Forest simulation models in Greece: main developments and challenges WG1-WG2-WG3 Dr. Ioannis Meliadis & Dr. Kostas Spanos Forest Research Institute, Thessaloniki, Greece COST ACTION FP0603: Forest models for research and decision support in sustainable forest management 1st Workshop and Management Committee Meeting. Institute of Silviculture, BOKU. 8-9 of May 2008 Vienna, Austria

Main features of country forests Forest cover (total/share): 6,513,000 Ha - about 49.5% of the land (industrial forests 25.4% of the land, non-industrial forests 23.9%). Growing stock, annual growth and cuts:  138,107,130 m 3 (41 m 3 /Ha) - industrial forests (56.10% conifers, 43,90% broadleaves).  3,812,538 m 3 (2.76%) - mean annual growth- industrial forests  1.14 m 3 /Ha (2,76%) – mean annual net increment (for all species). Main species: Fir, Pine, Beech, Oak, Poplar, Plane tree Main non-wood products and services: water, grazing, game, recreation Main risks: illegal cutting, fires, biotic hazards, drought Management and silvicultural characteristics: Plenty of unmanaged forests- Low profitability of timber High value of some non-timber products and services Complex forests: mixed and irregular Specialised areas on plantations (mainly poplars and pines)

Main features of high forests in Greece Forest cover (total/share): ha - 19,63% Growing stock, annual growth and cuts: 153,5 mil m 3, 4 m 3 /year (2,3 for coniferous and 1,5 m 3 /ha for broadleaves), 2,5 mil m 3 (or 1,2 /ha/year) Main species: Quercus spp (22,6%), Pinus nigra (8,72%), Abies cephalonica (8,34%), Fagus silvatica (5,17%), Pinus nigra (4,33%). Main non-wood products and services: Biodiversity, recreation, soil protection, water source protection, hunting. Main risks: Forest fires, overgrazing, drought, air pollution, illegal cuttings and land use changes. Management and silvicultural characteristics: - Bad forest quality and health - non strategic plants for the next years

Forest modelling approaches and trends Empirical models Main types of models developed Tree level models exist for the main forest trees species. Diameter distribution models for the main species in given areas to implement individual- tree models with stand-level data. Trends in modelling The trend has been towards individual tree-level modelling due to the type of forests and silvicultural systems. Recent research is concentrating in:  Modelling regeneration  Modelling site quality in uneven-aged and mixed forests  Modelling non-timber products and services  Modelling risk  Developing forest management information systems based on models Trends in modelling The existing trend in modelling can be found in some research programs, but is to ebvaluate existing models. Recent research is concentrating in Forest fire models, biodiversity, soil erosion, GIS-based forest information system.

Mechanistic models Forest modelling approaches and trends

Examples of GROWTH MODELS in Forestry in Greece (Developed by the FRI in Athens – Lab. of Silviculture and Forest Genetics) Model for Pinus halepensis (Alepo pine) a 1 = (HE)(EXP( / dHE)) h = a1(EXP( /d)) Vα = d h Vά = ( –5 d – 4 d – ) ( h ) Vε = Vα Ρ ΚΛ/Vα = d Dε = d Dα = Dε ΔΠΤ 60 = Ηο / (EXP ( /A)) D = d

Model for Pinus brutia (calabrian pine) a 1 = (HE)(EXP( / d HE )) h = a 1 (EXP( /d)) Vα = d h Vά = ( –5 d – 4 d – ) ( h ) Vε = Vα Ρ ΚΛ/Vα = d(EXP( – /d)) Dε = d Dα = Dε ΔΠΤ 60 = Ηο / (EXP ( /A)) D 1 = d D 2 = d D 3 = d

Model for Pinus nigra (black pine) a 1 = (HE)(EXP( / d HE )) h = a 1 (EXP( /d)) Vα = d h Vά = ( –5 d –4 d – ) ( h ) Vε = Vα Dε = d Dα = Dε ΔΠΤ 70 = Ηο / (EXP ( /A)) D = d(EXP( /d))

Model for Abies borissi regis (hybrid fir) a 1 = (HE)(EXP( / d HE )) h = a 1 (EXP( /d)) Vα = d h Vά = ( –4 d – 3 d – ) ( h ) Vε = Vα Dε = d Dα = Dε ΔΠΤ 110 = Ηο / (EXP ( /A)) D = d

Model for Quercus spp. (oak) a 1 = (HE)(EXP( /d HE )) h = a1(EXP( /d)) Vα = d h Vά = ( –5 d –4 d – ) ( h ) Vε = Vα Ρ ΚΛ/Vα = EXP( – /d) Dε = d Dα = Dε ΔΠΤ 90 = Ηο / (EXP ( /A)) D = d(EXP( /d))

