Advancing population ecology with integral projection models: A practical guide

Merow, Cory, Dahlgren, Johan P., Metcalf, C.J essica E., Childs, David Z., Evans, Margaret E. K., Jongejans, Eelke, Record, Sydne, Rees, Mark, Salguero-Gomez, Roberto and Mcmahon, Sean M. (2014) Advancing population ecology with integral projection models: A practical guide. Methods in Ecology and Evolution, 5 2: 99-110. doi:10.1111/2041-210X.12146

Author Merow, Cory
Dahlgren, Johan P.
Metcalf, C.J essica E.
Childs, David Z.
Evans, Margaret E. K.
Jongejans, Eelke
Record, Sydne
Rees, Mark
Salguero-Gomez, Roberto
Mcmahon, Sean M.
Title Advancing population ecology with integral projection models: A practical guide
Journal name Methods in Ecology and Evolution   Check publisher's open access policy
ISSN 2041-210X
Publication date 2014-01-01
Year available 2014
Sub-type Critical review of research, literature review, critical commentary
DOI 10.1111/2041-210X.12146
Open Access Status Not Open Access
Volume 5
Issue 2
Start page 99
End page 110
Total pages 12
Place of publication Oxford, United Kingdom
Publisher Wiley-Blackwell Publishing Ltd.
Language eng
Subject 1105 Dentistry
2302 Ecological Modelling
Abstract Summary: Integral projection models (IPMs) use information on how an individual's state influences its vital rates - survival, growth and reproduction - to make population projections. IPMs are constructed from regression models predicting vital rates from state variables (e.g. size or age) and covariates (e.g. environment). By combining regressions of vital rates, an IPM provides mechanistic insight into emergent ecological patterns such as population dynamics, species geographic distributions or life-history strategies. Here, we review important resources for building IPMs and provide a comprehensive guide, with extensive R code, for their construction. IPMs can be applied to any stage-structured population; here, we illustrate IPMs for a series of plant life histories of increasing complexity and biological realism, highlighting the utility of various regression methods for capturing biological patterns. We also present case studies illustrating how IPMs can be used to predict species' geographic distributions and life-history strategies. IPMs can represent a wide range of life histories at any desired level of biological detail. Much of the strength of IPMs lies in the strength of regression models. Many subtleties arise when scaling from vital rate regressions to population-level patterns, so we provide a set of diagnostics and guidelines to ensure that models are biologically plausible. Moreover, IPMs can exploit a large existing suite of analytical tools developed for matrix projection models.
Keyword Demography
Life history
Matrix projection model
Population growth rate
Population projection model
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Critical review of research, literature review, critical commentary
Collections: Official 2015 Collection
School of Biological Sciences Publications
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