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).

THANK YOU!