1. Analysis of Taper Responses to Sulfur Treatments in Coastal Oregon Doug-fir Western Mensurationists’ 2006 Annual Meeting June 19, 2006 Nicole Younger MS student, Department of Forest Resources, Oregon State University And Hud

2. What is Swiss Needle Cast Disease? Blame the Swiss! Tree rust caused by a fungus Clogs stomata with pseudothecia Pseudothecia count increases with age of needle Needle eventually dies Needle retention 3-4 years in healthy trees, two or less in infected trees 3 rd year 2 nd year Current year

3. Volume loss estimated at 23% with a high of 50% in the severely infected stands. Spread over the target population of 187,000 acres, this means that approximately 40MMBF were lost to this disease in 1996 alone! (Maguire et al. 1998) What is Swiss Needle Cast Disease?

4. What do we do now?!?

5. Essential ingredient for plant nutrition --component of amino acids, proteins, fats, and other plant compounds In the soil, sulfur (SO4) also plays a pivotal role in the movement of acidic cations such as H+, and Al3+, as well as nutrient cations such as Ca2+ and Mg2+ (Johnson and Mitchell 1998) Critical C/N ratio in the OR coast range Factory emmisions are being tightened resulting in less atmosheric Sulfur Recent discoveries of plants actually producing sulfur as a natural fungal defense (Williams and Cooper 2003) “Considered essentially non-toxic by ingestion” (MSDS)

7. Three treatments: 1.Sulfur 2.Sulfur and nutrients 3.Control 10 plots/treatment 4 Trees/plot (40 trees per treatment, 120 total)

8. Experimental Site Nilsen Creek, Lincoln County, Oregon Aerial applications took place 2000-2004 Ca prils

9. Planted in 1983 with all the same stock, 430 TPA Total height (H) ranged from 16.92 – 26.20 meters, with the mean at 21.53 m (std dev 1.61 m) DBH outside bark (D) ranged from 104.50 – 336.00 mm with a mean of 208.87 mm (std dev 42.25 mm) Early vegetation control, hack and squirt of hardwoods pre- canopy closure Slope/elevation/aspect all similar between treatment sites

10. Felled in April 2005 Trees measured and disks collected July – August 2005 Stump disk DBH disk Disk 1 Crown base disk Disk 2

11. Approximately 9 disks per tree were taken (1063 disks total) Diameter (inside and outside bark), height of disk as well as sapwood area of CB disk recorded 6” DBH 1 2 CB 3 4 5 Each tree measured for: Total height, crown ratio, lowest live branch location, crown width Needle characteristics (LA, width, length)

13. control – sulfur comparison p-value = 0.51 control – sulfur and nutrient comparison p-value = 0.85 Control Sulfur and Nutrient Sulfur

14. control – sulfur comparison p-value = 0.94 control – sulfur and nutrient comparison p-value = 0.67 Control Sulfur and Nutrient Sulfur

15. control – sulfur comparison p-value = 0.14 control – sulfur and nutrient comparison p-value = 0.16 Control Sulfur and Nutrient Sulfur

16. control – sulfur comparison p-value = 0.998 control – sulfur and nutrient comparison p-value = 0.073 Control Sulfur Sulfur and Nutrient

17. control – sulfur comparison p-value = 0.276 control – sulfur and nutrient comparison p-value = 0.028 Control Sulfur Sulfur and Nutrient

18. control – sulfur comparison p-value = 0.0039 control – sulfur and nutrient comparison p-value = <0.0001 Treatment LS mean (mm) 95% Conf. Int. Control3.263.12 – 3.39 Sulfur/Nutrient3.703.57 – 3.84 Sulfur3.543.40 – 3.67 “pre-treat” increment = (1996+1997+1998+1999)/4 “post-treat” increment = (2001+2002+2003+2004)/4

20. Ignoring autocorrelations in taper data sets causes (Kozak 1997): 1.Estimators which no longer have a minimum variance property 2.Underestimation of standard errors on parameter estimates 3.Unreliable tests of significance

21. Question: Does ignoring these autocorrelations in my taper dataset cause tests of treatment effects to be falsely significant?

