Genomic Selection in Douglas-fir

Transcription

1 Genomic Selection in Douglas-fir Glenn Howe PNWTIRC Oregon State University October 17, 2018 PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

2 Collaborative project Key funding PNWTIRC Conifer Translational Genomics Network (AFRI) Northwest Advanced Renewables Alliance (AFRI) NWTIC Key roles SNP discovery (PNWTIRC) SNP chip design (PNWTIRC) Population design (NARA) Foliage collection and DNA isolation (NARA) SNP chip manufacture and genotyping (NARA) SNP data processing (PNWTIRC) Genomic selection analyses (PNWTIRC/NARA/NWTIC) Personnel PNWTIRC Glenn Howe Jennifer Kling Scott Kolpak NARA/NWTIC Keith Jayawickrama Terrance Ye Matt Trappe PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

3 What is genomic selection? The selection of genetically superior trees based on genomic information rather than on directly measured phenotypes Goal is to predict phenotypes using DNA information It does not involve genetic modification! When selections are made, it is called genomic selection PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

4 What is genomic selection? The selection of genetically superior trees based on genomic information rather than on directly measured phenotypes Field tests are costly but why? Field testing takes a long time Some traits have low heritabilities PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

5 Genomic selection can be integrated into existing tree breeding programs BC Forest Service

6 Genomic selection Field tests are costly Testing and selection take a long time Low heritability traits Mature traits PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

7 Genomic selection Skip field testing Make selections early Increase heritabilities Select for difficult to measure traits PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

8 Potential advantages of genomic selection Skip entire cycles of field testing Reduce the size of field tests by using genomic selection for early culling Shorten the generation interval Increase heritabilities Select for difficult to measure traits (e.g., mature traits at an early age) PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

9 Livestock breeders have led the way It is already widely used in dairy cattle breeding (Dalton, 2009) and is expected to revolutionize all livestock genetic improvement programmes and can be extended to plants Goddard et al Genomic selection in livestock populations. Genet. Res. 92: PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

10 Why do we think genomic selection will work? Proven in livestock and crop plants Ibtisham et al (2017) Thai J Vet Med. 47(3): PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

11 Why do we think genomic selection will work? Proven in livestock and crop plants Genetic gains have proven economic value

12 However... We re engaged in genomic selection research because In theory, there's no difference between theory and practice. In practice, there is! attributed to Jan L. A. van de Snepscheut PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

13 How does genomic selection work?

14 Single nucleotide polymorphism (SNP) SNP Tree 1 A C G T G T C G G T C T T A Maternal chrom. A C G T G T C A G T C T T A Paternal chrom. Tree 2 A C G T G T C G G T C T T A Maternal chrom. A C G T G T C G G T C T T A Paternal chrom. Tree 3 A C G T G T C A G T C T T A Maternal chrom. A C G T G T C A G T C T T A Paternal chrom. Tree 1 is heterozygous Trees 2 and 3 are homozygous

15 Genomic selection Relies on markers linked to quantitative trait loci 0 10 Location (cm) Chromosome PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

16 Genomic selection Relies on markers linked to quantitative trait loci 0 10 Location (cm) Chromosome PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

17 Genomic selection How does it work? Objective is to predict breeding values using a genome-wide set of markers (e.g., tens of thousands of SNPs) With enough markers, at least one marker will be linked to each important gene No need to identify which specific genes or markers are important Highly effective in livestock breeding

18 Genomic selection Genomic selection markers work for any measured trait Growth Height, diameter, volume growth Adaptability Cold hardiness Spring bud flush Stem form Ramicorn branches and forks Sinuosity Wood stiffness PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

19 Genomic selection Particularly valuable for within-family selection Parent 1 x Parent 2 offspring 1 offspring 2 offspring 3 etc All offspring have the same expected phenotype (= parental average) Field testing is used to find which offspring are superior Genomic selection could be used instead PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

