1. New Analysis Projects in the Interior West FIA Program John D. Shaw Interior West Forest Inventory and Analysis USDA Forest Service Interior West FIA User Group Webcast April 13, 2010

2. State Reports UT, CO, and AZ making their way through the end of the pipeline ID and MT next – will hold special topics workshop next week NM will be done using 70% of plots, starting at end of 2012 10-year reports will likely have a trend analysis emphasis Forest Reports Clearwater, ID Panhandle, and Nez Perce Humboldt-Toiyabe and Bridger-Teton Basic Reporting

3. Stand Density Index Consistency of definitions and analysis From: Reineke, L.H. 1933. J. Agr. Res. 46(7):627-638.

4. Stand Density Index Wrapping up work on SDI max and related issues Paper given at National Silviculture Workshop Work has progressed in consultation with silviculturists and Forest Management Service Center FIA-based SDIs to be included in FVS Series of papers to be submitted this year 529 830 450

5. Growth and Growing Stock Analysis: What is the influence of compositional and structural diversity on productivity? Ponderosa pine example 1474 plots with PP component Jim Long Department of Wildland Resources

6. Ponderosa pineOther spp Pure, even-aged Mixed, irregular Pure, irregular Mixed, even-aged Dividing Stands (plots) by Composition and Structure From Long and Shaw. 2010. Forestry (Oxford). In press.

7. Analysis Approach FIA data include periodic increment (remeasurement, cores) Divide plots into categories based on composition and structure Model current annual increment as a function of stocking (SDI), stand height, and site quality (site index) Fit model to pure, even-aged stand data (reference condition) PAI (m 3 /ha/yr) = 0.0302 * SDI sum 0.7050 * HT -0.4783 * SI 1.5191 (r 2 = 0.86) Use reference equation to predict PAI of other classes Analyze residuals for differences

8. Pure, even-aged Mixed, irregular Pure, irregular Mixed, even-aged Reference Model Residuals vs Reference Model From Long and Shaw. 2010. Forestry (Oxford). In press.

9. No significant differences among groups There are lots of reasons to manage for compositional and structural diversity, but productivity doesn’t appear to be one of them Greater variability in irregular, mixed stands may be related to diversity of associates Easy to repeat this test for other target species Take-home Points

10. From: Thompson, M.T. et al. 5-year Interim Report for Colorado (in press) FIA Annual Inventory Data Capture Temporal Trends Average annual mortality rates for aspen and coniferous species in Colorado by measurement year, 2002-2006.

11. Analysis Approach Lots of need to address emerging issues – e.g., MPB See presentations by Mike Thompson (next) and Ray Czaplewski (3:15) Many studies on insect effects analyze the aftermath FIA plots in place during onset, peak, and decline of large- scale events Anatomy of an MPB epidemic Develop a series of postulates based on literature Test each postulate against FIA time series Jim Long Department of Wildland Resources

12. Relative density Mortality (%) early late Ln TPHA Ln QMD early late Rank of % mortality - + Stand-Level Mortality 100% early late Relative DBH - + Cumulative Mortality 100% early late Susceptibility vs density (Anhold et al. 1996) Progression in size-density space Progression by size class Progression of severity

13. Relative density Mortality (%) early late Possible asymmetry over time… Simplified from Anhold et al. (1996) WJAF

14. Preliminary results for stand-level mortality over time Mortality of lodgepole pine component Percentile rank of stand-level mortality Rank of % mortality - + Stand-Level Mortality early late

15. Early results look promising Results appear to uphold some models e.g., Anhold et al. Some surprises Mixtures don’t appear to have special “immunity” Appears to be moving into low-density stands faster than into high-density stands (implications for thinning) Statistical methods are somewhat trailing Czaplewski and Thompson are refining analysis of pseudo-panel data Not looking at the spatial component -- yet Take-home Points

16. USING FOREST INVENTORY AND ANALYSIS DATA TO QUANTIFY WILDLIFE HABITAT IN FORESTED LANDSCAPES: AN OVERVIEW OF POTENTIAL APPLICATIONS Chris Witt USFS Inventory, Monitoring and Analysis, Rocky Mountain Research Station, Ogden, Utah

17. Mexican Spotted Owl Recovery Plan Listed as “Threatened” by USFWS in 1993 Recovery Plan issued in 1995 Goals of the Plan include: – no loss of existing habitat – review of plan effectiveness after ten years

