Isozymatic variation and phylogenetic relationships between henequen (Agave fourcroydes) and its wild ancestor A. angustifolia (Agavaceae).
TL;DR: The hypothesis of the yucatecan origin of SK and YK from the SF ecotype, as well as the hypothesis of a recent introduction of KK to the Yucatan Peninsula in a domestication trend that probably included also Chelem White, are supported.
Abstract: Isozymatic variation and phylogenetic relationships among extant henequen (Agave fourcroydes) germplasm and wild populations of its ancestor A. angustifolia in the Yucatan Peninsula in Mexico were analyzed. Analysis of three isozyme systems using starch gel electrophoresis indicated that while A. angustifolia populations have relatively high levels of variation, within each henequen cultivar all individuals were identical. This result corresponds to previous ethnobotanical and morphological analyses, which indicated severe loss of genetic variation of this domesticated plant as a consequence of the promotion by means of asexual propagation of only one cultivar since the middle of the last century. The three extant cultivars of henequen were distinct from each other. Two of them, Sac Ki (SK) and Yaax Ki (YK), could be matched within the progenitor, but Kitam Ki (KK) has a MDH electrophenotype not found in any of the plants growing inside the Yucatan Peninsula, but found in some A. angustifolia plants growing in the Mexican states of Oaxaca and Veracruz. A parsimony analysis of the morphological data indicated two lineages: that of SK and YK, cultivated cordage plants selected for stronger and longer fibers, whose sister group is the Tropical subdeciduous forest ecotype (SF), and that of all the other wild populations, which also included KK, the cultivated textile plants selected for finer fibers and nearly extinct in Yucatan. These results support the hypothesis of the yucatecan origin of SK and YK from the SF ecotype, as well as the hypothesis of a recent introduction of KK to the Yucatan Peninsula in a domestication trend that probably included also Chelem White (its cultivation being abandoned later).
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TL;DR: Unweighted pair group method with arithmetic means (UPGMA) analysis of allozymes suggests the existence of at least two distinct groups of populations, one consisting of both subspecies of C. argyrosperma and another consisting ofC.
Abstract: Cultivated squash (Cucurbita argyrosperma ssp. argyrosperma and C. moschata) are important in the Mexican traditional agroecosystem. They are typically cultivated within maize fields where adjacent populations of a wild, close relative, C. argyrosperma ssp. sororia, occur. Consequently, there are ample opportunities for gene flow between domesticated and free-living Cucurbita populations. We used allozymes to examine genetic variation and gene flow among these three Cucurbita taxa in the state of Jalisco in Western Mexico. Twelve polymorphic allozyme loci were used to calculate genetic diversity for 16 populations of Cucurbita. We found high levels of genetic variation: polymorphism of 0.96, mean allelic diversity of 2.08, average expected heterozygosity 0.407, and little differentiation among conspecific populations (D = 0.081; F S T = 0.087; N e m = 5.22). These findings indicate that Cucurbita possess a high pollen dispersal potential, a somewhat surprising result considering they have specialist pollinators. Unweighted pair group method with arithmetic means (UPGMA) analysis of allozymes suggests the existence of at least two distinct groups of populations, one consisting of both subspecies of C. argyrosperma and another consisting of C. moschata.
76 citations
TL;DR: The results of RAPD analysis suggest that A. tequilana var.
