact of ecological factors on the habitat suitability and bioactive components accumulation of the medicinal holoparasitic plant Cynomorium songaricum using machine learning models |
| title_short | Evaluation of the impact of ecological factors on the habitat suitability and bioactive components accumulation of the medicinal holoparasitic plant Cynomorium songaricum using machine learning models |
| title_sort | evaluation of the impact of ecological factors on the habitat suitability and bioactive components accumulation of the medicinal holoparasitic plant cynomorium songaricum using machine learning models |
| topic | Cynomorium songaricum Rupr. environmental factors habitat suitability machine learning models bioactive components high-quality growing zones |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2025.1586682/full |
| work_keys_str_mv | AT jiachengji evaluationoftheimpactofecologicalfactorsonthehabitatsuitabilityandbioactivecomponentsaccumulationofthemedicinalholoparasiticplantcynomoriumsongaricumusingmachinelearningmodels AT jiachengji evaluationoftheimpactofecologicalfactorsonthehabitatsuitabilityandbioactivecomponentsaccumulationofthemedicinalholoparasiticplantcynomoriumsongaricumusingmachinelearningmodels AT xinxinwei evaluationoftheimpactofecologicalfactorsonthehabitatsuitabilityandbioactivecomponentsaccumulationofthemedicinalholoparasiticplantcynomoriumsongaricumusingmachinelearningmodels AT xinxinwei evaluationoftheimpactofecologicalfactorsonthehabitatsuitabilityandbioactivecomponentsaccumulationofthemedicinalholoparasiticplantcynomoriumsongaricumusingmachinelearningmodels AT huanguan evaluationoftheimpactofecologicalfactorsonthehabitatsuitabilityandbioactivecomponentsaccumulationofthemedicinalholoparasiticplantcynomoriumsongaricumusingmachinelearningmodels AT huanguan evaluationoftheimpactofecologicalfactorsonthehabitatsuitabilityandbioactivecomponentsaccumulationofthemedicinalholoparasiticplantcynomoriumsongaricumusingmachinelearningmodels AT zikangjin evaluationoftheimpactofecologicalfactorsonthehabitatsuitabilityandbioactivecomponentsaccumulationofthemedicinalholoparasiticplantcynomoriumsongaricumusingmachinelearningmodels AT zikangjin evaluationoftheimpactofecologicalfactorsonthehabitatsuitabilityandbioactivecomponentsaccumulationofthemedicinalholoparasiticplantcynomoriumsongaricumusingmachinelearningmodels AT xinyue evaluationoftheimpactofecologicalfactorsonthehabitatsuitabilityandbioactivecomponentsaccumulationofthemedicinalholoparasiticplantcynomoriumsongaricumusingmachinelearningmodels AT zhuoranjiang evaluationoftheimpactofecologicalfactorsonthehabitatsuitabilityandbioactivecomponentsaccumulationofthemedicinalholoparasiticplantcynomoriumsongaricumusingmachinelearningmodels AT youlasu evaluationoftheimpactofecologicalfactorsonthehabitatsuitabilityandbioactivecomponentsaccumulationofthemedicinalholoparasiticplantcynomoriumsongaricumusingmachinelearningmodels AT youlasu evaluationoftheimpactofecologicalfactorsonthehabitatsuitabilityandbioactivecomponentsaccumulationofthemedicinalholoparasiticplantcynomoriumsongaricumusingmachinelearningmodels AT shuyingsun evaluationoftheimpactofecologicalfactorsonthehabitatsuitabilityandbioactivecomponentsaccumulationofthemedicinalholoparasiticplantcynomoriumsongaricumusingmachinelearningmodels AT shuyingsun evaluationoftheimpactofecologicalfactorsonthehabitatsuitabilityandbioactivecomponentsaccumulationofthemedicinalholoparasiticplantcynomoriumsongaricumusingmachinelearningmodels AT guilinchen evaluationoftheimpactofecologicalfactorsonthehabitatsuitabilityandbioactivecomponentsaccumulationofthemedicinalholoparasiticplantcynomoriumsongaricumusingmachinelearningmodels AT guilinchen evaluationoftheimpactofecologicalfactorsonthehabitatsuitabilityandbioactivecomponentsaccumulationofthemedicinalholoparasiticplantcynomoriumsongaricumusingmachinelearningmodels |