relative contributions of crown and phenological traits to...

ORIGINAL PAPER

Relative contributions of crown and phenological traits to growthof a pseudo-backcross pine family ((slash×loblolly)×slash)and its pure species progenitors

Patricio R. Muñoz Del Valle & Dudley A. Huber &

Timothy A. Martin

Received: 17 February 2011 /Revised: 17 April 2012 /Accepted: 25 April 2012# Springer-Verlag 2012

Abstract One pseudo-backcross [(slash×loblolly)×slash](BC1) and open-pollinated families of the pure speciesprogenitors were established in a single test in North CentralFlorida. Multivariate analysis was used to estimate the intra-trait correlation among the taxa, and path analyses wereused to determine the relative contributions of crown archi-tectural and phenological traits to first-year height growth.The multivariate analysis indicated that BC1, slash, andloblolly pine have different relationships among the traitsstudied, suggesting that a separate path analysis was re-quired for each taxon. Path analysis coefficients of determi-nation of the final models were 0.69, 0.73, and 0.65 for thepseudo-backcross, loblolly, and slash pine families, respec-tively. The ranking of traits by relative magnitude of effecton total growth was, for the pseudo-backcross crown pro-jected area (CPA), fascicle length (FL), number of nodes(NN), number of branches (NB), number of needles perfascicle (NF), and fascicle diameter. For loblolly, this wasCPA, NB, FL, NN, NF, initiation, and specific leaf area. Forslash, this was CPA, NN, FL, NF, and NB. The studyindicated that all crown traits considered in the path analysishad moderate effects on first-year height growth, with theexceptions of the consistently large effect of CPA and theminimal effect of the phenological traits.

Keywords Pinus taeda .Pinus elliottii . Pseudo-backcross .

Path analysis . Crown . Growth . Hybrid

Introduction

The use of hybrids in breeding has been motivated primarilyas an alternative to increase genetic diversity within taxathat have low or almost null genetic variability for certaintraits or as a tool to combine desirable characteristics. Oftenin plant science, the traits of commercial importance are pestresistance, growth, or adaptation to extreme or difficultenvironments. For example, hybrids between Eucalyptusgrandis and Eucalyptus urophylla often combine high resis-tance to canker (Cryphonectria cubensis) and high wooddensity in selected genotypes used for clonal propagation(Campinhos et al. 1998). The common rule is that hybridsshow characteristics intermediate to the parents (Wright1976), but they can also perform better or worse than theparental average, a phenomenon known as heterosis (Wright1976; Zobel and Talbert 1984; White et al. 2007).

In pine, hybrids have provided an attractive approach forimprovement. In southeast Queensland, Australia, the hy-brid between Pinus elliottii var. elliottii (slash pine) andPinus caribaea var. hondurensis (Caribbean pine) was su-perior in growth when compared to either parent. As aresult, plantations of the Caribbean pine and slash pine inQueensland have been almost entirely replaced by theslash×Caribbean hybrid (Nikles 2000). In addition to desir-able growth, this hybrid has superior stem form, woodquality, and wind firmness compared to the mean of theparental species (Harding and Copley 2000). The samehybrid has been reported to outperform slash pine forgrowth rate in South Africa at 13.5 years of age with 2.5times the volume and better stem form (Sijde and Roelofsen

Communicated by R. Burdon

P. R. M. Del Valle (*) :D. A. Huber : T. A. MartinSchool of Forest Resources and Conservation,University of Florida,P.O. Box 110410, Gainesville, FL, USAe-mail: [email protected]

D. A. Hubere-mail: [email protected]

T. A. Martine-mail: [email protected]

Tree Genetics & GenomesDOI 10.1007/s11295-012-0514-7

1986). This documented hybrid performance motivated theCooperative Forest Genetics Research Program (CFGRP) atthe University of Florida to investigate the potential of pinehybrids. In 1994, CFGRP established 11 field trials withseven taxa in the Lower Coastal Plain of the southeasternUSA, including the P. elliottii var. elliottii×Pinus taeda(loblolly pine) hybrid (Lopez-Upton 1999).

In the southeastern USA, loblolly and slash pine, respec-tively, cover close to 12.0 and 5.3 million hectares in natu-rally regenerated and planted stands (Jokela and Long2000). The study of loblolly and slash pine hybrids isimportant not only because they are the most importantcommercial tree species in the southeastern USA (Dorman1976; Borders and Harrison 1989; McKeand et al. 2003) butalso because they contrast in commercially important char-acteristics, making the introgression of traits from one spe-cies to the other attractive. Some of these differences are: (1)Loblolly pine is widely known for high-volume produc-tion and is the fastest growing species of the southernpines on most sites (Dorman 1976), except for very poor-ly drained flatwoods where slash pine performs better(Borders and Harrison 1989); (2) loblolly pine has a lessdesirable form compared to slash pine (Dorman 1976;Huber et al. 2007, 2008); (3) loblolly is more susceptibleto tip moth (Rhyacionia spp.) than slash pine (Lopez-Upton et al. 2000); (4) slash pine is more susceptible tofusiform rust (Cronartium quercuum) than loblolly pine(Lopez-Upton et al. 1999); (5) loblolly pine branches aretypically larger in length and diameter (Dorman 1976;Xiao et al. 2003) and have a larger numbers of sidebranches or bifurcations (Dalla-Tea and Jokela 1991);and (6) loblolly needles are shorter (Richardson 1998;Chmura et al. 2007) with higher specific leaf area(McGarvey et al. 2004; Chmura et al. 2007) and loblollypine has greater whole-tree leaf area (Dalla-Tea andJokela 1991; Xiao et al. 2003; Martin and Jokela 2004;Emhart et al. 2007).

