Genetic variation in Broadleaf Lupine (Lupinus latifolius) on the Mt ...

ables obta<strong>in</strong>ed from the PRISM model (Daly and others 1994)<strong>in</strong>cluded: average monthly temperature (Jan–Dec, °C), averagemonthly precipitation (Jan–Dec, cm), average monthly snow(Jan–Dec, cm), average monthly grow<strong>in</strong>g degree-days (Jan–Dec,days above 10 °C [50 °F]), average date of last spr<strong>in</strong>g frost (Juliandate), and average date of first fall frost (Julian date). Plant associationseries, def<strong>in</strong>ed by Hall (1998), used for classify<strong>in</strong>g siteswere: Pacific silver fir, grand fir, western hemlock, mounta<strong>in</strong>hemlock, eastside Douglas-fir, and ponderosa p<strong>in</strong>e.PORTLANDCarsonCorvallisOREGONGreenhouse and Field SitesSeeds were scarified and sown <strong>in</strong>to 5-cm (2-<strong>in</strong>) peat pots <strong>in</strong>early February 1997 at the Natural Resources Conservation ServicePlant Materials Center (PMC) greenhouse <strong>in</strong> Corvallis, Oregon.Plants were arranged <strong>in</strong> 2 replications of a 16-plant plot ofeach family for greenhouse growth. By May 1 seedl<strong>in</strong>gs haddeveloped extensive root systems and were ready for outplant<strong>in</strong>g.Seedl<strong>in</strong>gs were outplanted to 2 field sites, one at the PMC onMay 8 and 9, and the other at the USDA Forest Service W<strong>in</strong>d RiverNursery (WRN) <strong>in</strong> Carson, Wash<strong>in</strong>gton, on May 21 and 22 (Figure1). Plants were spaced 46 x 91 cm (18 x 36 <strong>in</strong>) <strong>in</strong> a designwhere family row plots (4 plants of the same family) were randomized<strong>in</strong> 2 replications at each location (1216 plants at eachsite). Experimental plots were surrounded by a two-row buffer tom<strong>in</strong>imize edge effects. Weeds were controlled by cover<strong>in</strong>g the plotwith woven plastic mulch material, and test plants were planted <strong>in</strong>10-cm (4-<strong>in</strong>) holes cut <strong>in</strong> the mulch fabric (Figure 2). Outplant<strong>in</strong>gsurvival was 95%. Plants were grown and observed for 2 y.TraitsGenerally, traits could be grouped <strong>in</strong>to 5 broad categories:greenhouse traits (such as time to emergence), plant size(crown height or diameter), morphology (height-to-diameterratios, erectness), phenology (flower<strong>in</strong>g date or date of budburst), and pest resistance (mildew susceptibility) (Figure 3).Measurements were taken at each site and evaluated <strong>in</strong>dependently,for example, first year crown height at PMC and firstyear crown height at WRN are 2 separate traits. I evaluated 74traits over the course of the study.Mt. Hood0 10 miles (16 km)SourceFifth field watershedFourth field watersheFigure 1. Location of the 2 field plot sites used <strong>in</strong> the common-garden studywith a detail of the Lup<strong>in</strong>us <strong>latifolius</strong> sampl<strong>in</strong>g sites on the Mt Hood NationalForest.Photo by Joan Tr<strong>in</strong>dleStatistical AnalysisAnalysis generally follows the methods outl<strong>in</strong>ed <strong>in</strong> Campbell(1986) and Sorensen and Weber (1994). SAS software for personalcomputers (SAS 1999) was used for all statistical computations.To determ<strong>in</strong>e significance of effects and estimate variancecomponents for <strong>in</strong>dividual traits, analyses of variance (PROCMIXED) were performed on plot means us<strong>in</strong>g the nested model:Y ijkl = µ + B i + S j + F k(j) + E l(ijk)Figure 2. The field site at Corvallis, Oregon, show<strong>in</strong>g plot layout.where µ is the grand mean, B is the effect of block<strong>in</strong>g, S is theeffect of source location, F is the family with<strong>in</strong> source effect,39DAVID L DOEDE NATIVEPLANTS | SPRING 2005

