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Classification and Characterization of the Manoor Valley's (Lesser Himalaya) Vegetation from the Subtropical-Temperate Ecotonal Forests to the Alpine Pastures along Ecological Variables.

Inayat Ur RahmanAftab AfzalZafar IqbalMashail Nasser AlzainAl-Bandari Fahad Al-ArjaniAbdulaziz A AlqarawiElsayed Fathi Abd AllahNiaz AliShazia SakhiMuhammad Azhar KhanUzma KhanFarhana IjazSamina MumtazEduardo Soares Calixto
Published in: Plants (Basel, Switzerland) (2021)
Plant species are distributed in different types of habitats, forming different communities driven by different sets of environmental variables. Here, we assessed potential plant communities along an altitudinal gradient and their associations with different environmental drivers in the unexplored Manoor Valley (Lesser Himalaya), Pakistan. We have implemented various ecological techniques and evaluated phytosociological attributes in three randomly selected 50 m-transects within each stand (a total of 133) during different seasons for four years (2015-2018). This phytosociological exploration reported 354 plant species representing 93 different families. The results revealed that the Therophytic life form class dominated the flora, whereas Nanophyll dominated the leaf size spectra. There were a total of twelve plant communities identified, ranging from the lowest elevations to the alpine meadows and cold deserts. The maximum number of species were found in Cedrus-Pinus-Parrotiopsis community (197 species), in the middle altitudinal ranges (2292-3168 m). Our results showed that at high altitudes, species richness was reduced, whereas an increase in soil nutrients was linked to progression in vegetation indicators. We also found different clusters of species with similar habitats. Our study clearly shows how altitudinal variables can cluster different plant communities according to different microclimates. Studies such as ours are paramount to better understanding how environmental factors influence ecological and evolutionary aspects.
Keyphrases
  • climate change
  • human health
  • risk assessment
  • mental health
  • machine learning
  • healthcare
  • genome wide
  • plant growth