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Corona virus disease 2019 (COVID-19) caused by corona virus SARS-CoV-2 is believed to be originated in Wuhan province of China, which has spread all over the world. It has severely impacted the social, cultural, educational, research and development programmes throughout the globe. Since the inception of various lifeforms on planet earth, forests' plays an important role in the evolutionary history of organisms and fitness persuades the existence of modern-day species in different ecosystems. The visible effect of pandemic leads to the lockdown with positive implications on the earth biosphere as a whole; while on other hand, lockdown seized and reduced the urbanization and developmental projects, especially the research and development progress in the field of forestry and allied sciences. This viewpoint addresses key questions and point-out with detailed exploration of the problemas how COVID-19 pandemic impacts forestry research all over the world? We also suggest possible solutions, and define the role and association of disciplines, such as bioinformatics, remote sensing and statistical modules along with previously explored and collected field data as a sustainable measure to resolve the problems of forestry sector of this mighty challenge.

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The last five decades (since 1980) have witnessed the introduction of exotic trees as a popular practice in India to fulfill the demand of forest-based products for utilization in afforestation programmes. This study examines the distribution and habitat suitability of exotic Grevillea robusta trees in the northwestern Himalayas (state: Uttarakhand), focusing on the interaction between G. robusta and abiotic factors, such as climate, soil, and habitat suitability. This multipurpose agroforestry species is mainly grown by farmers as a boundary tree, windbreak, or shelterbelt and among intercrops on small farms in agroforestry systems worldwide. The results indicate that phenotypic plasticity is determined by tree height and diameter, indicating a higher frequency of young and adult trees. The study also highlights spatio-temporal modeling coupled with geological analysis to address the current distribution pattern and future habitat suitability range through MaxEnt modeling. The AUC ranged from 0.793 ± 3.6 (RCP 6.0_70) to 0.836 ± 0.008 (current) with statistical measures, such as K (0.216), NMI (0.240), and TSS (0.686), revealing the high accuracy of the model output. The variables, which include the minimum temperature of the coldest month (Bio 6), the slope (Slo), the mean temperature of the driest quarter (Bio 9), and the precipitation of the driest quarter (Bio 17), contribute significantly to the prediction of the distribution of the species in the Himalayan state. The model predicts a significant habitat suitability range for G. robusta based on bio-climatic variables, covering an area of approximately ~1641 km2 with maximal occurrence in Pauri (~321 km2) and Almora (~317 km2). Notably, the future prediction scenario corroborates with the regions of Tons (Upper Yamuna, Uttarkashi), Kalsi (Mussoorie, Dehradun), the Kedarnath Wildlife Sanctuary, and the Badrinath Forest Division for the potentially suitable areas. The climate was found to have a strong influence on the species’ distribution, as evidenced by its correlation with the Köppen–Geiger climate classification (KGCC) map. While the species demonstrated adaptability, its occurrence showed a high correlation with bedrocks containing an elevated iron content. Furthermore, the study also provides the first trees outside forests (TOF) map of G. robusta in the region, as well as insight into its future habitat suitability.

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