Abstract: By federal law in Mexico, A. tequilana Weber var. Azul is the only variety of agave permitted for the production of any tequila. Our objective was to assay levels of genetic variation in field populations of A. tequilana var. Azul using randomly amplified polymorphic DNA (RAPD) markers. Ten plants were collected from each of four different fields, with two fields being located in each of two principal regions of Mexico for the cultivation of A. tequilana var. Azul. The two regions are separated geographically by approximately 100km. Genetic relationships between A. tequilana var. Azul and two other varieties of A. tequilana Weber, ‘Chato’ and ‘Siguin’, were also investigated using RAPDs. Among the three varieties, 19 decamer primers produced 130 markers, of which 20 (15.4%) were polymorphic betweenA. tequilana var. Chato and A. tequilana var. Siguin. The results of RAPD analysis suggest that A. tequilana var. Siguin is more closely related to A. tequilana var. Azul than is A. tequilana var. Chato. Among the 40 field selections of A. tequilana var. Azul, only 1 of124 RAPD products (0.8%) was polymorphic and 39 of 40 plants were completely isogenic. This is one of the lowest levels of polymorphism detected to date for the analysis of a crop species, and is proposed to be the result of the promotion of a single conserved genotype over many years due to an exclusive reliance on vegetative propagation for the production of new planting materials. The significance of these results is discussed in relation to breeding programs focused on the improvement of A. tequilana var. Azul.
75 citations
Cites background or methods or result from "Isozymatic variation and phylogenet..."
...Similarly, Colunga-GarcíaMarín et al. (1999) investigated genetic diversity among a wild population of A. angustifolia and observed 34 different electrophenotypes for the three isozymes used in the study....
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...Similarly, Colunga-GarcíaMarín et al. (1999) investigated genetic diversity among a wild population of A....
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...Similar results were observed by Colunga-GarcíaMarín et al. (1999) for the case of A. fourcroydes, a plant species cultivated for fiber production in the Yucatan Peninsula of Mexico....
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...…coding for ribosomal DNA (Bolger & Simpson ,1996), variations in the sequence of the chloroplast rbcL gene (Eguiarte et al., 1994), isozyme analysis (Massey & Hamrick, 1998; Massey & Hamrick, 1999; Martínez-Palacios et al., 1999; Colunga-GarcíaMarín et al., 1999), and RAPDs (Trame et al., 1995)....
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TL;DR: Using intersimple sequence repeats and Bayesian estimators of diversity and structure, it is found that A. angustifolia traditional landraces had a genetic diversity similar to its wild populations and a higher genetic structure than the blue agave commercial system.
Abstract: Traditional farming communities frequently maintain high levels of agrobiodiversity, so understanding their agricultural practices is a priority for biodiversity conservation. The cultural origin of agave spirits (mezcals) from west-central Mexico is in the southern part of the state of Jalisco where traditional farmers cultivate more than 20 landraces of Agave angustifolia Haw. in agroecosystems that include in situ management of wild populations. These systems, rooted in a 9000-year-old tradition of using agaves as food in Mesoamerica, are endangered by the expansion of commercial monoculture plantations of the blue agave variety ( A. tequilana Weber var. Azul), the only agave certifi ed for sale as tequila, the best-known mezcal. Using intersimple sequence repeats and Bayesian estimators of diversity and structure, we found that A. angustifolia traditional landraces had a genetic diversity ( H BT = 0.442) similar to its wild populations ( H BT = 0.428) and a higher genetic structure ( θ B = 0.405; θ B =0. 212). In contrast, the genetic diversity in the blue agave commercial system ( H B = 0.118) was 73% lower. Changes to agave spirits certifi cation laws to allow the conservation of current genetic, ecological and cultural diversity can play a key role in the preservation of the traditional agroecosystems.
71 citations
TL;DR: The economic importance of Opuntia in Mexico is discussed and a method to predict the invasion of the alien species C. cactorum is proposed, which indicates that the possible routes of invasion are along the northern border through Texas and via southeastern Mexico.
Abstract: The appearance of the cactus moth Cactoblastis cactorum in Florida has roused concern over its possible effects on the Opuntia-rich areas of Mexico and the southwestern United States. In this paper we discuss the economic importance of Opuntia in Mexico and propose a method to predict the invasion of the alien species C. cactorum. In Mexico, the products derived from Opuntia are mainly human food and fodder for livestock. Both cultivated and wild populations of Opuntia are currently used for these two purposes. By using bioclimatic modeling, we predicted the potential distribution of C. cactorum and overlaid this on the actual distribution of Opuntia species. The resulting maps indicate that the possible routes of invasion to Mexico are 1) along the northern border through Texas (most likely) and 2) via southeastern Mexico (less likely). The impacts of an invasion of C. cactorum on Opuntia products could be significant as well as being a threat to endemic species. Bioclimatic modeling can help to predict the areas of highest probability of attack and facilitate planning to mitigate future impacts.
