current and future growth potential of douglas-fir in central...
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Current and future growth potential of
Douglas-fir in Central Europe
Tamara Eckhart, Elisabeth Pötzelsberger, Roland Koeck, Dominik Thom, Georg J. Lair, Marcela van Loo and Hubert Hasenauer
Tamara Eckhart
Institute of Silviculture University of Natural Resources and Life Sciences, Vienna
14 September 2018
Key Objectives
1) Assessing the importance of specific climatic and physico-
chemical soil characteristics for the growth potential of mature Douglas-fir stands Site parameters versus productivity of mature Douglas-fir stands (defined by
the Site Index: mean dominant tree height at age 60 yrs.) Important criteria: Stands originate from recommended provenances from
the western Cascades and coastal regions in WA and OR
2) Prediction of the current and future growth potential for Austria and Germany Maps of the current and future growth potential
Study sites
− 28 Douglas-fir stands (23 siliceous, 5 carbonate bedrock) − Age: 40-120 years − 300 – 900 m.a.s.l. − Annual mean temperature (1981 – 2010) 7.0°C to 9.9°C − Annual precipitation (1981 – 2010) 570 mm to 2100 mm
Figure: Overview study sites (Eckhart et al., submitted)
Austria
Germany
Switzerland
Czech Republic
Douglas-fir stand productivity versus site parameters Correlate Site Index (SI) to a set of 25 site parameters 1. Climate
2. Soil
3. Geology
Variable group Acronym Description Unit Mean Min Max Site Index SI Site index m 35.4 28.2 42.1 Climate Tmean Mean summer temperature
[JJA] °C 17.4 15.8 19.3
Psum Summer precipitation [JJA] mm 313 219 690 Soil pH H2O Actual pH [-] 5.0 4.0 7.8 C Carbon t/ha 43 14 119 N Nitrogen t/ha 3 0.5 9 C/N C/N ratio [-] 18 11 30 Ca Calcium kg/ha 3,929 23 25,298 Mg Magnesium kg/ha 312 3 2,508 K Potassium kg/ha 108 22 250 Fe Iron kg/ha 18 0 76 Al Aluminum kg/ha 592 0 1,859 Mn Manganese kg/ha 72 0.1 275 CEC eff. Cation-exchange capacity mmol/kg 157 40 657 Bsat Base saturation % 47 10 100 NO3
- Nitrate kg/ha 13 0.4 58 NO2
- Nitrite kg/ha 1 0 6 PO4
3- Phosphate kg/ha 1 0 10 SO4
2- Sulfate kg/ha 13 1 69 Clay Clay % 19 6 47 Sand Sand % 36 0 60 Skeleton Soil skeleton % 14 1 40 PV Pore volume % 69 50 84 Soildepth Effective soil depth cm 82 33 149 WHC Water holding capacity mm 275 128 565 Discrete variable group
Description Unit Allocation
Geology Carbonate or siliceous bedrock dummy (1/0)
5 sites (1), 23 sites (0)
Table : Summary of dependent variable and the 25 candidate variables (Eckhart et al., submitted)
Random Forests (Breiman, 2001)
− Non-parametric method with a high prediction accuracy even if predictor variables are moderately collinear (Dormann et al., 2013)
− Multiple regression trees are constructed using bootstrap samples (2/3 of the data set for prediction, 1/3 are “out-of-bag” observations to calculate error rate)
− Results are aggregated and form an ensemble Random Forest
Douglas-fir stand productivity versus site parameters Statistical method
Fig.: Visualization of the regression trees (adapted after Hänsch 2015)
Random Forests analysis:
1. Variable pre-selection: Selection of relevant variables using the Random Forests-based variable selection procedure of the VSURF package in R (Genuer et al., 2016)
2. Building final Random Forests model: - Final Random Forests model with pre-selected variables - Results:
- Variable importance plot: mean square error (MSE) is used as a measure of importance, indicating how much the prediction of the same model would get worse by omitting the variable
- Partial effect plots: show the effect of each explanatory variable on the site index variation while holding the other variables constant
Douglas-fir stand productivity versus site parameters Statistical method
Fig.: Partial effect plots showing the marginal influence of the explanatory variable on site index variation (Eckhart et al., submitted)
