Partial altitudinal movement of Himalayan Bulbul Pycnonotus leucogenys in Himalayan Siwalik range

Abstract

Himalayan Bulbul Pycnonotus leucogenys is widely spread in the Himalayan Siwalik range but information is available on its migratory behavioural patterns. Hence, a two-year study was undertaken to investigate its distribution range of the species in the summer and winter seasons in Punjab and Himachal Pradesh. A total of twenty variables including 19 bioclimatic variables and elevation were selected for the development of Species distribution Modelling (SDM). Occurrence records of P. leucogenys in summer and winter were processed at the Maximum entropy model (MaxEnt) using the ENMeval data package. The results suggested a downward movement from the upper Siwalik Himalayan ranges to the lower Siwalik in winter, and with return migration movement in summer. However, a large fraction of the distribution range was found overlapping in both the models, which suggests partial altitudinal migration of P. leucogenys. The data recorded on the ground subordinate the finding of the model. A wide range of tolerance for bioclimatic variables was observed in P. leucogenys, however temperaturerelated factors played a vital role in the variation in the species distribution range in the annual cycle of partial migration. The finding of this study would be a valuable reference for future studies on ecological and behavioral aspects of partial-migration of bird species in the Himalaya.

Potential Geographical Distribution of Lagerstroemia excelsa under Climate Change

“…Among these, MaxEnt stands out due to its advantages of requiring a smaller sample size, offering greater stability, and delivering superior prediction results. Consequently, it has emerged as the most widely utilized SDM [21][22][23][24]. Given MaxEnt's Constructing an ecological niche model (ENM) using occurrence data and environmental attributes proves to be an effective approach for delineating potential geographical distributions under varying climates [15,16].…”

“…Among these, MaxEnt stands out due to its advantages of requiring a smaller sample size, offering greater stability, and delivering superior prediction results. Consequently, it has emerged as the most widely utilized SDM [21][22][23][24]. Given MaxEnt's demonstrated benefits, we opted for this model to predict the potential geographical distribution of L. excelsa.…”