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Genetic and environmental contributions to multivariate morphological pattern in Puccinellia (Poaceae)
JERROLD I. DAVIS L. H. Bailey Hortorium, 467 Mann Library, Cornell University, Ithaca, NY 14853, U.S.A.
Received January 29, 1988
DAVIS, J. I. 1988. Genetic and environmental contributions to multivariate morphological pattern in Puccinellia (Poaceae). Can. J. Bot. 66: 2436-2444.
The contributions of genetic and environmental variation to multivariate patterns in morphology were investigated in a com- parative analysis of two samples of plants representing the Puccinellia nuttalliana complex (Poaceae). The first sample is a series of individuals (genotypes) collected live, vegetatively divided, and grown under controlled environmental conditions. Phenotypic variation in this sample, in individual characters and in multivariate factors, can be apportioned between genetic (among genotype) and environmental (among treatment) causes. The second sample consists of field-collected individuals from throughout the North American range of the complex. Variation in this sample, as in most field-collected samples, can- not be assigned directly to its underlying causes. Multivariate patterns in the two samples were analyzed by identical principal- components analyses of 48 morphological characters. The strongest factor identified by the greenhouse principal-components analysis correlates with the strongest of the field principal-components analysis; they are similar in character makeup, both reflecting spikelet size and plant scabrousness. These factors have a genetic component and no environmental component and appear to differentiate Puccinellia distans from the rest of the complex. The second strongest factor of the greenhouse principal-components analysis correlates with the second of the field principal-components analysis. These axes reflect general vegetative stature; they have genetic and plastic components. The overall analysis indicates that multivariate patterns in phenotype can reflect both genetic and environmental effects, in varying proportions; patterns of genetic affinity therefore may be difficult to dissociate from those reflecting plasticity.
DAVIS, J. I. 1988. Genetic and environmental contributions to multivariate morphological pattern in Puccinellia (Poaceae). Can. J. Bot. 66 : 2436-2444.
Les contributions de la variation gCnCtique et Ccologique aux modkles multidimensionnels en morphologie ont CtC CtudiCes dans une analyse comparCe de deux Cchantillons de plantes representant le complexe Puccinellia nuttalliana (Poaceae). Le premier Cchantillon est une sCrie d'individus (genotypes) rCcoltCs vivants, sCparCs vCgCtativement, qu'on a fait pousser sous conditions controlCes. La variation phCnotypique, dans cet Cchantillon, tant pour les caractkres individuels que pour les fac- teurs multidimensionnels, peut &tre rkpartie i des causes gCnCtiques (entre genotypes) et Ccologiques (entre traitements). Le deuxikme Cchantillon est constituC d'individus rCcoltCs partout en AmCrique du N O I ~ oh l'on rencontre le complexe. La varia- tion dans cet Cchantillon, comme dans la plupart des Cchantillons rCcoltCs aux champs, ne peut &tre attribuCe directement i ses causes fondamentales. Les modkles multidimensionnels dans les deux Cchantillons ont CtC analyses par des analyses en composantes principales identiques de 48 caractkres morphologiques. Le plus important facteur identifie par l'analyse en com- posantes principales de serres est en correlation avec celui de l'analyse en composantes principales de terrain; ils sont sembla- bles en ce qui concerne la composition des caractkres, les deux exprimant la taille de 1'Cpillet et la rugositC de la plante. Ces facteurs qui ont un composant gCnCtique et n'ont pas de composant Ccologique semblent distinguer le Puccinellia distans du reste du complexe. Le second facteur le plus fort de l'analyse en composantes principales de serres est en correlation avec le second de l'analyse en composantes principales de terrain. Ces axes reflktent l'importance vCgCtative gCnCrale; ils ont des composants gCnCtiques et plastiques. L'analyse globale indique que les modkles multidimensionnels chez le phenotype peu- vent exprimer tant les effets gCnCtiques qu'Ccologiques, dans differentes proportions; les modkles d'affinitC gCnCtique peuvent donc &re difficilement dissociables de ceux qui expriment la plasticitC.
[Traduit par la revue]
Introduction The recent growth of interest in phenotypic plasticity should
not be surprising, since this phenomenon bridges the fields of genetics, development, population biology, and evolution. Evidence is accumulating that plasticity represents more than mere developmental noise and that it may indeed play an important role in adaptive evolution (cf. Waddington 1953, 1960; Bradshaw 1965, 1974; Morisset and Boutin 1984; Schlichting 1986; Sultan 1987). However, phenotypic plas- ticity has received little attention by systematists. It should receive more, for at least two reasons. First, if plasticity is a significant force in the evolutionary process (i.e., diversifica- tion in genetically determined traits), it has influenced the pat- terns of diversity that exist in the biological world. Second, if plasticity is a contributor to phenotypic patterns, its very pres- ence may obscure the underlying patterns of genetic relation- ship that systematists seek to clarify. It is this second point of interest that motivates the current analysis.