Water budget: Low summer precipitation (< 270 mm) and low WHC (< 300 mm) reduce SI Temperature: Summer Temperature > 18°C reduces productivity
Soil nutrients: High phosphate, sulfate and nitrogen content important High iron content reduce growth
Physical soil properties: Sandy and clayey soils decline growth
pH optimum between 4.5 – 7.2
R² 30.3 %
Douglas-fir stand productivity versus site parameters Results: Partial effect plots ranked by variable importance
Prediction of the current and future growth potential
Prediction function of Random Forests analysis :
− Generating maps of current and future growth potential of Douglas-fir in Austria and Germany with the prediction function for site index
− Prediction only valid within the range of the 28 investigated Douglas-fir plots (no extrapolation of non-linear behaviour)
− Model resolution 1 x 1 km raster data
− Input data prediction model − 7 explanatory variables − No data available for phosphate, sulfate and iron : Input was set as
constant by taking the mean value of the investigated study plots
Input data prediction model – mean summer precipitation
200 400 600
33.0
34.0
35.0
36.0
Psum
SI
Decrease SI < 270 mm
RCP 4.5 RCP 8.5
Source: Worldclim Data
No extrapolation for summer prec. < 200 mm possible
15 16 17 18 19
34.8
35.2
35.6
Tmean
SIRCP 4.5 RCP 8.5
Optimum 17°-18° C, > 18° C decrease SI
Input data prediction model – mean summer temperature
Source: Worldclim Data
200 400
34.5
35.0
35.5
36.0
WHC
SI
Decrease SI < 300 mm WHC
Input data prediction model – water holding capacity
Source: European Soil Data Centre
0 10 30 50
35.2
35.4
35.6
NO3
SI
pos. correlated
4 5 6 7
35.3
35.5
pH
SI
Broad optimum range 4.5 – 7.2
Input data prediction model – nitrate, pH value
Source: Data Nitrogen CCTAME, European Soil Data Centre
0.0 0.2 0.4 0.6
35.0
35.4
Sand
SI
Sand > 45 % decrease SI
0.1 0.2 0.3 0.435
.30
35.4
035
.50
Clay
SI
Optimum 18-26 %, > 35 % decrease SI
Input data prediction model – soil texture
Source: European Soil Data Centre
Results: Growth potential of Douglas-fir
Sand content and summer prec. in eastern Germany outside verified range, although DF grows there in the alpine region summer temperatures below verified range
Current climate
in the alpine region summer temperatures below verified range
Results: Growth potential of Douglas-fir
Moderate CC Scenario RCP 4.5 2070
Summer prec. < 270 mm
Summer temp. > 20°C
Summer temp. > 15°C
Results: Growth potential of Douglas-fir
Severe CC Scenario RCP 8.5 2070
Summer prec. < 270 mm + Summer temp. > 20°C
Conclusions
Importance of specific climatic and physico-chemical soil characteristics
− Forest site productivity of Douglas-fir growth correlates with 10 out of 25 climatic and physico-chemical soil parameters
− Effects on the site index were non-linear, demonstrating comprehensible optimum ranges, critical levels as well as saturation levels
− SI was similar on carbonate and siliceous bedrock; results cannot be extrapolated to e.g. Rendzina sites
Conclusions
Current and future growth potential for Austria and Germany − Approach allowed to predict Douglas-fir growth potential including
important climatic and soil variables
− Current climate: − Highest growth potential: southern Germany and in the Northern Alpine
foothills − Lowest growth potential: drier regions in Central Germany and in the
summer warm East in Austria
− Future climate: − Moderate warming (RCP 4.5): Growth potential slightly decreases in the
most productive areas − Strong climate warming (RCP 8.5): Decrease in most productive areas, large
areas in Germany outside verified range (Summer prec., Summer temp.) − Temperature increase in Alpine regions: SI can be predicted with the model
function. Other study predict an increase in productivity in this region (Chakraborty et al., 2016).