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Both fire and climatic variability have monumental impacts on the dynamics of temperate ecosystems. These impacts can sometimes be extreme or devastating as seen in recent El Nino/La Nina cycles and in uncontrolled fire occurrences. This volume brings together research conducted in western North and South America, areas of a great deal of collaborative work on the influence of people and climate change on fire regimes. In order to give perspective to patterns of change over time, it emphasizes the integration of paleoecological studies with studies of modern ecosystems. Data from a range of spatial scales, from individual plants to communities and ecosystems to landscape and regional levels, are included. Contributions come from fire ecology, paleoecology, biogeography, paleoclimatology, landscape and ecosystem ecology, ecological modeling, forest management, plant community ecology and plant morphology. The book gives a synthetic overview of methods, data and simulation models for evaluating fire regime processes in forests, shrublands and woodlands and assembles case studies of fire, climate and land use histories. The unique approach of this book gives researchers the benefits of a north-south comparison as well as the integration of paleoecological histories, current ecosystem dynamics and modeling of future changes.
Two years in the making, this book represents the combined effort of scientists in Australia, New Zealand, and the United States and of some 16 organizations and agencies including the American Forage and Grassland Council. It has been adopted by these 16 and numerous other organizations world-wide as the definitive standard for word usage in the science.
The fitting of a curve or surface through a set of observational data is a very frequent problem in different disciplines (mathematics, engineering, medicine, ...) with many interesting applications. This book describes the algorithms and mathematical fundamentals of a widely used software package for data fitting with (tensor product) splines. As such it gives a survey of possibilities and benefits but also of the problems to cope with when approximating with this popular type of function. In particular it is demonstrated in detail how the properties of B-splines can be fully exploited for improving the computational efficiency and for incorporating different boundary or shape preserving constraints. Special attention is also paid to strategies for an automatic and adaptive knot selection with intent to obtain serious data reductions. The practical use of the smoothing software is illustrated with many examples, academic as well as taken from real life.
In the past, wildlife living in urban areas were ignored by wildlife professionals and urban planners because cities were perceived as places for people and not for wild animals. Paradoxically, though, many species of wildlife thrive in these built environments. Interactions between humans and wildlife are more frequent in urban areas than any other place on earth and these interactions impact human health, safety and welfare in both positive and negative ways. Although urban wildlife control pest species, pollinate plants and are fun to watch, they also damage property, spread disease and even attack people and pets. In urban areas, the combination of dense human populations, buildings, impermeable surfaces, introduced vegetation, and high concentrations of food, water and pollution alter wildlife populations and communities in ways unseen in more natural environments. For these ecological and practical reasons, researchers and mangers have shown a growing interest in urban wildlife ecology and management. This growing interest in urban wildlife has inspired many studies on the subject that have yet to be synthesized in a cohesive narrative. Urban Wildlife: Theory and Practice fills this void by synthesizing the latest ecological and social knowledge in the subject area into an interdisciplinary and practical text. This volume provides a foundation for the future growth and understanding of urban wildlife ecology and management by: • Clearly defining th e concepts used to study and describe urban wildlife, • Offering a cohesive understanding of the coupled natural and social drivers that shape urban wildlife ecology, • Presenting the patterns and processes of wildlife response to an urbanizing world and explaining the mechanisms behind them and • Proposing means to create physical and social environments that are mutually beneficial for both humans and wildlife.
R. S. GOVINDARAJU and ARAMACHANDRA RAO School of Civil Engineering Purdue University West Lafayette, IN. , USA Background and Motivation The basic notion of artificial neural networks (ANNs), as we understand them today, was perhaps first formalized by McCulloch and Pitts (1943) in their model of an artificial neuron. Research in this field remained somewhat dormant in the early years, perhaps because of the limited capabilities of this method and because there was no clear indication of its potential uses. However, interest in this area picked up momentum in a dramatic fashion with the works of Hopfield (1982) and Rumelhart et al. (1986). Not only did these studies place artificial neural networks on a firmer mathematical footing, but also opened the dOOf to a host of potential applications for this computational tool. Consequently, neural network computing has progressed rapidly along all fronts: theoretical development of different learning algorithms, computing capabilities, and applications to diverse areas from neurophysiology to the stock market. . Initial studies on artificial neural networks were prompted by adesire to have computers mimic human learning. As a result, the jargon associated with the technical literature on this subject is replete with expressions such as excitation and inhibition of neurons, strength of synaptic connections, learning rates, training, and network experience. ANNs have also been referred to as neurocomputers by people who want to preserve this analogy.