A number of studies have quantified the relationshipsbetween crown traits and growth (Allen and Scarbrough1970; Govindaraju 1984; Jokela and Martin 2000) andbetween phenology and growth (Jayawickrama et al. 1998;Emhart et al. 2006) in loblolly and slash pine. From thesestudies, growth potential differences between loblolly andslash pine appear to be due to the different growth strategiesthey exhibit in the early stages (Colbert et al. 1990), whereslash pine allocates more resources to stem growth andloblolly pine to branch and foliage, resulting in larger crownarea (Martin and Jokela 2004).

F1 hybrids between slash and loblolly pine have beenreported to be very heterogeneous and on average did notshow heterosis; however, some outstanding individuals havebeen found (Nikles 2000). Barnes and Mullin (1978) alsoreported greater within-family variation in the hybrid than

the pure species (slash or loblolly pine) for third-year height.In the CFGRP's growth trials of the F1 hybrids at 8 years afterplanting, the slash×loblolly pine hybrid was equal to theaverage of the two parental species with several outstandingindividuals (Huber et al. 2000). A few of the most outstandingindividuals out of the CFGRP hybrid population were select-ed and crossed to a different slash pine parent (therefore,called pseudo-backcrossed), as is recommended when in-breeding depression exists in the species and there is not astrong heterosis in the F1 hybrid (Kinghorn 2000; Shelbourne2000). The backcross breeding program generated the basegenetic material for the present study with the overall objec-tive of introgression of loblolly traits into the slash pinebreeding program. This pseudo-backcross has already beenshown to be successful with heterosis for height growth andother traits when compared to the parental average in acommon-garden trial (Muñoz Del Valle et al. 2011). Thisresult is comparable with successful backcrosses in pinefound in the literature (Slee 1972; Kraus 1986). The nextlogical step for tree improvement was to study whether thepure species relationships among component traits are main-tained or altered in the pseudo-backcross.

When starting a pine hybrid breeding program, it isimportant to understand the intra-trait relationship in thepseudo-backcross and to compare the hybrid against thepure species in terms of the relative contribution of thedifferent traits to total growth. This is a simple task whentwo or even three traits are involved and where a correlationmatrix would give sufficient insight. However, as morevariables are considered, a correlation matrix is increasinglydifficult to use and interpret because of the positive ornegative associations among traits. An approach for analyz-ing and interpreting complex relationships is path analysis, aform of multiple regression described by Wright (1921) as“a flexible means of relating the correlation coefficientsbetween variables in a multiple system to the functionalrelations among them.” A primary advantage of path anal-ysis is the graphical representation of the relationshipsamong the different traits that are assumed to exist (Bollen1989). Path analysis also allows the researcher to estimatethe relative influence of each variable on the response var-iable within the network.

Path analysis has been fully detailed in the literature(Kremer 1985; Bollen 1989; Lynch and Walsh 1998)and has been used in crop plants to dissect relationshipsbetween productivity and its components (Dewey andLu 1959; Duarte and Adams 1972; Cramer and Wehner1998; Bidgoli et al. 2006; Babar et al. 2007) and inforestry for these and other purposes, such as changein crown morphology traits with plantation density, ef-fect of fungal infection in the forest, survival of theunderstory in the function of crown properties, andphysiological relationships (Kremer and Larson 1983;

Tree Genetics & Genomes

Govindaraju 1984; Lundquist 2000; Sterck et al. 2003;Weisberg 2004; Parisi 2006). Although the usefulness ofpath analysis is limited by the knowledge that the re-searcher has of the biological phenomena underlying thetraits (Wilkinson et al. 1996), it is a valuable tool tocompare different populations under the same model(Wright 1960). This study was developed in two stages:the first stage was to determine and compare the intra-trait relationships of the pseudo-backcross against thepure species progenitors; the second stage was to usethe estimated correlations among traits to compare therelative contributions of crown architectural and pheno-logical traits to first-season height growth in the pseudo-backcross and the pure species progenitors on one sitein North Central Florida.

