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(American Journal of Botany. 1999;86:1615-1623.)
© 1999 Botanical Society of America, Inc.

Identifying inflorescence phytoliths from selected species of wheat (Triticum monococcum, T. dicoccon, T. dicoccoides, and T. aestivum) and barley (Hordeum vulgare and H. spontaneum) (Gramineae)

Terry B. Ball2, John S. Gardner and Nicole Anderson

Department of Botany, Brigham Young University, Provo, Utah 84602

Received for publication September 10, 1998. Accepted for publication March 2, 1999.


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 LITERATURE CITED
 
Analysis of microfossil silica phytoliths is becoming an increasingly important research tool for taxonomists, archaeobotanists, and paleoecologists. Expanded use of phytolith analysis by researchers is dependent upon development of phytolith systematics. In this study phytoliths produced by the inflorescence bracts from four species of wheat, Triticum monoccocum, T. dicoccon, T. dicoccoides, and T. aestivum, and two species of barley, Hordeum vulgare, and H. spontaneum, were analyzed using computer-assisted image and statistical analysis with the intent to develop taxonomic tools to distinguish among the taxa. A classification key based on significant differences among the mean morphometries of the inflorescence phytoliths produced by each species was created and tested. Discriminant analysis of the morphometries of several morphotypes of phytoliths was also conducted to determine whether this computer-assisted statistical procedure could be used as another method to classify the taxa and to determine which morphotypes have measurements that can best be used in discriminant functions. Test results indicated that, at the genus level, both the classification key and discriminant analysis of certain morphotypes of phytoliths were relatively reliable tools for distinguishing among phytoliths produced in the inflorescence bracts of the taxa considered. For distinguishing among the taxa at the species level, the classification key was most reliable. Of the discriminant analyses tested, that based on all the phytolith morphotypes combined was more reliable than those based on only one morphotype.

Key Words: barley • Gramineae • Hordeum • inflorescence bract • phytolith • Triticum • wheat


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 LITERATURE CITED
 
Solid deposits of SiO2 form in many plants at specific intracellular and extracellular locations (Jones and Handreck, 1967 ; Sangster, 1970 ; Raven, 1983 ). These deposits, as well as other types of plant mineral deposits, are called "phytoliths," literally meaning "plant-rocks." Many taxa produce phytoliths with characteristic morphologies, giving them taxonomic significance. Phytoliths are released from plants when the plant tissue in which they formed decays, is burned, or is digested. Released phytoliths thus become microfossils of the plant that produced them.

Analysis of microfossil phytoliths is becoming an increasingly important research tool for taxonomists, archaeobotanists, and paleoecologists. Microfossil phytoliths have been collected from such diverse sources as paleosols exposed by erosion or excavation (Bush and Colinvaux, 1994 ; Fisher, Bourn, and Fisher, 1995 ; Jiang, 1995 ; Piperno and Becker, 1996 ), tooth tartar and coprolites of herbivores (Cummings, 1989 ; Middleton and Rovner, 1994 ; Fox, Juan, and Albert, 1996 ), ceramics and bricks made from clay upon which vegetation once grew or to which plant fibers were added (Helbaek, 1961 ), the surface of stone tools used to process vegetation (Kamminga, 1979 ; Anderson, 1980 ), residues in vessels (Jones, 1993 ; Tyree, 1994 ), and sedimentary rocks (Jones, 1964 ).

Analysis of phytoliths has been useful to researchers in a wide variety of disciplines. For example, since 1990 phytolith analysis has been used to reconstruct paleoenvironments (Kalisz and Boettcher, 1990 ; Kelly et al., 1991 ; Dinan and Rowlett, 1993 ; Fisher, Bourn, and Fisher, 1995 ; Piperno and Becker, 1996 ), to make inferences about phylogenetic relationships (Piperno and Pearsall, 1993 ; Whang and Hill, 1995 ), to study the diet of man and herbivores (Ciochon, Piperno, and Thompson, 1990 ; Fox, Perez-Perez, and Juan, 1994 ; Fox, Juan, and Albert, 1996 ), to trace the use and development of cultivars (Piperno, 1990 ; Fujiwara, 1993 ; Rosen, 1993 ; Jiang, 1995 ), to study cultural ecology (Bush and Colinvaux, 1994 ; Henry, 1994 ), to conduct radiocarbon and thermoluminescence dating (Mulholland and Prior, 1993 ; Rowlett and Pearsall, 1993 ), and to study the micromorphology and formation of soil (Waltman and Ciolkosz, 1995 ). In reviewing the value and advances of phytolith research, Rovner (1983) suggested that it has the potential to become a second palynology.

