Biodiversity Analytics: From Foundations to Applications
Tools, Methods, and Case Studies in Computational Ecology
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Beschreibung
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.
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.
Unites ML, NLP, KGs, and graph theory to solve real biodiversity challenges Delivers end‑to‑end, AI‑driven DSS to turn ecological data into policy action Tackles sparse, noisy data with interpretable models for reliable predictions
Autor*in
Moumita Ghosh
Themen in »Biodiversity Analytics: From Foundations to Applications«
Biodiversity Monitoring ML Applications in Ecological Research Ecological Network Analysis Knowledge Graphs Natural Language Processing Mangrove Ecosystem Restoration Graph Neural Networks