Abstract
Key message
In this exploratory study, we show how combining the strength of tree diversity experiment with the long-term perspective offered by forest gap models allows testing the mixture yielding behavior across a full rotation period. Our results on a SW France example illustrate how mixing maritime pine with birch may produce an overyielding (i.e., a positive net biodiversity effect).
Context
Understanding the link between tree diversity and stand productivity is a key issue at a time when new forest management methods are investigated to improve carbon sequestration and climate change mitigation. Well-controlled tree diversity experiments have been set up over the last decades, but they are still too young to yield relevant results from a long-term perspective. Alternatively, forest gap models appear as appropriate tools to study the link between diversity and productivity as they can simulate mixed forest growth over an entire forestry cycle.
Aims
We aimed at testing whether a forest gap model could first reproduce the results from a tree diversity experiment, using its plantation design as input, and then predict the species mixing effect on productivity and biomass in the long term.
Methods
Here, we used data from different forest experimental networks to calibrate the gap model ForCEEPS for young pine (Pinus pinaster) and birch (Betula pendula) stands. Then, we used the refined model to compare the productivity of pure and mixed pine and birch stands over a 50-year cycle. The mixing effect was tested for two plantation designs, i.e., species substitution and species addition, and at two tree densities.
Results
Regarding the comparison with the experiment ORPHEE (thus on the short term), the model well reproduced the species interactions observed in the mixed stands. Simulations showed an overyielding (i.e., a positive net biodiversity effect) in pine-birch mixtures in all cases and during the full rotation period. A transgressive overyielding was detected in mixtures resulting from birch addition to pine stands at low density. These results were mainly due to a positive mixing effect on pine growth being larger than the negative effect on birch growth.
Conclusion
Although this study remains explorative, calibrating gap models with data from monospecific stands and validating with data from the manipulative tree diversity experiment (ORPHEE) offers a powerful tool for further investigation of the productivity of forest mixtures. Improving our understanding of how abiotic and biotic factors, including diversity, influence the functioning of forest ecosystems should help to reconsider new forest managements optimizing ecosystem services.
Using forest gap models and experimental data to explore long-term effects of tree diversity on the productivity of mixed planted forests