22. Where: d i = diameter inside bark of i th disk h i = height from ground of i th disk H = total height of tree Z = h i /H p =(HI/H)*100 D = diameter outside bark at breast height a 0 – a 2 and b 1 – b 5 = parameters to be estimated X =

23. Properties of Model: 1.d i = 0 when h i /H = 1.0 2.d i = DI (estimated dib at inflection point) when HI/H = P 3.function changes direction when h i /H = p

25. a0a0 a1a1 a2a2 b1b1 b2b2 b3b3 b4b4 a1a1 -0.999 a2a2 0.984-0.989 b1b1 0.007-0.001-0.003 b2b2 -0.0240.017-0.018-0.91 b3b3 0.023-0.0150.0140.883-0.987 b4b4 -0.0160.008-0.006-0.9370.982-0.988 b5b5 -0.0490.053-0.070.392-0.110.151-0.275 Parameter Correlation Matrix

26. Value Std.Error t-value p-value a 0 3.1370 1.26482.480 0.0133 a 1 0.7066 0.0923 7.653 <.0001 a 2 1.0009 0.0004 2332.256 <.0001 After Removal of a2 parameter: a 0 1.3607 0.0993 13.6992 <.0001 a 1 0.8989 0.0135 66.6847 <.0001

27. Treatment Indicators added to exponent: I S = 1 if treatment = Sulfur, 0 otherwise I SN = 1 if treatment = Sulfur and Nutrient, 0 otherwise Sulfur treatment insignificant (p = 0.3588) Sulfur and nutrients treatment does effect taper! (p = 0.0017)

31. Sulfur treatment still insignificant (p = 0.6689 vs. p = 0.3588 without car(1)) Sulfur and nutrient treatment still significant (p = 0.0010 vs. p = 0.0017 without car(1))

32. Sulfur treatment still insignificant (p = 0.1230) Sulfur and nutrient treatment still significant (p = 0.0135)

33. Sulfur treatment still insignificant (p = 0.0930) Sulfur and nutrient treatment more significant (p = <0.0001)

34. Model df AIC BIC log Likelihood GNLS 10 8447.159 8496.847 -4213.579 GNLScar 12 8370.610 8430.236 -4173.305 NLME 11 8460.590 8515.248 -4219.295 NLMEcar 13 8452.210 8516.805 -4213.105

35. Test log likelihood ratio p-value GNLS vs GLNScar 80.54881 <.0001 GNLScar vs NLME 91.98045 <.0001 NLME vs NLMEcar 12.38081 0.002 GNLS vs NLME11.43164 0.0007 GNLScar vs NLMEcar79.59964 <.0001

36. Sulfur/ SulfurNutrient GNLS-0.01-0.04 GNLS car(1)-0.01-0.04 NLME-0.02-0.04 NLMEcar(1)-0.02-0.05 Parameter estimates experienced little change:

37. P-values of treatment parameters show no clear patterns: Sulfur/ SulfurNutrient GNLS0.35880.0017 GNLS car(1)0.66890.0010 NLME0.12300.0135 NLMEcar(1)0.0930<0.0001

38. Parameters relatively unchanged as hypothesized Standard errors of treatment parameters fluxuated, did not necessarily become less significant as expected Adding car(1) to GNLS or NLME significantly fit data better Adding random tree effect also helped to fit data significantly better

39. Special Thanks Starker Forests Inc. for project funding supplying treated field sites Sean Garber for sharing his S-Plus knowledge and taper enthusiasm Temesgen Hailemariam for his guidance and the opportunity to attend this meeting

40. Works Cited Johnson DW, Mitchell MJ (1998) Responces of forest ecosystems to changing sulfur inputs. In 'Sulfur in the Environment'. (Ed. D Maynard) pp. 219-262. (Marcel Dekker, Inc.: New York) Maguire DA, Kanaskie A, Johnson R, Johnson G, Voelker W (1998) 'Swiss needle cast growth impact study: report on results from phases I and II.' College of Forestry, Oregon State University, Corvallis, OR. Material Safety Data Sheets (2005) Williams JS, Cooper RM (2003) Elemental sulfur is produced by diverse plant families as a component of defense against fungal and bacterial pathogens. Physiological and Molecular Plant Pathology 63, 3-16.