Materials and methods

Experimental site and genetic material

This material was the subject of a previous paper (MuñozDel Valle et al. 2011) where, specifically, hybrid vigorwas investigated and where more details for the soil char-acteristics, climate, site preparation, and experimental de-sign can be found. In summary, the experiment consistedof one trial planted on 18 December 2007 in AlachuaCounty, Florida. The potential productivity for both slashand loblolly pine has been qualified as relatively high inthis site (Thomas et al. 1985). The genetic material for thisexperiment included four families: (1) open-pollinatedfamily (Slash1_OP) from an original slash pine selection;(2) open-pollinated family (Slash3_OP) from a third cycleof improvement slash pine selection; (3) open-pollinatedfamily (Lob_OP) from an original loblolly pine selection;and (4) a pseudo-backcross (Slash1×Lob)×Slash3 (BC1)(Fig. 1), called pseudo-backcross as the genotype Slash3is different from Slash1.

The original F1 slash×loblolly pine hybrids were tested,along with other taxa, in 11 trials established in 1994 by theCFGRP (Lopez-Upton 1999). At age 8, 30 F1 individualswere selected and needle samples were collected and sentto two genetic marker labs. In 5 of the 30 selections, thepresence of a good quality loblolly parent was detected

(Lob) and F1s from that parent were chosen to start an intro-gression program into the slash pine breeding program (Gezanet al. 2005), the primary species for the CFGRP. The particularslash×loblolly (SL1) corresponded to one F1 pine elite selec-tion and was thematernal parent of BC1 (Fig. 1). Finally, the F1slash×loblolly pine hybrid was not included in the test becauseits performance is already known (see Lopez-Upton 1999) andthe practical application is to ascertain whether the backcrosswill perform better than current families in the breedingprogram.

Traits evaluated

Numerous traits were measured in the first year of growthin this test (phenology, growth, pest resistance, crownarchitecture, and needle traits) and used to evaluate familymeans and heterosis (Muñoz Del Valle et al. 2011). Asubset of traits (Table 1) was chosen for this study con-sidering the relationships between crown size and growth(Jokela and Martin 2000) and seasonal phenology withgrowth (Bollman and Sweet 1977; Jayawickrama et al.1998). These growth and phenological traits were: (1)Height (in centimeters) was assessed 14 times during thefirst growing season beginning in 15 February until 30November and more frequently at the beginning and endof the growing season (details in Muñoz Del Valle et al.2011) with a graduated pole by measuring the distancefrom the ground to the tip of the highest bud; (2) initiationand cessation of height growth (days) were estimated bylinear interpolation to determine when the plants reached5 and 95 % of their annual growth, respectively (Mirov etal. 1952; Jayawickrama et al. 1998; Emhart et al. 2006);(3) total growth (TG) for the period calculated as the

Fig. 1 Pedigree of the pseudo-backcross family (BC1). Slash1 and Lobwere first cycle selections and Slash3 a third cycle selection. SL1 was theslash×loblolly F1 hybrid selection (from Muñoz Del Valle et al. 2011)

Table 1 Details of number of seedlings, least-squares means, and stan-dard error (in parenthesis) for traits used in this study for each taxon

Lob_OP BC1 Slash1_OP Slash3_OP

No. ofseedlings

352 738 282 228

IN (days) 59.2 (0.44) 57.5 (0.37) 54.6 (0.39) 57.4 (0.42)

CS (days) 274 (1.6) 270.7 (1.4) 270.2 (1.8) 281.3 (1.5)

DU (days) 214.6 (1.6) 212.9 (1.3) 215.4 (1.7) 223.9 (1.5)

TG (cm) 24.5 (0.7) 26.1 (0.6) 18.3 (0.5) 27.7 (0.6)

NN (#) 3.4 (0.8) 3.33 (0.8) 2.31 (0.7) 3.7 (0.8)

NB (#) 7.72 (0.2) 6.88 (0.18) 4.53 (0.18) 7.94 (0.23)

CPA (cm2) 638.8 (20.9) 670 (21) 528.7 (16.4) 921.6 (24.2)

FL (mm) 143.8 (2.07) 164.1 (1.9) 182.8 (2.45) 199.8 (2.53)

NF (#) 3.04 (0.01) 2.93 (0.01) 2.66 (0.02) 2.88 (0.02)

FD (mm) 1.75 (0.02) 1.94 (0.01) 2.06 (0.02) 2.22 (0.02)

SLA (cm*g−1) 50.8 (0.55) 44.3 (0.5) 39.7 (0.53) 42.4 (0.51)

# to indicate that the unit of the variable was numeric


difference in height between initiation (IN) and cessation(CS) dates; and (4) duration of the growing season (DU)as the difference between the initiation and the cessationdates. Crown architecture and needle traits were collectedat final measurement and recorded for each tree: (1) totalnumber of nodes on the primary stem (NN); (2) number ofprimary branches (NB); (3) crown projected area (CPA, insquare centimeters) estimated with digital images, and thethreshold technique (King et al. 2008) where the digitalimages of each tree taken with a Nikon D40x camera wereprocessed with the software ImageJ (Rasband 1997–2005; seeMuñoz 2009 for details); (4) fascicle length (FL, in milli-meters) from three fully expanded needle fascicles measuredwith a graduated rule to the nearest millimeter; (5) number ofneedles per fascicle (NF) counted in each of five fasciclesamples per tree; (6) fascicle diameter (FD, in decimeters)measured in three fascicles with a graduated magnifying lens(×10); and (7) projected specific leaf area (SLA, in squarecentimeters per gram) estimated using the ratio between sur-face area and dry weight of needles times 1/π (Gonzalez2008), where surface area was estimated according to Murtyand Dougherty (1997) and needles were oven-dried for 48 h at65°C and weighed to the nearest 0.0001 g (XA-100, DenverInstruments, Denver, CO, USA).

