1. EFIMED Advanced course on MODELLING MEDITERRANEAN FOREST STAND DYNAMICS FOR FOREST MANAGEMENT Mediterranean forest management and planning: the need for simulation models MARC PALAHI Head of EFIMED Office

2. 20.8.20042 Contents 1 Some features of Mediterranean forests 2 Some concepts on forest planning 3 Simulation: a key step

3. 20.8.20043 Features of Mediterranean forests Long history of manipulation by man Many types of natural vegetation and high biological diversity Relevance of their protective, social and ecological functions versus the productive ones (externalities) Fragility, instability, over-exploitation (south) & fires (north)

4. 20.8.20044 The natural vegetation The climate and the altitude factor makes possible various vegetation zones: -ThermoMeso -Thermo-, Meso-: Q. ilex, Q. suber, P. halepensis, P. brutia -Supra-: Q. robur, Fraxinus spp., P. nigra -Montane- and Oro-: Cedrus, Fagus sylvatica, P. sylvestris Agro-silvo-pastoral systems: dehesa or montado

5. 20.8.20045 High biological diversity The Mediterranean area harbours 25000 plant species (50 % endemic) whereas in central and northern Europe (an area 4 time greater) 6000 plant species can be found In forest tree species: 100 vs. 30, respectively

6. 20.8.20046 Many non-wood forest outputs highly demanded by the society non-wood products: cork, grazing, resin, mushrooms, aromatic plants, honey, fruits (pinecones and acorns), truffles, game, services and externalities: soil protection, flood and avalanche prevention, landscape quality, nature conservation, recreation possibilities, micro-climate regulation

7. 20.8.20047 Forest management planning Complex problem because of multiple competing/complementary objectives/products NEEDS New models, techniques and tools to support decision making in forestry

8. 20.8.20048 Multi-objective forest planning 1 Features of Mediterranean forests 2 Some concepts on forest planning 3 Simulation: a key step

9. 20.8.20049 Forest Planning? Planning finds the optimal way to use forest resources maximises the production of goods and services those goods and services considered which are important to the forest owner/society (goal based) always utility maximisation & optimisation

10. 20.8.200410 Planning, not an easy task… Many goals to address the multiple functions Many parties (locals, government, ecologists) Long time horizons (as compare to agri) Risk and uncertainty Many alternatives

11. 20.8.200411 Framework of modern planning - A quantitative approach Decision makerForest ecosystem Inventory dataPreferencesModels Objectives and constraints Information about alternatives Comparisons Decision SIMULATION OPTIMIZATION Growth models Fire risk models Habitat models Mushroom models

12. 20.8.200412 Simulation: a key step in planning Decision Support Planning Simulation Data Management Measurement Forest Ecosystem Decision makerForest ecosystem Inventory dataPreferencesModels Objectives and constraints Information about alternatives Optimizations Plan Simulation

13. 20.8.200413 Stand development Growth Mortality Ingrowth

14. 20.8.200414 Forest stand development affected by Regeneration Growth of trees Mortality Human interventions Models should be able to predict these processes which are affected by factors like Productive capacity of an area – site quality Degree to which the site is occupied – density/competition Point in time in stand development - age

15. 20.8.200415 Modeling forest development is needed to provide tools that enable foresters to compare alternative silvicultural treatments predict the economic returns of a management schedule but also to produce information about the dynamic change of less tangible attributes of forests generate silvicultural instructions for different species, sites and management types

16. 20.8.200416 Simulation models comprise of: a series of mathematical equations numerical values embedded in those equations the logic necessary to link these equations the computer code required to implement the model on a computer Mathematically, e.g.; Dbh-Increment = a + b dbh + c Site + d Basal area Height = a + b dbh Volume = a + b Height + c dbh 2 …. Modelling

17. 20.8.200417 Simulation Treatment schedules for stands Example: 1.Do nothing 2.Thinning 3.Clear-felling / selective cutting Purpose: produce information for planning Stand development Harvested timber/firewood volume Costs and incomes Biodiversity indices Non-wood forest products Results in a Decision Space = Combinations of stands’ treatment schedules