In a recent study of geographic variation in the Carpinus caroliniana complex, Furlow (1987) notes that correlations observed between phenotypic traits and environmental condi- tions or geography, in field-collected plant samples, can have arisen in either of two ways. This variation might be attribut- able either to genetic differentiation of populations (environ- mental conditions having favored particular genotypes) or to plasticity (environmental conditions having evoked particular phenotypes from a common genotype). Hence, much variation that is described as racial may have no genetic basis, or less of a genetic basis than often is assumed. This caveat clearly applies to a voluminous literature dealing with local and geo- graphic races, infraspecific taxa, and weakly differentiated species.
While little enough is known of the interplay of genotype and environment in the determination of individual traits, we know even less about the determination of multivariate pheno- typic patterns. Yet systematists often turn to numerical analyti-
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TABLE 1. Provisional identifications of eight collections of Puccinellia constituting the
greenhouse sample in this study
Species and province or state in Genotype which collected
A P. purnila, New Brunswick B P. distans, Prince Edward Island C P. purnila, Alaska D P. primila, Alaska E P. distatls, Washington F P. distans, Washington G P. nutmlliana, Oregon H P. grandis, California
cal methods when confronting dubious taxonomic distinctions. In this paper I explore the contributions of genetic and environmental factors to the determination of multicharacter phenotypic patterns. The analysis demonstrates that genetics and environment both contribute to the multicharacter axes that are detected by numerical analyses, and that these forces can contribute together to a single multivariate factor, just as they do to an individual character. The patterns described in this paper provide tentative support for certain taxonomic con- clusions, but the major goal here is to explore the interplay of genetics and environment in the multivariate patterns them- selves.
In the most common use of ordination in biological sys- tematics, the individuals that are ordinated are operational taxonomic units (generally organisms, species, etc.), and the variates are character scores (Sneath and Sokal 1973). The analysis detects patterns of correlation among the characters and describes a multivariate phenetic space in which the taxo- nomic units can be projected (Sokal 1986). An implicit assumption of most such-studies is that the characters con- tribute independent elements of information to the analysis. Hence, the patterns that are detected can be interpreted as overall representations of "relationship." ~ o r o l l a r ~ to the assumption of character independence is the postulate that mis- leading evidence in one or a few characters will be outweighed by the accurate information provided by the majority. There- fore, the patterns of affinity that are detected will approximate actual affinities more closely as more characters are included in the analysis. It is not my object to examine the assumptions and methodology of phenetic analysis in depth, but to point out that independence of characters is one of its pillars. By the nature of most multivariate analytical techniques, any biologi- cal factor that influences character correlations will influence the results.
One cause of character correlation among organisms, indeed the one that justifies the search for correlations by sys- tematists, might be designated evolutionary history. Two organisms (or species, etc.) related by descent resemble one another in a particular character or group of characters because the state or states they share have been transmitted to them from a common ancestor. Evolutionary history is not, however, the only factor that contributes to correlation among characters; two of the best understood causes of phenotypic character correlation are genetic and environmental corre- lation (Falconer 1981). The present study is a preliminary inquiry into the influences these factors can have on a numeri-
Materials and methods
The basic strategy of this work is to contrast independent analyses of variation in two complementary plant samples. The "field" sample consists of 97 field-collected individuals from temperate and boreal North America, and represents variation among plants of the Puccinellia nuttalliana complex taken from natural habitats. Patterns in this sample are representative of those that exist among natural populations.
The "greenhouse" sample consists of individuals derived from plants collected live, divided, and cultured under controlled condi- tions. These clones represent four of the five species provisionally recognized in the Puccinellia nuttalliana complex (Table 1); Pucci- nellia nutkaensis is not represented. Collection localities and details of culturing methods are reported in Davis (1983). In brief, divided ramets of each clone (or genotype) were cultured hydroponically in a control nutrient solution and in a series of solutions augmented with NaCl to simulate naturally encountered edaphic conditions. For this study a subset of the greenhouse sample was chosen, with the goal of maximizing the number of genotypes and treatments represented, and the requirement that there be no missing plants in the resulting genotype-by-treatment matrix. This sample comprises eight geno- types (nos. 2-4, 6-8, 10, and 12 (cf. Davis 1983, Table 6)), and designated genotypes A through H, respectively. Each genotype was grown under three treatments, the control (two replicates), -0.5 MPa NaC1, and - 1.0 MPa NaC1, for a total of 32 plants.
