Optimized MaxEnt Model Predicts Future Suitable Habitats for Chinese Caterpillar Fungus Under Climate Change
The Chinese Caterpillar Fungus (CCF) is a precious and rare traditional Chinese medicinal material that is extremely sensitive to environmental changes, making wild resources scarce. Therefore, studying the impact of climate change on the potential distribution and changes of the CCF is of great sig...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/15/11/1144 |
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| Summary: | The Chinese Caterpillar Fungus (CCF) is a precious and rare traditional Chinese medicinal material that is extremely sensitive to environmental changes, making wild resources scarce. Therefore, studying the impact of climate change on the potential distribution and changes of the CCF is of great significance. Employing an enhanced MaxEnt approach (optimized with ENMeval), this study determined the primary ecological constraints on CCF and mapped its potential present and future ranges. The results indicated that elevation, bio05, bio04, bio12, bio11, slope, d1_ph_water, and hf were the driving environmental factors influencing the survival of the CCF. The ideal habitat zones for the CCF were mainly distributed in the plateau and alpine climate zones of northwestern and southwestern China, covering an area of 7.42 × 10<sup>4</sup> km<sup>2</sup>. Compared with the current climate scenario, the area of suitable habitats for the CCF was expected to increase in the future. In the 2090s, under the SSP1–2.6 scenario, the highly suitable areas for the CCF will have increased the most, by 67.54%, while the low–suitability areas will have decreased by 6.87%. Overall, the highly suitable areas for the CCF will shift towards higher latitudes. The outcomes of this study can inform subsequent conservation strategies for CCF resources and facilitate research on other ecological variables affecting CCF distribution patterns. |
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| ISSN: | 2077-0472 |