Data-driven Modelling of Structured Populations

Authors: Ellner, Stephen P., Childs, Dylan Z., Rees, Mark
The only comprehensive monograph on Integral Projection Models in ecology
Accessible to beginners with minimal math and statistics background, but also includes cutting-edge research
Presents a wide range of models, from simple to complex
All steps are illustrated in the text by code in the open-source R language
Complete code for all analyses and results provided online via GitHub

This book is a “How To” guide for modeling population dynamics using Integral Projection Models (IPM) starting from observational data. It is written by a leading research team in this area and includes code in the R language (in the text and online) to carry out all computations. The intended audience are ecologists, evolutionary biologists, and mathematical biologists interested in developing data-driven models for animal and plant populations. IPMs may seem hard as they involve integrals. The aim of this book is to demystify IPMs, so they become the model of choice for populations structured by size or other continuously varying traits. The book uses real examples of increasing complexity to show how the life-cycle of the study organism naturally leads to the appropriate statistical analysis, which leads directly to the IPM itself. A wide range of model types and analyses are presented, including model construction, computational methods, and the underlying theory, with the more technical material in Boxes and Appendices. Self-contained R code which replicates all of the figures and calculations within the text is available to readers on GitHub.
Stephen P. Ellner is Horace White Professor of Ecology and Evolutionary Biology at Cornell University, USA; Dylan Z. Childs is Lecturer and NERC Postdoctoral Fellow in the Department of Animal and Plant Sciences at The University of Sheffield, UK; Mark Rees is Professor in the Department of Animal and Plant Sciences at The University of Sheffield, UK.

Number of Illustrations and Tables
38 b/w illustrations, 29 illustrations in colour
Topics
Mathematical and Computational Biology
Computer Applications in Life Sciences

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