Soil organic matter (SOM) represents a major pool of carbon within the biosphere, roughly twice than in atmospheric CO2. SOM models embody our best understanding of soil carbon dynamics and are needed to predict how global environmental change will influence soil carbon stocks. These models are also required for evaluating the likely effectiveness of different mitigation options. The first important step towards systematically evaluating the suitability of SOM models for these purposes is to test their simulations against real data. Since changes in SOM occur slowly, long-term datasets are required. This volume brings together leading SOM model developers and experimentalists to test SOM models using long-term datasets from diverse ecosystems, land uses and climatic zones within the temperate region.

One of the central questions of ecology is why there are so many different kinds of plants and animals. Here David Tilman presents a theory of how organisms compete for resources and the way their competition promotes diversity. Developing Hutchinson's suggestion that the main cause of diversity is the feeding relations of species, this book builds a mechanistic, resource-based explanation of the structure and functioning of ecological communities. In a detailed analysis of the Park Grass Experiments at the Rothamsted Experimental Station in England, the author demonstrates that the dramatic results of these 120 years of experimentation are consistent with his theory, as are observations in many other natural communities. The consumer-resource approach of this book is applicable to both animal and plant communities, but the majority of Professor Tilman's discussion concentrates on the structure of plant communities. All theoretical arguments are developed graphically, and formal mathematics is kept to a minimum. The final chapters of the book provide some testable speculations about resources and animal communities and explore such problems as the evolution of "super species," the differences between plant and animal community diversity patterns, and the cause of plant succession.

Volume III includes more selections of articles that have initiated fundamental changes in statistical methodology. It contains articles published before 1980 that were overlooked in the previous two volumes plus articles from the 1980's - all of them chosen after consulting many of today's leading statisticians.

The book informs about agricultural landscapes, their features, functions and regulatory mechanisms. It characterizes agricultural production systems, trends of their development, and their impacts on the landscape. Agricultural landscapes are multifunctional systems, coupled with all nexus problems of the 21th century. This has led to serious discrepancies between agriculture and environment, and between urban and rural population. The mission, key topics and methods of research in order to understanding, monitoring and controlling processes in rural landscapes is being explained. Studies of international expert teams, many of them from Russia, demonstrate approaches towards both improving agricultural productivity and sustainability, and enhancing ecosystem services of agricultural landscapes. Scientists of different disciplines, decision makers, farmers and further informed people dealing with the evolvement of thriving rural landscapes are the primary audience of this book.

Advances in Ecological Research is one of the most successful series in the highly competitive field of ecology. This thematic volume focuses on large scale ecology, publishing important reviews that contribute to our understanding of the field. - Presents the most updated information on the field of large scale ecology, publishing topical and important reviews - Provides all information that relates to a thorough understanding of the field - Includes data on physiology, populations, and communities of plants and animals

This book should be on the shelf of every practising statistician who designs experiments. Good design considers units and treatments first, and then allocates treatments to units. It does not choose from a menu of named designs. This approach requires a notation for units that does not depend on the treatments applied. Most structure on the set of observational units, or on the set of treatments, can be defined by factors. This book develops a coherent framework for thinking about factors and their relationships, including the use of Hasse diagrams. These are used to elucidate structure, calculate degrees of freedom and allocate treatment subspaces to appropriate strata. Based on a one-term course the author has taught since 1989, the book is ideal for advanced undergraduate and beginning graduate courses. Examples, exercises and discussion questions are drawn from a wide range of real applications: from drug development, to agriculture, to manufacturing.

Geostatistics is essential for environmental scientists. Weather and climate vary from place to place, soil varies at every scale at which it is examined, and even man-made attributes – such as the distribution of pollution – vary. The techniques used in geostatistics are ideally suited to the needs of environmental scientists, who use them to make the best of sparse data for prediction, and top plan future surveys when resources are limited. Geostatistical technology has advanced much in the last few years and many of these developments are being incorporated into the practitioner’s repertoire. This second edition describes these techniques for environmental scientists. Topics such as stochastic simulation, sampling, data screening, spatial covariances, the variogram and its modeling, and spatial prediction by kriging are described in rich detail. At each stage the underlying theory is fully explained, and the rationale behind the choices given, allowing the reader to appreciate the assumptions and constraints involved.

Getting numbers is easy; getting numbers you can trust is hard. This practical guide by experimentation leaders at Google, LinkedIn, and Microsoft will teach you how to accelerate innovation using trustworthy online controlled experiments, or A/B tests. Based on practical experiences at companies that each run more than 20,000 controlled experiments a year, the authors share examples, pitfalls, and advice for students and industry professionals getting started with experiments, plus deeper dives into advanced topics for practitioners who want to improve the way they make data-driven decisions. Learn how to • Use the scientific method to evaluate hypotheses using controlled experiments • Define key metrics and ideally an Overall Evaluation Criterion • Test for trustworthiness of the results and alert experimenters to violated assumptions • Build a scalable platform that lowers the marginal cost of experiments close to zero • Avoid pitfalls like carryover effects and Twyman's law • Understand how statistical issues play out in practice.