Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Ghosh, Oindrilla; Kumar, Binod Title A name given to the resource AI-Driven Policy Frameworks and Decision Support Systems for Invasive Species Management and Biodiversity Conservation Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Harnessing AI for Invasive Species Management and Biodiversity Conservation;pp.1-32 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.4018/979-8-3373-4616-8.ch001" target="_blank" rel="noreferrer noopener">https://doi.org/10.4018/979-8-3373-4616-8.ch001</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105032588460?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105032588460?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ghosh O., Christ University, India; Kumar B., Radha Govind University, India Description An account of the resource Invasive species pose significant threats to biodiversity and ecosystem health. AI-driven policy frameworks and decision support systems can enhance invasive species management and biodiversity conservation efforts. By integrating machine learning algorithms and big data analysis, these systems provide real-time insights, enabling stakeholders to make informed decisions quickly. AI can identify patterns in species distribution, predict invasion potential, and assess ecological impacts. Additionally, these tools facilitate collaboration among policymakers, scientists, and conservationists, ensuring that strategies are evidence-based and tailored to local contexts. Effective implementation requires considering socio-economic factors and stakeholder engagement. As climate change continues to alter ecosystems, AI-driven systems can adapt to new challenges, promoting resilience. By harnessing technology, we can develop proactive strategies that balance human interests with ecological health, ultimately leading to sustainable environmental management. 2026 by IGI Global Scientific Publishing. All rights reserved. Publisher An entity responsible for making the resource available IGI Global Relation A related resource ISBN: 979-833734618-2; 979-833734616-8; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online