Harnessing AI for Invasive Species Management and Biodiversity Conservation - Softcover

Softcover

ISBN 13: 9798337346175

Publisher: Engineering Science Reference, 2026

Synopsis

Invasive species represent a major threat to biodiversity, ecosystems, and global economies, contributing to habitat destruction, species loss, and the disruption of ecological functions. Conventional methods for managing these species often prove inadequate due to limitations in resources, inefficiencies in monitoring large areas, delayed responses, and challenges in forecasting their spread. With increasing pressure from factors like climate change and human activities, there is a pressing need for more effective, scalable, and adaptable approaches. Artificial intelligence applications in conservation efforts may transform the management of invasive species and strengthen biodiversity conservation efforts, enhancing early detection, tracking, and control. Harnessing AI for Invasive Species Management and Biodiversity Conservation examines the challenges of integrating AI into ecological management, including concerns about data privacy, algorithm transparency, and the need for rigorous validation processes. It highlights how AI can serve as a powerful complement to traditional conservation strategies, providing innovative and sustainable solutions to combat the growing issue of invasive species. This book covers topics such as climate change, ecology, and sustainable development, and is a useful resource for business owners, engineers, policymakers, biologists, academicians, researchers, and environmental scientists.

About this title

About the Authors

Adnčne Arbi is a distinguished academic affiliated with the University of Carthage in Tunisia, where he has contributed significantly to the fields of mathematics and engineering. His tenure at the university spans over a decade, during which he has held various positions, including roles at the Ecole Polytechnique de Tunisie and the National Institute of Applied Sciences and Technology. His research interests primarily focus on neural networks, dynamical systems, and mathematical modeling, with a particular emphasis on the stability and controllability of complex systems. Arbi's recent publications reflect his commitment to advancing knowledge in applied mathematics, particularly through innovative approaches such as wavelet neural networks and the dynamics of nonlinear systems. His work has been published in reputable journals, contributing to the understanding of mathematical phenomena in both theoretical and practical contexts.

Dr. Walid Ben Ameur earned his PhD from the University of Carthage. He currently serves as a teacher-researcher at the Department of Life Sciences, Faculty of Sciences of Gabčs, Tunisia, and is a member of the Laboratory of Ecology of Terrestrial Fauna at the University of Gabčs, Tunisia. Walid Ben Ameur has authored more than 20 peer-reviewed scientific papers published in international journals. His work has made useful contributions to the field of environmental chemistry, with a particular focus on persistent organic pollutants. He has developed analytical techniques for assessing emerging pollutants and evaluating their effects on both the environment and human health. His research spans a diverse range of samples, including those from environmental, biological, human, and food sources. Dr. Ben Ameur has mentored three Master's students and regularly serves as a reviewer for respected journals such as Science of the Total Environment, Chemosphere, Environmental Science and Pollution Research, Marine Pollution Bulletin, Environmental Pollution, Ecotoxicology and Environmental Safety, and Chemistry Africa.

Edition details

• Publisher: Engineering Science Reference
• Publication date: 2026
• Language: English
• ISBN 13: 9798337346175
• Binding: Paperback
• Number of pages: 320
• Editor: Arbi Adnčne, Ben Ameur Walid