the source effect, the better the model expla<strong>in</strong>ed genetic <strong>variation</strong>among sources.Transfer risk among locations with<strong>in</strong> a zone was estimated asthe average proportion of genotypes that differ between 2 populationswith<strong>in</strong> the zone. This was calculated as the proportion ofnon-overlap between 2 normal curves (one represent<strong>in</strong>g thetransferred population and one represent<strong>in</strong>g the local, nativepopulation) with variances equal to the additive genetic variancewith<strong>in</strong> locations and separation equal to the average distancebetween location means <strong>in</strong> the zone (Campbell 1986).Additive genetic variance was calculated as 3X the familywith<strong>in</strong>-sourcecomponent of variance. A factor of 3 was usedbecause there is some level of <strong>in</strong>breed<strong>in</strong>g <strong>in</strong> these populations(Wilson and Hipk<strong>in</strong>s 2002) that would <strong>in</strong>crease the correlationamong progeny of open-poll<strong>in</strong>ated plants. Also, the estimate of<strong>variation</strong> among location means with<strong>in</strong> a zone <strong>in</strong>cludes sampl<strong>in</strong>gerrors associated with sampl<strong>in</strong>g 1 to 4 families per locationand genetic effects such as drift and/or migration fromnon-local pollen sources. An overall risk value of 0.51 impliesthat 50% of the seedl<strong>in</strong>g population is outside the genetic distributionof the local population.Variation <strong>in</strong> chromosome number was revealed <strong>in</strong> isozymeanalysis of a subset of the sources used <strong>in</strong> this study (Wilsonand Hipk<strong>in</strong>s 2002). This trait was not quantified directly <strong>in</strong> thisstudy, however, an attempt was made to classify sources as totetraploid and diploid cytology <strong>in</strong>directly. A discrim<strong>in</strong>antfunction was developed to classify the cytology of the subset ofsources with known cytology (identified by isozyme analysis).A stepwise selection procedure was used to select traits for thediscrim<strong>in</strong>ant function. This function was then applied to allthe sources <strong>in</strong> this study.RESULTSModerate amounts of genetic <strong>variation</strong> (source plus familywith<strong>in</strong>-source)were found for broadleaf lup<strong>in</strong>e <strong>in</strong> this study.Of the 74 traits analyzed, 64 had significant (P > 0.05) sourceor with<strong>in</strong>-source <strong>variation</strong>. For the majority of traits, thesource component was greater than the with<strong>in</strong>-source variancecomponent. In 59 traits where genetic <strong>variation</strong> was more than25% of the total phenotypic <strong>variation</strong>, source <strong>variation</strong> averaged28% of the phenotypic <strong>variation</strong> while with<strong>in</strong>-source<strong>variation</strong> was 13% of the total. There is also abundant with<strong>in</strong>source<strong>variation</strong> and several traits displayed much more <strong>variation</strong>among families than among sources. For example, genetic<strong>variation</strong> <strong>in</strong> emergence was 70% of the phenotypic variance,yet the magnitude of the with<strong>in</strong>-source component was 2.8X41DAVID L DOEDE NATIVEPLANTS | SPRING 2005

