, Volume 182, Issue 4, pp 1031–1043

Population viability analysis of plant and animal populations with stochastic integral projection models

Population ecology – original research


Integral projection models (IPM) make it possible to study populations structured by continuous traits. Recently, Vindenes et al. (Ecology 92:1146–1156, 2011) proposed an extended IPM to analyse the dynamics of small populations in stochastic environments, but this model has not yet been used to conduct population viability analyses. Here, we used the extended IPM to analyse the stochastic dynamics of IPM of small size-structured populations in one plant and one animal species (evening primrose and common lizard) including demographic stochasticity in both cases and environmental stochasticity in the lizard model. We also tested the accuracy of a diffusion approximation of the IPM for the two empirical systems. In both species, the elasticity for λ was higher with respect to parameters linked to body growth and size-dependent reproduction rather than survival. An analytical approach made it possible to quantify demographic and environmental variance to calculate the average stochastic growth rate. Demographic variance was further decomposed to gain insights into the most important size classes and demographic components. A diffusion approximation provided a remarkable fit to the stochastic dynamics and cumulative extinction risk, except for very small populations where stochastic growth rate was biased upward or downward depending on the model. These results confirm that the extended IPM provides a powerful tool to assess the conservation status and compare the stochastic demography of size-structured species, but should be complemented with individual based models to obtain unbiased estimates for very small populations of conservation concern.


Extinction Life cycle Population viability analysis Trait-based approach 

Supplementary material

442_2016_3704_MOESM1_ESM.docx (271 kb)
Supplementary material 1 (DOCX 271 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Département de BiologieÉcole Normale SupérieureParisFrance
  2. 2.CNRS, UMR 7618, iEES Paris, Université Pierre et Marie CurieParisFrance
  3. 3.CNRS, UMS 3194, CEREEP-Ecotron Ile De France, École Normale SupérieureSt-Pierre-lès-NemoursFrance

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