20 What do we need to practice genomic selection?

21 Genomic selection What do we need? Phenotypes We have these from existing genetic tests New trees to evaluate We generate these by crossing our known superior trees DNA samples We can isolate DNA from leaf samples A way to measure genetic markers (SNPs) We developed the methods for doing this (Axiom genotyping array) Statistical methods for estimating breeding values This is a very active area of research PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

22 Northwest Advanced Renewables Alliance Northwest Advanced Renewables Alliance

23 NARA pedigree and phenotypes PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

24 Collected foliage from seed orchards Orchard Number of samples CTC David T. Mason Seed Orchard 102 Roseburg Forest Products Seed Orchard - Lebanon 61 BLM Tyrrell Seed Orchard 6 Plum Creek Seed Orchard 33 PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

25 Collected foliage from progeny tests Lyons Ridge Moon Creek Big Creek Plantation Samples Moon Creek 293 Lyons Ridge 208 Big Creek 55

26 Needle collection Collected 5-10 fresh needles Placed in vial with desiccant 1920 samples were used for genotyping Photo from Matt Trappe PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

27 New crosses were outplanted Plum Creek nursery 25 full-sib families 1146 trees Planted on Roseburg property near Elkton, Oregon in March, 2015 Photos from Matt Trappe

28 Genomic selection validation NARA field test

29 Genomic selection Valuable for within-family selection Many more trees per family in third cycle Empirical test of genomic selection internal_id geno_id plate_well_cel female male _B _E _B _D _F _H _B _D _G _H _G _H _G _E _G _A _F _A _F _E _A _E _G _H _D _E _C _D _F _C _D _H _E _G _H _G _H _H _F _G _H _H _H _E _G _G _F _F _F _E _F _F _H _E _H _G _H _H _C _H _D _F _A _D _H _G

30 DNA isolation Dry needle samples minced Placed in a 96 well plate Records of location on the plates maintained in Excel Samples sent to the NFGEL Laboratory in Placerville, CA for extraction (Dr. Valerie Hipkins) Minimum concentration of 10ng/µl DNA required PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

31 NFGEL helped with DNA isolation

32 Affymetrix Axiom Genotyping Array for Douglas-fir Glenn Howe Keith Jayawickrama Scott Kolpak Jennifer Kling Matt Trappe Valerie Hipkins Terrance Ye Stephanie Guida Rich Cronn Sam Cushman Susan McEvoy PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

33 PNWTIRC report PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

34 Axiom SNP characteristics (CR = 60%) Unrelated Coastal Douglas-fir only 55,766 SNPs attempted 27,699 SNPs polymorphic and called 24,574 SNPs = polymorphic, called, HWE PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

35 Genotyping by GeneSeek PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

36 Affymetrix Axiom Analysis Suite PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

37 What have we learned so far?

38 Across family genomic selection works Predictive ability is the correlation between breeding values estimated from phenotypes versus SNPs PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

39 How many SNPs are needed? PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

40 Jury is out on within family GS Many more trees per family in third cycle Empirical test of genomic selection internal_id geno_id plate_well_cel female male _B _E _B _D _F _H _B _D _G _H _G _H _G _E _G _A _F _A _F _E _A _E _G _H _D _E _C _D _F _C _D _H _E _G _H _G _H _H _F _G _H _H _H _E _G _G _F _F _F _E _F _F _H _E _H _G _H _H _C _H _D _F _A _D _H _G

41 What s next? Genomic selection workplan PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

42 Genomic selection validation NARA field test

43 Test approaches using simulated data Breeding program simulations Simulates SNPs Simulates phenotypes Various mating designs are available Can use real pedigrees as input Updated to account for the structure of NWTIC breeding programs Being used to optimize sampling design for SNP genotyping PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

44 Thank you! I ll stop here so you can let this information sink in PACIFIC NORTHWEST TREE IMPROVEMENT RESEARCH COOPERATIVE