Statistical analyses

Multivariate analysis was performed to test whether differ-ent families (BC1, slash1_OP, slash3_OP, and Lob_OP) haddifferent relationships among traits and to estimate correla-tion matrices to serve as input for the path analysis. Amultivariate analysis of the 11 traits mentioned above was

performed using the software ASReml v.2 (Gilmour et al.2006). The statistical linear mixed model considered was

y ¼ X b þ Zuþ e ð1Þwhere y0[TG′ NN′ NB′ CPA′ FL′ NF′ FD′ SLA′ IN′ CS′ DU′]′, i.e., a stacked vector with dependent variables; β is avector of family fixed effects; u contains vectors of row andcolumn random effects; X and Z are incidence matrices; ande is a vector of random errors. The variance–covariancematrix of errors, R0var(e′e), was defined with three differ-ent structures: model 1—a pooled unstructured error matrixcommon for all families (55 covariance components); model2—an unstructured error matrix for each taxon (165055×3covariance components); and model 3—an unstructurederror matrix for each family (220055×4 covariance compo-nents). Likelihood ratio test (LRT) (Wolfinger 1996; Littellet al. 2006) was used to test different covariance matrices,and here, it was used to determine whether one (pooled),three [by taxon: slash13 (Slash1_OP and Slash3_OP together),Lob_OP, and BC1], or four (by family) correlation matriceswere needed. Prior to the analysis, each trait was standardizedby dividing each observation by the square root of the errorvariance for the trait–taxon combination. This makes all theresidual variances equal to 1 upon reanalysis. Once the residualvariances are set to “1” for the multivariate analysis, the off-diagonal elements become correlations. Thus, a determinationwas made whether correlation varied among traits varied bytaxon or by family.

Based on the relationships that are assumed to holdamong the traits studied, a hypothetical path diagram wasproposed (Fig. 2). The diagram was divided into two mainbranches: one based on crown architectural traits and the

IN(9)

p71

p72

p73

p74

p75

p87

r12

r13

r14

r15 r23

r24

r25 r34

r35

r45

r16

r26

r36

r46

r56

p811

p76 p119

p1110

E7 E8

E11

r910

r711

NN(1)

NB(2)

FL(3)

FD(4)

NF(5)

SLA(6)

CPA(7)

CS(10)

DU(11)

TG(8)

Fig. 2 Hypothetical pathdiagram showing that NN(number of nodes), NB (numberof branches), FL (fasciclelength), NF (number of needleper fascicle), FD (fasciclediameter), and SLA (projectedspecific leaf area) directlyimpact CPA (crown projectedarea) and interacting with eachother to indirectly impact TG(total growth). Also, IN(initiation) and CS (cessation)directly impact DU (duration ofgrowth) and interact with eachother to indirectly impact TG.pij are inferred path coefficientsand ρkl are observed correlationcoefficients. Ei is the residualerror term that reflected theunexplained variance andmeasurement error


other on phenological traits. In this diagram, CPA was anendogenous variable whose variance was theorized to beexplained by exogenous variables NN, NB, FL, NF, FD, andSLA plus the residual error term E7 that reflected the unex-plained variance and measurement error. The variance of theendogenous variable DU was theorized to be explained by theexogenous variables IN and CS plus the residual error termE11. At the same time, the endogenous variable TG wastheorized to be explained by CPA, DU, and the residual termE8. Directional arrows indicate this relationship where eachconnection is associated to a path coefficient (pij), which is ameasure of the effect of i on the cause j (direct effect). Thelines with arrows at both ends indicate correlation betweenboth variables (rkl), a measure of the pairwise relationshipbetween the first variable “k” and the second variable “l.”

Using the estimated correlation matrices from the multi-variate analysis, path analyses were performed with SAS/PROC CALIS® software (SAS Institute Inc. 2002–2003);direct and indirect effects were extracted. The indirecteffects are the effects of one variable on another mediatedby a third variable (e.g., effect of NN on TG in Fig. 2).Starting with the proposed diagram (Fig. 2), alternativepaths for the exogenous variables were tested for eachtaxon; that is, in addition to the original diagram, exogenousvariables were tested as having only a direct effect and bothdirect and indirect effects on TG. The final model waschosen based on the criteria of fitting assessed with a chi-square test, parsimony with Akaike information criteria(AIC), and an approximated t test (P<0.05) were used toevaluate the individual path parameters.