Sytematics remains the most crucial area of phytolith research. The potential for phytoliths in systematics was demonstrated early by researches such as Grob (1896) , Metcalfe (1960) , and Prat (1932) who used phytoliths as taxonomic features of grass epidermis. However, to date, phytolith classification keys that provide taxonomic resolution at the genus and species level are rare or lacking because the types, morphologies, and morphometries (measurements of size and shape) of phytoliths produced by closely related taxa are often similar. Consequently, individual phytoliths produced by one taxa usually cannot be distinguished from those produced by closely related taxa. Ball, Gardner, and Brotherson (1996) have demonstrated that although individual phytoliths often cannot be reliably classified, an adequately large sample of phytoliths from a given taxa can be distinguished from closely related taxa through either the use of classification keys based on the mean morphometries of the phytolith sample or the use of the phytolith morphometries in discriminant functions. In this study, computer-assisted image and statistical analysis were used to develop a classification key and discriminant functions for identifying sample populations of phytoliths produced by the inflorescence bracts of selected species of wheat and barley. Such classification keys and discriminant functions can be useful tools for researchers seeking to identify cereal grain phytoliths recovered from archaeological excavations and for taxonomist working with these species. The species analyzed include Triticum monoccocum L. (einkorn wheat), T. dicoccon Schrank. (emmer wheat), T. dicoccoides Körn. (wild emmer), T. aestivum L. (bread wheat), Hordeum vulgare L. (two-rowed and six-rowed barley), and one wild relative of cultivated barley, H. spontaneum C. Koch. These species were selected because of their historical, economical, agricultural, and archaeological import.

Previous research of wheat and barley phytoliths has been limited. Several studies of the deposition of silica in the plants have been conducted (Blackman, 1969 ; Hayward and Parry, 1973 ; Hutton and Norris, 1974 ; Hayward and Parry, 1975 ; Bennett, 1982a, b ; Hodson and Sangster, 1988, 1989 ), though these studies did not address taxonomic issues. Kaplan, Smith, and Sneddon (1992) identified different phytolith morphotypes produced by various cereals, including wheat and barley, and found that most species produce similar morphotypes and cannot usually be distinguished on the basis of morphotypes alone. Rosen (1992) conducted comparative studies of silica skeletons (relatively large articulated pieces of silicified tissue) of cereals recovered from archaeological sites, but did not attempt identification based upon disarticulated morphotypes. Tubb, Hodson, and Hodson (1993) explored the potential of identifying species of Triticeae based solely upon papillae phytolith morphometry, but have not yet considered other phytolith morphotypes. Ball, Brotherson, and Gardner (1993) , and Ball, Gardner, and Brotherson (1996) have reported morphometric studies of wheat inflorescence phytoliths. This report is a continuation of those studies and includes an additional species of wheat as well as the first comprehensive morphometric analysis of inflorescence phytoliths from two species of barley.


   MATERIALS AND METHODS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 LITERATURE CITED
 
Accessions of the taxa analyzed were obtained from several sources as described in Table 1. Phytoliths from the mature inflorescence bracts of each accession were extracted and prepared for scanning electron microscopy (SEM) following the procedures outlined by Ball, Brotherson, and Gardner (1993) .


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  		Table 1. Description of sample populations (N = 50 for each phytolith morphotype from each accession)

 
Images of the extracted phytoliths were recorded using a videocassette recorder connected to the video output of a Zeiss LSM1 microscope. Standard light microscopy was used at magnifications of 2000x (obtained through computer enlargement of 400x images).

Phytolith morphotypes produced by each species were noted and described. Morphometric analyses of the images were then performed using a MacIntosh Centris 650 computer equipped with a Data Translation DT-2255 frame grabber board (Data Translation, Inc., Marlboro, Maryland) and "Prism" image analysis software (Dapple Systems, Inc., Sunnyvale, California). Eighteen morphometric parameters relative to size and shape (Table 2) were evaluated for silica cell, papilla, and trichome base phytoliths. The length, narrowest width, and widest width were measured for dendriform phytoliths. Fifty phytoliths of each morphotype from each accession of each species were measured in every case.


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  		Table 2. Descriptions of the morphometric parameters measured

 
Statistical analysis of the data was then conducted using a PC 486 computer and the SAS System for Windows data analysis software (SAS Institute, Cary, North Carolina). Descriptive statistics by species of the means, minima, maxima, standard deviations, and variances were obtained for each of the morphometries evaluated. This information was then used to create a classification key based on the characteristic differences between the mean morphometries of the silica cell, dendriform, and papilla phytolith morphotypes produced by the taxa. Minimum adequate sample sizes to assure a 90% confidence level that the sample means were within 5% of the actual population means were calculated using the following standard equation.

where Nmin = the minimum adequate sample, Z2{propto}/2 = 1.64, which is the square of the two-tailed Z value at {propto} = 0.10, S2 = the variance, and (ME)2 = the square of the desired margin of error, in this case 0.05 x the sample mean.

