Population Assessment and Habitat Distribution Modelling of High Value Corylus jacquemontii for Conservation in the Indian North-Western Himalaya

Abstract

Corylus jacquemontii is one of the ecologically and economically important high value multipurpose tree in Indian Himalaya Region. Continued over-exploitation and habitat degradation of the species for fodder, fuel, food and medicinal purposes have caused rapid depletion from natural habitats. Therefore, twenty five populations of the C. jacquemontii were assessed in Lahaul & Spiti, Kinnaur, Chamba and Kullu districts of Himachal Pradesh. Soil sampled from each population were analyzed. The density of the species was highest in dry slope habitat. Total tree density had a significant positive correlation with organic carbon of soil (r=0.42, P<0.05 and n=25). C. jacquemontii seedling density had significant negative correlation with altitude (r =-0.43, P<0.05 and n=25) and pH (r=-0.60, P<0.05 and n=50). Most of the populations had poor regeneration of species. MaxEnt model predicted 3300 Km 2 suitable area for reintroduction of species in Himachal Pradesh. Precipitation of driest peroid emerged as a major factor in determining the distribution of potential habitats of C. jacquemontii. Therefore, mass multiplication using seed germination protocol, their establishment in identified habitats and regular monitoring of the habitats with the active participation of local inhabitants, Non-Government Organizations (NGOs) and Forest Department are essentially required.

Potential distribution model of Leontochir ovallei using remote sensing data

“…Consequently, Wang et al (2012) recommend using predictive models to evaluate the distribution pattern of species with scarce available data. For example, the species distribution models derived from MaxEnt (Maximum Entropy, Phillips et al, 2004) show a high performance in predicting species distribution based on a small sample or species presence data (Hernández et al, 2006;Wisz et al, 2008;Kumar and Stohlgren, 2009;Baldwin, 2009;Jafari et al, 2018;Shiv et al, 2019). MaxEnt is a machine learning algorithm that uses multivariate distributions of habitat capability deducted from species presence records to generate a species occurrence probability considering the restrictions and suitability of the environmental conditions.…”