1. Predicting climate change impacts on southern pines productivity in SE United States using physiological process based model 3-PG Carlos A. Gonzalez-Benecke School of Forest Resources and Conservation University of Florida

2. Outline 1.Southern forests in SE United States 2.3-PG Model 3.Model Calibration for Pinus elliottii (slash pine) 4.Model Validation 5.Case Study Climate Change Impacts on Productivity of Slash Pine Stands

3. Background Forests have multiple goods and services: wild-life, water, soil, C seq,… wood. In SE United States : 60% of landscape if forested including 28 million ha of southern pines. SE U.S. produces 58% of the total U.S. timber harvest and 18% of the global supply of roundwood (more than any other country). SE pine forests contain 1/3 of the contiguous U.S. forest C and can sequester 23% of regional GHG emissions. Most important southern pine species: Pinus taeda (loblolly pine), Pinus elliottii (slash pine) and Pinus palustris (longleaf pine).

4. BackgroundSlash Pine (Pinus elliottii Engelm.) Medium-Long-Lived. Fast-growing Important commercial species in SE United States Objectives: Pulpwood and sawtimber production Area of timberland: 4.2 million ha http://www.forestryimages.org

5. Tree growth model based on : P hysiological P rinciples that P redict G rowth Light Interception Carbon Acquisition Carbon Allocation Forest Production : 3-PG (Landsberg and Waring, 1997)

6. 3-PG Model BA Landsberg and Waring 1997

7. All modifiers affect canopy production: Temperature Frost Nutrition VPD ASW Age Max Canopy Quantum Efficiency (0  f i  1)  C = f T f F f N min{ f D, f  } f age f C   Cx CO 2 3-PG Model

8. Parameterization for Slash Pine Canopy Quantum Yield = 0.056 mol CO 2 / mol PAR 3-PG Model  C = f T f F f N min{ f D, f  } f age f C   Cx where D = current VPD k D = strength of VPD response Gonzalez-Benecke et al. 2014

9.  C = f T f F f N min{ f D, f  } f age f C   Cx 3-PG Model Parameterization for Slash Pine Teskey et al. 1994 Teskey et al. (in preparation)

10. Results Validation Sites 14 sites in US 7 sites in Uruguay 118 permanent plots 686 year x plot observations

11. Above Ground Biomass (Mg ha -1 ) Basal Area (m 2 ha -1 ) Volume (m 3 ha -1 ) Height (m) Trees per hectare Above Ground Biomass (Mg ha -1 ) Basal Area (m 2 ha -1 ) Volume (m 3 ha -1 ) Trees per hectare Height (m) Results Validation X=observed Y=predicted Gonzalez-Benecke et al. 2014 Variable Bias (%) R2R2 AGB (Mg/ha) -5.30.89 BA (m 2 /ha) -6.90.93 Height (m) 0.40.96 Nha (ha -1 ) 0.80.98 VOB (m 3 /ha) 4.10.97

12. Case Study: Climate Change Effect on Slash Pine Productivity Future Climate Data: CanESM2 model Downscaled using MACA method (Multivariate Adaptive Constructed Analogs) http://maca.northwestknowledge.net/ Scenarios (combination of climate and site quality) : Based on 2 RCPs (Representative Concentration Pathways) ScenarioClimate DataCO 2 - Historical1950 – 2010400 ppm - RCP 4.52070 – 2100550 ppm - RCP 8.52070 – 2100850 ppm Based on Site Quality (site index) ProductivitySite Index - Low19 m - Medium23 m - High28 m

13. 19.8 +2.1 +2.8 19.6 +2.0 +2.8 18.8 +2.8 +4.8 18.3 +2.1 +3.0 21.1 +2.0 +2.8 22.9 +1.8 +2.6 20.1 +2.0 +2.8 19.1 +2.9 +4.8 18.0 +2.1 +3.0 18.3 +2.1 +2.9 19.4 +2.0 +2.7 Historical Mean Annual Temperature (°C) and Mean Increment in Temperature due to Climate Change (RCP 4.5 and 8.5) Sites location 11 sites in SE US 4 sites in Northern Limit

14. Case Study Climate Change Scenarios Summary VariableRCP4.5RCP8.5 Tmax (C)+ 1.8 to +2.9+ 2.6 to +4.8 Tmin (C)+ 1.8 to +3.0+ 2.7 to +4.8 Rain (mm)- 49 to + 67- 66 to 45 Radiation (%)+2% to + 6%+1% to + 6%

15. L: 18 - 29 M: 8 - 12 H: 4 - 8 L: 18 - 28 M: 8 - 12 H: 4 - 10 L: 24 - 41 M: 17 - 34 H: 16 - 28 L: 26 - 39 M: 29 - 37 H: 14 - 20 L: 18 - 29 M: 8 - 12 H: 5 - 9 L: 10 - 20 M: 8 - 10 H: 3 - 6 L: 22 - 37 M: 12 - 19 H: 7 - 13 L: 18 - 29 M: 8 - 12 H: 5 - 8 L: 32 - 46 M: 32 - 42 H: 16 - 25 L: 25 - 41 M: 10 - 22 H: 6 - 23 L: 26 - 42 M: 16 - 22 H: 12 - 15 Climate Change Effect on Slash Pine Productivity Change in Above Ground Biomass (Mg/ha) at age=25 years RCP's v/s Historical Scenarios L: Low Productivity M: Medium Productivity H: High Productivity

16. Climate Change Effect on Slash Pine Productivity Change in Above Ground Biomass (Mg/ha) at age=25 years RCP's v/s Historical Scenarios RCP 4.5RCP 8.5 Site Quality Low MediumHigh Site Quality Low MediumHigh

17. Conclusions: For Sites with Mean Annual Temperature > 19 C: Under RCP4.5 : AGB can be increased between 2% to 27% (Mean=8%). Under RCP8.5 : AGB can be increased between 2% to 44% (Mean=13%). For Sites with Mean Annual Temperature < 19 C (North Limit): Under RCP4.5 : AGB can be increased between 2% to 44% (Mean=17%). Under RCP8.5 : AGB can be increased between 8% to 63% (Mean=27%). Climate Change Effect on Slash Pine Productivity Under Future Climate Scenarios Used:

18. Conclusions: Responses to Climate Change should be larger in colder range of distribution. Responses to Climate Change should be larger in low productivity sites. Climate Change Effect on Slash Pine Productivity Under Future Climate Scenarios Used:

19. Acknowledgements