The reliability of the classification key was then evaluated by using it to classify each accession separately
As an alternative method of classification, two approaches to discriminant analysis of the morphometric data were then conducted. First, discriminant analyses were performed on the morphometric data from each individual phytolith morphotype, i.e., the silica cell, papilla, trichome base, and dendriform phytoliths, to determine which morphotype could best be used in this computer-assisted statistical procedure to classify each accession by genus, and then by species. In each case the morphometric data for the accession being classified were removed from the training or calibration data set to avoid prejudicing the discriminant functions computed. We began the discriminant analysis of each accession with a stepwise discriminant procedure to identify those morphometries for each morphotype that best discriminates among the taxa in the calibration data set. The variables so identified were then used in standard parametric discriminant analysis of the calibration data set to compute the discriminant functions, which were then used to classify the accession being analyzed (for an explanation of these procedures see SAS, 1988 ). In this procedure, all of the phytoliths of each morphotype from each accession sample were individually classified. The taxon that produced any given accession being analyzed was then identified as that to which the majority of the phytoliths were classified by the analysis. For example, if in the discriminant analysis for genus, 36 out of 50 of a given accession's silica cell phytoliths were classified as Hordeum, while 14 were classified as Triticum, then Hordeum was identified as the genus of that accession.

In contrast to this first approach to discriminant analysis, based on individual phytolith morphotypes, the second approach was based on all four phytolith morphotypes combined. This was done to determine whether discriminant analysis based on all the morphotypes combined was more reliable than analysis based on only one morphotype. In this analysis, the calibration or training data set consisted of the mean of each morphometric parameter for each morphotype for each accession. Again stepwise discriminant analysis was used on the data to identify which of all the morphometric parameters best distinguished among the taxa, and the variables so identified were then used in standard parametric discriminant analysis to compute the discriminant functions. The functions were then used to classify each accession based on the means of the morphometric parameters of the phytoliths it produced.


   RESULTS
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 LITERATURE CITED
 
The morphotypes of the phytoliths produced by all the wheat and barley species analyzed were virtually identical to those described by Ball, Brotherson, and Gardner (1993) . They include silica cell phytoliths (Figs. 1, 2), papilla phytoliths (Figs. 3–5), trichome base phytoliths (Figs. 6, 7), dendriform phytoliths (Figs. 8, 9), small-prickle phytoliths, large-prickle phytoliths, hair cell phytoliths, stomata phytoliths, epidermal long-cell phytoliths, and subepidermal rod-shaped phytoliths (for illustrations of types not pictured see Ball, Gardner, and Brotherson, 1996 ). No morphotypes were unique to any of the species.



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  		Figs. 1–7. Phytoliths extracted from the inflorescence bracts of Triticum sp. Figure Abbreviations: S, silica cell phytoliths; P, papillae phytoliths; T, trichome base phytoliths. 1. Light micrograph of silica cell phytoliths from T. monococcum. Bar = 25 µm. 2. Scanning electron micrograph of silica cell phytoliths from T. aestivum. Bar = 10 µm. 3. Light micrograph of papillae phytoliths from T. aestivum. Bar = 25 µm. 4. Light micrograph of papillae phytoliths from T. monococcum. Bar = 25 µm. 5. Scanning electron micrograph of papillae phytolith from T. aestivum. Bar = 10 µm. 6. Light micrograph of trichome base phytoliths from T. monococcum. Bar = 25 µm 7. Scanning electron micrograph of trichome base phytoliths from T. aestivum. Bar = 10 µm

 


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  		Figs. 8–9. Phytoliths extracted from the inflorescence bracts of Triticum sp. Figure Abbreviations: S, silica cell phytoliths; P, papillae phytoliths; T, trichome base phytoliths. 8. Light micrograph of dendriform phytolith from T. aestivum. Bar = 25 µm. 9. Scanning electron micrograph of dendriform phytolith from T. aestivum. Bar = 10 µm

 
Descriptive statistics for the distinctive morphometries (those used in the classification key and the best discriminant functions) of the silica cell, trichome base, papilla, and dendriform phytoliths sampled from each species are found in Tables 3–5. Table 6 is the classification key for the taxa based upon differences in the mean morphometries of the phytoliths from the sample populations. Results of the test of this key to classify each accession are reported in Table 7. Table 8 reports the overall results of the discriminant analyses using the morphometric data for each individual morphotype, and using the data from all four morphotypes combined. These results indicate that discriminate analysis based on all four morphotypes combined is most reliable (100% correct classification at the genus level and 82.5% correct classification at the species level), while silica cell phytoliths are the most reliable for analysis based on only one morphotype (95% correct classification at the species level and 75% correct classification at the species level). Table 9 shows the detailed results and the variables used in the silica cell discriminate analysis, while Table 10 does the same for the analysis based on all four morphotypes combined.