the source component. Other traits display<strong>in</strong>g this trend weretraits relat<strong>in</strong>g to flower<strong>in</strong>g date, number of flower spikes, andsenescence. Traits <strong>in</strong> which the majority of genetic <strong>variation</strong> isexhibited as with<strong>in</strong>-source <strong>variation</strong> do not provide <strong>in</strong>formationabout geographic patterns of <strong>variation</strong>, therefore thesetraits were dropped from consideration, as were traits highlycorrelated with other traits occurr<strong>in</strong>g later <strong>in</strong> the life cycle.I reta<strong>in</strong>ed 24 traits quantify<strong>in</strong>g size, growth rate, phenology,flower color, and disease resistance for further analysis (Tables1 and 2). In pr<strong>in</strong>cipal component analysis of 24 traits, 4 componentsreta<strong>in</strong>ed more than 72% of the orig<strong>in</strong>al variability(Table 3). The first component was by far the largest, and summarized44% of the variability <strong>in</strong> the dataset. Most of the traitscontributed to the <strong>variation</strong> <strong>in</strong> this component. The positivecoefficients for size, growth rate, and phenology traits <strong>in</strong>dicatethat PC1 score <strong>in</strong>creases as these traits <strong>in</strong>crease, and suggestthat this component could be a usable surrogate for plant sizeand vigor. PC2 accounted for 14% of the total <strong>variation</strong>, andalthough the pattern<strong>in</strong>g of coefficients is not as easily <strong>in</strong>terpreted,there was an emphasis on flower color traits and size atWRN. The rema<strong>in</strong><strong>in</strong>g 2 components accounted for much less<strong>variation</strong> (comb<strong>in</strong>ed about 14%), and there is no clear pattern<strong>in</strong>gto the coefficients. Consequently, PC3 and PC4 weredropped from further consideration.Variation <strong>in</strong> component scores among sources was significantlycorrelated with geographic variables (Table 4), with latitudehav<strong>in</strong>g the strongest correlation with PC1 and departurehav<strong>in</strong>g the strongest correlation with PC2. Scatter plots ofpr<strong>in</strong>cipal component scores plotted by latitude and departurealso show trends across the landscape (Figure 4). RegressionTABLE 1Description of traits used to exam<strong>in</strong>e geographic patterns of genetic <strong>variation</strong> <strong>in</strong> Lup<strong>in</strong>us <strong>latifolius</strong>.Trait Description, location, z date Measurement unitsCV1H1D1 Crown height-to-diameter ratio, PMC, 7 Jul 1997 cm/mmCV1HT1 Crown height, PMC, 7 Jul 1997 cmCV1HT2 Crown height, PMC, 27 Jul 1997 cmCV1MLD Mildew score, PMC, 3 Sep 1997 0 = none to 3 = heavyCV1VIG Vigor rat<strong>in</strong>g, PMC, 3 Sep 1997 1 = unthrifty to 5 = vigorousCV1VRATE Rate of crown volume <strong>in</strong>crease, PMC, 1997 cm 3 /dayCV1WHC Amount of white color <strong>in</strong> flowers, PMC, 15 Jun 1997 0 = light to 3 = strongCV2BB Bud burst class, PMC, 17 Mar 1998 1 = dormant to 6 = advancedCV2DI1 Crown diameter, PMC, 20 Apr 1998 mmCV2DI2 Crown diameter, PMC, 15 Jun 1998 mmCV2SH2 Stalk height, PMC, 15 Jun 1998 cmWR1DI2 Crown diameter,WRN, 1997 mmWR1GRS Growth stage,WRN, 15 Sep 1997 1 = green to 5 = senescedWR1H1D1 Crown height-to-diameter ratio,WRN, 20 Jul 1997 cm/mmWR1H2D2 Crown height-to-diameter ratio,WRN, 11 Aug 1997 cm/mmWR1HT2 Crown height,WRN, 11 Aug 1997 cmWR1RDC Amount of red color <strong>in</strong> flowers,WRN, 23 Jun 1997 0 = light to 3 = strongWR1WHC Amount of white color <strong>in</strong> flowers,WRN, 23 Jun 1997 0 = light to 3 = strongWR1V2 Crown volume,WRN, 20 Jul 1997 cm 3WR2BB Bud burst class,WRN, 1 Apr 1998 1 = dormant to 6 = advancedWR2FORM Plant form,WRN, 9 Jul 1998 1 = upright to 5 = prostrateWR2HGT Crown height,WRN, 27 Jul 1997 cmWR2LFW Leaflet width,WRN, 9 Jul 1998 mmFRSTLEAF First true leaf, GRN, 1997 Julian datez PMC = Plant Materials Center, Corvallis, Oregon; WRN = W<strong>in</strong>d River Nursery, Carson,Wash<strong>in</strong>gton; GRN = Greenhouse at PMC42NATIVEPLANTS | SPRING 2005GENETIC VARIATION IN BROADLEAF LUPINE