70 citations
TL;DR: Despite the many unknowns regarding agaves, they provide a means to resolve disparities in resource availability and needs between natural and human systems in semi-arid regions and show considerable promise as an alternative source for food, alternative sweeteners, and even bioenergy.
Abstract: As climate change leads to drier and warmer conditions in semi-arid regions, growing resource-intensive C3 and C4 crops will become more challenging. Such crops will be subjected to increased frequency and intensity of drought and heat stress. However, agaves, even more than pineapple (Ananas comosus) and prickly pear (Opuntia ficus-indica and related species), typify highly productive plants that will respond favorably to global warming, both in natural and cultivated settings. With nearly 200 species spread throughout the U.S., Mexico, and Central America, agaves have evolved traits, including crassulacean acid metabolism (CAM), that allow them to survive extreme heat and drought. Agaves have been used as sources of food, beverage, and fiber by societies for hundreds of years. The varied uses of Agave, combined with its unique adaptations to environmental stress, warrant its consideration as a model CAM crop. Besides the damaging cycles of surplus and shortage that have long beset the tequila industry, the relatively long maturation cycle of Agave, its monocarpic flowering habit, and unique morphology comprise the biggest barriers to its widespread use as a crop suitable for mechanized production. Despite these challenges, agaves exhibit potential as crops since they can be grown on marginal lands, but with more resource input than is widely assumed. If these constraints can be reconciled, Agave shows considerable promise as an alternative source for food, alternative sweeteners, and even bioenergy. And despite the many unknowns regarding agaves, they provide a means to resolve disparities between natural and human systems in semi-arid regions.
57 citations
Cites background from "Isozymatic variation and phylogenet..."
...As with A. tequilana, henequen descended from A. angustifolia (Gentry, 1982; Colunga-GarcíaMarín and Maypat, 1997; Colunga-GarcíaMarín et al., 1999; Colunga-GarcíaMarín, 2003), which is a fertile hexaploid (Castorena-Sanchez et al., 1991), and has a wide native distribution throughout Mexico,…...
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References
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Book•
01 Jan 1966
TL;DR: In this article, the authors presented a case of two means regression method for the family error rate, which was used to estimate the probability of a family having a nonzero family error.
Abstract: 1 Introduction.- 1 Case of two means.- 2 Error rates.- 2.1 Probability of a nonzero family error rate.- 2.2 Expected family error rate.- 2.3 Allocation of error.- 3 Basic techniques.- 3.1 Repeated normal statistics.- 3.2 Maximum modulus (Tukey).- 3.3 Bonferroni normal statistics.- 3.4 ?2 projections (Scheffe).- 3.5 Allocation.- 3.6 Multiple modulus tests (Duncan).- 3.7 Least significant difference test (Fisher).- 4 p-mean significance levels.- 5 Families.- 2 Normal Univariate Techniques.- 1 Studentized range (Tukey).- 1.1 Method.- 1.2 Applications.- 1.3 Comparison.- 1.4 Derivation.- 1.5 Distributions and tables.- 2 F projections (Scheffe)48.- 2.1 Method.- 2.2 Applications.- 2.3 Comparison.- 2.4 Derivation.- 2.5 Distributions and tables.- 3 Bonferroni t statistics.- 3.1 Method.- 3.2 Applications.- 3.3 Comparison.- 3.4 Derivation.- 3.5 Distributions and tables.