18. 20.8.200418 Simulation program Simulation program combines inventory data models rules for producing alternatives (”instructions”) Models used models on stand dynamics regeneration growth mortality models on allometric relationships height = f(diameter) volume = f(diameter, height) other models (fire risk, mushroom, habitat models)

19. 20.8.200419 Multi-objective Planning Write a planning model using information from simulations by using models information on preferences -> objectives Planning model writer Solve the model using mathematical programming heuristics

20. 20.8.200420 Decision support systems Computer system which supports rather than replaces the decision maker User-friendly interface Planning system (data base, simulation and optimisation) augmented with e.g. Comparison tools Visualisation tool

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23. 20.8.200423 EFIMED Annual meeting 26-27 of october 2007 University of Valladolid, Palencia, Spain Modelling the production of wild mushrooms in Scots pine (Pinus sylvestris L.) forests in the Central Pyrenees José Antonio Bonet, Timo Pukkala, Christine Fischer, Marc Palahí, Juan Martínez de Aragón, Carlos Colinas

24. 20.8.200424 New forestry context Recent socio-economic changes have accentuated the multifunctionality of forest ecosystems - economic development/ increasing living standards - time for leisure and environmental awareness - urbanization of society/depopulation of rural areas - lack of man power and profitability (high costs, prices-globalization) From Productive functions to Environmental and Social functions, which need to be addressed in forest management planning (biodiversity, recreation, scenic beauty, non-timber products, etc)

25. 20.8.200425 Importance of mushrooms Contrary to timber, non-timber products have maintained their prize Market demand has increased Annual revenue from 478 metric tons of L. deliciosus sold in the central Barcelona market (Mercabarna) is estimated at 1.5 - 2 million €. Mushroom picking is a major recreational activity valuation studies estimate that Catalans are willing to pay an average of 6 € year -1 to be able to pick mushrooms Pinus sylvestris forests in Catalonia can produce 60 kg ha -1 of edible mushrooms

26. 20.8.200426 Managing forests for mushroom production The social and economic importance of mushroom picking requires mushroom production to be an explicit management objective in forest planning Quantitative scientifically based forest planning requires models to predict the yield of mushroom according to forest stand characteristics and management practices No such predictive models were found in the literature for mushrooms

27. 20.8.200427 Aim of the study To develop empirical models for predicting the production of wild mushrooms in Scots pine (Pinus sylvestris L.) forests in the Central Pyrenees based on mushroom production data from three consecutive years.

28. 20.8.200428 Mushroom data In 1995, 36 plots of 10 x 10 meters were established in Pinus sylvestris plantations of the Central Pyrenees to evaluate the productivity and diversity of ectomycorrhizal and edible fungi in this forest community. The plots were sampled at 1-week intervals from September through November during the 1995, 1996 and 1997 autumn seasons We used the following groupings in the model: all species, the edible species, the marketed edible species, and the marketed edible Lactarius species.

29. 20.8.200429 Forest data 24 plots inventoried in 2006 to measure site and growing stock variables (other plots had been cut or significantly transformed through management) Plots (area varying between 0.04-0.16 ha) were established so that at least 100 trees with dbh> 7.5 cm were within the plot. Dbh and the growth for the last ten years were measured for all trees and tree heights, tree age and bark thicknesses were recorded for a sample of at least 20 trees per plot. VariableMeanStandard deviationMinimumMaximum Stand variables T (yr)27.912.410.455.3 H dom (m)12.34.43.320.0 G (m 2 ha -1 )20.613.61.054.8 N trees (trees ha -1 )1171.7392.3717.22196.3 D m (cm)17.27.04.934.2 SI (m)22.32.913.327.5 Elevation (m)1238.8220.2846.01528.0 Aspect (degrees)179.9130.44.0356.0 Slope (%)24.17.27.038.0 Mushroom productions Total (kg ha -1 )123.7135.20.2466.6 Edible (kg ha -1 )63.075.00.2283.4 Marketed (kg ha -1 )25.639.10.2153.4 Lactarius (kg ha -1 )7.921.60.0104.5

30. 20.8.200430 Modelling mushroom production Mushroom production depends very much on weather conditions but also on the forest site and growing stock characteristics However, in forest planning, variables that can be known trough regular inventories and simulation tools and that can be influenced through forest management need to be used Weather conditions cannot accurately be predicted beyond a few weeks

