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Advancing population ecology with integral projection models: a practical guide

Author(s): Cory Merow1, Johan Dahlgren2, Jessica Metcalf3, Dylan Childs4, Margaret Evans5, Eelke Jongejans6, Sydne Record7, Mark Rees4, Roberto Salguero-Gómez8, Sean McMahon9

1. Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA 2. Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, Sweden 3. Department of Zoology, Oxford University, Oxford, UK 4. Department of Animal and Plant Sciences, University of Sheffield, Sheffield, UK 5. Laboratory of Tree-Ring Research and Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, USA 6. Department of Animal Ecology and Ecophysiology, Institute for Water and Wetland Research, Radboud University Nijmegen, Nijmegen, The Netherlands 7. Harvard University, Harvard Forest, Petersham, MA, USA 8. Centre for Biodiversity and Conservation Science, University of Queensland, St Lucia, Qld, Australia 9. Smithsonian Environmental Research Center, Edgewater, MD, Edgewater, MD, USA

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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 using regression models and covariates.

Description

  1. 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.
  2. 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.
  3. 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.

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