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Principles' eighteen chapters introduce the major themes and concepts of this diverse and dynamic field. The biological and social underpinnings of conservation problems and potential solutions are interwoven throughout the text, which is divided into three sections: foundations of the field, threats to biodiversity, and approaches to solving conservation problems. Guest essays and case studies provide a diversity of perspectives and real-world examples that add insight and provoke discussion. The text is richly illustrated, and concludes with an extensive glossary and bibliography.

In Earth in Mind, noted environmental educator David W. Orr focuses not on problems in education, but on the problem of education.

Much of what has gone wrong with the world, he argues, is the result of inadequate and misdirected education that: alienates us from life in the name of human domination; causes students to worry about how to make a living before they know who they are; overemphasizes success and careers; separates feeling from intellect and the practical from the theoretical; deadens the sense of wonder for the created world.

The crisis we face, Orr explains, is one of mind, perception, and values. It is, first and foremost, an educational challenge.

The author begins by establishing the grounds for a debate about education and knowledge. He describes the problems of education from an ecological perspective, and challenges the "terrible simplifiers" who wish to substitute numbers for values. He follows with a presentation of principles for re-creating education in the broadest way possible, discussing topics such as biophilia, the disciplinary structure of knowledge, the architecture of educational buildings, and the idea of ecological intelligence. Orr concludes by presenting concrete proposals for reorganizing the curriculum to draw out our affinity for life.

The abstract concept of “information” can be quantified and this has led to many important advances in the analysis of data in the empirical sciences. This text focuses on a science philosophy based on “multiple working hypotheses” and statistical models to represent them. The fundamental science question relates to the empirical evidence for hypotheses in this set—a formal strength of evidence. Kullback-Leibler information is the information lost when a model is used to approximate full reality. Hirotugu Akaike found a link between K-L information (a cornerstone of information theory) and the maximized log-likelihood (a cornerstone of mathematical statistics). This combination has become the basis for a new paradigm in model based inference. The text advocates formal inference from all the hypotheses/models in the a priori set—multimodel inference.

This compelling approach allows a simple ranking of the science hypothesis and their models. Simple methods are introduced for computing the likelihood of model i, given the data; the probability of model i, given the data; and evidence ratios. These quantities represent a formal strength of evidence and are easy to compute and understand, given the estimated model parameters and associated quantities (e.g., residual sum of squares, maximized log-likelihood, and covariance matrices). Additional forms of multimodel inference include model averaging, unconditional variances, and ways to rank the relative importance of predictor variables.

This textbook is written for people new to the information-theoretic approaches to statistical inference, whether graduate students, post-docs, or professionals in various universities, agencies or institutes. Readers are expected to have a background in general statistical principles, regression analysis, and some exposure to likelihood methods. This is not an elementary text as it assumes reasonable competence in modeling and parameter estimation.