45 UPDATE ON GRAYS HARBOR REALIZED GAIN TRIAL Terrance Ye, Keith Jayawickrama, Sukhyun Joo, and Doug Maguire Oregon State University Eric Turnblom University of Washington Brad St. Clair USFS PNW Research Station NWTIC Annual Meeting, October 16, 2018

46 Basic Concepts Breeding Population Selection Base Population Progeny Tests Further Selection Selected Population Progeny tests play important roles in testing and selecting genetically superior materials in tree breeding. Progeny tests differ from operational plantations in many ways, e.g., they usually contain highly heterogeneous materials and established using small, non-contiguous plots. External Population Propagation Population Deployment Decisions Operational Plantation

47 Basic Concepts One limitation for progeny tests is that as the test matures, trees face increasing competitive effects. This biases estimates of tree family differences over time, as slower growing families will eventually be suppressed. As a result, we typically do not measure progeny tests beyond about 1/4 or 1/3 rotation. In contrast, realized gain trials are designed to compare genetically improved materials against unimproved woods-run trees in operational settings (such as large block-plots, each plot contains genetically similar individuals, etc.). With realized gain trials, we could predict improvements in standlevel productivity through rotation.

48 Grays Harbor realized gain trial A joint project among NWTIC, SMC, and USFS PNW Research Station Genetics Team

49 Test Materials And Silvicultural Treatments * Genetic levels Elite seedlot (a mix of crosses among the 20 best parent trees) Intermediate seedlot (a mix of crosses among 20 intermediate parent trees) Woods-run seedlot (a random sample of 50 wild trees in Grays Harbor) Spacings 15 x 15 (200 SPA) 10 x 10 (440 SPA) 7 x 7 (889 SPA) Vegetation control Standard (one complete weed kill during site preparation and no weed control thereafter) Complete (maintains at least 80% bare ground until crown closure) * The intermediate seedlot was only planted at 10 spacing and received the complete vegetation control.

50 Field Trials # sites = 6 # plots / site = 22 # trees / plot 64 (15 spacing) 100 (10 spacing) 250 (7 spacing)

51 Purposes of establishing realized gain trials To understand the long-term effects on productivity, quality, and diversity of Douglas-fir trees and stands when genetic improvement and silviculture are deployed in combination. To compare the growth of genetically selected trees to unselected woods-run trees and estimate realized genetic gains. To provide data to modify or update growth models for effects caused by genetic selection, intensive weed control and different spacing. To demonstrate volume gains on an area basis.

52 Project Growth Using ORGANON Simulator

53 Why? The growth rate of the present young stands does not fully predict what we can expect from the future. Growth projections are needed for cooperators to estimate their expected return on investments in tree breeding and to choose optimal silvicultural treatments. ORGANON is an individual tree growth model developed for several species in Oregon and Washington. Some of our cooperative members use this simulator for growth projection. It is well documented and available to use for OSU staff.

54 Objectives To examine if the growth of improved seedlots differ in any quantifiable way from the woods-run seedlot. To predict rotation-age stand growth and yield based on age-13 height, diameter, and crown ratio.

55 How? One common approach of incorporating genetic gain into growth models is to change the site index based on height-age curve to reflect increased height growth of improved materials. Site index for each plot was calculated as followings: Sample 40 largest trees per acre in DBH (9 ~ 13 trees per plot, depending on spacing), and calculate mean age-13 height. Fit a height-age curve based on height measurements at ages 3, 5, 7, 9, and 13 to determine the average age at the breast height. Calculate site index for the plot using Bruce s (1981) method. Results - average by-plot site index: The Elite: (7.9% higher than the woods-run plots) The Intermediate: (7.2% higher than the woods-run plots) The Woods-run:

56 Another approach is to use genetic multipliers. Genetic multipliers are developed to reflect the relative growth difference between improved and unimproved materials, and are used to account for genetic gain in the growth rate. The estimated realized genetic gains for age-13 height: The elite seedlot: 10.2% The intermediate seedlot: 9.3% The estimated realized genetic gains for age-13 diameter: The elite seedlot: 8.8% The intermediate seedlot: 4.4% Use these estimated gains as genetic worth in ORGANON. Average site index by site.

