1. Uneven-aged management options to promote forest resilience:
effects of group selection and harvesting intensity
Presented by Guillaume LAGARRIGUES
Co-authors : Valentine Lafond , Thomas Cordonnier , Benoît Courbaud
National Research Institute of Science and Technology for Environment and Agriculture (IRSTEA) – Center of Grenoble
With the collaboration of Andreas Zingg
Swiss Federal Institute for Forest, Snow and Landscape Research (WSL)
Good morning everybody ! My presentation today is based on a work that we carried out 2 years ago and that has been published in “Annals of Forest Science”. We have recently updated this work, improving several points thanks to advances in my PhD, feedbacks on the initial study and data from the WSL of Zurich. Today, I’m going to present you some of the most important results obtained with this new work.
The 9th IUFRO International Conference on Uneven-aged Silviculture
17/06/2014

2. Adaptation of forest management to climate change
Uncertainties about climate change consequences
(Beniston et al. 2007)
The general context of this study is the adaptation of the management of mountain forests to climate change. However, in this study, we did not consider explicitly climate change scenarios. Indeed, as many uncertainties remain about the future climate in mountain areas, we considered that a first adaptation strategy should be to promote forest resilience, in particular by enhancing species diversity and uneven-aged structure.
Precautionary approach: promote forest resilience by enhancing species diversity and uneven-aged structure
(Puettman 2011 ; Seidl et al. 2011)

3. Managements options to promote forest resilience
Uneven-aged silviculture framework
(Cordonnier et al. 2008)
Uneven-aged silviculture give managers many options to harvest wood while preserving forest resilience. Among them, we chose group selection and harvesting intensity, that several studies found efficient to promote forest resilience. However, many questions remain about the scales at which they should preferably applied.
For example, there is no consensus about the size of gaps to create. Applying gap-based management with too small openings may not lead to different results than individual-based harvestings. Inversely, with too large gaps, we may switch from balance uneven-aged stands to even-aged large patches.
A similar trade-off occurs for harvesting intensity: too low harvesting intensity may lead to cover closure and disappearance of shade-intolerant species, whereas too intensive treatments will result in very clear stands and the loss of several forest services.
Create forest gaps by group selection
(Streit 2009)
Enhance natural regeneration
Regenerate shade- intolerant species
Intensify harvesting
(Diaci and
Firm 2011)

4. Simulation experiments
To explore these two management drivers, we proceeded by simulation experiments, using mathematical models. This diagram represents the general process we used in this study: from an initial state described as a set of trees of different sizes and species, we simulated the demographic evolution of the stand using a forest dynamics simulation model. At regular intervals, we simulated harvesting operations, using a silviculture algorithm which has several parameters, including parameters allowing to drive group size and amount of harvested trees. In the following, I will give you some details about each of these 3 concepts.
Initial state
Forest dynamics simulation model
Silviculture algorithm

5. Design of simulation experiments (Lafond et al. 2014)
Stand area : 4ha ; Simulation period: 150 years, with cuts every 10 years
ILLUSTRATION
1st experiment : spatialization of cuts
Standard harvesting intensity
7 modalities of spatialization
Individual selection
Small groups (20 – 1 000m²)
One large gap at a time
First, here is an overview of the simulation experiments we carried out. We performed experiments over 150 years of simulation, with cuts every 10 years, using a simulated stand of four hectares. We first tested seven modalities of cut spatialization, from individual selection to one large gap at a time, including intermediate values to create multiple small gaps (here in this graph, we used five hundred squared meters as unit group area).
In the second experiment, we tested four modalities of harvesting intensification, applying individual selection. Finally, we checked the interaction between these two drivers running a third experiment which was similar to the second one excepted that we applied group selection.
2nd : harvesting intensification with individual selection
4 modalities of harvesting intensity
3rd : harvesting intensification with group selection (500 m²)
4 modalities of harvesting intensity

6. Initial state
Diameter class
Abundance (trees / ha)
1905
We used a single stand as initial state. Here is the diameter distribution of this stand, which was reconstructed from data collected in a permanent plot in the canton of Bern in Swiss in nineteen oh five.
Permanent plot located in the canton of Bern in Swiss, monitored by the WSL of Zürich

7. Samsara : an individual-based forest model
The dynamics model in this study (called Samsara) has been successfully developed and used by B. Courbaud and his team at the Irstea of Grenoble for more than ten years. It is an individual-based and spatially explicit model, that is to say that each tree is modeled with its dimensions, species and spatial position on a plot. The main demographic driver of the model is the light. Light interception and radiation in the stand are explicitly computed using a set of light beams as an input, and the tree processes (growth, mortality and regeneration) are deduced from the light conditions in the stand and the spatial organization of trees.