TABLE 2Overall means, coefficients of <strong>variation</strong>, and relative variances (as percentage) for traits measured <strong>in</strong> the common-garden study of Lup<strong>in</strong>us <strong>latifolius</strong> from Mt HoodNational Forest, and 4 pr<strong>in</strong>cipal components.Trait z Mean Coefficient of <strong>variation</strong> Total phenotypic <strong>variation</strong> y(%)Sources Families (Sources) Plot errorCV1H1D1 0.91 18.8 43.1 ••• x 11.7 • 45.1CV1HT1 23.8 27.0 27.7 ••• 24.4 •• 47.9CV1HT2 31.6 22.6 40.5 ••• 16.9 •• 42.6CV1MLD 1.1 53.3 25.2 ••• 21.7 •• 53.0CV1VIG 3.1 11.5 34.7 ••• 0.0 65.3CV1VRATE 542.4 55.2 30.1 ••• 8.9 60.1CV1WHC 1.7 24.3 40.0 ••• 7.3 52.6CV2BB 3.9 28.2 43.8 ••• 2.2 54.0CV2DI1 23.7 35.3 37.9 ••• 4.1 58.0CV2DI2 58.0 27.3 39.4 ••• 0.0 60.6CV2SH2 40.5 29.1 40.6 ••• 0.0 59.4WR1DI2 21.7 19.7 34.6 ••• 4.2 61.1WR1GRS 3.7 27.3 34.3 ••• 10.9 • 54.8WR1H1D1 1.1 24.6 41.0 ••• 15.2 •• 43.8WR1H2D2 1.1 21.9 36.0 ••• 10.6 • 53.3WR1HT2 22.9 25.9 45.2 ••• 12.2 •• 42.6WR1RDC 1.4 33.6 44.3 ••• 10.8 • 44.9WR1WHC 1.4 31.2 34.4 ••• 6.2 59.4WR1V2 1717.7 43.9 30.5 ••• 5.4 64.1WR2BB 4.3 17.2 35.4 ••• 5.8 58.8WR2FORM 2.6 18.8 50.3 ••• 0.8 49.0WR2HGT 19.5 27.2 47.3 ••• 9.5 • 43.2WR2LFW 10.6 16.6 35.6 ••• 4.0 ns 60.4FRSTLEAF 92.1 4.2 35.8 ••• 28.0 ••• 36.2PC1 0.0 79.7 ••• 20.3PC2 0.0 62.5 ••• 37.5PC3 0.0 53.3 ••• 46.7PC4 0.0 17.0 • 83.0z See Table 1 for trait def<strong>in</strong>itions.y Calculated as Σ 2TP = Σ 2S + Σ 2F(S) + Σ 2P where Σ 2S = <strong>variation</strong> due to source location, Σ 2F(S) = <strong>variation</strong> due to families-with<strong>in</strong>-sources, and Σ 2P = <strong>variation</strong> due to plot error.x ••• = P < 0.001; •• = P < 0.01; • =P < 0.05; ns = not significant43DAVID L DOEDE NATIVEPLANTS | SPRING 2005

TABLE 3Pr<strong>in</strong>cipal component analysis of the 24 traits used <strong>in</strong> this study, with factor load<strong>in</strong>gs and proportions of location <strong>variation</strong> expla<strong>in</strong>ed.Trait zFactor load<strong>in</strong>gsPC1 PC2 PC3 PC4CV1H1D1 0.18877 0.12923 -0.35632 -0.08614CV1HT1 0.25167 0.14654 -0.00565 -0.10971CV1HT2 0.27070 -0.03515 -0.02776 -0.02253CV1MLD -0.01777 -0.14292 0.06120 0.63134CV1VIG 0.24935 -0.05391 0.07771 -0.13662CV1VRATE 0.26867 0.07128 0.03361 -0.13339CV1WHC 0.12761 -0.26531 0.16264 0.11600CV2BB 0.22636 -0.17566 0.15862 0.01430CV2DI1 0.25080 -0.08058 0.02488 -0.12305CV2DI2 0.25931 -0.02032 0.06226 -0.07851CV2SH2 0.25831 -0.09111 -0.01800 -0.06485WR1DI2 0.07896 0.29605 0.46914 0.22022WR1GRS 0.05462 0.44274 0.06836 0.13490WR1H1D1 0.23998 0.20432 -0.23075 0.04779WR1H2D2 0.21106 0.14553 -0.34572 0.10030WR1HT2 0.24715 0.27679 -0.01242 0.13539WR1RDC -0.15620 0.27836 -0.01425 -0.36505WR1V2 0.16398 0.33652 0.25854 0.23421WR1WHC 0.17327 -0.30272 0.03854 0.21040WR2BB 0.20490 -0.13906 0.02446 -0.04242WR2FORM -0.16720 0.16398 0.41640 -0.13454WR2HGT 0.26901 -0.00349 0.02168 0.03431WR2LFW 0.11292 -0.24272 0.21026 -0.09674FRSTLEAF -0.14104 0.07065 -0.34641 0.38160Proportion of total 0.4397 0.1384 0.0908 0.0533<strong>variation</strong> expla<strong>in</strong>edCumulative proportion 0.4397 0.5781 0.6689 0.7222z See Table 1 for trait def<strong>in</strong>itions.44NATIVEPLANTS | SPRING 2005GENETIC VARIATION IN BROADLEAF LUPINE