18. Error bars represent 95% C.I. Pine-Oak Forest Type Gila Mountains Recovery Unit: Arizona

19. Lewis’ Woodpecker Breeding and Nesting Photo: Tom Grey Used annual inventory data (2000 – 2007) to quantify forests providing important breeding habitat for M. lewis.  Crown cover  Woody understory  Snag densities

20. Results: Forest Type, Crown Cover, Woody Cover, Snags Error bars represent 95% confidence intervals

21. Additional Research: Utah State University/UDWR Established “phantom” plots at known M. lewis nest sites in aspen (n = 16 in 2009) Compare data to existing P2 plot data Visit FIA plots to conduct bird counts (with emphasis on M. lewis ) Produce a perpetual monitoring tool for M. lewis and other forest vertebrates on Utah’s Sensitive Species List

22. Aspen, Heartrot, and Cavity-Nesting Birds Aspen trees infected with Phellinus tremulae facilitates cavity excavation. Tree and stand characteristics that promote infection in the western U.S. are largely unknown Regional differences in tree and stand characteristics could play a role in bird assemblages.

23. Infected vs. Non-infected stands Not infected: n = 3023, mean = 17.89, SD = 8.31 Infected: n = 3023, mean = 21.21, SD = 9.29 F (1, 6044) = 213.66, p < 0.0001 Not infected: n = 392, mean = 31.64, SD = 33.35 Infected: n = 392, mean = 48.99, SD = 33.92 F (1, 782) = 52.11, p < 0.0001

24. Ecoregional Analysis Compared purity, age, diameter and infection rate between six ecoregions. These areas contain > 95% of all aspen in the Intermountain region

25. Ecoregional Comparisons: Stand Purity Infection rate: 31.1 13.619.612.319.811.7

26. Infection rate: 31.1 13.619.612.319.811.7 Ecoregional Comparisons: Tree Age

27. Summary FIA data are being used to: – Help managers with habitat treatment expectations (large sample size, systematic sampling of resource) – Track changes (or lack thereof) in habitat quality after management plan implementation (remeasurement of plots over time) – Quantify existing habitat for target species or guilds (plot and condition-level variables that reflect species needs) – Identify limiting habitat features on a landscape-scale (strategic-level population estimates of habitat features)

28. Forest Genetics Sampling IW-FIA cooperating with RMRS and other geneticists to explore grid-based genetics sampling All tree species + select pathogens Expect ~15K samples Focal species for first round of analysis: Aspen – Bryce Richardson (RMRS-Provo), Karen Mock (USU) Douglas-fir (Sam Cushman (RMRS-Moscow), FS genetics lab Armillaria – Ned Klopfenstein (RMRS-Moscow), Mee-Sook Kim (Kookmin University, South Korea), Marylou Fairweather (R3 FHP) Several objectives: Collection feasibility Database proof-of-concept (i.e., genetic traits stored like DIA and HT) Biogeographic analyses Future opportunities (winners vs losers after future events) E-Genetics: grab a sample and see what is (and was) there

29. Tree Ring Archiving and Analysis Project Complete cores were collected by IW-FIA during 1980s and 1990s periodic inventories Primarily used for aging and recent increment Had done limited full-core reading and no analysis Initial estimate of ~8000 cores from most IW states ~90% considered salvageable Established contract with Utah State University dendroecology lab for reading and archiving (2-year project) JVA for initial analysis in two areas (overlapping 2-year project) Basic growth and yield modeling e.g., FVS large-tree diameter growth model Climate studies About 8 months in on reading / archiving R. Justin DeRose Department of Wildland Resources

30. About 50 of these in FIA storage…

31. Sorting the good, the bad, and the ugly…

33. Getting organized by county within state

35. What we have so far…

36. (all species)

37. What we have so far… 40 pinyons >300 years old

38. Tree ring chronologies are typically on the most climate-sensitive sites ITRDB sites are relatively sparse, but have been used to develop climate surfaces for space in between Chronologies on FIA grid can test relationships across landscapes and elevation gradients Tree Rings and Climate

39. CO 2 ppm 1451 Pinyon – Raw Ring Width Data CO 2 ppm Smoothed ring width Ring width

40. CO 2 ppm 1451 Pinyon – Growth for Diameter Expected diameter growth Normalized ring width

41. Questions? John D. Shaw USDA Forest Service Rocky Mountain Research Station Ogden Forestry Sciences Lab 507 25 th Street Ogden, UT 84401 Phone: (801) 598-5902 Email: jdshaw@fs.fed.us Web: www.fs.fed.us/rm/ogden