Influence of tree crown parameters on the seasonal CO2-exchange of a pine forest in Brasschaat, Belgium. Jelle Hofman Promotor: Dr. Sebastiaan Luyssaert Co-promotoren: Bert Gielen Prof. Dr. Ivan Janssens

Introduction Carbon cycle (soils-oceans-biosphere-atmosphere) CO2-exchange vegetation: Photosynthesis  Respiration Depends on meteorological (temp, VPD, radiation,…) and ecological (LAI, N, chlorophyl,…) parameters Ecosystems affect atmospheric CO2-concentrations

Introduction Insight in the influence of climate and ecology on the underlying processes that determine CO2-exchange: Short term: climate parameters are suitable to predict C02-exchange. Longer term: ecological parameters become increasingly important. The response of ecosystems (ecological parameters) on both slow and abrupt climate changes will therefore dominate the CO2-flux on the long term.

Goal of master thesis Determine the influence of ecological tree crown parameters on the GPP-course for a tree plot (‘De Inslag’) in Brasschaat. Influence on different time scales: Seasonal (2009): LAI, SLA, N, chlorophyl, FAPAR Interannual (2002-2009): FAPAR

Materials and methods Fluxnet sampling tower in experimental plot ‘De Inslag’: NEE: “netto ecosystem exchange” by eddy covariance measurements  GPP (µmol/m²s) is derived Meteorological parameters: Temperature (°C), soil temperature (°C), radiation (W/m²), precipitation (mm), soil water (%vol), vapour pressure (kPa). FAPAR: “fraction of absorbed photosynthetically active radiation” Radiation, absorption (chlorophyl)  combination meteo/ecology

Materials and methods Own measurements (March 2009 - March 2010): LAI (m²/m²)  hemispherical photography SLA (m²/g)  surface/weight ratio needle samples N-content (w%)  C/N analysis needle samples Chlorophyl content (µg/ml)  spectrophotometer 34 sampling days: 34 LAI-values (average from 23 photos) 24 needle samples: SLA, N en chlorophyl All data series were interpolated to daily values in MATLAB.

Materials and methods Neural networks: Used parameters: Arithmetic network of different processing units (“nodes”). Output (GPP-course) can through nodes (functions) be explained by input variables (LAI, SLA, FAPAR, …). Network “trained” in MATLAB to improve relation between input-output Technique is used to represent complex, non-linear relations between variables  natural ecosystems. Used parameters: Non linear influence on GPP Coupled: vb. temp-humidity, radiation-FAPAR,...

Materials en methods Network was created in which GPP 1997-2009 was explained by meteo parameters of 1997-2009  trained/optimalised. With this network, GPP was estimated based on meteo parameters for 2009 (seasonal) and 2002-2009 (interannual). The estimated GPP (network) and the measured GPP (tower) were plotted next to eachother: Difference: Residual GPP-course Difference can’t be explained by the meteo parameters  caused by biological responses or noise Residual GPP-course is explained in neural networks by the different ecological tree crown parameters.  How much of the residual variation is explained by ecological parameters?

Results Influence of ecological parameters on residual seasonal GPP-variation (2009): 74% of GPP-course was explained by meteo All ecological parameters individualy explained a part of the residual GPP-variation: LAI (51%)>chlorophyl (48%)>SLA (33%)>FAPAR (32%)>N (27%) Random (25%) All parameters together explained 40% (Random: 20%)

Results Influence of FAPAR on residual interannual GPP-variation (2002 - 2009): 72% of GPP-course was explained by meteo parameters FAPAR explained a part of the residual GPP-variation: FAPAR: 37% Random: 14%

Conclusion Ecological parameters correlated both mutually as with meteo parameters as with GPP-course  proves their influence on GPP and the complex relations between the different parameters! All ecological tree crown parameters explained residual GPP-variation that couldn’t be explained by the meteo parameters. Residual GPP-variation 2009 (seasonal): LAI 26%, chlorofyl 23%, SLA 8%, FAPAR 7%, N 2% more than the random data set. Residuele GPP-variatie 2002-2009 (interannual): FAPAR 23% more than the random data series. Influence FAPAR increases on longer timescales: 7  23% Influence meteo decreases on longer timescales: 74  72%

Conclusion Ecological parameters lead both on seasonal and interannual timescales to better GPP-estimates by explaining a part of the residual variation. Climate and ecology hereby have a combined influence on the CO2-exchange between vegetation and atmosphere through complex mutual relations. Physical/financial costs of a permanent sampling of ecological parameters must be considered:  remote sensed: FAPAR! Worldwide Different ecosystems Permanent sampling More reliable results by evolution in resolution/sensor technology

Questions?