Dead wood modelling at stand-level Jari Hynynen & Harri Mäkinen Finnish Forest Research Institute Vantaa Research Unit
MOTTI stand simulator Salminen et al. (2005), Hynynen et al. (2005) A stand-level analysis tool for assessing the effects of alternative forest management on development of stand and trees profitability of forest management biomass production and carbon sequestration forest biodiversity A workbench for modellers a tool for model building and testing A stand-level decision support system
User-defined input Stand inventory data MOTTI-system Stand simulation State update Growth prediction Management options Statistical model Process-based model Regeneration - Growth - Mortality Timber assortment criteria Economical parameters Analysis Yield Wood quality Forest economics Biomass & Carbon Biodiversity Request for reports Output of the results
Prediction of dead wood dynamics in Motti Prediction of mortality tree-level: survival models stand-level: self-thinning The amount of produced dead wood Decomposition of dead wood in terms of stem volume, density, mass The amount of actual dead wood Assessment of the quality of dead wood diversity index The "value" of dead wood
Survival models for individual trees
pcomp5 = Survival probability of a tree 1+ exp Mortality due to competition Haapala 1983, Hynynen et al. 2002 1 ( ) a0 + a1 d + a2 BA + a3 BAL p comp5 d = probability of a tree to die during the next five years = breast height diameter, cm BA = stand basal area, m 2 ha -1 BAL = basal area of trees larger than subject tree, m 2 ha -1 P comp5 0.3 0.25 Basal area, m 2 ha -1 5 20 40 0.2 0.15 0.1 0.05 0 0 5 10 15 20 d, cm 25
Mortality due to aging of a tree Hynynen et al. 2002 Survival probability of a tree pold( age) = e 1+ e 10 age 10 + 10 082. A max 10 age 10 + 10 082. A Maximum tree age (A max ) max Survival probability, % 100 90 Tree species Temperature sum, ddy 1200 800 Maximum age, years Scots pine (Pinus sylvestris) 450 750 Norway spruce (Picea abies) 350 450 Silver birch (Betula pendula) 200 300 Pubescent birch (Betula pubescens) 150 225 Aspen (Populus tremula) 180 270 Grey alder (Alnus incana) 100 150 Red alder (Alnus glutinosa) 150 225 Mountain ash (Sorbus aucuparia) 80 120 Goat-willow (Salix caprea) 80 120 Other coniferous sp. 350 400 Other broadleaved sp. 100 150 80 70 60 50 40 30 20 10 0 0 FINNISH 0.5 FOREST RESEARCH 1.0 INSTITUTE 1.5 age/a max
Stand-level model for self-thinning
Model for self-thinning Hynynen 1993, Hynynen et al. 2002 ( ) 0 2 ( ) 1 ( ) ln N = a + a ln SI + a ln D + b + e ij i gkij i ij N = stem number, trees/ha SI = site index, m D = mean diameter, cm Stem number, n ha -1 5000 4500 4000 3500 3000 2500 Scots pine Norway spruce Silver birch Pubescent birch 2000 1500 1000 500 0 10 15 20 25 30 35 40 Mean diameter at stump height, FINNISH cm FOREST RESEARCH INSTITUTE
Prediction of mortality in MOTTI Survival on tree-level pcomp5 p comp5 d = 1+ exp 1 ( ) a0 + a1 d + a2 BA + a3 BAL Self-thinning on stand-level ( ) ( ) ( ) = probability of a tree to die during the next five years ln Nij = a0+ a2ln SIi + a1ln Dgkij + bi + eij = breast height diameter, cm BA = stand basal area, m 2 ha -1 BAL = basal area of trees larger than subject tree, m 2 ha -1 P comp5 0.3 0.25 0.2 Basal area, m 2 ha -1 5 20 40 Stem number, n ha -1 5000 4500 4000 3500 3000 2500 IF Scots pine Norway spruce Siver birch Pubescent birch 0.15 0.1 0.05 0 0 5 10 15 20 25 d, cm 2000 1500 1000 500 0 10 15 20 25 30 35 40 Mean diameter at FINNISH stumpforest height, RESEARCH cm INSTITUTE
Models for decomposition of stems
Modelling data Data from permanent sample plots Scots pine (36), Norway spruce (13), birch (9) Stand treatment history is known Mortality of trees Time of death is known size of trees at the time of death is known Field measurement both living and dead trees were measured sample discs from stems Laboratory analysis wood density estimation volume, density and mass of stems Tree species Scots pine Norway spruce Birch