57 Traits BFV Board Foot Volume (bf/ac) BFV32 - log length of 32 feet BFV16 - log length of 16 feet BFV08 - log length of 8 feet CFV Cubic Foot Volume (ft 3 /ac)

58 Change of Least-Squares Mean of BFV with Age * Use by-plot site index Use genetic multipliers * LS means were estimated at 10 x 10 spacing.

59 Change of Least-Squares Mean of BFV Increment with Age * Use by-plot site index Use genetic multipliers * LS means were estimated at 10 x 10 spacing.

60 Change of Realized Gain of BFV with Age * Use by-plot site index Use genetic multipliers * Gains were estimated at 10 x 10 spacing.

61 Change of LS Mean / Increment / Realized Gain of CFV with Age * Use by-plot site index Use genetic multipliers * Estimated at 10 x 10 spacing.

62 Summary Results from the two approaches showed similar patterns but differed in scales. The approach using genetic multipliers appeared to be more conservative than the approach based by-plot site index (height-age curve). When the genetic multipliers were implemented in regional growth models (e.g., ORGANON and FVS), data from old-age large-scale block-plot realized gain trials were not yet available.

63 While the differences in BFV (or CFV) between the improved and the woods-run seedlots increased with age, the realized gains of the improved seedlots (expressed as % of the woods-run seedlot) decreased with age. For example, realized gain of BFV 16 decreases from 23.2% at age 18 to 12.0% at age 53 for the elite seedlot, and from 14.9% at age 18 to 10.0% at age 53 for the intermediate seedlot. Increments of BFV8 and BFV32 peaked at ages and respectively for the improved seedlots. By contrast, the increments reached their maximum at ages and respectively for the woods-run seedlot. The differences between the elite and the intermediate were small.

64 Impact of Genetic and Silvicultural Effects

65 Traits Age-13: Ht_13: total height Dbh_13: diameter at breast height Vol_13: stem volume Slenderness_13: = ht_13 / dbh_13 Surv_13: survival rate Forking_13: number of forks Ramicorns_13: number of ramicorn branches Sinuosity_13: stem sinuosity Age-9: Age-43 Av_9: squared acoustic velocity Sg_9: wood specific gravity Bfv08_43 board foot volume with log length of 32 feet Bfv16_43 board foot volume with log length of 16 feet Bfv32_43 board foot volume with log length of 8 feet Cfv_43: cubic foot volume

66 Statistical Model for Analysis of Variance and Contrast Test For analyzing plot-level data For analyzing individual-tree data S site effect; G effect of genetic type or seedlot; V effect of vegetation control; D effect of stand density or spacing; F effect of family within seedlot

67 Table 1. Significance levels (p values) from analyses of variance and contrast tests using plot level data Significant site effect for all traits Significant seedlot (genetic) effect for all growth and wood quality traits Significant spacing effect for all traits except for age-13 survival No significant effect of vegetation control except for age-13 survival

68 Table 1. Significance levels (p values) from analyses of variance and contrast tests using plot level data No significant site x seedlot and site x spacing effects for any trait Significant seedlot x spacing effect for age-13 volume and age-43 yield traits The improved seedlots performed significantly better in growth rate and stem sinuosity than the woods-run seedlot.

69 Table 2. Significance levels (p values) from analyses of variance and contrast tests using individual-tree level data Significant differences among full-sib families. Site x family interaction was significant for age-13 growth, branching, and stem form traits

70 Realized Genetic Gain Realized gain of genetically improved seedlots where LSMs are the predicted least-squares means.

