Two general topics

1. Using FIA Data for Understanding Plant-Climate RelationshipsNicholas L. CrookstonGerald E. RehfeldtMarcus V. WarwellRocky Mountain Research StationMoscow, ID

2. Two general topics • Define contemporary climate profiles for western United States tree species, predict and map where those climates will be in the future. • Modify the Forest Vegetation Simulator (FVS) to account for climate change.

3. The climate model (Rehfeldt, 2005) uses Hutchinson’s (Australian National University) splines to interpolate monthly values of total precipitation and average, max, and min temperature. 3,000 weather stations used in the fit. Climate model and variables

4. Weather station locations

5. Period is 1961-1990. Predictions are a function of latitude, longitude, elevation, and spline coefficients. Derived 19 variables broadly related to physiological processes of plant growth (map, mat, mtcm, gsp, dd5). With interactions, 35 variables are defined. Climate model (continued…)

6. Definitions: A model that predicts species presence/absence as a function of contemporary climate. The realized climatic niche for contemporary climates. Based on climate metrics and presence/absence data compiled from FIA plots that cover our study area FIA-Portland, Oregon (PNW) FIA-Ogden, Utah (RMRS) Species climate profile

7. Modeled using Breiman’s Random Forests (2001) regression tree program. A forest of CART-like regression trees are built, each based on a separate boot-strap sample. Species climate profiles (continued…)

8. At each node, the predictor that minimizes classification error is used to define the split. The predictor is the one from a randomly selected set; a new set is picked for each node. Splits are made until no improvement can be achieved. Random Forests (continued…)

9. Random Forests (continued…) • A prediction is made by running a case down each tree; each tree casts a vote. • The class that receives the plurality of votes is the predicted class. • We used the version in R.

10. Classification errors: 9.4% for Douglas-fir 9.9% for Engelmann spruce Our opinion: Results are superb. Predictedprofiles

11. DF

12. ES

13. Used predictions from the average of two global circulation models. Hadley Center (HadCM3GGa1) Canadian Center (CGCM2_ghga) Scenario: 1% per year increase in greenhouse gasses. Predicted where each species’ climate profile will be, not where the species will be. Future location of climate profiles

14. DF

15. ES

16. DF climate area: +8%, 61% stays DF profile. ES climate area: -62%, 22% stays ES profile. Places that support a given species throughout the century will support different genotypes of the species the maps understate the magnitude of the potential impact. Conclusions and Discussion

17. Rehfeldt et al. (2002, 2003, In prep, and in the poster session) reports on the implications of climate change on plants and Brown’s biotic communities. Conclusions and Discussion

18. Forest management plans, management assessments, and project plans, are often based on FVS projections. The plans are based on 100+ year projections, within the time span of significant climate change and the expected life of many existing trees. Modifying FVS…Justification

19. Growth predictions must be consistent with thousands of FIA plots plus other observations, and genetic reality: for many species, individual trees have less adaptive potential than evident within a species overall. Geneticists might say this differently: For genetic specialists, genotypic plasticity is narrower than species niche breadth. Major considerations

20. Growth observations reflect the range of adaptation throughout a species (true of one-time observations). Long-term repeated measurements of climate and growth measures the plastic responses within the range of conditions experienced by the trees. Forecasted climate change is larger than recent experience. Major considerations (continued…)

21. Replace site variables with climate variables. Calibrate a model that represents genotypic response to climate change. Combine these two models. Proposed modification of diameter growth model, 3 steps:

22. Modeled delta diameter squared: dds, proportional to BAI dds is a multiplicative function of tree size measured by dbh; competition measured by basal area in larger trees; and site measured by slope, aspect, elevation, location and habitat type. Modification of Wykoff (1990):

23. Replaced habitat type and location with mat and map. We left in other predictors even though they may be surrogates for climate. The resulting model is preliminary. Douglas-fir is presented. Proposed modification of dds

24. 200 150 Diameter increment (mm/10 yr) 100 50 0 0 5 10 15 Mean Annual Temperature (C)

25. Precipitation (mm) 3000 2600 80 2000 1600 1200 800 600 60 400 200 Diameter increment (mm/10 yr) + 7 mm + 4 40 20 0 5 10 15 Mean Annual Temperature (C)

26. Based on results of common garden experiments Genotypes are planted across environmental gradients. Observations directly measure plasticity. Available information is limited to few species and not the full range of expected climates. Extend results based on related studies and supporting ecological and genetics theory. Step 2: Model genotypic response

27. D E C B F A Size or Survival Mean Annual Temperature (C)

28. The location of the genotypic response curves is made consistent with the population wide curves fit using the FIA data. If climate does not change, predictions are consistent with contemporary growth observations. If the climate changes, predictions show the departure from contemporary observations. Step 3: Combine the models

29. 10 5 Diff = 14.5 0 + 4 70 60 50 40 Diameter growth (mm/10 yr) 30 20 10 0 -2 0 2 4 6 8 10 12 Mean Annual Temperature (C)

30. To represent climate change in FVS, the genotype of sample trees will be necessary input. The genotype must be defined in terms of the climate variables used in the biometric growth model. Height growth, mortality, and other components will also need to be modeled. Implications for FVS:

31. Wensel, Turnblom, and Yeh modeled short-term deviations caused by changes in the weather. We need to carefully consider this work. Milner et al. adapted Forest-BGC. These mechanistic approaches also assume that the trees present on the site are properly adapted, yet they may be needed to represent factors like changes in CO2 concentration. Consider other approaches

32. Random Forests worked very well; our climate profile mapping seems very successful. Growth prediction techniques must account for the genetic reality that individual trees have less adaptive potential than observed over the population (from FIA and similar data). We are working towards making FVS sensitive to climate. Some take-home messages:

33. Rehfeldt, G.E.; Ying, C.C.; Spittlehouse, D.L.; Hamilton, D.A. 1999. Genetic responses to climate in Pinus contorta: niche breadth, climate change, and reforestation. Ecological Monographs. 69(3):375-407. Rehfeldt, G.E.; Tchebakova, N.M.; Parfenova, Y.I.; Wykoff, W.R.; Kuzmina, N.A.; Milyutin, L.I. 2002. Intraspecific responses to climate in Pinus sylvestris. Global Change Biology 8:912-929. Some take-home references:

34. FIA-Ogden Sharon Woudenberg Ron Tymcio FIA-Portland Jeremy Fried Dale Weyermann Sally Campbell Andy Gray Acknowledgements • RMRS-Moscow, ID • Dennis Ferguson • Andy Hudak • Jeff Evans • National Forest System- Fort Collins, CO • Gary Dixon • Richard Teck