Preprints
https://doi.org/10.5194/gmd-2021-422
https://doi.org/10.5194/gmd-2021-422

Submitted as: model evaluation paper 12 Jan 2022

Submitted as: model evaluation paper | 12 Jan 2022

Review status: this preprint is currently under review for the journal GMD.

Tree migration in the dynamic, global vegetation model LPJ-GM 1.0: Efficient uncertainty assessment and improved dispersal kernels

Deborah Zani1,2, Veiko Lehsten1,2, and Heike Lischke1 Deborah Zani et al.
  • 1Dynamic Macroecology/Land Change Science, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerland
  • 2Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden

Abstract. The prediction of species geographic redistribution under climate change (i.e. range shifts) has been addressed by both experimental and modelling approaches and can be used to inform efficient policy measures on the functioning and services of future ecosystems. Dynamic Global Vegetation Models (DGVMs) are considered state-of-the art tools to understand and quantify the spatio-temporal dynamics of ecosystems at large scales and their response to changing environments. They can explicitly include local vegetation dynamics relevant to migration (establishment, growth, seed production), species-specific dispersal abilities and the competitive interactions with other species in the new environment. However, the inclusion of more detailed mechanistic formulations of range shift processes may also widen the overall uncertainty of the model. Thus, a quantification of these uncertainties is needed to evaluate and improve our confidence in the model predictions. In this study, we present an efficient assessment of parameter and model uncertainties combining low-cost analyses in successive steps: local sensitivity analysis, exploration of the performance landscape at extreme parameter values, and inclusion of relevant ecological processes in the model structure. This approach was tested on the newly-implemented migration module of the state-of-the-art DGVM, LPJ-GM 1.0. Estimates of post-glacial migration rates obtained from pollen and macrofossil records of dominant European tree taxa were used to test the model performance. The results indicate higher sensitivity of migration rates to parameters associated with the dispersal kernel (dispersal distances and kernel shape) compared to plant traits (germination rate and maximum fecundity) and highlight the importance of representing rare long-distance dispersal events via fat-tailed kernels. Overall, the successful parametrization and model selection of LPJ-GM will allow simulating plant migration with a more mechanistic approach at larger spatial and temporal scales, thus improving our efforts to understand past vegetation dynamics and predict future range shifts in a context of global change.

Deborah Zani et al.

Status: open (until 09 Mar 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Deborah Zani et al.

Data sets

Input climate and landscape data Deborah Zani, Veiko Lehsten https://doi.org/10.18161/20211127

Deborah Zani et al.

Viewed

Total article views: 205 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
170 31 4 205 13 1 2
  • HTML: 170
  • PDF: 31
  • XML: 4
  • Total: 205
  • Supplement: 13
  • BibTeX: 1
  • EndNote: 2
Views and downloads (calculated since 12 Jan 2022)
Cumulative views and downloads (calculated since 12 Jan 2022)

Viewed (geographical distribution)

Total article views: 189 (including HTML, PDF, and XML) Thereof 189 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Jan 2022
Download
Short summary
The prediction of species migration under rapid climate change remains uncertain. In this paper, we evaluate the importance of the mechanisms underlying plant migration and increase the performance in the dynamic global vegetation model LPJ-GM 1.0. The improved model will allow to understand past vegetation dynamics and predict the future redistribution of species in a context of global change.