8. Model calibration for this study
1905 – Initial state
Observed data in 2009
Abundance (trees / ha)
Prior to the simulation experiments, key parameters of samsara were recalibrated especially for this study, using inverse modeling so that simulated stand history fit to observed stand history. This calibration method is currently in development in the course of my PhD.
Calibration of model parameters
Samsara
Predictions
Diameter class
Inverse modeling to calibrate the stand dynamics model so that model predictions fit to historical data (Lagarrigues et al., submitted)

9. Modeling natural regeneration
Number of recruits
Norway spruce
Silver fir
Limitation by low density of seed bearers and competition with pioneer species
Limitation by low light conditions
In our simulation experiments, natural regeneration is a key process as the management efficiency was assessed according to the regeneration of the different species. The initial calibration of Samsara coupled with the recalibration presented previously lead to these response curves for two species (Norway spruce and Silver fir). The number of recruits for each species in function of the light proportion were modeled as bell-shaped curves: in the left, recruitment is limited by the low light conditions, typically found when applying individual selection; in the right, it is limited by the distance of seed bearers and by the competition with pioneer species, which are the conditions experienced in large gaps. As expected, the calibration gave as a result that fir outperforms spruce in shadowy conditions and inversely in more lightly conditions. The accurate calibration of this process is crucial since our results were very sensitive to variations of regeneration parameters.
% of full light

10. Management modeling by a silviculture algorithm (Lafond et al. 2013)
G (m²/ha)
𝑃𝑜𝑡𝑒𝑛𝑡𝑖𝑎𝑙 ℎ𝑎𝑟𝑣𝑒𝑠𝑡=
G h ×𝐻𝑎𝑟𝑣𝑒𝑠𝑡𝑖𝑛𝑔 𝑝𝑟𝑜𝑝𝑜𝑟𝑡𝑖𝑜𝑛
G max
G standard
N (t/ha) Gh
G min
In this slide, I symbolized how the silviculture algorithm works to apply a harvesting. We start from a stand, here described by a virtual diameter distribution. First, the user must select a minimum diameter for harvesting, here set as 27.5cm. The basal area above this bound, called here Gh, is then computed. A proportion to harvest, defined as an input parameter, is then applied. This is the parameter we used to vary the harvesting intensity. That gives a potential amount to harvest. This value is then compared to three harvesting quantities, called here Gmax, standard and min. The closer quantity just below the potential harvest is selected. That solution was the best trade-off we found between exact control of harvesting quantity and adaptation to stand stocking. The quantity to harvest is then applied to the stand, but the trees harvested will be different according to the spatialization mode chosen. In both cases, largest trees were targeted in priority, but in case of individual selection, only the largest trees are effectively harvested, whereas, in group selection, trees of various sizes can be harvested if they are located close to a group of large trees.
Ø (cm)
Øh = 27,5 cm

11. Results of the 1st experiment (spatial aggregation of cut trees)
Size diversity
(Gini index)
Species mix
(% of spruce basal area)
Uneven-aged
Species balance
Now, here are the results : for the three experiments, I present 2 graphics, namely the Gini index computed on individual basal areas (which gives a measure of size diversity) and the proportion of spruce in the stand basal area (which give a measure of species diversity). These two indexes were plotted in function of harvesting modalities. Dotted lines are the initial values, and points are the final state at the end of the simulation period (after 150 years).
Here, we have the results for the first experiment, which concerned the spatialization gradient. Group selection had a positive effect on size and species diversity, by preserving large trees and enhancing regeneration, in particular that of spruce. For structure diversity, a threshold of this positive effect is reached from 500 squared meters of area for cutting groups, whereas larger groups are necessary to have a significant positive effect on spruce maintenance.
Even-aged
Aggregation area (m²)
Aggregation area (m²)

12. 2nd experiment (harvesting intensity with individual selection)
Size diversity
(Gini index)
Species mix
(% of spruce basal area)
Uneven-aged
Species balance
For the second experiment, where harvesting intensity was varying under individual selection, we observed a negative effect of intensification on size diversity (due mainly to the loss of large trees) but a positive effect on spruce maintenance (thanks to the more successful spruce regeneration under lower canopy cover).
Even-aged
Proportion of potential harvesting (%)
Proportion of potential harvesting (%)

