1. Climatic and biophysical controls on conifer species distributions in mountains of Washington State, USA D. McKenzie, D. W. Peterson, D.L. Peterson USDA Forest Service, Pacific Northwest Research Station P. E. Thornton National Center for Atmospheric Research

2. What are the impacts of spatial and temporal climatic variability on critical plant resources and species distribution? –What is the range of variability in biophysical environments of mountain landscapes across a marine-to-continental climatic gradient? –How would climate-driven changes in the biophysical environment affect species distribution?

3. Why model species distributions? Plant community composition affects ecosystem properties and processes. Forest management practices are often based on forest cover types. Species have responded individually to past climatic changes.

4. Objectives Define empirical environmental niches for major tree species in the Pacific Northwest. Model changes in spatial distribution of environmental niches under future climate. Identify core habitat areas, stress zones, and potential for species migrations.

5. Forested Bioregions of the PNW Pacific silver fir, subalpine fir and mountain hemlock Western hemlock, Douglas-fir, and Western red cedar Sitka spruce Douglas-fir, grand fir Ponderosa pine, Douglas-fir Franklin and Dyrness 1973

8. Br. A. Brousseau – St. Mary’s College C. Webber – California Academy of Sciences

9. N. Snell – California Academy of Sciences

10. Methods (1) Tree species data from Area Ecology Program. 1-km climate coverages from DAYMET. Biophysical variables computed from VIC and MT-CLIM.

12. Relative abundance of 6 key species along a geographic gradient

13. (DAYMET raw data)

14. Interpolation: assign weights to stations

15. Annual Total Precipitation (1991)

17. Methods (2) Generalized linear models (GLMs) to predict probability of occurrence. From proxy sets (correlated predictor variables), select no more than one. Quadratic terms identify unimodal responses. Models at multiple scales for each species. Bootstrapped receiver operating characteristic (ROC) curves estimate accuracy and robustness.

18. Summer = June, July, August, September. Winter = December, January, February. Variables used to predict species distributions a Soil water indices were computed at three depths in the soil layer: 0-10 cm, 10-40 cm, and 40-100 cm.

19. PDE ~ steeper environmental gradients

20. Grizzly bear – ponderosa pine All 4 forests – Engelmann spruce Area under ROC curve = 0.941Area under ROC curve = 0.723 Area under ROC curve represents the ability of the model to discriminate between presence and absence at all cut levels

22. Probability of occurrence Ponderosa pine – Wenatchee NF Douglas-fir – Wenatchee/Grizzly Bear combined

23. Response of Douglas-fir is consistent across scales

24. Mountain hemlock

25. Douglas-fir – Grizzly Bear habitat study plots

26. Douglas-fir – Grizzly Bear/Wenatchee NF combined

27. Changes in winter precipitation may alter species dominance

28. Applications to modeling the effects of climatic change? Is there a disconnect between scales? –17 or 50 years of current climate predicts distributions in 75-300 yr-old forests. Are equilibrium models useful? –Climate annual means and species presence/absence are snapshots. –What about “process-based” modeling?

29. Other dimensions of the problem Competitive effects – species composition Distribution (presence/absence) vs. abundance Mature niche vs. regeneration niche

30. Annual Variability in growth Mean Productivity Subalpine fir Pacific silver fir/western hemlock Pacific silver fir/mountain hemlock Douglas-fir mixed conifer Pacific silver fir Douglas-fir/grand fir Western red cedar What forest types are most sensitive to climatic variability? Hessl et al.

31. Ponderosa pineDouglas-fir Subalpine fir Mountain hemlock Constraint lines representing limiting factors (Grizzly bear data)

32. Climate variable 1 Climate variable 2 Species 1 Species 2 Species 3 Mature niche Regeneration niche Species that currently coexist may not have equal capacity to regenerate under changing climate

33. Conclusions Focus on climate and biophysical variables allows predictions under changing climate. Models are consistent across scales – robust estimation of environmental niches. More complete picture will emerge from complementary studies of abundance, composition, and regeneration.

34. Thanks! Amy Hessl Dan Fagre Bud Kovalchik Robert Norheim PeopleInstitutions