New Analysis Projects in the Interior West FIA Program

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. Basic Reporting • 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

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

4. 529 830 450 Stand Density Index • Wrapping up work on SDImax 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

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. Dividing Stands (plots) by Composition and Structure Mixed, even-aged Pure, even-aged Pure, irregular Mixed, irregular Ponderosa pine Other spp 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 (m3/ha/yr) = 0.0302 * SDIsum0.7050 * HT-0.4783 * SI1.5191 (r2 = 0.86) • Use reference equation to predict PAI of other classes • Analyze residuals for differences

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

9. Take-home Points • 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

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

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

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

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

15. Take-home Points • 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

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

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

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

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 13.6 19.6 12.3 19.8 11.7

26. Ecoregional Comparisons:Tree Age Infection rate: 31.1 13.6 19.6 12.3 19.8 11.7

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. What we have so far… (all species)

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

38. Tree Rings and Climate • 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

39. 1451 Pinyon – Raw Ring Width Data CO2 ppm Ring width Smoothed ring width CO2 ppm

40. 1451 Pinyon – Growth for Diameter CO2 ppm Normalized ring width Expected diameter growth

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