Evaluation of Two European Growth Models for Douglas Fir

Assessment of two European growth models for Douglas fir in France, focusing on their ability to simulate new management scenarios based on actual field data. The study evaluates the models' performance against observed data from field experiments with varied initial densities and thinning intensities. The aim is to compare a fully empirical model and a semi-functional model in terms of their robustness and realistic simulation capabilities for forest management scenarios. The models considered are Gymnos and Perin, Ligot et al. (2012), parameterised with extensive data sets from Belgium.

• Growth models
• European
• Douglas fir
• Forest management
• Simulation

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1. Evaluation of two European growth models for Douglas fir Minna Pulkkinen, Holger Wernsd rfer & Fran ois Ningre LERFoB, AgroParisTech, INRA, F-54000, Nancy, France Journ es CAQSIS, Montpellier, 5 7/4/2016 1

2. Background Project "Assessment of new forest production systems for Douglas fir towards a simulation tool linking research, development and teaching" General driving problematics: need to renew forest management - Increasing demand for wood as renewable material and source of energy - Requirement of sustainability, favourable carbon and nutrient balances, maintenance of biodiversity, ... 2

3. Growth models as tools in development of new management scenarios - Key question: how well are models able to simulate new management scenarios? - Evaluation of model simulations against observed data: if a model is able to reproduce actualised contrasting management scenarios, it is likely to be able to simulate new scenarios as well - Evaluation data: repeatedly measured field experiments with (i) highly contrasting initial densities and (ii) varied timings and intensities of thinnings - Sensitivity analysis: Identifying relations and phases of development critical for model performance model improvement 3

4. Different types of growth models may have different ability to produce robust results outside their construction / parameterisation conditions - modelling paradigm: empirical, semi-functional, functional - growth unit and spatial explicitness - initial state and input data requirements - time step - incorporation of randomness in modelled processes Aim of this study: evaluate two tree-based European growth models fully empirical vs. semi-functional against independent data in view of realistic simulation of new management scenarios for Douglas fir in France 4

5. Models: Gymnos Donn es initiales: Age, Nha, I0 - Perin, Ligot et al. (2012) - For even-aged pure stands of Douglas fir, Norway spruce and two larch species - Empirical, spatially inexplicit - Parameterised with extensive data sets from S Belgium - Fundamental relationships: (i) Hdom growth (ii) tree circumference growth (as function of Hdom, stand density and tree size) - Mortality: maximum stand basal area w.r.t. mean tree circumference - Initial state at age t: plot size, stand age, tree diameters, site index parameter of Hdom vs. age curve (H50) Initialisation Sorties : Peuplement : Gha, Nha, RDI Cdom, Cmoy, Cg Hdom, Age, Surface Liste d arbres Age, Hdom, Suppression des arbres claircis oui Eclaircie ? Croissance : Aget+1= Aget+ 1 Hdomt+1= Hdomt+ dHdomt Cit+1= Cit+ dCit non oui non Auto claircie Nha > Nhamax ? (Perin 2013) 5

6. - Ottorini (1991, 1995); based on work by Mitchell (1975) - For even-aged pure Douglas fir stands - Semi-functional, spatially explicit - Parameterised with very detailed tree analysis data from N-E France - Basic idea: height growth (Hdom growth adjusted with vigour and foliar volume) crown growth in 3D (restricted by neighbouring crowns) effective foliar volume stem volume growth - Mortality with (i) suppression by neighbouring trees and (ii) Reineke s self-thinning rule - Initial state at age 0: tree coordinates, tree vigours, site index parameter of Hdomvs. age curve (H50) Models: SimCoP 6

7. Evaluation data A subset of 62 plots from permanent field experiments in Baden-W rttemberg, S-W Germany (FVA Freiburg) Measurements at irregular time intervals - diameter on all trees at all observation time points - height on (a subset of) trees at some observation time points - cause and time of removal on all trees exact information on mortality and thinnings - co-ordinates of trees not available in all plots 7

8. Plot selection criteria - pure one-storey Douglas fir stands - tree coordinates available - no interventions before first observation time point - Hdom known at least at three observation time points Ninit (ha-1) H50 (m) No. of. obs. time points Length of obs. period (years) No. of cuttings Cutting removal (%) Cutting factor Min. 150 27.1 3 5 1 0.2 0.013 1st quart. 1000 32.5 6 20 3 16.2 0.802 Median 1056 34.0 7 22 4.5 21.4 0.937 3rd quart. 2017 36.8 7 22 6 28.4 1.026 Max. 4017 39.6 7 24 7 91.7 1.382 8

9. Simulations: Initial state & parameterisation Initial state in Gymnos: tree diameters at first observation time point - measured values from data social statuses of trees - drawn from theoretical distribution Initial state in SimCoP: tree vigours (and coordinates) at age 0 - estimated from data (h/Hdom) at first observation time point and adjusted for mean and variance of default theoretical distribution social statuses of trees - drawn from theoretical distribution 9

10. Site index parameter H50 - estimated by fitting Hdomvs. age curve into measured data, conditional on default values of other parameters of curve - with SimCoP, fitting used also to select Hdomvs. age curve among three alternatives Otherwise original parameterisation used without modifications 10

