1. Climate Impacts: Mountain pine beetle in Eastern Washington Elaine Oneil PhD. Rural Technology Initiative College of Forest Resources Climate Impacts Group Seminar January 24, 2008

2. The Study  Context

3. Tree Mortality Mountain Pine Beetle caused by = Host Type Pinus spp. USDA Forest Service PNW Region Forest Health Protection Washington Department of Natural Resources Forest Health Program Note: Shaded areas show locations where trees were killed. Intensity of damage is variable and not all trees in shaded areas are dead. Sources: Annual aerial insect and disease surveys flown by USDA Forest Service, Oregon Department of Forestry, and Washington Department of Natural Resources; 250m forest type map developed by USDA Forest Service - Remote Sensing Application Center.

4. Source: http://www.dnr.wa.gov/htdocs/rp/forhealth/ Mountain Pine Beetle Year: Acres 2005: 554,000 2006: 267,000 Acres not surveyed in 2006 (fires): ~ 200,000

5. Photo credit: Don Hanley

8. 1979-1999 Mortality Rate = 2.2 TPA 2000+ Mortality Rate 8.4 TPA

9. 2005 aerial survey results Mountain pine beetle ranks as the number 1 causal agent of tree mortality in Washington State accounting for an estimated 75% of the observed mortality in Eastern Washington 2005 and 56% of the mortality state wide.

10. Eastern WA 26.6 million acres total 9 million acres forestland Western WA 15.9 million acres total 11.9 million acres forestland

12. Over 1 million acres of Douglas-fir types potentially affected Approximately 80% of state and private acres have a pine component

13. The Study  Context  Climate Characteristics of interest

18. Western regional Climate Data www.wrcc.dri.edu

20. Maximum precipitation Minimum Precipitation Minimum Summer temperature Maximum Summer Temperature 1980’s outbreak in PP starts Current MPB outbreak in LP starts 2000 Western regional Climate Data www.wrcc.dri.edu

21. The Study  Context  Literature review and definitions  Conceptual model and research questions

22. MPB risk and susceptibility  Risk  Linked to the likelihood of MPB attack as a function of MPB population dynamics and proximity to host trees  Climate change enhancing insect survival and reproduction  Susceptibility  Linked to the likelihood of a tree, or stand, being attacked as a function of poor vigor.  Warmer and drier summers leading to increased moisture stress and reduced vigor within pine forests  Warmer and/or drier winters reducing snowpack and effective moisture retention into late spring/early summer

23. MPB susceptibility rating systems  Differ for MPB in lodgepole pine (LP) and ponderosa pine (PP)  Various combinations of stand density, vigor, basal area, age, diameter, crown competition, and/or growth rates are used to rate stand susceptibility  Stand susceptibility as measured by these metrics is widely variable across the geographic ranges of host species and differs by species. (Shore et al 1989, Amman & Anhold 1989)  Rating systems need to account for beetle population dynamics and climate (Shore et al 1989, 2001)

24. Braun and Gara, 1990 15.5 º C

25. Braun and Gara, 1990 When the flying population ‘switches’ from attacking stressed focus trees to attacking adjacent healthy trees, the switching mechanism has occurred and an epidemic outbreak has begun

26. Fire Risk Fuels Topography Weather

27. Fire Risk Mountain Pine Beetle Susceptibility Fuels Stand parameters Topography Stand carrying capacity Weather Weather/Climate

28. Research Questions  What role does the relationship between stand and site variables play in host susceptibility to MPB attack?  What role do climate and weather play in host susceptibility to MPB attack?

29. The Study  Context  Literature review and definitions  Conceptual model and research questions  Methods

30. Continuous Vegetation Survey Plots

31. DAYMET data 18 year monthly average temperature, precipitation at 1 km resolution Daily weather data for 1980 to 2003 on a square km grid Courtesy of the Numerical Terradynamic Simulation Group University of Montana at http://www.daymet.org/default.jsp

32. Where is MPB attack located? # Unique plots – some attacked more than once

33. Okanogan National Forest Colville National Forest

34. MPB attack on the Okanogan NF

35. MPB attack on the Colville NF

36. Integrating the Data  GIS analysis  Exploratory Data Analysis  Calculating carrying capacity  Calculating stand variables  Accounting for prior mortality

37. How much MPB attack has there been? Count by year – 1981-2003 Count over time

38. Where is MPB attack located? Elevation range

39. Carrying capacity metrics  Site Index  A species specific measure of actual or potential forest productivity and site quality  Tells us something about stand growth independent of stand density.

41. Carrying capacity metrics  Site Index  A species specific measure of actual or potential forest productivity and site quality  Tells us something about stand growth independent of stand density.  Growth Basal Area  A measure of stocking that relates the site carrying capacity to a stand of 100 years of age that maintains a diameter increment of 1 inch/decade (20 rings/inch)  Poor sites have lower inherent carrying capacity and therefore a lower GBA (Cochran et al 1994)  GBA has been correlated to bark beetle susceptibility (Sartwell 1971, Sartwell & Stevens 1975)  Multiple GBA values in a single site index or site class  GBA varies by species for a given site

42. VIVIIIII I

43. Analyses  Binary response variable MPB [0,1]  Generalized linear model with a link function  Binomial if it is [0,1]  Poisson if it is [0, number of attacks/plot]  Zero-inflation  Zero-inflated negative binomial  Zero-inflated Poisson

44. The Study  Context  Literature review and definitions  Conceptual model and research questions  Methods  Results  Implications

45. Assessing stocking rates using GBA

46. Carrying Capacity and MPB attack

47. Does Carrying Capacity Matter?

48. For trees over 10” this stand has: SDI =83; DBHq =13.7 ; 72 =TPA; BA =60

49. Significant Predictors 1980-1999 Yearly tests (10% PDE) –Temperature (S/W), precipitation, VPD, DBH, BA Cumulative tests (36% PDE) –Precipitation, first warm day, temperature (S/W) 2000-2003 Yearly tests (17% PDE) –VPD, Temperature (W), site variables Cumulative tests (42% PDE) –VPD, length of drying period

50. 1981-1999

51. 2000-2003

52. The Study  Context  Literature review and definitions  Conceptual model and research questions  Methods  Results  Implications

53. Implications – Carrying Capacity  Incorporating stand carrying capacity  Can improve our MPB susceptibility assessment procedures  Can be extended to other species with perhaps greater impact

54. Implications - Climate Stand variables are not significant predictors of MPB attack Winter temperature  Not about insect survival  Water balance Summer Temperature  VPD is key after 2000

55. Adapted from Waring and Running (1998) Humidity 100% 60% 30% Vapor Pressure Deficit (VPD)

56. 2003 1980

57. DeLucia, E. H., H. Maherali, et al. (2000). "Climate-driven changes in biomass allocation in pines." Global Change Biology 6(5): 587-593.

58. A potential Stress Index? Summer VPD change as a precursor to MPB out breaks?

59. How to predict the future climate impacts from MPB outbreaks Consider tree physiology Use measures of relative change Incorporate site specific parameters Account for current stand structure Climate change will fundamentally alter how we manage for bark beetles  What is the carrying capacity of a site under climate change?  Can the mature trees adapt?  Can they adapt quickly enough?

60. Research Needs  Determine a stress index for existing forest species in their current niches  Estimate future stress based on climate scenarios  Determine how tree species respond physiologically to climate shifts  Determine how stand carrying capacity changes in response to climate shifts Determine how habitat types will move and change in their constituency with climate change Determine how to model that change to increase forest ecosystem resilience  Refine our estimation of disturbance rates