TABLE 4Correlation coefficients (r) between factor scores for 2 pr<strong>in</strong>cipal components and5 geographic and topographic variables.50600005050000PC1 CLASSGeographic or topographic variable PC1 PC2Departure -0.04 -0.55•••Latitude 0.52 ••• -0.15Elevation -0.28 •• -0.33••North–south aspect 0.10 0.10East–west aspect 0.09 -0.08••• = P < 0.001; •• = P < 0.01; • = P < 0.05TABLE 5Latitude504000050300005020000Mt Hood50100005000000499000049800004970000570000 580000 590000 600000 610000 620000 630000PC1 score < –2.57PC1 score ≥ –2.57 and < –0.51PC2 CLASSDeparturePC1 score ≥ –0.51 and < 2.68PC1 score > 2.685060000Amounts of <strong>variation</strong> among locations (percentage of total sums-of-squares)expla<strong>in</strong>ed by geographic, topographic, and climatic variables for 2 pr<strong>in</strong>cipal components.PC Independent variable (sign of coefficient) (%)Latitude50500005040000503000050200005010000Mt Hood50000001 Intercept 2.2Annual precipitation (+) 14.4Grow<strong>in</strong>g degree-days (+) 12.8Frost-free days (-) 5.1Annual precip x east–west aspect (-) 2.4Latitude (+) 2.3East–west aspect (+) 2.0Full model (adjusted for number of variables) 46.72 Intercept 2.9Grow<strong>in</strong>g degree-days (-) 5.7Elevation (-) 5.0Frost-free days (+) 4.6Departure (-) 4.6Full model (adjusted for number of variables) 34.0analysis revealed that moderate amounts of the <strong>variation</strong> <strong>in</strong>PC1 could be expla<strong>in</strong>ed by climatic, geographic, and topographicvariables (Table 5). Total precipitation and grow<strong>in</strong>gdegree-days were the most important <strong>in</strong>dependent variables,account<strong>in</strong>g for approximately 28% and 25% of the modelsums-of-squares, respectively. As one would expect, PC1 score(or plant size and vigor) <strong>in</strong>creased as precipitation and grow<strong>in</strong>gdays <strong>in</strong>creased, and as aspect became more favorable (eastor northeast). Of the source <strong>variation</strong> <strong>in</strong> PC2, 34% could beexpla<strong>in</strong>ed by frost-free period, grow<strong>in</strong>g degree-days, departure,and elevation, each account<strong>in</strong>g for approximately 25% ofthe model sums-of-squares.499000049800004970000570000 580000 590000PC1 score < –1.34PC1 score ≥ –1.34 and < 0.30600000 610000 620000 630000DeparturePC1 score ≥ 0.30 and < 1.38PC1 score > 1.38Figure 4. Pr<strong>in</strong>cipal component scores (PC), divided <strong>in</strong>to 4 classes, plotted by latitudeand departure. Pr<strong>in</strong>cipal component 1 (top). Pr<strong>in</strong>cipal component 2 (bottom).Results of analyses on watershed and plant associationseries classification models are shown <strong>in</strong> Table 6. All levels ofwatershed subdivision were significant effects for PC1 andPC2 account<strong>in</strong>g for 61% and 57% of the sums of squares forsources, respectively. The rema<strong>in</strong><strong>in</strong>g source <strong>variation</strong>, or lackof fit, was significant for both components, signify<strong>in</strong>g therewere other important variables, such as aspect or soil type, not<strong>in</strong>cluded <strong>in</strong> the model. Interest<strong>in</strong>gly, elevation was not a significanteffect <strong>in</strong> classification models, nor <strong>in</strong> the regressionmodel for PC1. Source location effects were consistentlysmaller <strong>in</strong> the watershed model, <strong>in</strong>dicat<strong>in</strong>g it expla<strong>in</strong>ed genetic<strong>variation</strong> better than the plant association model.In the plant association models, associations were significantonly for PC2, as were elevation bands. Source effects weresignificant and large for both PCs, and because the watershedmodels had consistently less lack-of-fit, plant associationmodels were not explored any further.The stepwise selection procedure selected 8 traits to use <strong>in</strong> thediscrim<strong>in</strong>ant function: 1) the amount of red color <strong>in</strong> flowers atWRN; 2) the amount of red color <strong>in</strong> flowers at PMC; 3) the45DAVID L DOEDE NATIVEPLANTS | SPRING 2005

