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Nematode community structure along elevation gradient in high altitude vegetation cover of Gangotri National Park (Uttarakhand), India.

ABSTRACT: The study was undertaken to understand the dynamics of soil nematode communities association along elevation gradient and associated variables in high-altitude regions of Western Himalaya. The diversity and distribution pattern of soil nematodes were investigated at four elevation ranges (Elv1, Elv2, Elv3, and Elv4 each of 500 m class) along altitudinal gradient (3000-5000 m). The nematode community comprised 58 genera of which 37 genera showed sensitivity towards altitudinal variation and the community structure also varied among elevation classes. It was found that elevation significantly affect the nematodes trophic group, diversity composition, and the nematode indices. Nematode generic richness and evenness index indicated a relatively low biodiversity of soil nematodes is supported at higher altitudes. Moreover, maturity indices reflected lower sustenance of k-strategic nematodes at higher elevations. Structure index depicted fewer connections in the soil food web at higher elevations. Nematode faunal profile showed low nutrient and low disturbance in the region. Carbon footprint of the whole nematode assemblage declined along the elevation. Overall substantial differences in the nematode composition, abundance, trophic structure, and contribution to belowground carbon cycling were observed with change in elevation. These findings could be utilized as useful tool in the long-term monitoring and to understand the region's soil health.

SUBMITTER: Kashyap P

PROVIDER: S-EPMC8792017 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Vegetation characteristics are an important proxy to measure the outcome of ecological restoration and monitor vegetation changes. Similarly, the classification of remotely sensed images is a prerequisite for many field ecological studies. We have a limited understanding of how the remote sensing approach can be utilized to classify spontaneous vegetation in post-industrial spoil heaps that dominate urban areas. We aimed to assess whether an objective a priori classification of vegetation using remotely sensed data allows for ecologically interpretable division. We hypothesized that remote sensing-based vegetation clusters will differ in alpha diversity, species, and functional composition; thereby providing ecologically interpretable division of study sites for further analyses. We acquired remote-sensing data from Sentinel 2A for each studied heap from July to September 2020. We recorded vascular plant species and their abundance across 400 plots on a post-coal mine in Upper Silesia, Poland. We assessed differences in alpha diversity indices and community-weighted means (CWMs) among remote sensing-based vegetation units. Analysis of remotely sensed characteristics revealed five clusters that reflected transition in vegetation across successional gradients. Analysis of species composition showed that the 1st (early-succession), 3rd (late-succession), and 5th (mid-succession) clusters had 13, 10, and 12 exclusive indicator species, respectively, however, the 2nd and 4th clusters had only one species. While the 1st, 2nd, and 4th can be combined into a single cluster (early-succession), we found the lowest species richness in the 3rd cluster (late-succession) and the highest in the 5th cluster (mid-succession). Shannon's diversity index revealed a similar trend. In contrast, the 3rd cluster (late-succession) had significantly higher phylogenetic diversity. The 3rd cluster (late-succession) had the lowest functional richness and the highest functional dispersion. Our approach underscored the significance of a priori classification of vegetation using remote sensing for vegetation surveys. It also highlighted differences between vegetation types along a successional gradient in post-mining spoil heaps.

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Nematode community structure along elevation gradient in high altitude vegetation cover of Gangotri National Park (Uttarakhand), India.

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