- 4 Studentized maximum modulus.- 4.1 Method.- 4.2 Applications.- 4.3 Comparison.- 4.4 Derivation.- 4.5 Distributions and tables.- 5 Many-one t statistics76.- 5.1 Method.- 5.2 Applications.- 5.3 Comparison.- 5.4 Derivation.- 5.5 Distributions and tables.- 6 Multiple range tests (Duncan).- 6.1 Method.- 6.2 Applications.- 6.3 Comparison.- 6.4 Derivation.- 6.5 Distributions and tables.- 7 Least significant difference test (Fisher).- 7.1 Method.- 7.2 Applications.- 7.3 Comparison.- 7.4 Derivation.- 7.5 Distributions and tables.- 8 Other techniques.- 8.1 Tukey's gap-straggler-variance test.- 8.2 Shortcut methods.- 8.3 Multiple F tests.- 8.4 Two-sample confidence intervals of predetermined length.- 8.5 An improved Bonferroni inequality.- 9 Power.- 10 Robustness.- 3 Regression Techniques.- 1 Regression surface confidence bands.- 1.1 Method.- 1.2 Comparison.- 1.3 Derivation.- 2 Prediction.- 2.1 Method.- 2.2 Comparison.- 2.3 Derivation.- 3 Discrimination.- 3.1 Method.- 3.2 Comparison.- 3.3 Derivation.- 4 Other techniques.- 4.1 Linear confidence bands.- 4.2 Tolerance intervals.- 4.3 Unlimited discrimination intervals.- 4 Nonparametric Techniques.- 1 Many-one sign statistics (Steel).- 1.1 Method.- 1.2 Applications.- 1.3 Comparison.- 1.4 Derivation.- 1.5 Distributions and tables.- 2 k-sample sign statistics.- 2.1 Method.- 2.2 Applications.- 2.3 Comparison.- 2.4 Derivation.- 2.5 Distributions and tables.- 3 Many-one rank statistics (Steel).- 3.1 Method.- 3.2 Applications.- 3.3 Comparison.- 3.4 Derivation.- 3.5 Distributions and tables.- 4 k-sample rank statistics.- 4.1 Method.- 4.2 Applications.- 4.3 Comparison.- 4.4 Derivation.- 4.5 Distributions and tables.- 5 Signed-rank statistics.- 6 Kruskal-Wallis rank statistics (Nemenyi).- 6.1 Method.- 6.2 Applications.- 6.3 Comparison.- 6.4 Derivation.- 6.5 Distributions and tables.- 7 Friedman rank statistics (Nemenyi).- 7.1 Method.- 7.2 Applications.- 7.3 Comparison.- 7.4 Derivation.- 7.5 Distributions and tables.- 8 Other techniques.- 8.1 Permutation tests.- 8.2 Median tests (Nemenyi).- 8.3 Kolmogorov-Smirnov statistics.- 5 Multivariate Techniques.- 1 Single population covariance scalar unknown.- 1.1 Method.- 1.2 Applications.- 1.3 Comparison.- 1.4 Derivation.- 1.5 Distributions and tables.- 2 Single population covariance matrix unknown.- 2.1 Method.- 2.2 Applications.- 2.3 Comparison.- 2.4 Derivation.- 2.5 Distributions and tables.- 3 k populations covariance matrix unknown.- 3.1 Method.- 3.2 Applications.- 3.3 Comparison.- 3.4 Derivation.- 3.5 Distributions and tables.- 4 Other techniques.- 4.1 Variances known covariances unknown.- 4.2 Variance-covariance intervals.- 4.3 Two-sample confidence intervals of predetermined length.- 6 Miscellaneous Techniques.- 1 Outlier detection.- 2 Multinomial populations.- 2.1 Single population.- 2.2 Several populations.- 2.3 Cross-product ratios.- 2.4 Logistic response curves.- 3 Equality of variances.- 4 Periodogram analysis.- 5 Alternative approaches: selection, ranking, slippage.- A Strong Law For The Expected Error Rate.- B TABLES.- I Percentage points of the studentized range.- II Percentage points of the Bonferroni t statistic.- III Percentage points of the studentized maximum modulus.- IV Percentage points of the many-one t statistics.- V Percentage points of the Duncan multiple range test.- VI Percentage points of the many-one sign statistics.- VIII Percentage points of the many-one rank statistics.- IX Percentage points of the k-sample rank statistics.- Developments in Multiple Comparisons 1966-).- 3.5 Allocation.- 3.6 Multiple modulus tests (Duncan).