Results

It was not possible to fit the multivariate analysis with alltraits of interest because of a strong positive collinearitybetween CS and DU. Therefore, to retain the phenologybranch, CS was dropped from all subsequent analyses.Some details of the fitting and comparison of the multivar-iate models for the three variance–covariance matrices forthe residuals are presented in Table 2. The log-likelihoodratio test indicates that the model with one error structure

(model 1) differed significantly (P<0.05) from both three(model 2) and four (model 3) error matrices. However, themodel with four error structures was not significantly dif-ferent from the model with three error structures (P00.245).These results showed that the model with the best fit had threecorrelation matrices (model 2), one for each taxon (Slash13,Lob_OP, and BC1). From the multivariate analysis, almost allcorrelations among variables were significant (P<0.05); how-ever, no significant correlations were found in BC1 for DU–IN and SLA–NB; in Lob_OP for CPA–DU, SLA–NB, FD–IN, FD–SLA, and NF–SLA; and in Slash13 for CPA–DU,SLA–NN, SLA–NB, SLA–CPA, FD–SLA, NF–FL, and NF–SLA. Additionally, a moderate to highly significant correla-tion (0.20–0.74) was found between CPA and needle andcrown traits across all taxa, with the exception of SLA forSlash13 (correlation matrices found in Muñoz 2009).

Path analyses were performed by taxon using the threecorrelation matrices from the multivariate analysis. In thethree cases, the hypothesized path model (Fig. 2) wasrejected as not adequately fitting the data. The variants ofthe hypothesized model were tested for each taxon separate-ly, and the best path model was chosen for each taxon basedon goodness of fit measured by the higher likelihood (chi-square test, P>0.1), parsimony (smallest AIC), and signifi-cance of the individual path (approximate t test, P<0.05).Based on these criteria, the final model parameters for eachtaxon are presented in Table 3.

All traits were kept in the final diagrams presented foreach taxon; however, traits with non-significant paths werenot included in the final model. In the path diagram, lineswithout path coefficients signify non-significant paths; allcorrelation lines for non-significant traits were removed fora better visual comparison of the different models (Figs. 3,4, and 5). The best model for Lob_OP (Fig. 3) differs fromthe proposed hypothesized model. That is, NN and NF had apositive direct effect on TG with no indirect effect throughCPA. SLA and IN had a negative direct effect on TG withno indirect effect through CPA and DU, respectively. Inaddition, FD and DU had no effect on TG. Although thebest path models for Slash13 and BC1 were very similar andgraphically differed only by the significant path from FD toCPA present only in BC1 (Figs. 4 and 5), they differed fromthe original proposed model. That is, NN and NF, in addi-tion to their indirect effect on TG through CPA, had asignificant positive direct effect on TG. It was the case forNB, but with a negative direct effect on TG. Additionally,neither the phenology branch nor SLA had a significanteffect on TG for both Slash13 and BC1.

The path coefficients for CPA–TG were the largest, vary-ing from 0.73 to 0.82, but were not significantly differentamong taxa (confidence intervals overlapping). Asexpected, the path coefficients for NB–CPA and NB–TGwere found to be more similar between Slash13 and BC1

Table 2 P values for the likelihood ratio test for the error structure inthe multivariate model testing

Model No. ofparameters

No. oftraits

LogLa χ2 Pvalue

Model comparisonhypothesis

1 55 10 8,530.94

2 165 10 8,129.82 0.00000 2 to 1

3 220 10 8,067.98 0.24512 2 to 3

a −2×[log (likelihood)]


than either with Lob_OP. In general, path coefficients be-tween crown traits and CPA were similar for Slash13 andBC1, with the exception of NF that was significantly largerfor Slash13 and FD–CPA that only was significant for BC1.Only the path coefficient NB–CPA was significantly differ-ent between Lob_OP and either Slash13 or BC1 whencomparable paths were considered. Differences betweenLob_Op and either Slash13 or BC1 were found in thenumber of traits affecting TG as well as in the form of theireffect (direct or indirect) on TG. IN and SLA were onlysignificant for Lob_OP, with a direct negative effect on TG.NF and NN had only a direct effect on TG for Lob_OP,while it had direct and indirect effects in Slash13 and BC1.Finally, NB had an additional negative direct effect on TGfor Slash13 and BC1.

The partitioning of the total relative effects into direct andindirect effects is presented in Fig. 6 where the sum of thedirect and indirect effects is the total effect. Also, the across-

trait summation of effects on TG standardized by the totaleffect across all traits is presented. Mathematically, theindirect effect is the product of the path coefficients, i.e.,the indirect effect of NB on TG for Lob_OP is 0.602×0.74800.45. For BC1, the total effect of FL and FD onTG was wholly explained by indirect effects, while theeffects of NB, NN, and NF on TG were explained byindirect effects at 64, 48, and 43 %, respectively (Fig. 6).In the case of Lob_OP, the total effects of NB and FL on TGwere fully explained by indirect effects while for NN, NF,SLA, and IN were fully explained by direct effects. ForSlash13, the indirect effects explained all the total effectfor FL, 58 % for NB and 57 % for NF (Fig. 6).

