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One of the enduring questions about our planet is how it has remained continuously habitable over vast stretches of geological time despite the fact that its atmosphere and climate are potentially unstable. James Lovelock's Gaia hypothesis posits that life itself has intervened in the regulation of the planetary environment in order to keep it stable and favorable for life. First proposed in the 1970s, Lovelock's hypothesis remains highly controversial and continues to provoke fierce debate. This book undertakes the first in-depth investigation of the arguments put forward by Lovelock and others—and concludes that the evidence doesn't stack up in support of Gaia. The book draws on the latest findings in fields as diverse as climate science, oceanography, atmospheric science, geology, ecology, and evolutionary biology. The book takes readers to obscure corners of the natural world, from southern Africa where ancient rocks reveal that icebergs were once present near the equator, to mimics of cleaner fish on Indonesian reefs, to blind fish deep in Mexican caves. It weaves these and many other intriguing observations into a comprehensive analysis of the major assertions and lines of argument underpinning Gaia, and finds that it is not a credible picture of how life and Earth interact. The book reflects on the scientific evidence indicating that life and environment mutually affect each other, and proposes that feedbacks on Earth do not provide robust protection against the environment becoming uninhabitable—or against poor stewardship by us.

A severe thunderstorm morphs into a tornado that cuts a swath of destruction through Oklahoma. How do we study the storm’s mutation into a deadly twister? Avian flu cases are reported in China. How do we characterize the spread of the flu, potentially preventing an epidemic? The way to answer important questions like these is to analyze the spatial and temporal characteristics—origin, rates, and frequencies—of these phenomena. This book introduces advanced undergraduate students, graduate students, and researchers to the statistical and algebraic methods used to analyze spatiotemporal data in a range of fields, including climate science, geophysics, ecology, astrophysics, and medicine. The book begins with a concise yet detailed primer on linear algebra, providing readers with the mathematical foundations needed for data analysis. It then fully explains the theory and methods for analyzing spatiotemporal data, guiding readers from the basics to the most advanced applications. This self-contained, practical guide to the analysis of multidimensional data sets features a wealth of real-world examples as well as sample homework exercises and suggested exams.