Models for dead wood dynamics Models for Scots pine, Norway spruce and birch probability that a tree remains standing as a snag after its death Models for the remaining fraction of stem volume stem density stem mass as a function of time since the death of a tree
Models for the probability that a tree remains standing as a snag after its death Mäkinen et al. (2006) Pr( snag ln 1 Pr( snag spti ) ) spti = g 1 + g 4 y spti + g 7 dbh spt0 + ε + ε 1s 1spt Probability 1 0.8 0.6 0.4 Scots pine Norway spruce Birch 0.2 0 0 20 40 60 80 100 Time since death, years
Models for the remaining fraction of stem volume Mäkinen et al. (2006) v v spti spt0 = exp ( exp( c + c y + c dbh + γ ) 1 4 spti 7 spt0 1spt Remaining fraction 1 0.8 0.6 0.4 Scots pine Norway spruce Birch 0.2 0 0 20 40 60 80 100 Time since death, years
Models for the remaining fraction of wood density Mäkinen et al. (2006) r r spti spt0 = exp ( exp( b + b y + b dbh + β ) 1 4 spti 7 spt0 1spt Remaining fraction 1 0.8 0.6 0.4 Scots pine Norway spruce Birch 0.2 0 0 20 40 60 80 100 Time since death, years
Models for the remaining fraction of stem mass Mäkinen et al. (2006) m m spti spt0 = exp ( exp( d + d y + d dbh + δ ) 1 4 spti 7 spt0 1spt Remaining fraction 1 0.8 0.6 0.4 Scots pine Norway spruce Birch 0.2 0 0 20 40 60 80 100 Time since death, years
Conclusions Dead wood decomposition models Decomposition rate depends on the time since death slow initial decomposition period: 0 => 5 to 10 years period of radip decomposition period of modearte decomposition rate Conifers stem mass decreased by 50 % in 25-35 years stems competely decayed in 60-80 years Birch stem mass decreased by 50 % in 10 years stems competely decayed in 25-40 years The models provide a framework for predicting dead wood dynamics in managed and dense unthinned stands
Further analysis
Diversity Index of dead wood An index based on properties of dead wood tree species stem diameter (diameter classes) degree of decomposition (decay classes) type of dead stem (snag/log) Can be used to assess the "value" of dead wood to biodiversity
An example The effect of alternative management practices on the dead wood production and on the profitability of forest management
A simulation study for a Norway spruce stand (one rotation - 65 years) Management schedules forest management recommendations (Tapio) active production of dead wood in intermediate thinnings 10 m3/ha 20 m3/ha 40 m3/ha The effects management on the amount of dead wood diversity index of dead wood profitability of forest management
The volume of dead wood at the end of the rotation (Norway spruce, age 65 years) Amount of deadwood, m3/ha 70 60 50 40 30 Log Snag 20 10 0 Managed (Tapio) Unmanaged Management
Diversity index of dead wood A Norway spruce stand diversity index 60 50 Management 40 Tapio 30 2. thinning 10 m3/ha 20 m3/ha 20 precommercial thinning 1. thinning 40 m3/ha Unmanaged 10 0 0 10 20 30 40 50 60 70 Stand age, years
relative NPV, % 120 Relative net present values lnterest rate 3 % 100 80 60 40 20 0 Tapio 10 m3/ha 20 m3/ha 40 m3/ha Unmanaged Management
References Haapala, P. 1983. Luonnonpoistuman ennustaminen puun kuolemistodennäköisyysmallilla. Metsäntutkimuslaitoksen puuntuotoksen tutkimussuunta. Stencil. 33 p. [In Finnish]. Hynynen, J. 1993. Self-thinning models for even-aged stands of Pinus sylvestris, Picea abies and Betula pendula. Scandinavian Journal of Forest Research 8(3): 326-336. Hynynen, J., Ojansuu, R., Hökkä, H., Siipilehto, J., Salminen, H. & Haapala, P. 2002. Models for predicting stand development in MELA System. Finnish Forest Research Institute, Research Papers 835. 116 p. Hynynen, J., Ahtikoski, A., Siitonen, J., Sievänen, R. & Liski, J. 2005. Applying the MOTTI simulator to analyse the effect of alternative management schedules on timber and non-timber production. Forest Ecology and Management 207: 5-18. Mäkinen, H., Hynynen, J., Siitonen, J. & Sievänen, R. 2006. Predicting the decomposition of Scots pine, Norway spruce, and birch stems in Finland. Ecological Applications 16(5): 1865-1879. Salminen, H., Lehtonen, M. & Hynynen, J. 2005. Reusing legacy FORTRAN in the MOTTI growth and yield simulator. Computers and Electronics in Agriculture 49(1): 103-113.