Model for Fagus spp. (beech) a 1 = (HE)(EXP( / d HE )) h = a1(EXP( /d)) Vα = d h Vά = ( –5 d – 4 d – ) ( h ) Vε = Vα Ρ ΚΛ/Vα = EXP( – /d) Dε = d Dα = Dε ΔΠΤ 105 = Ηο / (EXP ( /A)) D = d

Where:  a 1 = a factor selection of a height curve (m)  HE = a mean height for estimation of the above factor (m)  d HE = the mean barked diameter at breast height corresponding to the above the mean height (cm)  h = a height of a tree (m)  d = the barked diameter of the same tree at breast height (cm)  Vα = the unbarked stem volume of the same tree (m3)  Vά = the stem rise factor of the same tree (m3/cm)  Ρ ΚΛ/Vα = the percentage of branch wood of the same tree with respect to unbarked stem volume (%)

 Vε = the barked stem volume of the same tree (m3)  Dε = the barked stump diameter of the same tree (cm)  Dα = the unbarked stump diameter of the same tree (cm)  ΔΠΤ α = the site index at an age α, i.e., α = 105 years  H 0 = the dominant height of a stand (m)  A = the age of the stand at breast height (years)  EXP(X) =the base e of the neperian logarithms at power X  D = crown diameter (m)  D1 = crown diameter for wood production (m)  D2 = crown diameter for wood production with tapping (m)  D1 = crown diameter for wood production with tapping or tapping and grazing (m)

If there are any models for predicting the yield of non- timber products or estimating different services (scenic beauty, recreation), list them. If not skip the slide.  The Soil Erosion Risk Assessment Maps. USLE equation: A tn/ha/year = R * K * LS * C * P In this project our institute includes the development of methodology and products generation (Soil erosion risk maps) using EO data and ancillary data into GIS environment. See also: 1. Spanos, K.A., Feest, A., A review of the assessment of biodiversity in forest ecosystems. Management of Environmental Quality, 18 (4): Modelling non-timber products and services

If there are any models for predicting the risk (occurrence/damage) of hazards (fires, wind, snow, etc), list them. If not skip the slide. SPREAD OF Heterobasidion IN STANDS OF Picea and Pinus (see MOHIEF project) Models for predicting risk of hazards

Describe a country research hihlight/finding in the context of modelling which can be relevant for other countries You can use more than on slide See references: 1. Spanos, K.A., Feest, A., A review of the assessment of biodiversity in forest ecosystems. Management of Environmental Quality, 18 (4): Woodward, S., J.E. Pratt, T. Pukkala, K.A. Spanos, G. Nicolotti, C. Tomiczek, J. Stenlid, B. Marçais & P. Lakomy, MOHIEF: MODELLING OF HETEROBASIDION IN EUROPEAN FORESTS, AN EU-FUNDED RESEARCH PROGRAMME. Research highlight

Future challenges To collaborate with other experts on forest models. To develop forest models for biodiversity indicators to use in forest biodiversity assessment and monitoring. To develop forest models to predict the effect of climate change on biodiversity quality. To improve the sustainable forest management practices. To incorporate the experience of other institutions to into our research plans. To facilitate our scope for our “models” for the Greek reality.

Innovative references 1) Apatsidis, L.D., Ziagas, E.Ch., Perris, I.G., Sotiropoulos, D.S., Tziovaras, E.Z., Models for Haleppo pine, Calabrian pine, Black pine, Fir, Oak and Beech. Forest research (New Series) Vol. 12, 104 p.. National Agricultural Research Foundation (N.AG.RE.F.), Athens (in Greek with English summary). 2) Kaloudis, S., A. Roussos and P. Kerkides, 1999 "Investigation of mountainous vegetation characteristics using GIS technology", International Journal of Balkan Ecology, 2 (3), ) Spanos, K.A., Feest, A., A review of the assessment of biodiversity in forest ecosystems. Management of Environmental Quality, 18 (4): ) Toth, B.B., Feest, A., A simple method to assess macrofungal sporocarp biomass for investigating ecological change. Can. J. Botany 85: ) Woodward, S., J.E. Pratt, T. Pukkala, K.A. Spanos, G. Nicolotti, C. Tomiczek, J. Stenlid, B. Marçais & P. Lakomy, MOHIEF: MODELLING OF HETEROBASIDION IN EUROPEAN FORESTS, AN EU- FUNDED RESEARCH PROGRAMME.