46TABLE 6Comparison of watershed and plant association classification models used toexpla<strong>in</strong> <strong>variation</strong> among sources <strong>in</strong> 2 pr<strong>in</strong>cipal components.Effect Number Proportion of sourceclasses<strong>variation</strong> (%) zPC1 PC2Watershed ModelFourth field watershed 4 12.3 ••• 12.5 •••Fifth field watershed 13 28.6 ••• 23.0 ••Elevation 5 2.0 6.7Sixth field watershed 24 20.1 • 21.9 ••Source 84 37.0 ••• 35.9 ••Plant Association ModelPlant Association 6 2.8 20.9 ••Elevation 5 25.4 •• 22.3 ••Source 84 71.8 ••• 56.8 •••z Percentage of sums of squares for sources.••• = P < 0.001; •• = P < 0.01; • = P < 0.05amount of blue color at PMC; 4) ratios of first-year crownheight and diameter at WRN; 5) the same ratios at PMC; 6) second-yearcrown height at PMC; 7) second-year crown diameterat PMC; and 8) the amount of fall regrowth at PMC. Probabilitiesof correct classification were very high (probabilities were100% for 74 of 84 sources), however the discrim<strong>in</strong>ant analysiswas not able to reflect the same variability <strong>in</strong> cytotype with<strong>in</strong>sources as was seen <strong>in</strong> the isozyme analysis. Consequently thiswas not pursued further. If variability <strong>in</strong> cytotype is noted for aspecies, any future studies <strong>in</strong> genetic <strong>variation</strong> for that speciesshould quantify or score the cytotype of <strong>in</strong>dividual plants, asstudies <strong>in</strong> other herbaceous species have shown chromosomenumber to be of some adaptive importance (Burton and Husband1999, 2000; Cronn 2004).DISCUSSIONThis study found significant amounts of source-related genetic<strong>variation</strong> <strong>in</strong> broadleaf lup<strong>in</strong>e, and furthermore, that patterns ofgenetic <strong>variation</strong> among sources correspond to patterns <strong>in</strong>environmental and geographic <strong>variation</strong>.The pattern<strong>in</strong>g of source <strong>variation</strong>, particularly <strong>in</strong> PC1,along moisture and precipitation gradients suggests that plantperformance is related to local conditions at the seed source,and that nonlocal seed sources may or may not perform as wellas the local seed source. This means that seeds should not bemoved <strong>in</strong>discrim<strong>in</strong>ately, and some level of control should beexercised on seed transfers. At the same time, the levels ofwith<strong>in</strong> family and family <strong>variation</strong> with<strong>in</strong> each source showthere is abundant genetic <strong>variation</strong> with<strong>in</strong> sources, and somedegree of transfer should be acceptable.Under natural regeneration, many seeds will drop to theground and sprout for each mature plant that develops. Forexample, Campbell (1979) estimated that a Douglas-fir <strong>in</strong> anold-growth forest is probably selected from more than 2000seedl<strong>in</strong>gs. A certa<strong>in</strong> amount of natural reproduction will be maladapteddue to <strong>in</strong>breed<strong>in</strong>g, migration, drift, and recomb<strong>in</strong>ationdur<strong>in</strong>g meiosis. Some reproduction, possibly more than half, willdie by chance, buried by fall<strong>in</strong>g debris, mov<strong>in</strong>g