Forest landscape disturbances are a global phenomenon. Simulation models are an important tool in understanding these broad scale processes and exploring their effects on forest ecosystems. This book contains a collection of insights from a group of ecologists who address a variety of processes: physical disturbances such as drought, wind, and fire; biological disturbances such as defoliating insects and bark beetles; anthropogenic influences; interactions among disturbances; effects of climate change on disturbances; and the recovery of forest landscapes from disturbances—all from a simulation modeling perspective. These discussions and examples offer a broad synopsis of the state of this rapidly evolving subject.

Restoration ecology, as a scientific discipline, developed from practitioners’ efforts to restore degraded land, with interest also coming from applied ecologists attracted by the potential for restoration projects to apply and/or test developing theories on ecosystem development. Since then, forest landscape restoration (FLR) has emerged as a practical approach to forest restoration particularly in developing countries, where an approach which is both large-scale and focuses on meeting human needs is required. Yet despite increased investigation into both the biological and social aspects of FLR, there has so far been little success in systematically integrating these two complementary strands. Bringing experts in landscape studies, natural resource management and forest restoration, together with those experienced in conflict management, environmental economics and urban studies, this book bridges that gap to define the nature and potential of FLR as a truly multidisciplinary approach to a global environmental problem. The book will provide a valuable reference to graduate students and researchers interested in ecological restoration, forest ecology and management, as well as to professionals in environmental restoration, natural resource management, conservation, and environmental policy.

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.

In North America, concepts of Historical Range of Variability are being employed in land-management planning for properties of private organizations and multiple government agencies. The National Park Service, U.S. Fish & Wildlife Service, Bureau of Land Management, U.S. Forest Service, and The Nature Conservancy all include elements of historical ecology in their planning processes. Similar approaches are part of land management and conservation in Europe and Australia. Each of these user groups must struggle with the added complication of rapid climate change, rapid land-use change, and technical issues in order to employ historical ecology effectively. Historical Environmental Variation in Conservation and Natural Resource Management explores the utility of historical ecology in a management and conservation context and the development of concepts related to understanding future ranges of variability. It provides guidance and insights to all those entrusted with managing and conserving natural resources: land-use planners, ecologists, fire scientists, natural resource policy makers, conservation biologists, refuge and preserve managers, and field practitioners. The book will be particularly timely as science-based management is once again emphasized in United States federal land management and as an understanding of the potential effects of climate change becomes more widespread among resource managers. Additional resources for this book can be found at: www.wiley.com/go/wiens/historicalenvironmentalvariation.

A measure of the degree of departure of a landscape from its range of historical conditions can provide a means for prioritizing and planning areas for restoration treatments. There are few statistics or indices that provide a quantitative context for measuring departure across landscapes. This study evaluated a set of five similarity indices commonly used in vegetation community ecology (Sorenson's Index, Chord Distance, Morisita's Index, Euclidean Distance, and Similarity Ratio) for application in estimating landscape departure (where departure = 1 - similarity). This involved comparing composition (vegetation type by area) of a set of reference landscapes to the compositions of 1,000 simulated historical landscapes. Stochastic simulation modeling was used to create a diverse set of synthetic reference and historical landscapes for departure index evaluation. Five reference landscapes were created to represent various degrees of expected departure from historical conditions. Both reference and historical landscapes were created to contain four important factors that could potentially influence departure calculation: (1) number of classes defining landscape composition, (2) dominance of the classes, (3) variability of area with the classes, and (4) temporal autocorrelation. We found that most evaluated indices are useful but not optimal for calculating departure. The Sorenson's Index appeared to perform the best with consistent and precise behavior across the ranges of the four treatments. The number of classes used to describe vegetation had the strongest influence on index performance; landscape composition defined by few classes had the least accurate, most imprecise, and most highly variable departure estimates. While results from this study show the utility of similarity indices in evaluating departure, it is also evident that a new set of statistics are needed to provide a more comprehensive analysis of departure for future applications.