This book provides a practical, end-to-end introduction to computational biodiversity science—showing how artificial intelligence, machine learning, graph theory, and natural language processing transform ecological monitoring, modeling, and conservation. Centered on mangrove ecosystems and climate resilience, it connects ecological foundations to reproducible analytics and policy-facing tools. This book explains the data revolution in ecology—from species-occurrence and environmental layers to image, text, and networked observations. It demonstrates machine learning for species distribution modeling, invasive-species prediction, and habitat classification from satellite imagery; introduces ecological network analysis to reveal keystone species and fragile interactions using degree, PageRank, and modularity; and shows how graph neural networks predict missing links in food webs and mutualistic networks. A dedicated chapter details building ecological Knowledge Graphs from literature with NLP—entity and relation extraction, ontology design, and semantic enrichment—and deploying them using tools such as Neo4j and PyKEEN. The closing chapter connects research to real decisions via AI-driven Decision Support Systems. It outlines scenario modeling for restoration planning, collapse risk, and resilience assessment; compares AI pipelines with conventional workflows for efficiency, accuracy, and reproducibility; and highlights community engagement, translating technical outputs into accessible formats that empower local stakeholders. 

Dr. Moumita Ghosh is currently working as a Postdoctoral Researcher in the Department of Computer Science and Engineering, Kalyani University, India, under a research project funded by the Department of Science and Technology (DST), Government of India. She obtained her B.Tech. in Information Technology from WBUT, India, in 2011, her M.E. from Jadavpur University in 2013, and her Ph.D. from Jadavpur University in 2024. Her doctoral research focused on Algorithms and Applications in Biodiversity. Dr. Ghosh has over a decade of experience in teaching and research and has served in various academic positions at reputed institutions, including Heritage Institute of Technology, Narula Institute of Technology, Jadavpur University, Institute of Engineering and Management, and Bengal College of Engineering and Technology. She was awarded the prestigious DST Women Scientists Fellowship (WOS-A) and successfully served as the Principal Investigator of a funded research project on the application of data mining on the littoral forest of West Bengal. Her research interests include Computational Biodiversity, Machine Learning, Data Mining, Artificial Intelligence, Complex Networks, Deep Learning, Ecological Informatics, and Knowledge Discovery. Dr. Ghosh has authored numerous research articles in reputed international journals and conferences. She has also contributed to an Australian innovation patent and actively collaborates with international researchers, including partners from Universitas Islam Indonesia. She is a member of the Computer Society of India (CSI) and serves as a reviewer and technical committee member for several international conferences and journals.

Dr. Sanjay Chakraborty is currently a postdoctoral research scientist with the Department of Computer and Information Science, REAL, AIICS, at Linköping University, Sweden. He is also on lien as an associate professor in the Department of Computer Science and Engineering, Techno International New Town, Kolkata, India. He received his B.Tech. degree in information technology from West Bengal University of Technology, India, in 2009, and his M.Tech. degree from the National Institute of Technology (NIT), Raipur, India, in 2011, and his Ph.D. degree from the A.K. Choudhury School of Information Technology, University of Calcutta, India, in 2022. He was awarded the University Silver Medal by NIT Raipur in 2011 for securing First Class Second position in M.Tech. Dr. Chakraborty has over 15 years of teaching and research experience and has published more than 90 research articles in international journals, conferences, and book chapters. He has authored four books published by Lambert Academic Publishing, Springer DIR, Springer STNIC, and Springer EAI. He is actively involved in several academia–industry collaborative projects funded by the European Union, Vinnova, WASP-WISE, and the Knut and Alice Wallenberg Foundation, Sweden. His research interests include machine learning, applied artificial intelligence, and quantum computing. He is a professional member of IAENG and UACEE and serves as a reviewer for numerous international journals, transactions, and conferences. His accolades include the IEEE Young Professional Best Paper Award (2017), recognition among the Top Five Best Papers by the Ain Shams Engineering Journal (Elsevier), and the Most Cited Author Award (2021) from the Biomedical Journal (Elsevier).