Multivariate axes detected in the greenhouse sample can be attributed to genetic and environmental causes. The greenhouse sample includes few genotypes, however, and the controlled condi- tions under which the plants were grown may differ substantially from natural conditions. Hence, multivariate patterns detected in this sample may differ from those that are found in a larger sample grown under natural conditions. By comparing numerical analyses of the two samples, the greenhouse sample can be used to demonstrate the contributions of genetic and environmental forces to patterns detected in the field sample, while the field sample serves to verify that pat- terns detected in the greenhouse sample are not systematically mis- leading.
Forty-eight characters (Table 2) were scored on each plant. Meas- urements greater than 5 mm were made with a ruler with 1-mm divi- sions; measurements 5 mm and smaller were made with an optical micrometer with 0.025-mm divisions. Separate principal-components analyses (the "greenhouse PCA" and the "field PCA") were con- ducted on the two samples; each analysis involved the same 48 char- acters and identical analytical procedures. The PCAs were based on Kendall T correlation coefficients among characters, with pairwise deletion; components were not rotated. Following Z-score transfor- mation of each character in the combined 129-plant sample, factor scores were calculated for plants of both samples on both sets of axes. Hence, individuals of both samples can be projected together in the phenetic space defined by each of the analyses.
Results of the two PCAs were compared in three ways. First, to determine which factors of the PCAs reflect genetic variation and which reflect plasticity, two-way factorial analysis of variance (ANOVA) was conducted on factor scores of the greenhouse sample for the first 10 factors of each analysis; the independent variables for these 20 ANOVAS were genotype and treatment, and the dependent variable for each was factor score: Second, to determine which fac- tors of the greenhouse PCA describe patterns similar to those in the field PCA, factors of the two PCAs were tested for Kendall T correla- tion with one another, using individuals of the field sample. Third, note was made of which characters contribute to each factor, and of spatial relationships among individuals plotted on the principal com- ponents. Numerical procedures were conducted on a microcomputer with the IBM-PC implementation of Systat (Wilkinson 1986).
Results
The first seven comDonents of the greenhouse PCA differen- cal taxonomic analysis.
- tiate among genotypes of the greenhouse sample at the 0.05
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TABLE 2. Forty-eight characters of Puccinellia used in principal-components analyses. The reference number, acronym, character description, and method of scoring (in parentheses) is given for each character
1. HEIGHT, height of plant (cm) 2. STEMDIAM, maximum stem diameter (mm) 3. NODEINDM, indument at and just below node of stem (three states: glabrous, sparsely scabrous, scabrous) 4. SHTHCOLR, presence-absence of anthocyanic color in leaf sheath (two states: present, absent) 5. INVOLUTE, leaf blades flat or involute (three states: most blades flat, nearly even mix of forms, most blades involute) 6. BLADWDTH, maximum width of leaf blade (mm) 7. LIGLNTH, length of ligule (mm) 8. LIGWDTH, width of ligule at base (mm) 9. LIGAPEX, shape of apex of ligule (six states: acute, right angle, obtuse, widely obtuse, truncate with apicule, truncate)
10. LIGMRGN, margin of ligule (two states: entire, erose) 11. LIGINDUM, indument of abaxial ligular surface (two states: glabrous, scabrous) 12. INFLNTH, length of inflorescence (cm) 13. I N T E R N D ~ , length of first internode of inflorescence (cm) 14. MAJBRLNG, length of longest branch arising from first node of inflorescence (cm) 15. MAJBRPED, distance along longest branch arising from first node of inflorescence, from base to first pedicel (cm) 16. INFINDUM, indument of branches of inflorescence (same as character 3) 17. INFBRCHS, number of branches at first node of inflorescence (actual count) 18. FLORETS, maximum number of florets in at least two spikelets (actual count) 19. G L ~ T O G L ~ , length of rachilla segment between first and second glumes (mm) 20. RACHSEG~, length of rachilla segment between first and second lemmas (mm) 21. RACHSEG~, length of rachilla segment between second and third lemmas (mm) 22. BRACTCLR, presence-absence of anthocyanic color in floral bracts (same as character 4) 23. G L M ~ L N T H , length of first glume (mm) 24. GLM~WDTH, width of first glume (mm) 25. G L M ~ N E R V , number of nerves of first glume (actual count) 26. G L M ~ M R G N , margin of first glume (three states: entire, distally ciliate <0.2 length, distally ciliate 20 .2 length) 27. GLM~INDM, indument of first glume (same as character 11) 28 GLM~SHAP, shape of second glume (three states: ovate, elliptic, obovate) 29. G L M ~ L N T H , length of second glume (mm) 30. GLM~WDTH, width of second glume (mm) 31. GLM~APEX, shape of apex of second glume (same as character 9) 32. G L M ~ M R G N , margin of second glume (same as character 26) 33. G L M ~ I N D M , indument of second glume (same as character 11) 34. LEMALNTH, length of first lemma (mm) 35. LEMAWDTH, width of first lemma (mm) 36. LEMWDHGT, longitudinal distance along center of first lemma, from base to widest point (mm) 37. LEMAKEEL, presence-absence and strength of keel of first lemma (three states: convex, slightly or apically keeled, markedly keeled) 38. LEMANERV, number of nerves of first lemma (actual count) 39. LEMMAPEX, shape of apex of first lemma (same as character 9) 40. LEMAMRGN, margin of first lemma (same as character 26) 41. LEMHRHGT, longitudinal extent of sericeous hairs from base of lemma (mm) 42. LEMHRLNT, maximum length of hair on lemma (mm) 43. LEMASCAB, presence-absence of scabrules on first lemma (same as character 4) 44. PALENGTH, length of palea of first floret (mm) 45. PALWIDTH, width of palea of first floret (mm) 46. PALHRHGT, longitudinal distance along nerves of palea from base to first hair (mm) 47. PALHRLNT, maximum length of hair on palea (mm) 48. ANTHLNTH, length of anther (mm, mean of three)
significance level (Table 3). Factors 2-6, all of which differ- entiate among genotypes, also describe plastic responses; fac- tor 9 reflects plasticity but not genetic variation (Table 3). No genotype -environment interaction is detected in any of these factors.
