Florian Hartig

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The NUCOMBog R package for simulating vegetation, water, carbon and nitrogen dynamics in peatlands

J. W. M. Pullens, M. Bagnara, R. Silveyra González, D. Gianelle, M. Sottocornola, M. M. P. D. Heijmans, G. Kiely & F. Hartig

Ecological Informatics, 40, 35–39 (2017)
Cite this
@article{pullens2017nucombog,
  author = {J. W. M. Pullens and M. Bagnara and R. Silveyra González and D. Gianelle and M. Sottocornola and M. M. P. D. Heijmans and G. Kiely and F. Hartig},
  title = {The NUCOMBog R package for simulating vegetation, water, carbon and nitrogen dynamics in peatlands},
  journal = {Ecological Informatics},
  volume = {40},
  pages = {35--39},
  year = {2017},
  doi = {10.1016/j.ecoinf.2017.05.001},
}

DOI: 10.1016/j.ecoinf.2017.05.001
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Funding

This study was supported through funding by STSM (COST Action FP1304 short-term scientific mission) (FP1304); DFG (DO 786/8-1).

View article (DOI) Software: NUCOMBog

Abstract

Since peatlands store up to 30% of the global soil organic carbon, it is important to understand how these ecosystems will react to a change in climate and management. Process-based ecosystem models have emerged as important tools for predicting long-term peatland dynamics, but their application is often challenging because they require programming skills. In this paper, we present NUCOMBog, an R package of the NUCOM-Bog model (Heijmans et al. 2008), which simulates the vegetation, carbon, nitrogen and water dynamics of peatlands in monthly time steps. The package complements the model with appropriate functions, such as the calculation of net ecosystem exchange, as well as parallel functionality. As a result, the NUCOMBog R package provides a user-friendly tool for simulating vegetation and biogeochemical cycles/fluxes in peatlands over years/decades, under different management strategies and climate change scenarios, with the option to use all the in-built model analysis capabilities of R, such as plotting, sensitivity analysis or optimization.

What the paper shows and why it matters

(AI-generated)

Peatlands hold up to about 30% of the world’s soil organic carbon, so it matters how they respond to climate change and to changes in management. Process-based ecosystem models are used to project peatland vegetation and carbon over years to decades. Such models are, however, often hard for new users to apply. They need specialised input formats and programming for tasks such as sensitivity analysis (finding out which parameters matter most) or calibration (fitting parameters to measurements). The problem the paper takes on is how to make such a model available in an established scientific computing environment such as R.

The paper presents NUCOMBog, an R package for running the peatland model NUCOM-Bog from within R. It argues that this makes the model easy to apply and opens it to the analysis tools available in R for sensitivity analysis, calibration and Bayesian inference. NUCOM-Bog (Heijmans et al. 2008) simulates, in monthly steps, how five plant groups (graminoids, shrubs and three kinds of peat moss) compete and how carbon, nitrogen and water cycle in a one-square-metre patch of bog. The package’s functions set up a run and read the results back. The package also adds heterotrophic respiration, the carbon released by decomposition, as an output. Combined with plant growth (net primary production), this gives net ecosystem exchange (NEE), the net carbon flux between ecosystem and atmosphere, so that results can be compared with flux measurements from eddy covariance towers and flux chambers. A further function runs many parameter combinations in parallel, to make sensitivity analysis and calibration practical. As an example, the paper simulates Walton Moss bog in England for 1766–1999 with test data supplied in the package; this illustrates the workflow rather than testing the model.

The paper offers the package as a way to lower the effort of applying the model, and names the study of peatland vegetation stability and teaching as uses. Where many years of flux data exist, the model could be evaluated against them, and sensitivity analysis and calibration are expected to show how important and how uncertain the parameters are. The authors hope that this will improve the model and reveal gaps in knowledge about how peatlands function under climate change and management. The paper’s contribution is the tool, not a finding about peatlands.

References

  • Heijmans et al. (2008). Long‐term effects of climate change on vegetation and carbon dynamics in peat bogs. Journal of Vegetation Science 19, 307–320. doi:10.3170/2008-8-18368