13. 3rd experiment (harvesting intensity with group selection)
Size diversity
(Gini index)
Species mix
(% of spruce basal area)
Species balance
Uneven-aged
Finally, with the third experiment, we obtained similar results than for the second, except that size diversity and spruce maintenance was systematically higher in this experiment thanks to group selection, whatever the harvesting intensity.
Even-aged
Proportion of potential harvesting (%)
Proportion of potential harvesting (%)

14. Management durability
1st experiment
2nd experiment
3rd experiment
As expected, high harvesting intensities lead to very low basal areas in the two last experiments. Here, we represent a line at 20 m² / ha as the minimum to preserve during all stand life. With intensities higher than 75%, we obtained basal areas lower than that minimum, which highlights the durability issue occurring with such intensive treatments.
Basal area (m²/ha)
Proportion of potential harvesting (%)
Proportion of potential harvesting (%)
Aggregation area (m²)

15. Main conclusions and limitations
Creating gaps and increasing harvesting intensity are both key management options to drive species mix and size diversity in spruce-fir stands
Small-sized gaps (around 500m²) are sufficient to enhance natural regeneration, but large openings (> 1 000m²) may be necessary to increase proportion of shade-intolerant species such as spruce
Harvesting intensity: trade-off between durability and spruce maintenance
Group selection amplify harvesting intensity effects : forest management coupling both options should be applied with care
Simulations are very sensitive to regeneration parameters
(see Lafond et al. 2014; Courbaud et al., submitted)
Regeneration response to light must be calibrated accurately
Conclusions only valid in forest conditions close to those used for model calibration
Here are our main conclusions considering the results of these experiments.

16. Research perspectives with this silviculture algorithm
Distinguishing thinning from harvesting operations
Driving species mix directly by choosing the trees to harvest according to their species
Driving forest stand structure and composition for biodiversity conservation
Preserving rare species
Sparing some very large trees
Leaving more dead wood in stands
Thinning potential
Harvesting potential
Harvesting diameter
Thinning diameter
I invite you to have a look on the poster mentionned here, where a study was carried out using these last parameters.
=> See poster session : Studying the response of timber production and biodiversity conservation to uneven-aged silviculture in mountain forests (Lafond et al.)

17. Funding and acknowledgments
Guillaume Lagarrigues PhD
The French Environment and Energy Management Agency (ADEME)
The French National Forest Office (ONF)
IRSTEA (Grenoble, France)
Projects
French research program “Biodiversity, Forest Management and Public Policy” (BGF)
European Research project “Advanced multifunctional forest management in European mountain ranges” (ARANGE)
Data for model calibration
Swiss Federal Institute for Forest, Snow and Landscape Research (Zürich)
To conclude, I would like to thank these organisms which fund my PhD and our team, and also give a special thank to the WSL and Andreas Zingg who have provided us long term data which were very precious for this study as well as for my PhD.
Thank you for your attention !

18. References
Beniston, M., Stephenson, D. B., Christensen, O. B., Ferro, C. A. T., Frei, C., Goyette, S., Halsnaes, K., Holt, T., Jylha, K., Koffi, B., Palutikof, J., Schoell, R., Semmler, T. & Woth, K. (2007). Future extreme events in European climate: an exploration of regional climate model projections. Climatic Change 81: Cordonnier, T., Courbaud, B., Berger, F. & Franc, A. (2008). Permanence of resilience and protection efficiency in mountain Norway spruce forest stands: A simulation study. Forest Ecology and Management 256(3): Courbaud, B., de Coligny, F. & Cordonnier, T. (2003). Simulating radiation distribution in a heterogeneous Norway spruce forest on a slope. Agricultural and Forest Meteorology 116(1-2): Courbaud, B., Goreaud, F., Dreyfus, P. & Bonnet, F. R. (2001). Evaluating thinning strategies using a tree distance dependent growth model: some examples based on the CAPSIS software uneven-aged spruce forests module. Forest Ecology and Management 145(1-2): Courbaud, B., Lafond, V., Lagarrigues, G., Cordonnier, T., Vieilledent, G. & De Coligny, F. (submitted). Critical steps to build and evaluate a mechanistic ecological model: a worked example with the Samsara.2 forest dynamics model. Ecological Modelling. Diaci, J. & Firm, D. (2011). Long-term dynamics of a mixed conifer stand in Slovenia managed with a farmer selection system. Forest Ecology and Management 262(6): Lafond, V., Lagarrigues, G., Cordonnier, T. & Courbaud, B. (2014). Uneven-aged management options to promote forest resilience for climate change adaptation: effects of group selection and harvesting intensity. Annals of Forest Science 71(2): Lagarrigues, G., Jabot, F., Lafond, V. & Courbaud, B. (Submitted). Approximate Bayesian Computation to recalibrate ecological models with large scale data: illustration with a forest simulation model. Ecological Modelling. Puettmann, K. J. (2011). Silvicultural Challenges and Options in the Context of Global Change: "Simple" Fixes and Opportunities for New Management Approaches. Journal of Forestry 109(6): Seidl, R., Rammer, W. & Lexer, M. J. (2011). Adaptation options to reduce climate change vulnerability of sustainable forest management in the Austrian Alps. Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere 41(4):