Among components of the field PCA, the first six differenti- ate genotypes of the greenhouse sample; three of these (factors 2, 5, and 6) differentiate treatments as well, and factor 9 dif- ferentiates treatments but not genotypes (Table 3). A geno- type-environment interaction is detected only in factors 5 and 6, both of which differentiate genotypes and treatments inde- pendently.
The first component of the greenhouse PCA correlates strongly with the first component of the field PCA (7 = 0.80, Table 4); the second components of the two PCAs also corre-
late strongly with one another (7 = 0.77). Significant correla- tions, although generally weaker, also exist among other components of the two analyses (Table 4).
The first factor of the greenhouse PCA comprises 26.5 % of variation in the sample; of 48 characters in the analysis, 23 exhibit strong loadings (absolute value r 0.5) on this factor (Table 5). With a genetic component and no detected plasticity (Table 3), this factor differentiates genotypes B, E, and F from genotypes A, C, D, and H, with genotype G situated in an intermediate position (Fig. 1). Individuals of the field and greenhouse samples are largely intermixed in the phenetic space defined by this factor, although genotypes B, E, and F exhibit slightly higher scores than individuals of the field sample (Fig. 1). Character loadings on this factor indicate that genotypes B, E, and F differ from other genotypes by a greater
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DAVIS 2439
TABLE 3. Tests for variation among genotypes (genetic variation) and among treatments (plasticity) within the greenhouse sample in factor scores from the greenhouse and field PCAs
Variance components
Greenhouse PCA Field PCA
Among Among Among Among genotypes treatments lnteraction Residual genotypes treatments lnteraction Residual
PCA - factor F-ratio F-ratio F-ratio MS F-ratio F-ratio F-ratio MS
0.1 lns 5.45" 0.0911s 4.2311s 4.49" 5.30" 1.9711s 4.2211s
16.52""" 0.3711s
NOTE: Degrees of freedom among genotypes = 7; among treatments = 2; for genotype-environment interaction= 14. *, P I 0.05; ** P 5 0.01; ***, P 2 0.031; ns, not significant.
TABLE 4. Kendall 7 correlation coefficients among factor scores of field collections of Puccinellia on factors of the greenhouse and field PCAs
Greenhouse PCA factor Field PCA
factor 1 2 3 4 5 6 7 8 9 10
NOTE: 11 = 83; critical absolute values of T for statistical signiticarice are 20 .21 (P 5 0.05). 20 .27 (P 5 0.01). and 20.35 (P I 0.M)I). T 2 0.35 is set in boldfaced type.
degree of scabrousness of the nodes, ligules, and inflorescence axes; more ciliate margins of the glumes and lemmas; more obtuse apices of the ligules and lemmas; smaller spikelets, as represented by shorter rachilla segments, shorter and nar- rower glumes and lemmas, and shorter anthers; and a lesser extent of hairs along the lemmas and paleas (Table 5).
The second factor of the greenhouse PCA encompasses 10.5% of variation in the sample; eight characters exhibit loadings of absolute value L 0.5 on this factor, all of these loadings positive (Table 5). This component reflects genetic variation and plasticity in the greenhouse sample (Table 3). Individuals of the field and greenhouse samples are intermixed in the phenetic space defined by this factor (Fig. 1). The factor describes overall vegetative size and inflorescence dimen- sions. Characters with the strongest loadings (absolute value L 0.5) include height, diameter of stem, width of leaf blade, lengths of four inflorescence axes, and number of nerves of the first lemma (Table 5). While genotypes differ from one another in average scores on this factor (Table 3, Fig. I), the plastic nature of the factor (Table 3) is reflected in the distance among individuals of most genotypes along the axis (Fig. 1).