- 3.7 Least significant difference test (Fisher).- 4 p-mean significance levels.- 5 Families.- 2 Normal Univariate Techniques.- 1 Studentized range (Tukey).- 1.1 Method.- 1.2 Applications.- 1.3 Comparison.- 1.4 Derivation.- 1.5 Distributions and tables.- 2 F projections (Scheffe)48.- 2.1 Method.- 2.2 Applications.- 2.3 Comparison.- 2.4 Derivation.- 2.5 Distributions and tables.- 3 Bonferroni t statistics.- 3.1 Method.- 3.2 Applications.- 3.3 Comparison.- 3.4 Derivation.- 3.5 Distributions and tables.- 4 Studentized maximum modulus.- 4.1 Method.- 4.2 Applications.- 4.3 Comparison.- 4.4 Derivation.- 4.5 Distributions and tables.- 5 Many-one t statistics76.- 5.1 Method.- 5.2 Applications.- 5.3 Comparison.- 5.4 Derivation.- 5.5 Distributions and tables.- 6 Multiple range tests (Duncan).- 6.1 Method.- 6.2 Applications.- 6.3 Comparison.- 6.4 Derivation.- 6.5 Distributions and tables.- 7 Least significant difference test (Fisher).- 7.1 Method.- 7.2 Applications.- 7.3 Comparison.- 7.4 Derivation.- 7.5 Distributions and tables.- 8 Other techniques.- 8.1 Tukey's gap-straggler-variance test.- 8.2 Shortcut methods.- 8.3 Multiple F tests.- 8.4 Two-sample confidence intervals of predetermined length.- 8.5 An improved Bonferroni inequality.- 9 Power.- 10 Robustness.- 3 Regression Techniques.- 1 Regression surface confidence bands.- 1.1 Method.- 1.2 Comparison.- 1.3 Derivation.- 2 Prediction.- 2.1 Method.- 2.2 Comparison.- 2.3 Derivation.- 3 Discrimination.- 3.1 Method.- 3.2 Comparison.- 3.3 Derivation.- 4 Other techniques.- 4.1 Linear confidence bands.- 4.2 Tolerance intervals.- 4.3 Unlimited discrimination intervals.- 4 Nonparametric Techniques.- 1 Many-one sign statistics (Steel).- 1.1 Method.- 1.2 Applications.- 1.3 Comparison.- 1.4 Derivation.- 1.5 Distributions and tables.- 2 k-sample sign statistics.- 2.1 Method.- 2.2 Applications.- 2.3 Comparison.- 2.4 Derivation.- 2.5 Distributions and tables.- 3 Many-one rank statistics (Steel).- 3.1 Method.- 3.2 Applications.- 3.3 Comparison.- 3.4 Derivation.- 3.5 Distributions and tables.- 4 k-sample rank statistics.- 4.1 Method.- 4.2 Applications.- 4.3 Comparison.- 4.4 Derivation.- 4.5 Distributions and tables.- 5 Signed-rank statistics.- 6 Kruskal-Wallis rank statistics (Nemenyi).- 6.1 Method.- 6.2 Applications.- 6.3 Comparison.- 6.4 Derivation.- 6.5 Distributions and tables.- 7 Friedman rank statistics (Nemenyi).- 7.1 Method.- 7.2 Applications.- 7.3 Comparison.- 7.4 Derivation.- 7.5 Distributions and tables.- 8 Other techniques.- 8.1 Permutation tests.- 8.2 Median tests (Nemenyi).- 8.3 Kolmogorov-Smirnov statistics.- 5 Multivariate Techniques.- 1 Single population covariance scalar unknown.- 1.1 Method.- 1.2 Applications.- 1.3 Comparison.- 1.4 Derivation.- 1.5 Distributions and tables.- 2 Single population covariance matrix unknown.- 2.1 Method.- 2.2 Applications.- 2.3 Comparison.- 2.4 Derivation.- 2.5 Distributions and tables.- 3 k populations covariance matrix unknown.- 3.1 Method.- 3.2 Applications.- 3.3 Comparison.- 3.4 Derivation.- 3.5 Distributions and tables.- 4 Other techniques.- 4.1 Variances known covariances unknown.- 4.2 Variance-covariance intervals.- 4.3 Two-sample confidence intervals of predetermined length.- 6 Miscellaneous Techniques.- 1 Outlier detection.- 2 Multinomial populations.- 2.1 Single population.- 2.2 Several populations.- 2.3 Cross-product ratios.- 2.4 Logistic response curves.- 3 Equality of variances.- 4 Periodogram analysis.- 5 Alternative approaches: selection, ranking, slippage.- A Strong Law For The Expected Error Rate.- B TABLES.