Ecological Detective: Confronting Models with Data

by Ray Hilborn, Marc Mangel

The modern ecologist usually works in both the field and laboratory, uses statistics and computers, and often works with ecological concepts that are model-based, if not model-driven. How do we make the field and laboratory coherent? How do we link models and data? How do we use statistics to help experimentation? How do we integrate modeling and statistics? How do we confront multiple hypotheses with data and assign degrees of belief to different hypotheses? How do we deal with time series (in which data are linked from one measurement to the next) or put multiple sources of data into one inferential framework? These are the kinds of questions asked and answered by "The Ecological Detective."

Ray Hilborn and Marc Mangel investigate ecological data much as a detective would investigate a crime scene by trying different hypotheses until a coherent picture emerges. The book is not a set of pat statistical procedures but rather an approach. The Ecological Detective makes liberal use of computer programming for the generation of hypotheses, exploration of data, and the comparison of different models. The authors' attitude is one of exploration, both statistical and graphical. The background required is minimal, so that students with an undergraduate course in statistics and ecology can profitably add this work to their tool-kit for solving ecological problems.

335 pages, ebook

First published January 1, 2013

Reviews

[Ann · Rating 4 out of 5]

This is a good resource for students beginning to do modeling in ecology. The authors make no bones about doing the math -- you have to have a good grip on basic statistics, calculus, and some knowledge of Bayesian methods. However, they provide clear development of their methods and helpful pseudocode as a framework for putting the analyses into a computer program. There are many good ecological examples with real and simulated data and several chapters focus on a particular ecological question and detail how they would explore the models and test them with data. They begin the book with a discussion of why models and testing models with data is essential. They also helpfully discuss different philosophical ideas about scientific method and hypotheses. They make a good case for multiple hypotheses (rather than the Popperian approach of one hypothesis which is the most familiar approach)which they use throughout the book. They also emphasize likelihood and Bayesian approaches. Highly recommended for ecologists doing modeling, especially useful for first time modelers and as an extra resource for a modeling class, etc.