Factors 3 and 4 of the greenhouse PCA summarize 9 .3 and 6.0% of variation in the sample, respectively, and exhibit strong loadings (absolute value L 0.5) of three characters and one character, respectively; with r L 0.4 as the criterion for strong loading, 11 characters load strongly on factor 3, and 7 on factor 4 (Table 5). Both axes reflect variation among geno- types and among treatments (Table 2). Factor 3 principally distinguishes genotype H from others of the sample, and fac- tor 4 distinguishes genotype G; these genotypes stand apart from nearly all individuals of the field sample in the phenetic space defined by their respective factors (Fig. 2). Genotype H differs from the rest of both samples, along factor 3, by virtue of a smaller stature, a more obovate second glume, and a greater number of nerves of the lemma (Table 5). Genotype G differs from the rest of the sample, along factor 4, principally on the basis of longer lemma hairs; additional differences, reflected in characters with loadings marginally lower than 0.5, include a greater development of anthocyanic color in the leaf sheaths, and longer hairs of the palea.
Two characters load strongly (r of absolute value L 0.5) on factor 5 of the greenhouse PCA, and one or none loads this
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- 2 0 2 FACTOR 1
-4 -2 0 2 FACTOR 3
FIG. 1. Distribution of field sample ( a ) and greenhouse sample FIG. 2. Distribution of field sample ( a ) and greenhouse sample (letters A-H) of Puccinellia on factors 1 and 2 of the greenhouse (letters A-H) of Puccinellia on factors 3 and 4 of the greenhouse principal-components analysis. principal-components analysis.
strongly on each of factors 6 - 10 (Table 5). Among green- house factors 5- 10, none exhibits a correlation stronger than 0.41 with any of the field PCA factors (Table 4).
The first factor of the field PCA correlates strongly with the first factor of the greenhouse PCA (Table 4) and summarizes 19.2 % of variation in the field sample (Table 6). Scores on this factor vary among genotypes of the greenhouse sample (Table 3). Characters that load strongly on field PCA factor 1 tend to load strongly on greenhouse PCA factor 1 as well (Tables 5 and 6).
The second factor of the field PCA summarizes 15.8% of variation in the field sample (Table 6) and correlates strongly with the second factor of the greenhouse PCA (Table 4). Scores on this factor differ among genotypes and among treat- ments of the greenhouse sample (Table 3). Thirteen characters exhibit loadings of absolute value 0.5 or greater on this factor; all of these loadings are positive (Table 6). Characters that load strongly on field PCA factor 2 tend to load strongly on greenhouse PCA factor 2 as well (Tables 5 and 6).
Among remaining components of the field PCA, there is less correlation with factors of the greenhouse PCA. This is reflected in lower correlation coefficients among factors (Table 4), and in a lesser match among factors in the charac- ters that load strongly on them (Table 5 and 6).
Discussion This analysis differs from many numerical analyses of mor-
phology. To facilitate the investigation of character associa- tion per se, the number of characters, relative to the number of individuals, is inflated beyond levels often used. There is nothing qualitatively unique in this, but the results should be interpreted with the understanding that the effects of using a large number of characters probably are accentuated in this study.
One observation that emerges from this study is that more than one multivariate axis reflects plastic variation, both in the field sample and in a sample reflecting the response of divided
clones to controlled environmental variation. Multivariate axes found to reflect plasticity usually reflect genetic variation as well. Hence, there is no observed tendency for plasticity to be confined to a single axis, nor for it to be isolated in general from the genetically determined factors of variation that are of specific interest to the systematist. On the contrary, the princi- pal-components analysis detects a series of factors that individ- ually reflect mixtures of both genetic and environmental forces, in varying proportions. This does not imply that genetic variation and the plastic response of a plant are parallel in each character contributing to each of these factors. Among the correlated aspects of variation in characters that contribute to any one factor, the component of variation in some charac- ters might be exclusively genetic, while the component of vari- ation in others is environmental. However, the directions and relative magnitudes of the factors reflect a complex interplay of genetic and plastic contributions.
The first factor of the greenhouse PCA describes a morpho- logical pattern that may have taxonomic significance. First, it is entirely genetic in origin. Second, this factor correlates strongly with the first factor of the field PCA, and reflects variation in much the same set of characters. Finally, a plot of both samples along the first factor of the greenhouse PCA indicates that individuals of the two samples are intermixed on this axis (Fig. 1). Hence, this factor appears to represent a broadly based pattern of genetic affinity that is detectable in both the greenhouse and the field samples.