- I Percentage points of the studentized range.- II Percentage points of the Bonferroni t statistic.- III Percentage points of the studentized maximum modulus.- IV Percentage points of the many-one t statistics.- V Percentage points of the Duncan multiple range test.- VI Percentage points of the many-one sign statistics.- VIII Percentage points of the many-one rank statistics.- IX Percentage points of the k-sample rank statistics.- Developments in Multiple Comparisons 1966-1976.- 1 Introduction.- 2 Papers of special interest.- 2.1 Probability inequalities.- 2.2 Methods for unbalanced ANOVA.- 2.3 Conditional confidence levels.- 2.4 Empirical Bayes approach.- 2.5 Confidence bands in regression.- 3 References.- 4 Bibliography 1966-1976.- 4.1 Survey articles.- 4.2 Probability inequalities.- 4.3 Tables.- 4.4 Normal multifactor methods.- 4.5 Regression.- 4.6 Categorical data.- 4.7 Nonparametric techniques.- 4.8 Multivariate methods.- 4.9 Miscellaneous.- 4.10 Pre-1966 articles missed in [6].- 4.11 Late additions.- 5 List of journals scanned.- Addendum New Table of the Studentized Maximum Modulus.- Table IIIA Percentage points of the studentized maximum modulus.- Author Index.
4,763 citations
Book•
01 Jan 2002
TL;DR: PAUP* 4.0 Beta is a major upgrade of the bestselling software for the inference of evolutionary trees, for use in Macintosh or Windows/DOS-based formats.
Abstract: PAUP* 4.0 Beta is a major upgrade of the bestselling software for the inference of evolutionary trees, for use in Macintosh or Windows/DOS-based formats. This version is for use in Macintosh. Please note it is currently only available as a Beta version. For more information (and the Linux/UNIX version), visit www.sinauer.com/detail.php?id=8060
4,329 citations
TL;DR: Woody species with large geographic ranges, outcrossing breeding systems, and wind or animal-ingested seed dispersal have more genetic diversity within species and populations but less variation among populations than woodyspecies with other combinations of traits.
Abstract: The plant allozyme literature was reviewed to: (1) compare genetic diversity in long-lived woody species with species representing other life forms, and (2) to investigate whether the levels and distribution of genetic diversity in woody species are related to life history and ecological characteristics. Data from 322 woody taxa were used to measure genetic diversity within species, and within and among populations of species. Woody species maintain more variation within species and within populations than species with other life forms but have less variation among populations. Woody species with large geographic ranges, outcrossing breeding systems, and wind or animal-ingested seed dispersal have more genetic diversity within species and populations but less variation among populations than woody species with other combinations of traits. Although life history and ecological traits explain a significant proportion (34%) of the variation among species for the genetic parameters measured, a large proportion of the interspecific variation is unexplained. The specific evolutionary history of each species must play an important role in determining the level and distribution of genetic diversity.
1,515 citations
TL;DR: An attempt to improve methods of analysis of fern enzymes in starch gel electrophoresis by experimenting with modifications of the method of sample preparation outlined by Soltis et al. (1980), and determining gel and electrode buffers that provide clear starch gel enzyme banding for 22 enzyme systems in ferns.
