1. Stephen Gray, USGS Tucson With: Julio Betancourt, Lisa Graumlich, Steve Jackson, Mark Lyford, Jodi Norris, and Greg Pederson Nonlinear Interactions Between Climate, Landscape Structure, and Plant Migration Nonlinear Interactions Between Climate, Landscape Structure, and Plant Migration

2. Global Change Impacts?

6. TNC Invasives Project

8. Plant Migration and Invasion Expect significant shifts in the distribution of plant species Will contribute to major vegetation/ ecosystem change across the West Driven by: –Changing climate –Land use –Exotic introductions –Human vectors, etc.

9. Forecasting Environmental Change Sustainable land management requires realistic predictions for future vegetation change –Provide viable scenarios for planning and policy –Tool for policy makers and stakeholders to explore potential ecological outcomes and the costs/consequences of management and mitigation efforts

10. Nonlinear Behavior and Environmental Forecasting Nonlinearity is a major obstacle to environmental forecasting Examples of nonlinear behavior- –Threshold responses –Feedbacks –Cascading responses –Cross-scale interactions

11. The Classic Example: Nonlinear Behavior in the Spread of Large Fires Peters et al (2004) PNAS Ignition- single tree Spread within patch Spread among patches Large Area: feedbacks and nonlinear interactions Predictability?

12. Nonlinearity in Western Ecosystems Focus on inherent complexity in biological processes or cross-scale interactions

13. Nonlinearity in Western Ecosystems Focus on inherent complexity in biological processes or cross-scale interactions But, non-stationary (i.e. regime-like) behavior in the climate system may also produce nonlinear dynamics in natural systems

14. Nonlinearity in Western Ecosystems Focus on inherent nonlinearity in biological processes or cross-scale interactions But, non-stationary (i.e. regime-like) behavior in the climate system may also produce nonlinear dynamics in natural systems Examples: Woody plant migration and invasion in western North America

15. Traditional View: Climate as stochastic variations around STATIONARY mean 010050 The Ecologist’s Concept of Climate 7525

16. North American Tree-ring Network Spring 2005 NOAA-NCDC

17. High variance explained (r 2 = 0.58) Well replicated (n = 133) Long segments (Avg. Length = 385 yr) Conservative detrending Test Case: Greater Yellowstone Precipitation Gray, Graumlich and Betancourt (in review) Quat. Res.

18. Test Case: Greater Yellowstone Precipitation

19. 21-yr Spline 60-yr Spline Test Case: Greater Yellowstone Precipitation

20. Rocky Mountain Climate-Reconstruction Network Gray et al. GRL (2003)

21. Gridded PDSI reconstructions from Cook et al. 2004, Science D2M variability and associated wet/dry regimes can become synchronized across large portions of the West

22. Non-stationary (regime-like) behavior Standard deviations Example: Upper Colorado Basin Annual Precipitation Year AD -The mean, SD, probability of extreme single year events, etc. changes over D2M timescales Hidalgo 2004; Gray et al. 2003, 2004

23. Is this D2M Variability Real? Not an artifact of tree-ring methodology Signals are coherent at regional to sub- continental scales Feature of winter and growing season temp/precip Recent modeling studies reproduce D2M variability –Schubert et al. (2004) Science –Seager et al. (2005) J. Climate –Sutton and Hodson (2005) Science But, will D2M variability continue in the future?

24. D2M Variability and Internal Ocean Processes

25. Ocean ‘thermostat’ mechanism (Clement et al. 1996) Uniform heating Largertemperature response in the West Cooling by upwelling opposes forcing in the East, reducing temperature response Coupled interactions (i.e. the Bjerknes feedback) amplify the East/west temperature difference Warm, mixed Surface layer Deep, cold ocean waters ~20ºC 0 m 100 50 150

26. The Big Question… How does D2M variability and associated climatic regimes impact plant invasion and migration processes?

27. Tree rings: Climate/Demography

30. Climatic Regimes Pace Migration/Invasion Events Dutch John Mtn., Utah -Northernmost P. edulis -Study encompasses 25 km 2 watershed -Reconstructed pinyon dynamics from woodrat middens and dated wood Jackson et al. (2005), J. Biogeography 32:1085-1106. Gray et al. (in press), Ecology

31. Migration Dynamics at the Landscape/Watershed Scale % Area Occupied no sites all sites Step-like change in the distribution and abundance of pinyon pine at the watershed/landscape scale no pinyon dominates

32. Medieval Dry Period Little or no successful establishment Migration Dynamics at the Landscape/Watershed Scale Modified drought index % Area Occupied no sites all sites

33. Migration Dynamics at the Landscape/Watershed Scale Small Population “Great Drought” Modified drought index % Area Occupied no sites all sites

34. Migration Dynamics at the Landscape/Watershed Scale “Great Wet” Step-like change in pinyon abundance & distribution Modified drought index % Area Occupied no sites all sites