The second factor of the greenhouse PCA is similar to the first in some ways. Like the first component, factor 2 has a significant genetic component and correlates strongly with a factor of the field KA (the second) reflecting variation in many of the same characters. Also, a plot of field and greenhouse individuals on the second factor reveals a broad overlap in dis- tribution of the two samples along this factor. Therefore, the second factor of the greenhouse K A , like the first, describes a genetically determined axis of multicharacter variation that is similar in character makeup to an axis that exists in the field sample.
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TABLE 5. Loadings of characters on first 10 factors of the greenhouse PCA. Eigenvalues are given in paren- theses and percent total variance in brackets
PCA factors
1 2 3 4 5 6 7 8 9 10 (12.7) (5.0) (4.5) (2.9) (2.4) (2.0) (1.9) (1.6) (1.2) (1.1)
Character [26.5] [10.5] [9.3] [6.0] [5.1] [4.3] [4.0] [3.3] [2.5] [2.2]
HEIGHT
STEMDIAM
NODEINDM
SHTHCOLR
INVOLUTE
BLADWDTH
LIGLNTH
LIGWDTH
LlGAPEX
LlGMRGN
LlGlNDUM
INFLNGTH
I N T E R N D ~
MAJBRLNG
MAJBRPED
lNFlNDUM
INFBRCHS
FLORETS
G L ~ T O G L ~
RACHSEG 1 R A C H S E G ~
BRACTCLR
GLM 1 LNTH
G L M ~ W D T H
G L M ~ N E R V
G L M ~ M R G N
G L M ~ I N D M
G L M ~ S H A P
G L M ~ L N T H
G L M ~ W D T H
G L M ~ A P E X
G L M ~ M R G N
G L M ~ I N D M
LEMALNTH
LEMAWDTH
LEMWDHGT
LEMAKEEL
LEMANERV
LEMMAPEX
LEMAMRGN
LEMHRHGT
LEMHRLNT
LEMASCAB
PALENGTH
PALWIDTH
PALHRHGT
PALHRLNT
ANTHLNTH
NOTE: See Table 2 for character descriptions. Character loadings of absolute value 20.50 are set in boldface.
Unlike the first factor of the greenhouse PCA, the second includes a significant plastic component of variation in addi- tion to its genetic component. This admixture of genetic and plastic variation, in contrast with the purely genetic aspect of factor 1, is reflected in the distribution of greenhouse plants in the plot of these two factors (Fig. I). Genetic variation is demonstrated in variation among the genotypes along both of these axes, e.g., by the tendency for genotype B to exhibit higher scores than genotype A on factor 1, while genotype H
exhibits higher scores than genotype D on factor 2. The envi- ronmental aspect of factor 2, which is lacking from factor 1, is demonstrated by the tendency for individuals of the same genotype (e.g., genotype C) to be separated by a greater dis- tance vertically than horizontally in this plot.
The third and fourth components of the greenhouse PCA differ from the first and second. Perhaps most significantly, factors 3 and 4 each differentiate one genotype from all others, as well as from the field sample (Fig. 2). These two factors
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TABLE 6. Loadings of characters on first 10 factors of the field PCA. Eigenvalues are given in parentheses and percent total variance in brackets
PCA factor
1 (9.2)
Character 119.21
HEIGHT
STEMDIAM
NODEINDM
SHTHCOLR
INVOLUTE
BLADWDTH
LlGLNTH
LlGWDTH
LlGAPEX
LIGMRGN
LIGINDUM
lNFLNGTH
~ N T E R N D ~
MAJBRLNG
MAJBRPED
INFlNDUM
lNFBRCHS
FLORETS
G L ~ T O G L ~
RACHSEG 1 R A C H S E G ~
BRACTCLR
GLM 1 LNTH
G L M ~ W D T H
G L M ~ N E R V
G L M ~ M R G N
G L M ~ ~ N D M
G L M ~ S H A P
G L M ~ L N T H
G L M ~ W D T H
G L M ~ A P E X
G L M ~ M R G N
G L M ~ I N D M
LEMALNTH
LEMAWDTH
LEMWDHGT
LEMAKEEL
LEMANERV
LEMMAPEX
LEMAMRGN
LEMHRHGT
LEMHRLNT
LEMASCAB
PALENGTH
PALWlDTH
PALHRHGT
PALHRLNT
ANTHLNTH
NOTE: See Table 2 for ch: .iptions. Char
-0.29 -0.16 -0.28 -0.17 +0.32 -0.42 -0.03 -0.36 -0.32 -0.03 +0.07 -0.41 -0.29 -0.40 -0.18 +0.21 -0.04 +0.01 +O. 17 +0.22 +O. 16 -0.28 -0.01 +0.09 +0.28 +0.20 -0.10 +0.23 -0.02 +0.01 +0.25 +0.23 -0.01 +0.01 +0.08 +0.10 -0.01 +O. 14 +0.06 +0.29 +0.22 +0.25 +0.13 -0.06 -0.09 -0.24 +0.32 +0.23
acter loadings
are depicted on a single graph, rather than separately, each in association with factor 1, to emphasize the kind of pattern they summarize. Factor 3 reflects differences between genotype H and the remainder of both samples, while factor 4 similarly differentiates genotype G (and a few other individuals). Most succeeding factors (e.g., numbers 5 and following, not illus- trated) exhibit a similar pattern. Genotypes G and H are pro- visionally the sole representatives in the greenhouse sample of Puccinellia nuttalliana and Puccinellia grandis, respectively.
