Florian Hartig Theoretical Ecology & Ecological Data Science

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r3PG — an R package for simulating forest growth using the 3-PG process-based model

Volodymyr Trotsiuk, Florian Hartig, David I. Forrester

Methods in Ecology and Evolution, 11(11), 1470–1475 (2020)
Cite this
@article{trotsiuk2020r3pg,
  author = {Volodymyr Trotsiuk and Florian Hartig and David I. Forrester},
  title = {r3PG — an R package for simulating forest growth using the 3-PG process-based model},
  journal = {Methods in Ecology and Evolution},
  volume = {11},
  number = {11},
  pages = {1470–1475},
  year = {2020},
  doi = {10.1111/2041-210X.13474},
}

DOI: 10.1111/2041-210X.13474
Cited by 58 (Google Scholar) · 49 (OpenAlex), as of 07 September 2026

View article (DOI)

Abstract

Process-based forest models (PBMs) are important tools for quantifying forest growth and vulnerability, particularly under climate change. The 3-PG model (Physiological Processes Predicting Growth) is one of the most widely used forest growth simulators for this purpose worldwide. Here, we present r3PG, a new Fortran implementation of 3-PG, wrapped into an R package. r3PG can simulate monospecific as well as mixtures of evergreen and deciduous tree species in even-aged or uneven-aged stands. The combination of Fortran functions with an R interface makes the model extremely fast, facilitating its use for extensive computer experiments and sensitivity analysis. We demonstrate this in a case study including single model runs, a sensitivity analysis and full Bayesian calibration of the model, and spatial simulations of forest growth across Switzerland. r3PG is faster and easier to use than previous implementations of 3-PG in Visual Basic, and we believe this will make 3-PG even more useful and popular for ecologists and climate change scientists.

What the paper shows and why it matters (AI-generated)

3-PG is one of the most widely used forest growth models worldwide, but for years its only implementations ran in Visual Basic — slow, and awkward to embed in the large computational experiments modern forest-model calibration needs. r3PG rebuilds it in Fortran with an R interface, fast enough for full Bayesian calibration and spatial simulation, demonstrated here across Switzerland. Ongoing use spans carbon accounting, alpine management optimisation and crown-width modelling — evidence the speed-up opened real doors rather than just being a convenience.