soil, predation,and so on. Nevertheless, natural th<strong>in</strong>n<strong>in</strong>g between seedl<strong>in</strong>g andmature plant results <strong>in</strong> a large amount of natural selection act<strong>in</strong>gto produce <strong>variation</strong> among and with<strong>in</strong> sourcesImplications for Seed TransferRelative seed transfer risk was quantified by compar<strong>in</strong>g thephenotypic <strong>variation</strong> with<strong>in</strong> a def<strong>in</strong>ed seed zone to the averagegenetic <strong>variation</strong> with<strong>in</strong> a source location. What constitutes anacceptable level of risk will probably vary depend<strong>in</strong>g on theobjectives for seedl<strong>in</strong>g outplant<strong>in</strong>g, availability of seeds andresources, and the philosophy of the organization. Sorensen andWeber (1994) suggested an upper limit for risk of 0.51 for reforestationpractices, based on f<strong>in</strong>al desired crop density adjustedfor mortality occurr<strong>in</strong>g throughout the life span of the plant<strong>in</strong>g.Follow<strong>in</strong>g a similar methodology, I arrived at an upper level forrisk of 0.61. Assumptions <strong>in</strong>cluded a f<strong>in</strong>al crop density of 47 840plants/ha (19 360/ac) on a 46-cm (18-<strong>in</strong>) spac<strong>in</strong>g; 76 289 seeds/kg(34 699/lb); broadcast sow<strong>in</strong>g at 22.4 kg/ha (20 lb/ac); 80% germ<strong>in</strong>ationrate; and 90% fall-down (10% success) because of<strong>in</strong>correct plant<strong>in</strong>g depth, desiccation, predation, and fungalpathogens (Darris 2004). These assumptions resulted <strong>in</strong> aneed for 35% of the seeds sown to become established(47 840/[7620 • 22.4 • 0.8 • 0.1] = 0.349), and 65% expendable.The difference between an R of 0.61 and the 65% that can be lostprovides an additional small cushion for the unexpected.Transfer risk was estimated for several different seed zonestrategies (Table 7). The strategies were chosen to display arange of options for limit<strong>in</strong>g seed transfer effects and weresuggested by results of regression and classification analyses.While climatic variables, such as grow<strong>in</strong>g degree-days andannual precipitation, were important explanatory variables <strong>in</strong>regression analyses, this data is not commonly available tofield personnel. Consequently geographic land divisions, suchas the watershed del<strong>in</strong>eations, were used as a convenient, readilyavailable method for construct<strong>in</strong>g zones. It should bestressed that the calculated risks are relative, have not beenfield-tested, and the utility of these estimates is primarily <strong>in</strong>compar<strong>in</strong>g the effectiveness of different strategies.When seeds are collected from and freely moved aboutwith<strong>in</strong> the entire Mt Hood National Forest, the proportion ofNATIVEPLANTS | SPRING 2005GENETIC VARIATION IN BROADLEAF LUPINE

TABLE 7Relative risk (R) estimates for seed transfer zones for Lup<strong>in</strong>us <strong>latifolius</strong> on the MtHood National Forest.Effect R for PC1 R for PC2 Comb<strong>in</strong>ed RNo subdivision 0.048 0.035 0.66Fourth field watersheds 0.040 0.031 0.059Fifth field watersheds 0.033 0.024 0.049Sixth field watersheds 0.027 0.020 0.042a seedlot outside the genetic distribution at the plant<strong>in</strong>g locationis 66%. Look<strong>in</strong>g at it another way, 34% of the seed populationis still with<strong>in</strong> the genetic distribution at the plant<strong>in</strong>glocation, attest<strong>in</strong>g to the significant amounts of genetic <strong>variation</strong>with<strong>in</strong> local