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This book explores the theory and methods of systems analysis and computer modeling as applied to problems in ecology and natural resource management. It reflects the problems and conflicts between competing uses of limited space and the need for quantitative predictors of the outcome of various management strategies.
This book will serve as a readable introduction to ecological modeling for people involved in resource management and will also review models for specific applications of interest to more experienced modelers. Successful uses of ecological models as well as discussions of important issues in modeling are addressed. The authors of this volume hope to close the gap between the state of the art in ecological modeling and the state of the practice in the use of models in management decision making.
Ecological Modeling:A Commonsense Approach to Theory and Practice explores how simulation modeling and its new ecological applications can offer solutions to complex natural resource management problems. This is a practical guide for students, teachers, and professional ecologists. Examines four phases of the modeling process: conceptual model formulation, quantitative model specification, model evaluation, and model use Provides useful building blocks for constructing systems simulation models Includes a format for reporting the development and use of simulation models Offers an integrated systems perspective for students, faculty, and professionals Features helpful insights from the author, gained over 30 years of university teaching "I can strongly recommend the book as textbook for all courses in population dynamic modeling particularly when the course is planned for the second or third year of a bachelor study in ecology, environmental science or ecological engineering. It uncovers very clearly for the readers the scientific idea and thinking behind modeling and all the necessary steps in the development of models." Ecological Modeling Journal, 2009
Brings scientists, policy makers, land and water managers and citizen stakeholders together to resolve natural resource and environmental problems.
The book gives a comprehensive overview of all available types of ecological models. It is the first book of its kind that gives an overview of different model types and will be of interest to all those involved in ecological and environmental modelling and ecological informatics.
Modeling is a key component to sciences from mathematics to life science, including environmental and ecological studies. By looking at the underlying concepts of the software, we can make sure that we build mathematically feasible models and that we get the most out of the data and information that we have. Systems Science and Modeling for Ecological Economics shows how models can be analyzed using simple math and software to generate meaningful qualitative descriptions of system dynamics. This book shows that even without a full analytical, mathematically rigorous analysis of the equations, there may be ways to derive some qualitative understanding of the general behavior of a system. By relating some of the modeling approaches and systems theory to real-world examples the book illustrates how these approaches can help understand concepts such as sustainability, peak oil, adaptive management, optimal harvest and other practical applications. - Relates modeling approaches and systems theory to real-world examples - Teaches students to build mathematically feasible models and get the most out of the data and information available - Wide range of applications in hydrology, population dynamics, market cycles, sustainability theory, management, and more
Managing today’s lands is becoming an increasingly difficult task. Complex ecological interactions across multiple spatiotemporal scales create diverse landscape responses to management actions that are often novel, counter-intuitive and unexpected. To make matters worse, exotic invasions, human land use, and global climate change complicate this complexity and make past observational ecological studies limited in application to the future. Natural resource professionals can no longer rely on empirical data to analyze alternative actions in a world that is rapidly changing with few historical analogs. New tools are needed to synthesize the high complexity in ecosystem dynamics into useful applications for land management. Some of the best new tools available for this task are ecological and landscape simulation models. However, many land management professionals and scientists have little expertise in simulation modeling, and the costs of training these people will probably be exorbitantly high because most ecosystem and landscape models are exceptionally complicated and difficult to understand and use for local applications. This book was written to provide natural resource professionals with the rudimentary knowledge needed to properly use ecological models and then to interpret their results. It is based on the lessons learned from a career spent modeling ecological systems. It is intended as a reference for novice modelers to learn how to correctly employ ecosystem landscape models in natural resource management applications and to understand subsequent modeling results.
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.