31. 20.8.200431 Modelling approach The predictors were chosen from stand and site variables as well as their transformations (Age, Site index, Hdom, Basal area, N, ELE, SLO, AS, …) Due to the hierarchical structure of the data, mushroom measurements of the same year were correlated observations as were the measurements on the same plot, the generalised least squares (GLS) technique was applied to fit mixed linear models Models for the total production, edible species, marketed species, and individual species or species groups were fitted

32. 20.8.200432 Results The regression analyses showed that stand basal area, elevation, aspect and slope were the most significant predictors: Total production ln(yij) = 0.981 +2.483ln(G) -0.128G +0.934cos(Asp) -0.0135Slo1.5 + ui + uj + eij Edible mushrooms ln(yij) = -4.329 +1.966ln(G) -0.118G +0.636cos(Asp) +0.00331Alt + ui + uj + eij Marketed mushrooms ln(yij) = -6.236 +1.246ln(G) -0.0599G +0.00459Alt + ui + uj + eij Marketed Lactarius ln(yij) = -0.192 +1.016ln(G) -0.106G +1.489cos(Asp) -0.0151Slo1.5 + ui + uj + eij

33. 20.8.200433 Results

34. 20.8.200434 Results

35. 20.8.200435 Results Productions were greatest when stand basal area was approximately 20 m 2 ha -1. Increasing elevation and northern aspect and decreasing slope increased total mushroom production, edible and marketed Marketed Lactarius spp., the most important group collected in the region, showed similar relationships. The annual variation in mushroom production correlated with autumn rainfall.

36. 20.8.200436 Discussion It seems that highest mushroom production coincides with the peak in forest volume growth Previous studies shows that mushroom production correlates with growth and photosynthetic rate of host trees Flux of current photosynthates is critical for soil respiration and ectomycorrhizal sporocarp production Since stand basal area is correlated with site conditions (soil quality, water availability, humidity, etc.), and other variables like age, volume, etc. estimating the effects of growing stock variables requires more plot measurements or a population in which stand variables are less correlated

37. 20.8.200437 Discussion Elevation, aspect and slope in the Prepyrenees range reflect water availability and soil quality which clearly affects mushroom production Stands near canopy closure with vigorous growth rates located at high elevations, in northern aspects and with low slopes seems to be optimal sites for mushroom production in Scots pine forests of the Spanish pre-Pyrenees. Such models can be used to optimized forest stand management for mushroom and timber production Despite of the limitations of our data in number of measurements and plots the results of the study are encouraging because they demonstrate that mushroom production are related to stand characteristics that can be influenced by silvicultural interventions

38. 20.8.200438 Next step is to collect a larger data set including more variability in growing stock characteristics as well as other tree species. Collecting large quantities of empirical data over several years is required because there are multiple factors responsible for high temporal variation in mushroom productions. The effect of silvicultural treatments needs to be studied Climate change should be considered as it will affect both forest growth (and composition) and weather conditions = mushroom production Final remarks

39. 20.8.200439 BONET, J.A. PUKKALA, T.; FISCHER, C.R.; PALAHÍ, M.; MARTÍNEZ DE ARAGON, J. i COLINAS, C. 2007. “Empirical models for predicting the yield of wild mushrooms in Scots pine forests in the Central Pyrenees”. Annals of Forest Sciences (in press). MARTÍNEZ DE ARAGÓN, J.; BONET, J.A.; FISCHER, C.R. i COLINAS, C. 2007. “Productivity and richness of ectomycorrhizal and edible forest fungi in three pine forests of the pre-Pyrenees, Spain: Development of predictive models as a basis for forest management of the mycologic resource”. Forest, Ecology & Management (doi:10.1016/j.foreco.2007.06.040). BONET, J.A.; FISCHER, C.R. y COLINAS, C., 2004. “The relationship between orientation and forest age on the production of sporocarps of ectomycorrhizal fungi in Pinus sylvestris forests of the Central Pyrenees”. Forest, Ecology and Management, 203: 157-175. REFERENCES

40. 20.8.200440 Dibujo transición corta setas THANK YOU!!