35. Switching between dry/wet regimes drives nonlinear invasion dynamics “D2M” Wet Regime Step-likeChange Rapid Recruitment Low Mortality

36. Switching between dry/wet regimes drives non-linear invasion dynamics “D2M” Wet Regime Broadscale Mortality Abundance of Open Niches Step-likeChange Rapid Recruitment Low Mortality “D2M” Dry Regime

37. Modern (shaded) Glacial (>13 kyr BP) Rocky Mts presentabsent Distribution of Utah Juniper: Holocene Migration Dynamics: Utah Juniper - Reconstructed from 205 woodrat middens at 14 sites -Lyford et al. (2003) Ecol. Monog. 73:567-583

38. cal yr B.P. 0123456 Sites Occupied 0 2 4 6 8 10 12 Lyford et al. (2003) Ecol. Monog. 73:567-583 CLIMATIC REGIMES AND UTAH JUNIPER MIGRATION 10,000 yr BP - Reconstructed from 205 woodrat middens at 14 sites -Climate inferred from lake sediments and dune records MT WY Current Dist.

39. cal yr B.P. 0123456 Sites Occupied 0 2 4 6 8 10 12 Migration Stalls During Cold Periods Lyford et al. (2003) Ecol. Monog. 73:567-583 CLIMATIC REGIMES AND UTAH JUNIPER MIGRATION 10,000 yr BP - Reconstructed from 205 woodrat middens at 14 sites -Climate inferred from lake sediments and dune records MT WY Current Dist.

40. Lyford et al. (2003) Ecol. Monog. 73:567-583 CLIMATIC REGIMES AND UTAH JUNIPER MIGRATION MT WY Oldest Youngest 10 kyr BP

41. Lyford et al. (2003) Ecol. Monog. 73:567-583 CLIMATIC REGIMES AND UTAH JUNIPER MIGRATION 10 kyr BP MT WY 5.7 kyr BP 6.4 kyr BP MT WY Youngest Oldest 10 kyr BP

42. Modern ClimateCold Scenario UTAH JUNIPER DISTRIBUTION IN RELATION TO CLIMATE AND SUBSTRATE (Lyford et al. 2003) WY ~ 60 km > 350 km Less suitable habitat in northern areas Requires long-distance dispersal Abundant habitat in northern areas Short distances between suit. hab. Lyford et al. (2003) Ecol. Monog. 73:567-583

43. Higher probability of survival Lower probability of survival INTERACTION BETWEEN CLIMATIC REGIMES AND LANDSCAPE STRUCTURE Favorable Climatic RegimeLess-favorable Regime + +

44. Reduced connectivityHigh connectivity INTERACTION BETWEEN CLIMATIC REGIMES AND LANDSCAPE STRUCTURE Favorable Climatic RegimeLess-favorable Regime

45. Climatic Regimes Nonlinear Dynamics Regime-like behavior in the climate system promotes step-like changes that may persist for decades to millennia Interactions between climate and other factors may introduce marked spatial and temporal complexity to ecological processes 10 kyr BP MT WY 5.7 kyr BP 6.4 kyr BP

46. How/why does climate drive nonlinear change?

47. Climate affects large areas simultaneously

48. CLIMATIC REGIMES MAY BECOME SYNCHRONIZED OVER WIDE AREAS After Fye et al. 2003

49. What Governs the Impact of Climatic Regimes? Magnitude/ Rate of Shift? PastPresent Magnitude/ Duration of regimes?

50. Does the Frequency of Regime Shifts Alter the Ecological Impact of Climate? Woodhouse, Gray and Meko (in review) = sig. (p < 0.05) decadal to multidecadal power

51. Decadal to Multidecadal Variability Lees Ferry 25 and 50 yr splines

52. How/why does climate drive nonlinear change? Climate affects large areas simultaneously Impacts depend on: Total area affected by regime Magnitude and duration of regimes Speed/amplitude of switching

53. How/why does climate drive nonlinear change? Climate affects large areas simultaneously Impacts depend on: Total area affected by regime Magnitude and duration of regimes Speed/amplitude of switching Possibility that the stressor and not the biological response behaves in a nonlinear manner?

54. Are current prediction methods adequate? Statistical biogeographic models cannot account for the impacts of D2M variability, land use/land cover, migration processes, etc. Thompson et al. 2003

55. Climate/Vegetation Change

57. What’s Next? Dynamic Vegetation Models are a good start (Neilson et al. 2005, Bioscience) DVMs model changes in vegetation based on knowledge of plant population and migration processes But, current DVMs capture spatial heterogeneity in the environment better than temporal variability

58. Thanks! Funding: U.S. Geological Survey-National Research Council Associates Program USGS Mapping Division National Science Foundation

59. Thanks! Funding: –U.S. Geological Survey-National Research Council Associates Program –USGS Mapping Division –National Science Foundation