71 Realized Genetic Gains Gains for rotation-age yield traits were fairly consistent, ranging from 10 ~ 14%.

72 Realized Genetic Gains At age 13, realized gains of the elite seedlot was 10.2% for HT, 8.8% for DBH, and 28.3% for VOL. Gains for the intermediate seedlot were a notch lower. Improved seedlots had 9~10% less stem sinuosity than woods-run seedlot.

73 Realized Genetic Gains Improved seedlots had slightly more forking and ramicorns, and slightly better survival rate than woods-run seedlot Small realized gains for wood quality traits

74 Effect of Seedlot x Spacing Interaction

75 Significant seedlot x spacing effect for age-13 volume and age-43 yield traits Woods-run: BFVs dropped sharply from 10 spacing to 15 spacing Elite: BFV08 increased with spacing; spacing had little impact on BFV16 and BFV32

76 Realized vs. Predicted Gains

77 Predicted Gain vs. Realized Gain (At Full-Sib Family Level)

78 Predicted Gain vs. Realized Gain (At Full-Sib Family Level) Are the correlations too weak?

79 Realized gains are calculated from large block-plot trials and refer to the gain actually achieved from breeding programs. By contrast, predicted gains are predicted based on estimates of genetic parameters from progeny trials with single-tree or small plots. There are many reasons why these two types of gains may differ. G x E effect Age-age correlation Inter-tree competition Within-family variation Pollen contamination Sampling issues Reports on correlation between predicted and realized gains in the literature: 0.87 for Scots pine by Jansson et al (1998) 0.77 for Douglas-fir by Ye et al. (2010) 0.82 for Eucalyptus globulus by Callister et al. (2013)

80 Sampling Issue: Effect of Truncated Selection on Correlation Coefficient 39 out of 522 parents were selected

81 Simulation: Effect of Truncated Selection on Correlation Coefficient * * Each scenario Repeated 1000 times.

82 Predicted Gain vs. Realized Gain (At Plot Level)

83 Difference in Competition Index between the Elite and Woods-run Seedlots

84 Competition for resources (e.g. nutrients, water and light) directly affects the growth processes and crown development of individual trees within a stand. Thus, the genetic effect and the competition effect could be confounded after canopy closure. As mentioned previously, one reason that realized gains did not meet predicted gains well is that between-tree competition in progeny trials is not representative of block planting where trees compete with genetically similar individuals.

85 Objective: To examine if the elite genetic entries selected from the narrowspaced progeny trials have different competitive pattern than woods-run trees.

86 At a single site: Tree s phenotype = Population mean + Plot effect (incl. seedlot, spacing, veg) + Genetic effects (incl. GCA and SCA) + Competition effect + Spatially correlated Residual + Spatially independent residual

87 At a single site: Tree s phenotype = Population mean + Plot effect (incl. seedlot, spacing, veg) + Genetic effects (incl. GCA and SCA) + Competition effect + Spatially correlated Residual + Spatially independent residual Methods We first removed the plot effect, the genetic effect, and the spatiallycorrelated residuals from the observed values by fitting individual-tree mixed models. Adjusted phenotype = Population mean + Residual (Competition effect + Spatially independent residual)

88 Using the adjusted phenotype as raw data, we calculated competition indices (Cs) for each tree based on the assumption that a tree s competitive ability can be indicated by an expression of the number of competitors, their size and distance and/or spatial distribution of neighboring trees. Competition index is considered to be a measure of the degree to which growth resources may be limited by the number, size, and proximity of neighbors.

89 Definitions of distance-dependent competition indices: neighbors Hegyi (1974) Modified after Hegyi (1974) Target tree Schütz (1989) dbh j, dbh i = diameter at breast height (cm) for neighbor (j) and target tree (i); dist ij = horizontal distance (m) between neighbor (j) and target (i) trees; ht j, ht i = height (m) for neighbor (j) and target (i) trees; cr j, cr i = crown radius (m) for neighbor (j) and target (i) trees; n = number of direct neighbors (n <= 8).

