Global Ecosystem Change: Next-Generation Biomonitoring Using Edna and Machine Learning - by Mark Molina (Hardcover)

About the Book

Biomonitoring is a powerful tool used to assess the health and condition of an ecosystem. It involves studying living organisms and their responses to environmental changes. Biomonitoring through environmental DNA (eDNA) and Machine Learning represents a cutting-edge approach in ecological research and conservation. eDNA allows scientists to detect traces of genetic material left behind by organisms in their environment, offering a non-invasive method to monitor biodiversity. Machine Learning enhances this process by analyzing vast amounts of eDNA data, identifying species presence, and even predicting ecosystem health trends. This combination revolutionizes biodiversity assessment, offering faster and more comprehensive insights into ecosystems than traditional methods. By making use of machine learning algorithms to interpret eDNA signals, researchers can uncover hidden biodiversity patterns and monitor changes over time effectively. This synergy holds promise for advancing conservation efforts and understanding ecological dynamics in an increasingly interconnected world. Most of the topics introduced in this book cover new techniques and the applications of biomonitoring. The various technological progress that have future implications are glanced at in this book. It will help the readers in keeping pace with the rapid changes in this field.

Book Synopsis

Biomonitoring is a powerful tool used to assess the health and condition of an ecosystem. It involves studying living organisms and their responses to environmental changes. Biomonitoring through environmental DNA (eDNA) and Machine Learning represents a cutting-edge approach in ecological research and conservation. eDNA allows scientists to detect traces of genetic material left behind by organisms in their environment, offering a non-invasive method to monitor biodiversity. Machine Learning enhances this process by analyzing vast amounts of eDNA data, identifying species presence, and even predicting ecosystem health trends. This combination revolutionizes biodiversity assessment, offering faster and more comprehensive insights into ecosystems than traditional methods. By making use of machine learning algorithms to interpret eDNA signals, researchers can uncover hidden biodiversity patterns and monitor changes over time effectively. This synergy holds promise for advancing conservation efforts and understanding ecological dynamics in an increasingly interconnected world. Most of the topics introduced in this book cover new techniques and the applications of biomonitoring. The various technological progress that have future implications are glanced at in this book. It will help the readers in keeping pace with the rapid changes in this field.