生态演替
山崩
植被(病理学)
地质学
林业
地理
自然地理学
土壤科学
生态学
地貌学
生物
医学
病理
作者
Deepesh Goyal,Varun Joshi
摘要
ABSTRACT Landslides are geological disasters that occur very frequently in the Indian Himalayan Region (IHR) and are considered as major perturbation processes to soil and vegetation. Natural recovery, one of the most effective ways of landslide recovery, was studied by referring to vegetational structure, soils, and environmental variables and their correlations about 6 years after a landslide in Garhwal Himalayas. The study illustrated that a nutrient‐deficient environment prevails in landslides. A total of 25 plant species were found with higher diversity and richness indices of herb species in disturbed sites than in undisturbed sites. The low values of similarity index between the landslide and control sites exhibit the impacts of environmental components on the recovery of vegetation on landslides. The correlation through redundancy analysis (RDA) reveals that the herb species are more inclined toward the higher landslide sites having high pH and low nutrient content, whereas the woody species are more oriented toward the middle landslide sites. It was also observed that Alnus nepalensis has a wide distributional range as it was placed near the center of the RDA biplot. There exists a research gap in apprehending the variations in soil carbon status, nutrient dynamics, and plant community structure following natural recovery along elevational gradients in landslides. The study indicates that the early stages of plant recovery following landslides are significantly influenced by abiotic environmental conditions associated with soil characteristics. This study also provides a reference for the recovery and restoration strategies in the landslide‐affected regions. The recovery in landslides is a complex process; hence, further long‐term studies should be continued to evaluate the spatio‐temporal variations during succession in landslides and interactions between the biotic and abiotic components.
科研通智能强力驱动
Strongly Powered by AbleSci AI