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Phenocam observed flowering anomaly of Rhododendron arboreum Sm. in Himalaya: a climate change impact perspective.

Sudeep ChandraAnkit SinghJincy Rachel MathewChandra Prakash SinghMehul R PandyaBimal K BhattacharyaHiteshkumar A SolankiM C NautiyalRajesh Joshi
Published in: Environmental monitoring and assessment (2022)
Flowering exhibits a significant relationship with environmental stimuli and changes. Effect of photoperiodism and vernalization have been well studied in flowering phenology; however, the effect of soil temperature on flowering is less explored which is one of the major factors of vegetation growth in alpine ecosystem. This study thus focuses on the effects of soil and air temperature on flowering response of Rhododendron arboreum Sm., a Himalayan tree species, which is also an indicator of spring initiation in high altitude regions. To monitor the flowering pattern, we employed automated phenocam, which was set up at 3356 masl in Tungnath (Indian Alpine region of Uttarakhand) for time-lapse photography of timberline ecotone. Soil and air temperature were recorded continuously at the timberline ecotone. Three years (2017 to 2020) of datasets were used for the present study. The phenocam observations displayed an interesting event in the year 2019-2020 with complete absence of flowering in R. arboreum population at Tungnath timberline ecotone. From the soil temperature data, an increase in winter (Dec-Jan, during which floral buds form) soil temperature, by > 1 °C, and no accumulation of freezing degree-days were found for the year 2019-2020. Air temperature however did not display any relationship with the failure of flowering, ruling out aerial chilling or frost injury of floral buds. From the results, a possible relationship between soil temperature and flowering can be suggested pointing towards necessary root apex vernalization stimulus in shallow rooted Rhododendrons. However, the dependency of flowering in Rhododendrons on winter soil temperature further requires continuous monitoring and more observations to make concrete inferences.
Keyphrases
  • arabidopsis thaliana
  • climate change
  • plant growth
  • electronic health record
  • human health
  • deep learning
  • high throughput
  • rna seq
  • artificial intelligence