Ecological Determinants of Woody Plant Species Richness in the Indian Himalayan Forest

Abstract

The ecological importance of woody plant species richness is well known. The role of abiotic ecological determinants on structuring the vegetation has been well studied. The present study evaluated the independent and integrated strength of the abiotic and biotic determinants in explaining species richness of woody plants in the Indian Himalayan forest. The primary field inventory data was collected using nested quadrat method (tree species at 10 × 10 m2, shrub species at 5 × 5 m2, and herb species at 1 × 1 m2 quadrats) for different life forms and for the abundance estimation within each 1 km transect. Each transect was laid in a 6.3 × 6.3 km2 grid on the study site. The biotic determinants included diameter at breast height (d.b.h.) and tree height, whereas the abiotic determinants were temperature, precipitation, soil moisture, relative humidity and elevation. A total of 302 woody plant species (233 genera and 53 families) were recorded from the field inventory. The woody plant species richness was found to range from 1 to 54 per ha at transect level. Structural Equation Model (SEM) evaluated different combinations of ecological determinants for woody plant species richness. The abiotic or biotic determinants were non-significant if considered independently; however, the integration of both resulted in a significant relation with woody plant species richness. The best combination of ecological determinants include density d.b.h. ≥ 2.5 cm, tree height, relative humidity, and elevation (R2 = 0.53). Overall, the integration of biotic and abiotic determinants better explained woody plant species richness in the Indian Himalayan forest.

Publication
Spatial Modeling in Forest Resources Management
Swapna Mahanand
Swapna Mahanand
Postdoctoral associate / Remote Sensing in Geo- and Ecosystem Research

I am a Postdoctorate Researcher at Leipzig University working on the FLEXPOOL project “Biodiversity effects on Plant-Atmosphere interactions analyzed with Remote Sensing (PARSe Biodiversity)” funded by iDiv. I use various machine learning techniques to assess plant diversity using the biophysical proxies derived from satellite data products. Further, I use that relationship in the prediction and monitoring of biodiversity.