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The influence of abiotic and spatial variables on woody and herbaceous species abundances in a woodland-grassland system in the Eastern Terai of India

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Journal of Plant Ecology 2022年第1期15卷 155-167页

作者： dhritiman das Subham Banerjee John Lehmkuhl Jagdish Krishnaswamy Robert John Academy of Conservation Science and Sustainability Studies Ashoka Trust for Research in Ecology and the Environment(ATREE)Royal EnclaveSriramapuraJakkur PostBangalore 560064India Manipal Academy of Higher Education Manipal 576104KarnatakaIndia Pygmy Hog Conservation Programme Durrell Wildlife Conservation TrustLes Augres ManorLa Profonde RueTrinityJersey JE35BPChannel Islands Department of Biological Sciences Indian Institute of Science Education and ResearchKolkataRoom 214Research ComplexMohanpur 741246West BengalIndia USDA Forest Service Pacific Northwest Research StationWenatcheeWA 98801USA

The environmental factors that influence tree-grass abundances in tropical savanna and grasslands are not well understood,particularly for woodland-grassland mosaics in humid *** studied the effects of abiotic and spatial variables on woody and herbaceous species distributions in a Terai ecosystem of northeastern *** evaluated the importance of climatic and non-climatic factors that maintain variable tree-grass ratios across the landscape,and also accounted for spatial connectivity and *** measured species abundances of woody and herbaceous plant species in 134 plots with each 30 m×30 m in a 519 km^(2)protected Terai habitat,and derived several climatic and non-climatic environmental *** constructed variables based on different models of spatial connectivity among sites,to test their influence on species *** then used redundancy analyses and variation partitioning to quantify the importance of environmental variables and spatial structure on variation in tree-grass *** found that environmental variables including rainfall,fire,water stress,topography and soil nutrients had statistically significant effects on species abundance and tree-grass *** structure was significant,and the best spatial model was an inverse distance-weighted model with linkage extending to 23.5 km,indicating weak dispersal *** 21%of the variation in species abundance was explained by the selected environmental and spatial *** results indicate that dynamic plant communities in which spatial-temporal variation in environmental factors may drive stochasticity in species distribution and abundance,thus dominantly influencing on the vegetation mosaic.

关键词： woodland-grassland Eastern Terai Manas National Park variation partitioning ground fires spatial connectivity

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Unravelling the Complex Networks of Social Physics:Exploring Human Behavior,Big Data,and Distributed Systems

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Journal of Social Computing 2024年第2期5卷 165-179页

作者： Subrata Paul dhritiman Mukherjee Anirban Mitra Anuradha Mitra Pushan Kumar Dutta the Department of Computer Science and Engineering Brainware UniversityKolkata 700125India. the Department of Computer Science and Engineering Amity University KolkataKolkata 700135India the Department of Management University of LiverpoolLiverpoolL697ZXUK the Department of Electronics and Communication Engineering Amity University KolkataKolkata 700135India

Networks form an integral part of our daily lives, comprising structures known as nodes connected via ties. These networks exhibit a complex interplay of random and structured features. We encounter networks regularly in our daily activities, whether it is logging into websites, commuting by train, going to work, or attending school. Networks are prevalent in various academic disciplines and practical applications, spanning biology, physics, engineering, social science, and numerous other fields. The early 21st century has witnessed a rapid expansion of techniques facilitating human interaction, significantly impacting behavior and social bonding in both urban and rural settings. The availability and accessibility of extensive population datasets have spurred ongoing research into understanding and shaping human inclinations and sociological phenomena in unconventional ways. This review delves into the nuanced aspects of social physics, exploring the rationale behind choosing this research topic, various conceptual frameworks, categorizations, and distinctions between machine learning and deep learning. The article briefly outlines the advantages of leveraging big data in social physics, highlighting its transformative potential. Lastly, the authors provide insights into several applications of social physics in subsequent sections, offering a glimpse into its real-world implications.

关键词： networks social physics machine learning deep learning social science

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