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Building integral projection models: a user's guide
Author(s) -
Rees Mark,
Childs Dylan Z.,
Ellner Stephen P.
Publication year - 2014
Publication title -
journal of animal ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.134
H-Index - 157
eISSN - 1365-2656
pISSN - 0021-8790
DOI - 10.1111/1365-2656.12178
Subject(s) - projection (relational algebra) , computer science , kernel (algebra) , population , estimator , range (aeronautics) , parameterized complexity , process (computing) , kernel density estimation , nonparametric statistics , statistical model , machine learning , econometrics , statistics , mathematics , algorithm , engineering , demography , sociology , aerospace engineering , operating system , combinatorics
Summary 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. For continuously structured populations, where some continuous measure of individual state influences growth, survival or reproduction, integral projection models ( IPM s) are commonly used. 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. An IPM based on population studies of S oay sheep is used to illustrate the complete process of constructing, implementing and evaluating an IPM fitted to sample data. 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 IPM s are briefly discussed. 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.