Advancing population ecology with integral projection models: a practical guide

Abstract

Summary Integral projection models ( IPM s) use information on how an individual's state influences its vital rates – survival, growth and reproduction – to make population projections. IPM s 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 IPM s and provide a comprehensive guide, with extensive R code, for their construction. IPM s can be applied to any stage‐structured population; here, we illustrate IPM s 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 IPM s can be used to predict species' geographic distributions and life‐history strategies. IPM s can represent a wide range of life histories at any desired level of biological detail. Much of the strength of IPM s 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, IPM s can exploit a large existing suite of analytical tools developed for matrix projection models.