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With its focus on the practical application of the techniques of multivariate statistics, this book shapes the powerful tools of statistics for the specific needs of ecologists and makes statistics more applicable to their course of study. It gives readers a solid conceptual understanding of the role of multivariate statistics in ecological applications and the relationships among various techniques, while avoiding detailed mathematics and the underlying theory. More importantly, the reader will gain insight into the type of research questions best handled by each technique and the important considerations in applying them. Whether used as a textbook for specialised courses or as a supplement to general statistics texts, the book emphasises those techniques that students of ecology and natural resources most need to understand and employ in their research. While targeted for upper-division and graduate students in wildlife biology, forestry, and ecology, and for professional wildlife scientists and natural resource managers, this book will also be valuable to researchers in any of the biological sciences.
This book provides a foundation for modern applied ecology. Much of current ecology research and conservation addresses problems across landscapes and regions, focusing on spatial patterns and processes. This book is aimed at teaching fundamental concepts and focuses on learning-by-doing through the use of examples with the software R. It is intended to provide an entry-level, easily accessible foundation for students and practitioners interested in spatial ecology and conservation.
Most projects in Landscape Ecology, at some point, define a species-habitat association. These models are inherently spatial, dealing with landscapes and their configurations. Whether coding behavioral rules for dispersal of simulated organisms through simulated landscapes, or designing the sampling extent of field surveys and experiments in real landscapes, landscape ecologists must make assumptions about how organisms experience and utilize the landscape. These convenient working postulates allow modelers to project the model in time and space, yet rarely are they explicitly considered. The early years of landscape ecology necessarily focused on the evolution of effective data sources, metrics, and statistical approaches that could truly capture the spatial and temporal patterns and processes of interest. Now that these tools are well established, we reflect on the ecological theories that underpin the assumptions commonly made during species distribution modeling and mapping. This is crucial for applying models to questions of global sustainability. Due to the inherent use of GIS for much of this kind of research, and as several authors’ research involves the production of multicolored map figures, there would be an 8-page color insert. Additional color figures could be made available through a digital archive, or by cost contributions of the chapter authors. Where applicable, would be relevant chapters’ GIS data and model code available through a digital archive. The practice of data and code sharing is becoming standard in GIS studies, is an inherent method of this book, and will serve to add additional research value to the book for both academic and practitioner audiences.
An attractive, promising, and frustrating feature of ecology is its complex ity, both conceptual and observational. Increasing acknowledgment of the importance of scale testifies to the shifting focus in large areas of ecology. In the rush to explore problems of scale, another general aspect of ecolog ical systems has been given less attention. This aspect, equally important, is heterogeneity. Its importance lies in the ubiquity of heterogeneity as a feature of ecological systems and in the number of questions it raises questions to which answers are not readily available. What is heterogeneity? Does it differ from complexity? What dimensions need be considered to evaluate heterogeneity ade quately? Can heterogeneity be measured at various scales? Is heterogeneity apart of organization of ecological systems? How does it change in time and space? What are the causes of heterogeneity and causes of its change? This volume attempts to answer these questions. It is devoted to iden tification of the meaning, range of applications, problems, and methodol ogy associated with the study of heterogeneity. The coverage is thus broad and rich, and the contributing authors have been encouraged to range widely in discussions and reflections. vi Preface The chapters are grouped into themes. The first group focuses on the conceptual foundations (Chapters 1-5). These papers exarnine the meaning of the term, historical developments, and relations to scale. The second theme is modeling population and interspecific interactions in hetero geneous environments (Chapters 6 and 7).
As Earth faces the greatest mass extinction in 65 million years, the present is a moment of tremendous foment and emergence in ecological science. With leaps in advances in ecological research and the technical tools available, scientists face the critical task of challenging policymakers and the public to recognize the urgency of our global crisis. This book focuses directly on the interplay between theory, data, and analytical methodology in the rapidly evolving fields of animal ecology, conservation, and management. The mixture of topics of particular current relevance includes landscape ecology, remote sensing, spatial modeling, geostatistics, genomics, and ecological informatics. The greatest interest to the practicing scientist and graduate student will be the synthesis and integration of these topics to provide a composite view of the emerging field of spatial ecological informatics and its applications in research and management.
This book introduces the key stages of niche-based habitat suitability model building, evaluation and prediction required for understanding and predicting future patterns of species and biodiversity. Beginning with the main theory behind ecological niches and species distributions, the book proceeds through all major steps of model building, from conceptualization and model training to model evaluation and spatio-temporal predictions. Extensive examples using R support graduate students and researchers in quantifying ecological niches and predicting species distributions with their own data, and help to address key environmental and conservation problems. Reflecting this highly active field of research, the book incorporates the latest developments from informatics and statistics, as well as using data from remote sources such as satellite imagery. A website at www.unil.ch/hsdm contains the codes and supporting material required to run the examples and teach courses.
Mammals in the genus Martes are mid-sized carnivores of great importance to forest ecosystems. This book, the successor to Martens, Sables, and Fishers: Biology and Conservation, provides a scientific basis for management and conservation efforts designed to maintain or enhance the populations and habitats of Martes species throughout the world. The twenty synthesis chapters contained in this book bring together the perspectives and expertise of sixty-three scientists from twelve countries, and are organized by the five key themes of evolution and biogeography, population biology and management, habitat ecology and management, research techniques, and conservation. Recent developments in research technologies such as modeling and genetics, biological knowledge about pathogens and parasites, and concerns about the potential effects of global warming on the distribution and status of Martes populations make new syntheses of these areas especially timely. The volume provides an overview of what is known while clarifying initiatives for future research and conservation priorities, and will be of interest to mammalogists, resource managers, applied ecologists, and conservation biologists.
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.
This book brings together a set of approaches to the study of individual-species ecology based on the analysis of spatial variations of abundance. Distribution ecology assumes that ecological phenomena can be understood when analyzing the extrinsic (environmental) or intrinsic (physiological constraints, population mechanisms) that correlate with this spatial variation. Ecological processes depend on geographical scales, so their analysis requires following environmental heterogeneity. At small scales, the effects of biotic factors of ecosystems are strong, while at large scales, abiotic factors such as climate, govern ecological functioning. Responses of organisms also depend on scales: at small scales, adaptations dominate, i.e. the ability of organisms to respond adaptively using habitat decision rules that maximize their fitness; at large scales, limiting traits dominate, i.e., tolerance ranges to environmental conditions.​