11. Simulations: Thinnings Trees killed by natural hazards (wind, ice / snow, game animals, ...) included in thinnings introducing additional, sometimes drastic thinnings in data Timing of thinnings determined by age (cf. Hdom, RDI, ...) easy comparison with observations Intensity of thinnings determined by stocking density to be removed (cf. target density after thinning) no artificial correction of possibly erroneous before- thinning mortality 11

12. Employing two thinning algorithms (Capsis) 1) List thinning - removing exactly same tree individuals as were removed in data close imitation of observed thinnings, but difficult to use in further work (scenario simulations) 2) Size-class thinning - removing trees in diameter classes in same proportion as in data - selecting removed trees within class randomly - spatial control fairly good imitation of observed thinnings, possible to use in further work 12

13. Simulations: Randomness Sources of randomness - Gymnos: generation of tree diameters at initial state selection of trees in mortality process - SimCoP: generation of tree vigours at initial state - Size-class thinning algorithm: selection of trees within each class With each simulation scheme (model + initial state generation + thinning algorithm) involving randomness 100 repeated simulations per plot 13

14. Source of randomness Model Simulation scheme Initial state Mortality mechanism Thinning algorithm Measured diameters & list thinning x Measured diameters & size-class thinning x x Gymnos Random diameters & list thinning x x Random diameters & size-class thinning x x x Adjusted vigours & list thinning Adjusted vigours & size-class thinning x SimCoP Random vigours & list thinnings x Random vigours & size-class thinning x x 14

15. Evaluation criteria Evaluation based on stand characteristics computed in a uniform manner from tree diameters and heights: stocking density, basal area, Hdom, Ddom No extra variation between models due to different volume, biomass etc. equations Output of each simulation scheme: 100 time series (repetitions) of stocking density, basal area, Hdom, Ddom in each of 62 plots Need to summarise variation (i) over repetitions at each observation time point in each plot, (ii) over observation time points in each plot, and (iii) over plots 15

16. Average performance of each simulation scheme in each plot - compare mean simulated time series to observed time series - summarise resulting time series of relative errors into plot-wise diagnostics (a) mean of relative errors over observation time points average systematic error over observation period ("relative bias") (b) mean of absolute values of relative errors aggregated measure of performance at individual observation time points (c) standard deviation of relative errors variation in performance between individual observation time points ("relative precision") 16

17. Variation between simulated time series of each simulation scheme in each plot - compare every simulated time series to observed time series - summarise resulting set of relative errors into plot-wise diagnostics (a) mean of absolute values of relative errors over observation time points (b) standard deviation of relative errors With Gymnos, first observation time point (having perfect match!) not included in diagnostics computation 17

18. Results: Average model performance in plot over simulation period Ddom Stocking density List thinning Size-class thinning List thinning Size-class thinning Gymnos SimCoP Gymnos SimCoP Gymnos SimCoP Gymnos SimCoP Basal area Both models had slight difficulty to reproduce observed mortality ( overestimation of stocking density) Both models predicted Ddomwell Both models overestimated basal area List thinning Size-class thinning 18 Gymnos Gymnos SimCoP SimCoP

19. Hdom: Gymnos predicted very well, SimCoP underestimated on average in majority of plots Hdom Hdom List thinning Size-class thinning List thinning Size-class thinning Gymnos Gymnos SimCoP SimCoP Gymnos Gymnos SimCoP SimCoP Small mean of relative errors over time does not necessarily mean good performance at individual time points! Mean of relative errors 0.4 % St. dev. of relative errors 11 % 19

20. Results: Effect of initial density Basal area Ninit 4000 n = 4 Ninit 2000 n = 22 Ninit= 150-1200 n = 36 Gymnos, list thinning SimCoP, list thinning Gymnos, size-class thinning SimCoP, size-class thinning Initial density affected average model performance: stronger overestimation of basal area with low densities, underestimation with high densities, best performance with medium densities (mostly present in parameterisation?) 20

21. Results: Importance of early performance Basal area Performance of SimCoP iat early phase of simulation affected its average performance over whole simulation period (in basal area and Ddom) If SimCoP was doing well at 1st observation time point, it was doing well over the whole observation period Basal area: plots with same (small) error range at 1st obs. time point Basal area: all plots SimCoP SimCoP SimCoP SimCoP 21

22. Concluding remarks Both models were able to predict relatively well with their original parameterisation stand growth and thinning reaction of independent contrasting data similar performance achieved with very different parameterisation effort Starting from age 0, SimCoP was sensitive to early phase performance Both models can be used for developing new management scenarios 22

23. Acknowledgements Access to growth models and help with implementation: T. Bronner & J.-M. Ottorini (SimCoP) J. Perin & G. Ligot (Gymnos) Evaluation data: A. Albrecht Capsis tools: F. de Coligny Funding: Chaire For ts pour demain , DGER, R gion Lorraine, AgroParisTech, INRA The UMR 1092 LERFoB is supported by a grant overseen by the French National Research Agency (ANR) as part of the Investissements d'Avenir program (ANR-11-LABX-0002-01, Laboratory of Excellence ARBRE) 23

24. Results: Effect of initial density Ddom Stocking density 24