populations of this species.The most conservative strategy, collect<strong>in</strong>g and deploy<strong>in</strong>gseeds with<strong>in</strong> a sixth field watershed (the smallest watersheddivision), had a relative risk of 0.42. This strategy would berelatively expensive, and necessitate us<strong>in</strong>g 87 seed zones forthe entire Mt Hood National Forest. However, if the objectivefor outplant<strong>in</strong>g was to mimic exist<strong>in</strong>g patterns of genetic <strong>variation</strong>,this gives an idea of the amount of mismatch therewould be <strong>in</strong> a collection area this size.Us<strong>in</strong>g fifth field watersheds is very similar to the exist<strong>in</strong>gseed collection and deployment policy of the USDA ForestService. Risk associated with mov<strong>in</strong>g seeds with<strong>in</strong> these del<strong>in</strong>eationsis 0.49, fairly conservative when compared to the 0.61allowable risk calculated us<strong>in</strong>g the procedure outl<strong>in</strong>ed above.It would result <strong>in</strong> 31 seed zones to cover the entire forest.Fourth field watersheds are recommended for seed zonesfor this species on the Mt Hood National Forest (Figure 1).Risk for this level of geographic subdivision is 0.59, below theupper allowable transfer risk of 0.61. This strategy wouldrequire only 4 seed zones, and provides a reasonable compromisebetween ma<strong>in</strong>ta<strong>in</strong><strong>in</strong>g many locally adapted seedlots andkeep<strong>in</strong>g management costs for collection and ma<strong>in</strong>tenancelow. Seeds should be collected from several sites (10 to 16) tosample with<strong>in</strong>-zone <strong>variation</strong>, but a large number of sitesshould not be necessary because of moderate amounts ofwith<strong>in</strong>-source <strong>variation</strong> found <strong>in</strong> this study and <strong>in</strong> allozymes(Wilson and Hipk<strong>in</strong>s 2002). With<strong>in</strong> a source, many plants,perhaps as many as 30 to 50, should be collected from to adequatelysample with<strong>in</strong>-source genetic <strong>variation</strong>.ACKNOWLEDGMENTSJoan Tr<strong>in</strong>dle and Dale Darris provided excellent field andtechnical assistance, and Caitl<strong>in</strong> Cray coord<strong>in</strong>ated the field47DAVID L DOEDE NATIVEPLANTS | SPRING 2005

collections. All are gratefully acknowledged. Frank Sorensen,Nancy Mandel, and Rich Cronn provided valuable <strong>in</strong>sights andcomments throughout the entire project, thank you. This projectwas funded by the USDA Forest Service.REFERENCESBurton TL, Husband BC. 1999. Population cytotype structure <strong>in</strong> the polyploidGalax urceolata (Diapensiaceae). Heredity 82:381–390.Burton TL, Husband BC. 2000. Fitness differences among diploids, tetraploids, andtheir triploid progeny <strong>in</strong> Chamerion angustifolium:mechanisms of <strong>in</strong>viability andimplications for polyploid evolution. Evolution 54(4):1182–1191.Campbell RK. 1975. Adaptational requirements of plant<strong>in</strong>g stock. In: Globalforestry and the western role, 1975 Permanent Association CommitteesProceed<strong>in</strong>gs. Portland (OR):Western Forestry and Conservation Association.p 103–107.Campbell RK. 1979. 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Placerville (CA):National Forest Gel Electrophoresis Laboratory.A UTHOR INFORMATIONDavid L DoedeArea <strong>Genetic</strong>istUSDA Forest ServiceGifford P<strong>in</strong>chot National ForestMt Adams Ranger District2455 Hwy 141Trout Lake,WA 98650ddoede@fs.fed.us48NATIVEPLANTS | SPRING 2005GENETIC VARIATION IN BROADLEAF LUPINE