of absolute
6 7 8 (1.5) (1.3) (1.3) [3.2] [2.8] [2.7]
-0.20 +0.13 -0.01 -0.08 +0.24 +0.20 -0.04 +0.01 +0.33 +0.29 +0.11 -0.23 +0.04 +0.46 -0.04 -0.11 +0.02 +0.09 -0.30 -0.05 -0.10 -0.01 +0.05 +0.20 +0.35 +0.03 +0.37 +0.36 +0.04 +0.22 +0.16 -0.03 +0.14 +0.03 +0.03 -0.11 +0.11 -0.02 -0.12 +0.06 +0.07 -0.13 +0.01 +0.16 -0.17 +0.10 +0.01 -0.12 -0.01 +0.16 +0.19 +0.40 -0.51 +0.12 -0.15 +0.13 +0.31 -0.03 +0.30 +O.OO -0.03 +0.28 +0.10 +0.26 +0.05 -0.18 +0.20 -0.04 -0.03 -0.05 -0.15 +0.05 -0.10 +0.11 +0.05 +0.01 -0.11 -0.13 -0.10 -0.01 +0.44 +0.39 +0.26 +0.26 +0.27 -0.06 -0.06 +0.06 -0.27 +0.03 -0.11 -0.13 +0.10 +0.05 -0.08 -0.1 1 -0.16 -0.14 +0.18 +0.18 +0.02 -0.03 -0.08 -0.17 +0.06 +0.21 +0.07 +0.05 -0.10 +O. 15 +0.24 +0.26 +0.30 -0.15 +0.09 -0.06 +0.13 +0.03 -0.04 -0.12 +0.16 -0.07 +0.16 +0.06 +0.14 -0.12 -0.20 -0.18 +0.01 +0.28 +0.04 +0.02 +0.10 -0.15 -0.07 -0.06 +0.17 -0.04 +0.21 -0.13 +0.10 +0.18 -0.07 +0.21
value 2 0.50 are set in boldface.
This might appear to suggest that the third and fourth com- ponents of the greenhouse PCA detect species-level dis- tinctions. However, the field sample represents a fairly comprehensive sampling of variation in the P. nuttalliana complex, and it is not likely that the greenhouse sample includes one or , especially, two or several species that are not represented in the field sample. If the weaker factors of the greenhouse PCA were describing character combinations that differentiate taxa, rather than simply individual genotypes,
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individuals of the field sample would be expected to be dis- tributed among the genotypes that are differentiated by these factors. Because ordinations are not statistical, one or a few individuals with unique trait combinations can affect the results considerably (Sokal 1986); this is why patterns in the small greenhouse sample must be verified by comparison with patterns in the field sample, if they are to be interpreted as tax- onomically meaningful. Factors 3 - 10 appear, then, to reflect idiosyncratic character combinations in genotypes of the greenhouse sample, rather than patterns of genetic affinity. This conclusion is supported by the generally weak correlation between greenhouse and field PCA axes, beyond the first two; the two analyses appear to diverge after factor 2 and, there- after, to detect different aspects of variation.
An examination of the character makeup of the first two axes of the two PCAs indicates that the characters fall into a few morphological categories. For instance, the strongest axis of variation in both analyses (factor 1) is an expression of spikelet size and plant scabrousness, with each of these general features reflected in several characters. Spikelet size is repre- sented in this axis by lengths and widths of glumes, the lemma, and the palea and by lengths of rachilla segments (Tables 5 and 6); scabrousness is manifested in ciliation of margins of both glumes and the lemma and in scabrousness of the inflorescence axes, nodes, and ligules. There are reasons to hypothesize developmental integration among traits in each of these cate- gories. In the category of scabrousness, the homology of scab- rules on various parts of the plant, and of scabrules and cilia, may be hypothesized. In the category of spikelet size, develop- mental integration may be hypothesized on the basis of homol- ogy (e.g., between lengths of glumes and length of the lemma) as well as on the basis of functional relationship of parts (e.g., length of lemma and length of rachilla segment). The general suggestion here is that the correlations detected in this multi- variate factor reflect multiple expression of polygenic sys- tems, one affecting the size of the spikelet, the other affecting the development of scabrules on the epidermis, with modify- ing genes separately affecting individual traits within each category and rendering the correlations imperfect. Other char- acters loading strongly on factor 1 are less obviously related to these two major categories, but not decidedly unrelated. For instance, anther length may be linked developmentally with size of spikelet. Similarly, apices of the lemma and ligule are more obtuse in plants with smaller spikelets; these characters might be linked developmentally with smaller spikelet size.