The results of path analysis were used to rank thetraits according to the magnitude of their total relativeeffect on TG (Fig. 6). The rank of trait effects on heightgrowth in order of relative importance for BC1 wasCPA, FL, NN, NB, NF, and FD; for Lob_OP, the rank

Table 3 Fitting parameter of thefinal path model for each taxon Best model parameters Lob_OP Slash13 BC1

Coefficient of determination CPA equations 0.65 0.65 0.72

Coefficient of determination TG equations 0.79 0.76 0.68

Total determination of all equations 0.69 0.65 0.73

Root mean square error of approximation (RMSEA) 0.040 0.000 0.044

Akaike information criterion (AIC) −2.73 −1.56 −0.03

Adjusted for df goodness of fit index (AGFI) 0.96 0.99 0.89

Chi-square 9.26 0.44 3.97

Degree of freedom (df) 6.00 1.00 2.00

Pr>chi-square 0.16 0.51 0.14

0.162 (0.03)

0.602 (0.04)

0.394 (0.04)

0.096 (0.03)

0.748 (0.03)

0.780.42

0.18 0.38

0.23-0.16

-0.10

-0.35

-0.05

-0.060(0.03)

-0.066 (0.03)

E7 E8

NN

NB

FL

FD

NF

SLA

CPA TG

IN

DU

E11

0.206 (0.02)

0.355 (0.03)

0.20

80 - 100

60 - 79

40 - 59

20 - 39

00 - 19

Fig. 3 Path diagram showinginferred path coefficient andsignificant (Pr<0.05)correlations for Lob_OP. NN(number of nodes), NB (numberof branches), FL (fasciclelength), NF (number of needleper fascicle), FD (fasciclediameter), SLA (projectedspecific leaf area), CPA (crownprojected area), TG (totalgrowth), IN (initiation), DU(duration of growth), E7, E8,and E11 are the residual terms.Standard errors for pathcoefficients are in parentheses.Non-significant (P>0.05) cor-relations and path coefficientsare omitted. Line width is pro-portional to the strength of thecoefficient/correlation (seelegend)


was CPA, NB, FL, NN, NF, IN, and SLA (last two withnegative effect); and for Slash13, the order of traits wasCPA, NN, FL, NF, and NB.

Discussion

Crown traits, particularly leaf area or leaf area index, haveconsistently been shown to be important determinants of

loblolly and slash pine growth rates (Colbert et al. 1990;Will et al. 2001; McGarvey et al. 2004; Chmura andTjoelker 2008). Studies of growth phenology, while lesscommon, have also shown that under some conditions, thetiming and duration of growth can have impacts on the totalgrowth achieved in a season (Lanner 1976; McCrady andJokela 1996; Jayawickrama et al. 1998; Emhart et al. 2006).While several studies have examined genetic variation inindividual crown or phenology traits (e.g., McCrady and

0.130 (0.04)

0.375 (0.04)

0.427 (0.04)

0.075 (0.03)

0.819 (0.04)

0.780.42

0.21 0.33

0.18

0.23

E7 E8

NN

NB

FL

FD

NF

SLA

CPA TG

IN

DU

E11

0.321 (0.02)

0.285 (0.02)

0.145 (0.04)

-0.168 (0.04)

0.068 (0.03)

80 - 100

60 - 79

40 - 59

20 - 39

00 - 19

0.40

0.31

0.78

0.31

0.084 (0.04)

Fig. 4 Path diagram showinginferred path coefficients andsignificant (Pr<0.05)correlations for BC1 (seecaption in Fig. 3 forabbreviations). Standard errorsfor path coefficients are inparentheses

0.238(0.04)

0.369 (0.05)

0.391 (0.03)

0.102 (0.03)

0.725(0.04)

0.750.20

0.20 0.22

0.18

0.07

E7 E8

NN

NB

FL

FD

NF

SLA

CPA TG

IN

DU

E11

0.374(0.02)

0.433 (0.03)

0.149 (0.05)

-0.198(0.05)

0.193(0.03)

80 - 100

60 - 79

40 - 59

20 - 39

00 - 19

Fig. 5 Path diagram showinginferred path coefficient andsignificant (Pr<0.05)correlations for Slash13 (seecaption in Fig. 3 forabbreviations). Standard errorsfor path coefficients are inparentheses


Jokela 1996; McGarvey et al. 2004), none, to our knowl-edge, has comprehensively examined genetic variation inthe traits underlying whole-plant leaf area and componentphenological traits as we have done in the current study.

In this analysis, the original model included 11 traits,although high correlation between CS and DU did not allowfitting the complete set of traits. Similar high correlationsbetween these traits have been reported for loblolly andslash families by Emhart et al. (2006) and for loblolly pineby Parisi (2006). These results further indicated that growthduration, in these conditions, was largely a function of thedate of growth cessation rather than the timing of growthinitiation. In addition, since DU is a function of CS and IN,there is inevitable collinearity among them; however, thevariation in CS is indirectly included through DU in thepresent analysis.

