Building integral projection models: a user's guide. (20th January 2014)

• Record Type: Journal Article
• Authors: Rees, Mark; Childs, Dylan Z.; Ellner, Stephen P.; Coulson, Tim
• Is Part Of: Journal of animal ecology. Volume 83:Number 3(2014:May)
• Page Start: 528
• Page End: 545
• Publication Date: 2014-01-20
• Subjects: Animal ecology -- Periodicals; 591.7
• DOI: 10.1111/1365-2656.12178
• Languages: English
• ISSNs: 0021-8790

Abstract

<abstract abstract-type="main" id="jane12178-abs-0001"> <title>Summary</title> <p> <list id="jane12178-list-0001" list-type="order"> <list-item> <p>In order to understand how changes in individual performance (growth, survival or reproduction) influence population dynamics and evolution, ecologists are increasingly using parameterized mathematical models. </p> </list-item> <list-item> <p>For continuously structured populations, where some continuous measure of individual state influences growth, survival or reproduction, integral projection models (IPMs) are commonly used.</p> </list-item> <list-item> <p>We provide a detailed description of the steps involved in constructing an IPM, explaining how to: (i) translate your study system into an IPM; (ii) implement your IPM; and (iii) diagnose potential problems with your IPM. We emphasize how the study organism's life cycle, and the timing of censuses, together determine the structure of the IPM kernel and important aspects of the statistical analysis used to parameterize an IPM using data on marked individuals.</p> </list-item> <list-item> <p>An IPM based on population studies of Soay sheep is used to illustrate the complete process of constructing, implementing and evaluating an IPM fitted to sample data.</p> </list-item> <list-item> <p>We then look at very general approaches to parameterizing an IPM, using a wide range of statistical techniques (e.g. maximum likelihood methods, generalized additive models, nonparametric kernel density estimators). Methods for selecting models for parameterizing IPMs are briefly discussed.</p> </list-item> <list-item> <p>We conclude with key recommendations and a brief overview of applications that extend the basic model. The online Supporting Information provides commented R code for all our analyses.</p> </list-item> </list> </p> </abstract>