Abstract: The homosporous pteridophytes have been largely uninvestigated by electrophoresis, despite the fact that they offer many exciting research possibilities (Soltis et al., 1980). The paucity of electrophoretic studies of ferns and fern allies may be due in large part to the high concentrations of condensed tannins that many species contain (Cooper-Driver, 1976 and pers. comm.). These compounds render enzymes inactive by binding with them following cellular disruption, thereby frustrating researchers who have attempted electrophoretic analysis utilizing standard methods of sample preparation. The method of sample preparation developed by Kelley and Adams (1977a, b) in their analysis of enzyme variation in Juniperus was an important procedural breakthrough in overcoming the difficulties that result from the liberation of large amounts of phenolic compounds during tissue preparation. Recently, a simplified version of that method was applied by Soltis et al. (1980) to fern leaf tissue, facilitating rapid preparation of active enzyme samples and thereby making electrophoretic analyses of large numbers of individuals more feasible. In an attempt to improve methods of analysis of fern enzymes in starch gel electrophoresis, we have experimented with modifications of the method of sample preparation outlined by Soltis et al. (1980). We also have examined several different methods of sample preparation such as those of Gottlieb (1981a), Mitton et al. (1979), and Werth et al. (1982), and have evaluated the relative merits of each with fern tissue. Finally, during the course of our electrophoretic investigations of ferns we found that standard gel and electrode buffers and staining schedules, such as those of Brewer (1970) and Shaw and Prasad (1970), often provided unsatisfactory results when applied to ferns. We have determined gel and electrode buffers, as well as staining schedules, that provide clear starch gel enzyme banding for 22 enzyme systems in ferns. Requests for advice resulting from the recent surge of interest in fern enzyme electrophoresis have prompted us to compile our procedural data so that other researchers can take advantage of our experimentation. We hope that these data will stimulate more extensive electrophoretic investigation of pteridophytes and other electrophoretically difficult taxa. Gottlieb (1981b) recently reviewed aspects of enzyme electrophoresis primarily in gymnosperms and angiosperms. His discussion is equally relevant to understanding the potential applications and limitations of electrophoretic evidence in pteridophytes. Since homosporous pteridophytes have high chromosome numbers, it is tempting to invoke polyploidy in interpreting their enzyme band patterns. It is well
1,432 citations
TL;DR: This work analyzed 8,000 random data matrices consisting of 10-500 binary or four-state characters and 5-25 taxa to study several options for detecting signal in systematic data bases, finding the skewness of tree-length distributions is closely related to the success of parsimony in finding the true phylogeny.
Abstract: DNA sequences and other molecular data compared among organisms may contain phylogenetic signal, or they may be randomized with respect to phylogenetic history. Some method is needed to distinguish phylogenetic signal from random noise to avoid analysis of data that have been randomized with respect to the historical relationships of the taxa being compared. We analyzed 8,000 random data matrices consisting of 10-500 binary or four-state characters and 5-25 taxa to study several options for detecting signal in systematic data bases. Analysis of random data often yields a single most-parsimonious tree, especially if the number of characters examined is large and the number of taxa examined is small (both often true in molecular studies). The most-parsimonious tree inferred from random data may also be considerably shorter than the second-best alternative. The distribution of tree lengths of all tree topologies (or a random sample thereof) provides a sensitive measure of phylogenetic signal: data matrices with phylogenetic signal produce tree-length distributions that are strongly skewed to the left, whereas those composed of random noise are closer to symmetrical. In simulations of phylogeny with varying rates of mutation (up to levels that produce random variation among taxa), the skewness of tree-length distributions is closely related to the success of parsimony in finding the true phylogeny. Tables of critical values of a skewness test statistic, g1, are provided for binary and four-state characters for 10-500 characters and 5-25 taxa. These tables can be used in a rapid and efficient test for significant structure in data matrices for phylogenetic analysis.
1,323 citations