Is survival of Himalayan Cedar (Cedrus deodara) threatened? An evaluation based on predicted scenarios of its growth trend under future climate change

Modern pollen dispersal in relation to present vegetation distribution and land use in the Baspa valley, Kinnaur, western Himalayas

Interpretation of a fossil pollen data for the vegetation and climate reconstruction of any region needs a modern pollen-vegetation analogue for its calibration. We analyzed the surface sediments and moss polsters for the pollen and microcharcoal records to understand the modern pollen-vegetation relationship and human activities in the Baspa Valley, Kinnaur, Himachal Pradesh. Presently, valley is occupied by the arboreal and non-arboreal vegetation of temperate to subalpine habitats and land use activities. The recovered pollen assemblages showed variability in the dispersal behavior of pollen of taxa growing along the valley transect and also captured the signals of human activities over land use. The overall dominance of arboreal pollen in the recovered pollen assemblage corresponds with the dominant growth of conifers and broadleaf tree taxa and represents the valley vegetation at a regional scale. However, the profuse pollen production of a few arboreal taxa and long distance pollen transport from one vegetation zone to other by the strong upthermic valley winds could bias the pollen representation of in-situ vegetation. The high pollen frequency of non-arboreal taxa in the open meadows represents the near vicinity to their plant source. Human activities like fire burning and cultivation by the local population are evident by the recovery of microcharcoal particles and pollen of plants belonging to Cerealia Poaceae, Asteraceae, Amaranthaceae, Polygonaceae, Rosaceae, Juglandaceae, etc. The dataset taken as modern pollen-vegetation analogue is useful to assess past changes in the vegetation and land cover in relation to climate and human factors for future sustenance.

INTRAGRO: A machine learning approach to predict future growth of trees under climate change

The escalating impact of climate change on global terrestrial ecosystems demands a robust prediction of the trees' growth patterns and physiological adaptation for sustainable forestry and successful conservation efforts. Understanding these dynamics at an intra-annual resolution can offer deeper insights into tree responses under various future climate scenarios. However, the existing approaches to infer cambial or leaf phenological change are mainly focused on certain climatic zones (such as higher latitudes) or species with foliage discolouration during the fall season. In this study, we demonstrated a novel approach (INTRAGRO) to combine intra-annual circumference records generated by dendrometers coupled to the output of climate models to predict future tree growth at intra-annual resolution using a series of supervised and unsupervised machine learning algorithms. INTRAGRO performed well using our dataset, that is dendrometer data of P. roxburghii Sarg. from the subtropical mid-elevation belt of Nepal, with robust test statistics. Our growth prediction shows enhanced tree growth at our study site for the middle and end of the 21st century. This result is remarkable since the predicted growing season by INTRAGRO is expected to shorten due to changes in seasonal precipitation. INTRAGRO's key advantage is the opportunity to analyse changes in trees' intra-annual growth dynamics on a global scale, regardless of the investigated tree species, regional climate and geographical conditions. Such information is important to assess tree species' growth performance and physiological adaptation to growing season change under different climate scenarios.