Prior to the multivariate analysis for correlation struc-tures, an LRT revealed that the variances were different forthe different taxa for growth traits. This is not surprisingsince the taxa were growing at different rates. Since we were

interested in the correlation between traits and whether thesewere different among taxa, the data were standardized asdescribed earlier. Thus, the best model (ten traits) had threecorrelation matrices (by taxon as Lob_OP, Slash13, andBC1), suggesting that the nature of the intra-plant traitrelationships changed according to taxon. The explanationfor two of the three different correlation matrices may comefrom different early growth strategies manifested by loblollyand slash pine (Colbert et al. 1990), where slash pine tendsto partition more biomass to stem tissue while loblolly pinepartitions more to branch and foliage (Martin and Jokela2004). In addition, the correlation matrices indicated con-sistency at the species level for the slash pine families. Thetwo slash families belong to different improvement cyclesand had different means for all traits measured (Muñoz DelValle et al. 2011). However, they were consistent in theirintra-trait relationships. For BC1, the mix of alleles led to adifferent allocation pattern. These results indicated that theintra-plant trait relationships varied among species and thatthose relationships were not totally maintained in thepseudo-backcross.

For the slash and loblolly pine families studied here, therewas no significant correlation between TG and DU. How-ever, previous research indicated that the relationship be-tween growth and phenology varied by family. Emhart et al.(2006) found a significant relationship between TG and DUfor one slash family and no significant relationship for otherslash families. In the case of BC1, a significant but weakcorrelation was found for TG and DU, while DU and INwere not correlated. In contrast, Parisi (2006) found that DUwas significantly but weakly correlated with IN for loblollypine. All of these results indicate that caution should betaken concerning inference when working with phenologi-cal traits in slash and loblolly pine and their hybrids. Spe-cific leaf area was significantly correlated with the fewesttraits, indicating near independence from most traits consid-ered in this study.

The path analysis had moderate to high coefficients ofdetermination for the final models, non-significant chi-square and small AIC (Table 3). These indicate that the pathanalysis models fit the data reasonably well and also are anindication that the biological assumptions considered toconstruct the final path analysis are valid. However, othermodels and assumptions may also fit the data satisfactorily(Bollen 1989). Path coefficients may be interpreted as thechange in the dependent variable caused by a change in theindependent variable (both in standard deviations) when allother background variables are held constant (Lynch andWalsh 1998). Large values for path coefficients betweenCPA and TG were found for all taxa, with no statisticaldifferences among taxa. These results coincided with otherpath analysis studies in jack pine (Govindaraju 1984) andconfirmed the importance of the crown for height growth

-0.2 -0.1

00.10.20.30.4 0.50.60.70.80.9

11.1

NN NB FL SLA FD NF CPA IN DU Across

Rel

ativ

e ef

fect

(a)

Direct Indirect Total

-0.2 -0.1

00.10.20.30.4 0.50.60.70.80.9

11.1


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(b)

-0.2 -0.1

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(c)Direct Indirect Total

Fig. 6 Direct, indirect, and total relative effects of crown architectureand phenology traits on total growth for BC1 (a), Lob_OP (b), andSlash13 (c) (see caption in Fig. 3 for abbreviations)


(Allen and Scarbrough 1970; Govindaraju 1984; Jokela andMartin 2000; Chmura et al. 2007). In addition, BC1 grewsimilarly to Slash3_OP, on average the largest family in thetest, while maintaining a significantly smaller crown(Muñoz Del Valle et al. 2011), indicating that BC1 wasmore efficient than either one of its pure parent species interms of height growth per unit of crown area.

The pattern of contribution of NN, NB, and NF to TGwas found to be more similar between Slash13 and BC1(Figs. 3, 4, and 5) than either with Lob_OP. Such similaritycould be explained by the fact that BC1 was expected toshare 75 % of the slash alleles. While the direct effect NN–TG was not materially different among taxa, there is anadditional indirect path to TG through CPA for BC1 andSlash13. NB–CPA was 61 % higher for Lob_OP than forSlash13 and BC1. In addition, branch number in Slash13and BC1 was negatively correlated with TG, indicating thatfor Lob_OP, the number of branches is more importantwhen building photosynthetic area. These results are con-sistent with the observation that, when controlling for treesize, loblolly pine tends to produce more branches than slashpine (Xiao et al. 2003) and allocates a greater proportion ofaboveground biomass to branches and leaf area (Colbert etal. 1990; Chmura et al. 2007) than does slash pine. Thesedifferences are largely attributable to differences in branchstructure between the species. In addition to having a greaternumber of branches, loblolly pine branches have a greaternumber of bifurcations or secondary/tertiary branches thanslash pine (Dalla-Tea and Jokela 1991). These crown struc-tural and biomass allocation differences enable loblolly pineto have a greater capacity to rapidly develop tree- and stand-level leaf area compared to slash pine (e.g., Martin andJokela 2004). In contrast, even when FL was found signif-icantly different among the taxa studied (Muñoz Del Valle etal. 2011), the coefficient for FL–CPA was not significantlydifferent among the three taxa; however, the coefficient wasslightly larger for Slash13 and BC1. This indicates that FL isslightly more important for the slash families consideredhere. This result is consistent with Chmura et al. (2007)where slash pines build more photosynthetic area by length-ening the needles rather than by increasing the number ofbranches.