However many underlying genetic factors actually are involved, the phenotypic characteristics summarized by fac- tor 1 are among those generally recognized as differentiating Puccinellia distans from other elements of North American Puccinellia (cf. A. S. Hitchcock 1950: C. L. Hitchcock 1969). The makeup of factor 1 is thus consistent with a distinction between the introduced weedy Eurasian species, P. distans, and the remaining taxa of the P. nuttalliana complex, which are native to ~ o r t h America. This analysis therefore corrobo- rates the recognition of P. distans (represented in the green- house sample by genotypes B, E, and F) and assigns an intermediate phenetic position to P. nuttalliana (represented by genotype G), between P. distans and other elements of the complex. Puccinellia nuttalliana is similar to P. distans in indumentum characters but has larger spikelets with more acute lemma apices.
The second factor of the greenhouse PCA largely reflects overall plant stature, including such attributes as height, width
of leaf blade, and lengths of inflorescence axes. The genetic and environmental contributions to this factor indicate that dif- ferences exist among genotypes in this set of characters, and that plastic variation among ramets of each genotype, in response to salinity, places them at different points along this axis. A distinct taxonomic interpretation for this axis is not apparent. The factor appears to reflect the occurrence of large- plant and small-plant genotypes within the Puccinellia nuttalli- ana complex, as well as the capability of individual genotypes to produce small plants under conditions of higher salinity and larger plants under conditions of lower salinity. Whether the genetic variation along this axis distinguishes species or indi- viduals within species remains to be determined. Leaving aside considerations of the systematic importance of green- house factor 2, one might wonder how, in general, systematics should treat a strong multivariate axis that reflects both genetic and environmental influences.
In numerical taxonomy, differential character weighting generally is rejected on theoretical grounds. Indeed, the search for multivariate patterns typically is justified on the basis of avoidance of the undue influence (weighting) of a few charac- ters. Yet differential weighting can arise in numerical analysis from the inclusion of several developmentally correlated manifestations of one genetic factor and few of another (Shaffer 1986). The present analysis confirms the potential for inadvertent weighting to arise from genetic and environmental character correlations. 'The environmental correlations are most obvious; several factors exhibit elements of plasticity, and several characters of demonstrated high plasticity (e.g., inflorescence axes, Davis 1983) contribute to factor 2 and to other factors. The contribution of genetic correlation to the multicharacter patterns detected in this analysis is more equi- vocal, but its presence is suggested strongly by a number of apparently homologous traits contributing to several factors. Scabrousness, for instance, is manifested throughout the grass plant; in this case it was scored at several points and treated as independent characters in the analysis. Although unequi- vocal evidence is not at hand, it is unlikely that these sepa- rately measurable traits are genetically and evolutionarily independent of each other.
Emerging from this analysis is the indication that genetic and environmental correlations may influence analyses of "relationship" simply because their effects can be measured repeatedly. To the extent that these correlations dominate a numerical analysis, they will obscure the objects of the analy- sis, correlations among independent characters. Do develop- mental correlations significantly affect typical numerical studies, or is the problem unduly exaggerated in this study? This question is impossible to answer definitively, in the absence of a quantitative genetic analysis of the organisms and characters in question. A survey of recent literature suggests, however, that the problem is widespread and significant. One study includes several length measurements from a single leaf, while another includes length, width, and additional measure- ments of leaf, sepal, and petal.
When a genetic analysis is not available, as it hardly ever is, I would suggest that the best understanding of phenetic varia- tion may emerge from an operational assumption that develop- mental correlations do exist, rather than that they do not. To hypothesize such correlations is not, of course, to identify which characters are correlated, and how strongly. It may be advisable to run series of parallel analyses involving succes- sively fewer characters, while removing those that reasonably
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2444 CAN. J. BOT. VOL. 66, 1988
might be hypothesized to be related developmentally to others, on the basis of homology or other considerations. The investi- gator then might determine how many characters can be elimi- nated from the analysis before major differences begin to emerge in the results, and to focus the analysis on characters that are critical in determining that one pattern, rather than another, is detected. Further research is required to clarify the interacting roles of environmental and genetic correlations in the determination of multivariate pattern.
Acknowledgements This work was supported in part by United States National
Science Foundation grant DEB 81-08677. I thank Dr. Jack Maze and two anonymous reviewers for comments on the manuscript.
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