90 Border trees as well as the trees with less than four direct neighbors were excluded from the analysis. All the competition indices calculated were evaluated and compared on the basis of simple correlation with adjusted diameter or height growth. In addition, changes in correlation between the indices and growth were examined by spacing and seedlot, and significance was tested using Fisher z transformation.

91 Correlation with Diameter: C1 The negative correlation between the competition indices and growth represents competitive stress. Both seedlots showed high negative correlation between the index C1 and diameter at both 7 and 10 spacings. No statistically significant differences in correlation was found between the elite and the woods-run seedlots (p = at 7 spacing and p = at 10 spacing).

92 Correlation with Diameter: C3 The competition index (C3), calculated based on crown characteristics and tree height, and thus also reveals the competition for light at canopy closure unlike other two indices. Both seedlots showed negative correlation between the index C3 and diameter at both 7 and 10 spacings. Differences between the elite and the woods-run seedlots were insignificant at 7 spacing (p = ) but significant at 10 spacing (p = ).

93 Correlation with Height: C2 Negative correlation between index C2 and height was found for both seedlots and both spacings. The elite seedlot exhibited much higher correlation (almost x3) than the woodsrun seedlot. Significant differences in correlation was found between the elite and the unimproved seedlots (p < at both spacings.

94 Correlation with Height: C3 Results were similar to the previous slide. Negative correlation between index C3 and height was found for both seedlots and both spacings. The elite seedlot exhibited much higher correlation than the woods-run seedlot. Significant differences in correlation was found between the elite and the unimproved seedlots (p < at 7 spacing and p = at 10 spacing).

95 In summary, tree height and breast height diameter were affected by competition from neighbors at this age, showing statistically significant negative correlations with the competition indices. The elite and the woods-run seedlots differed in the competition stress on height growth, with the elite seedlot being significantly higher tolerance to competition than the woods-run seedlot at narrow spacing settings. By contrast, there was generally little differences in the competition stress on diameter growth between seedlots.

96 Two implications: NWTIC progeny trials are normally established in narrow spacings, and selections are finally made between age 12 and 15. It might be necessary to incorporate competition index into genetic gain prediction model to increase the accuracy of prediction. Competition index is an important component in an individual-tree growth model as it is used to describe a tree s social status and quantify the surrounding environment. Regional growth models such as ORGANON or FVS use genetic multipliers to account for the differences in growth between genetically improved and unimproved seedlots while assumes no competitive advantage of the trees in the improved seedlot. Our results may suggest that it is necessary for growth modelers to study the impact of competition on the estimates of genetic multipliers.

97 Take-Home Message The genetically selected Douglas-fir seedlots exhibited significantly higher growth rate than the unimproved woods-run trees. Realized gains for the projected rotation-age BFV (or CFV) were 10 14%. Selecting for fast growth in the breeding cycles resulted in significant improvement on stem straightness (9 10%). In addition, wood stiffness and wood density (at age 9) were not reduced in any meaningful way. Spacing had a significant impact on the projected rotation-age stand productivity while vegetation control did not make much difference. While the competition stress was significant at narrow spacings (7 and 10 ) at age 13, the elite seedlot appeared to possess higher tolerance to the competition stress on height growth than the woods-run seedlot.

98 Acknowledgements 1. Rayonier Forest Resources (RFR) and Quinault Indian Nation (QIN) developed the Grays Harbor breeding population on which this trial was based. 2. Mike Bordelon and Jess Daniels were early supporters of establishing realized gain trials in the PNW. 3. The full-sib crosses were made by RFR (coordinated by Jessica Josephs), while the QIN (coordinated by Jim Hargrove) helped collect the unimproved seed. 4. These trials were established through the collaboration of the SMC, NWTIC and the US Forest Service PNW Research Station. 5. Green Diamond Resource Company, Port Blakely Tree Farms, QIN, RFR, WA Department of Natural Resources and Weyerhaeuser Company provided the test sites.