It has been shown that under similar growing conditions,loblolly pine has higher SLA than slash pine (Dalla-Tea andJokela 1991; Will et al. 2001; McGarvey et al. 2004; MuñozDel Valle et al. 2011). When examined across widely vary-ing species and functional groups, specific leaf area tends tobe highly correlated with leaf-level physiological traits suchas leaf life span, net photosynthesis, and respiration rates(Reich et al. 1992; Wright et al. 2004; Shipley et al. 2006)and, in turn, with plant-level traits such as growth andresource use efficiency (Reich et al. 2003). There are sur-prisingly few studies, however, examining similar within-

genotype variations. In this study, we found that SLA had anegative effect on TG within Lob_OP. However, this wasnot found in BC1 or Slash13. Our results are consistent withOsone et al. (2008) where SLA caused a decrease in growthrate when the relationship was not corrected for a specificnitrogen absorption rate of roots (not measured in ourstudy).

Indirect effects accounted for approximately 50 % of thecontribution of traits to growth for Slash13 and BC1 (acrosscolumn in Fig. 6), while for Lob_OP this was below 44 %,indicating that the indirect crown trait effects mediatedthrough CPA are as important as the direct effect on TG.The abundance and strength of the indirect effects in thisstudy emphasize the complexity of growth as a polygenic,integrative trait and highlight why it is often difficult torelate individual, simple traits to growth rate.

This study examined the genetic control of crown dy-namics during a growth stage critical in southern pine standdynamics. It is well established that leaf area and crown sizeare highly correlated with growth and productivity in south-ern pines (Martin and Jokela 2000; Will et al. 2005), but thatthese relationships hold primarily during the early period ofstand development before canopy closure when leaf area isstill accumulating (Gholz et al. 1991; Bracho et al. 2012)and before tree–tree crown interactions and strong inter-treecompetition come into play. Studies on crown traits duringthis growth stage are most informative regarding the geneticarchitecture of traits and are most relevant to stand dynam-ics. In summary, we found that the intra-plant trait relation-ships varied according to taxon and that the relativerelationships are not fully maintained in the pseudo-backcross hybrid. Even when most of the traits consideredhere are not directly used for improvement in breedingprograms, our results open the possibility that changes inthe intra-trait relationships within the hybrid could modifythe known indirect selection effect of the pure species. Thedifferences in relationships among traits were expected forloblolly and slash pine because of the early growth differ-ences already noted. Also, BC1 was expected to have acloser relationship with the slash pine families because ofgenome similarity. While the last did occur when consider-ing the form of the effects (i.e., direct or indirect), differ-ences were found in the effect on crown area at the needlelevel (i.e., NF and FD) and in the size of the total effect ofbranches in growth that was twice in BC1 when comparedwith Slash13, probably influenced by Lob_OP. While forLob_OP branches were the second more important traitinfluencing growth, it was the last in the rank for Slash13.While it was expected that the rank of effect on TG changedwith the taxa, it was not expected that the number of traitsinfluencing growth (directly and indirectly) was different.Seven, six, and five traits were found significant forLob_OP, BC1, and Slash13, respectively.


While a traditional introgression scheme in crop breedingwill take several generations of backcrossing to the recurrentparent, in pine breeding, the number of years required for asingle breeding cycle does not normally allow this practiceprior to deployment. In order to take advantage of this kindof material, a strategy that could be considered, under theassumption that BC1 performs well at selection age, is theinfusion of selected individuals from the BC1 into the eliteslash pine breeding program. At this point, recurrent selec-tion for phenotype and molecular markers could incorporatethe best characteristics from loblolly pine into the elite slashpine populations, avoiding the high cost of maintaining aseparate hybrid breeding program. However, this should bestudied in more detail and a formal strategy should begenerated closer to selection age.

Acknowledgments This research was funded by the CFGRP underthe Agriculture and Food Research Initiative of USDA's NationalInstitute of Food and Agriculture Conifer Translational Genomic Net-work (CTGN) grant. The molecular markers were developed under theCTGN grant. The authors thank the CFGRP cooperators, in particularthe University of Florida, Plum Creek Timber Company, and Smurfit-Stone Container Corporation for materials and land used in the project.We thank Greg Powell, Carlos Gonzales, Pablo Pinedo, Xiaobo Li,Andres Susaeta, Claudio Verdugo, Rodrigo Vergara, and AlejandroRiveros for their help in data collection. Thanks are also due toSalvador Gezan and Charlotte Germain for their useful commentsand discussion about the project. Three anonymous reviewers arethanked for their useful comments.

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