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  		Table 3. Mean, range, and standard deviation for distinguishing size morphometries of the top surface of inflorescence silica cell phytoliths. Nmin = minimum adequate sample size rounded to nearest 5

 

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  		Table 6. Key to Triticum monococcum, T. dicoccon, T. dicoccoides, T. aestivum, Hordeum spontaneum, and H. vulgare using mean morphometriesa of inflorescence phytoliths

 

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  		Table 7. Results of the test of the Key to Triticum monococcum, T. dicoccon, T. dicoccoides, T. aestivum, Hordeum spontaneum and H. vulgare.

 

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  		Table 8. Results of discriminant analysis using morphometric data from different types of inflorescence phytoliths of selected accessions of Triticum monococcum, T. dicoccon, T. dicoccoides, T. aestivum, Hordeum spontaneum, and H. vulgare.

 

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  		Table 9. Detailed results of discriminant analysis of inflorescence silica cell phytoliths from selected accessions of Triticum monococcum, T. dicoccon, T. dicoccoides, T. aestivum, Hordeum spontaneum, and H. vulgare.

 

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  		Table 10. Detailed results of discriminant analysis using the mean morphometries of inflorescence silica cell, papillae, trichome base, and dendriform phytoliths combined from selected accessions of Triticum monococcum, T. dicoccon, T. dicoccoides, T. aestivum, Hordeum spontaneum and H. vulgare.

 

   DISCUSSION
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
LITERATURE CITED
 
In constructing the classification key it was found that silica cell phytolith morphometries were adequate to discriminate among all the taxa at the genus level and even at the species level in the case of barley. This is fortunate as silica cell phytoliths are the most durable and frequently recovered in archaeological settings. However, best discrimination among the species of wheat required the use of the trichome base phytoliths as well.

Of the three types of classification paradigms considered in this study, i.e., the key, discriminant analysis based on one morphotype of phytolith, and discriminant analysis based on all four morphotypes combined, the key proved to be most reliable (100% correct classification at the genus level and 92.5% correct classification at the species level). This is fortunate as the key is much easier to use and does not require as many complex computer-assisted calculations as discriminant analysis. However, as more accessions and taxa are studied, discriminant analysis may yet prove to be necessary. Furthermore, because none of the classification methods is completely accurate at all taxonomic levels, researchers may want to use more than one paradigm to validate their conclusions.

There are a few important practical requirements that must be met by researchers intending to use the classification tools presented in this study. First, they are intended to classify only inflorescence phytolith populations. Ball, Brotherson, and Gardner (1993) noted that phytoliths produced in other plant parts, i.e., laminae and culms, may have morphometries that could be confused with these inflorescence phytoliths. To avoid the confusion, one must be certain that dendriform phytoliths, which are unique to inflorescence bracts, occur in the assemblage being analyzed. Second, the inflorescence of other cereal grains not analyzed in this study may produce phytolith assemblages that could be confused with the wheats and barleys considered herein. Consequently, before using these classification paradigms, one must be reasonably confident that the phytolith sample being analyzed is from one of the wheat or barley species included in this study. Finally, because these tools use population rather than individual phytolith differences for discrimination, one must be certain to obtain a sufficiently large sample of phytoliths from the taxon to be identified in order to be confident that the population is adequately represented. We have found that a sample of 50 phytoliths of each morphotype is usually adequate.

Analyses of other species of cereal grains and plant parts are planned to further develop the classification tools presented in this study.


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  		Table 4. Mean, range, and standard deviation for distinguishing shape morphometries of the top surface of inflorescence silica cell phytoliths. (Nmin = minimum adequate sample size rounded to nearest 5.)

 

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  		Table 5. Mean, range, and standard deviation for distinguishing morphometries of the trichome base, papillae, and dendriform phytoliths. (Nmin = minimum adequate sample size rounded to nearest 5.)

 

   FOOTNOTES
 
2 Author for correspondence. Back


   LITERATURE CITED
TOP
 ABSTRACT
 INTRODUCTION
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 LITERATURE CITED
 
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