The science of ecology and the practice of resource management are critical to our understanding of the Earth's ecosystems and our efforts to conserve them. This book attempts to bridge the gap between ecology and natural resource management and in particular, focuses on the discipline of plant ecology as a foundation for vegetation and wildlife management. It describes how concepts and approaches used by ecologists to study communities and ecosystems can be applied to their management. Guy R. McPherson and Stephen DeStefano emphasise the importance of thoughtfully designed and carefully conducted scientific studies to both the advancement of ecological knowledge and the application of techniques for the management of plant and animal populations. The book is aimed at natural resource managers, as well as graduate and advanced undergraduate students, who are familiar with fundamental ecological principles and who want to use ecological knowledge as a basis for the management of ecosystems.

This book explains ways that ecological science can be applied to solving some of the most crucial problems facing our world today. A major theme is how resources can be effectively managed and exploited in as near a sustainable manner as possible. The author draws together, in a single volume, major topics in environmental and resource management that have traditionally been dispersed among several different books. Applied Ecology starts with an analysis of our planet's basic natural resources - energy, water and soil; it moves on to the management of biological resources - fish, grazing lands and forests, and then to pest control and pollution. Finally, the book tackles conservation and management of wild species and the restoration of ecological communities. The second edition of this text has been radically redesigned and rewritten. Each chapter starts with a list of questions, setting out the various fundamental problems to be considered. Interwoven with these practical problems is a clear explanation of the underlying basic science - ecology - studied at scales ranging from global, landscape and ecosystem, down to the population and individual (and even their physiology and genetics). The science is illustrated by examples from every major geographic area of the world. This book is aimed primarily at undergraduate students taking courses in applied ecology, environmental science, environmental management and natural resources management. The author has extensive experience as a university teacher. Like his lectures, this book is scientifically rigorous yet clear and easy to understand. Draws together major topics in environmental and resource management, usually dispersed over many separate books. Questions, summaries and clearly structured chapters enhance usability. Emphasis on clarity and accessibility. Based on a proven and successful course.

Ecologists and natural resource managers are charged with making complex management decisions in the face of a rapidly changing environment resulting from climate change, energy development, urban sprawl, invasive species and globalization. Advances in Geographic Information System (GIS) technology, digitization, online data availability, historic legacy datasets, remote sensors and the ability to collect data on animal movements via satellite and GPS have given rise to large, highly complex datasets. These datasets could be utilized for making critical management decisions, but are often “messy” and difficult to interpret. Basic artificial intelligence algorithms (i.e., machine learning) are powerful tools that are shaping the world and must be taken advantage of in the life sciences. In ecology, machine learning algorithms are critical to helping resource managers synthesize information to better understand complex ecological systems. Machine Learning has a wide variety of powerful applications, with three general uses that are of particular interest to ecologists: (1) data exploration to gain system knowledge and generate new hypotheses, (2) predicting ecological patterns in space and time, and (3) pattern recognition for ecological sampling. Machine learning can be used to make predictive assessments even when relationships between variables are poorly understood. When traditional techniques fail to capture the relationship between variables, effective use of machine learning can unearth and capture previously unattainable insights into an ecosystem's complexity. Currently, many ecologists do not utilize machine learning as a part of the scientific process. This volume highlights how machine learning techniques can complement the traditional methodologies currently applied in this field.

An insightful guide to the concepts and practices of modern landscape ecology Elements of geography, conservation biology, soil science and other disciplines factor into landscape ecology's rich analyses of the ecological and environmental forces at play across different terrains. With its unique, organism-oriented approach to the subject, Applied Landscape Ecology considers the effects of ecological processes upon particular species and places its findings within the context of larger-scale concerns. Students, researchers, and practitioners alike will find this a rewarding and instructive read that offers practical and detailed information on the latest methods and technologies used in the field today. This essential resource: Takes an interdisciplinary approach to landscape ecology Examines the subject within the contexts of specific organisms Covers cutting-edge technologies and methods Represents a collaboration between an international team of landscape ecology experts Whether new to the practice or an established ecologist, anyone with an interest in this exciting and developing field should have a copy of Applied Landscape Ecology at their disposal.

The ability to anticipate the impacts of global environmental changes on natural resources is fundamental to designing appropriate and optimised adaptation and mitigation strategies. However, this requires the scientific community to have access to reliable, large-scale information on spatio-temporal changes in the distribution of abiotic conditions and on the distribution, structure, composition, and functioning of ecosystems. Satellite remote sensing can provide access to some of this fundamental data by offering repeatable, standardised, and verifiable information that is directly relevant to the monitoring and management of our natural capital. This book demonstrates how ecological knowledge and satellite-based information can be effectively combined to address a wide array of current natural resource management needs. By focusing on concrete applied examples in both the marine and terrestrial realms, it will help pave the way for developing enhanced levels of collaboration between the ecological and remote sensing communities, as well as shaping their future research directions. Satellite Remote Sensing and the Management of Natural Resources is primarily aimed at ecologists and remote sensing specialists, as well as policy makers and practitioners in the fields of conservation biology, biodiversity monitoring, and natural resource management.