19. Results of the 1st experiment (Spatial aggregation of cut trees)
Regeneration
(Number of poles/ha)
Species mix
(% of spruce basal area)
Here are the results of these simulation experiments : for the three sets of simulation, I present 2 graphics, namely the number of poles by ha (which gives a measure of natural regeneration) and the proportion of spruce in the stand basal area (which give a measure of species diversity), in function of harvesting modalities. Dotted lines is the initial state, whereas points are the final state at the end of the simulation period (after 150 years).
Here, we have the results for the first experiment, which concerned the spatialization gradient. Group selection had a positive effect on structure and species diversity, by preserving large trees and enhancing regeneration, in particular that of spruce. For structure diversity, a threshold of this positive effect is reached from 500 squared meters of aggregation area, whereas large groups are necessary to have a significant positive effect on spruce maintenance.
Species balance
Aggregation area (m²)
Aggregation area (m²)

20. 2nd experiment (harvesting intensity with individual selection)
Regeneration
(Number of poles/ha)
Species mix
(% of spruce basal area)
Species balance
For the second experiment, where harvesting intensity was varying under individual selection, we observed no effect of intensification on regeneration but a positive effect on spruce maintenance (thanks to the more successful spruce regneration under lower canopy cover). However, in high intensites (>75%), final basal areas are very low, leading to non durable management.
Aggregation area (m²)
Aggregation area (m²)

21. 2nd experiment (harvesting intensity with group selection)
Regeneration
(Number of poles/ha)
Species mix
(% of spruce basal area)
Species balance
Finally, we obtained similar results for the third experiment than for the second, except that regeneration abundance and spruce proportion was systematically higher in group selection than in individual selection, whatever the harvesting intensity. However, high harvesting intensities lead to very low basal areas at the end of simulations on this experiment.
Finally, we found that coupling an intensity of 50% with aggregation areas of 500m² was the best trade-off in terms of management durability and stand resilience.
Aggregation area (m²)
Aggregation area (m²)

22. Thank you for your attention !

23. 𝑁 𝑃𝑟𝑜𝑑,𝑖 = 𝛼 1 × 𝐺 𝑛,𝑖 𝛼 2 𝑝 𝑆𝑢𝑟𝑣 = 1 1+ 𝑒 −( 𝛽 1 + 𝛽 2 ×𝐸+ 𝛽 3 ×𝐸²) …
𝑁 𝑃𝑟𝑜𝑑,𝑖 = 𝛼 1 × 𝐺 𝑛,𝑖 𝛼 2 …
Individual selection
(0 m²)
Large gap
(5000 m²  One large gap)

24. Management modeling by a silviculture algorithm
Concerning the silviculture algorithm, we developed for this study a new algorithm that allow the aggregation of harvestings as well as a great flexibility to drive harvesting intensity. We also added other parameters and drivers to deal with other research issues on which we were interested otherwise, such as biodiversity conservation. That results in a algorithm with a large number of parameters. In this study, we only varied two of these, here framed in red, namely harvesting proportion to drive harvesting intensity and aggregation area to simulate groups of different sizes.

25. Uneven-aged silviculture give managers many options to harvest wood while preserving forest resilience. Among them, we chose group selection and is favorably considered as forest gaps can enhance natural regeneration, especially for shade-intolerant species. Intensify harvesting is another interesting option that can allow reduce the amount of very large trees, reducing thus the risk of tree senescence and diseases while enhancing also natural regeneration by providing more light to the ground. However, such fine details about uneven- aged management have been poorly studied until now, and many questions remains about the efficiency of these options and the scales at which they should preferably applied.