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Continuous Characters in Phylogenetic Analyses: Patterns of Corolla Tube Length Evolution in Lithospermum L

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Continuous Characters in Phylogenetic Analyses: Patterns of Corolla Tube Length Evolution in Lithospermum L bs_bs_banner Biological Journal of the Linnean Society, 2012, ••, ••–••. With 3 figures Continuous characters in phylogenetic analyses: patterns of corolla tube length evolution in Lithospermum L. (Boraginaceae) JAMES I. COHEN* Texas A&M International University, 5201 University Boulevard, 379D LBVSC, Laredo, TX 78041, USA Received 20 February 2012; revised 15 April 2012; accepted for publication 15 April 2012 The present study comprises an analysis of six different scoring schemes and eight different types of analytic methods aiming to investigate the evolution of a continuous character (i.e. corolla tube length) in Lithospermum L. (Boraginaceae). Corolla tube length in the genus is quite variable, ranging from 1 mm to 75 mm, and the length of the corolla tube has implications for pollination biology, such as longer corolla tubes (> 25 mm in length) being pollinated by hummingbirds or moths. In general, the various methods resolve similar ancestral character states; however, different states are reconstructed at nodes in which the descendants greatly differ in corolla tube length. Additionally, it is suggested that all of the variation of a continuous character should be included in analyses, and this may necessitate multiple analyses with different partitions of the data. The various analyses provide evidence that two maximum parsimony methods, linear parsimony and the TNT method, minimize the number of different rates of evolution. In Lithospermum, six origins of corolla tubes > 20 mm in length are resolved, and these origins occurred at two different times periods: (1) in the shadow of hummingbird diversi- fication in North America (approximately 6–8 Mya) and (2) more recently (approximately 1–1.5 MyA). Four substantial decreases in corolla tube length also are reconstructed, and these may be associated with the origin of self-pollination. © 2012 The Linnean Society of London, Biological Journal of the Linnean Society, 2012, ••, ••–••. ADDITIONAL KEYWORDS: ancestral character state reconstruction – evolution – molecular dating – pollination. INTRODUCTION and states often overlap, making effective state assig- nation problematic (Felsenstein, 1988; Stevens, 1991, Continuous or quantitative characters, such as corolla 2000; Gift & Stevens, 1997; Swiderski et al., 1998; length or leaf width, are commonly included in phy- Garcia-Cruz & Sosa, 2006). Presently, numerous logenetic analyses, particularly at lower taxonomic methods exist that allow for the investigation of evo- levels (e.g. among species or genera; Stevens, 1991). lutionary patterns of continuous characters without Utilizing these types of characters has often involved the need to create discrete states. These currently forcing quantitative variation into discrete (noncon- available phylogenetic methods differ as to whether or tinuous or binned) character states (Mickevich & not branch lengths are used, if and how estimation Johnson, 1976; Archie, 1985; Farris, 1990; Stevens, accuracy is calculated, which evolutionary model is 1991, 2000; Gift & Stevens, 1997; Rae, 1998; Swider- employed, and whether the data are analyzed in a ski, Zelditch & Fink, 1998; Garcia-Cruz & Sosa, 2006; maximum parsimony (MP), maximum likelihood Goloboff, Mattoni & Quinteros, 2006). Determining (ML) or Bayesian inference (BI) framework (Table 1). the appropriate manner in which to convert continu- Because of the particular parameters included in the ous characters into discrete states can be difficult, methodologies, each may reconstruct, for the same character, different ancestral values at the same node (Butler & Losos, 1997; Martins, 1999; Webster & *E-mail: [email protected] Purvis, 2002a, b). © 2012 The Linnean Society of London, Biological Journal of the Linnean Society, 2012, ••, ••–•• 1 2 J. I. COHEN MP methods attempt to minimize change, either locally (among adjacent branches) or globally (across the entire tree), throughout the tree (Table 1), and they do so without invoking an explicit model of BAYESTRAITS model B evolution (Webster & Purvis, 2002a). By contrast, ML cies; MAX/MIN, and BI methods involve global reconstructions and utilize a specific model of evolution [i.e. Brownian motion (Felsenstein, 1985) or some form of selection (Martins, 2004)] to resolve ancestral character states (Webster & Purvis, 2002a; Pagel & Meade, 2011). Additionally, some MP methods as well as all ML and BAYESTRAITS model A BI methods utilize branch lengths during the infer- ence of ancestral states (Table 1). The objectives of the present study are two-fold: (1) to compare and evalu- ate different methods for ancestral character state reconstruction of continuous characters as well as different schemes for scoring continuous character Compare exponential model 20 mm in length; RANGE, the range of values for each > data, and (2) to investigate the patterns of evolution of a particular continuous character (i.e. corolla tube length) as it relates to pollination biology in Lithos- permum L. (Boraginaceae). Compare linear model Butler & Losos (1997), Martins (1999), and Webster & Purvis (2002a, b) have undertaken comparative studies on the utility of various methods employed to examine the evolution of continuous characters, and each reached a different conclusion. Butler & Losos Weighted squared- change parsimony (1997) used three different MP methods (linear par- simony, squared-change parsimony, and weighted squared-change parsimony) and found that none accurately reflected the evolution of body size in Anolis lizards. Martins (1999) and Webster & Purvis Squared- change parsimony (2002a, b) analyzed data in both MP and ML frame- works. Martins (1999) favoured a ML approach because ML methods, compared to MP methods, provide greater flexibility in the inputs for evolution- ary models. In addition, ML methods can provide SDs Linear parsimony or SEs for reconstructed ancestral values, which MP methods do not (although some may provide a range of values; Butler & Losos, 1997; Goloboff et al., 2006) (Table 1). In more recent studies, Webster & Purvis TNT method (2002a, b) found conflicting results. The first (Webster & Purvis, 2002a) suggests that, according to residual sum of squares calculations, MP methods reconstruct the most predictive ancestral states. However, the second (Webster & Purvis, 2002b) concluded that, although a two-parameter ML model performed best, no model consistently provided good estimates of ancestral values. Given these varying results, each method for estimating ancestral character states appears to have its strengths and weaknesses, with no method consistently found to be best at recon- structing evolutionary history. Analytical methods, scoring schemes, and type of phylogenetic tree used in presented analyses, and properties of various analytical methods In the aforementioned studies, the authors only used single point values at the terminals (although, for the use of two values, see Webster & Purvis, Error estimates includedType of reconstructionExplicit model of evolutionBranch lengths used NA NA Local NA NA Local NA NA NA Global NA Global NA NA NA Global X X Global X X X Global X X X Global X X X X X MINMEANMAXMAX/MINRANGE±1/2 RANGEType of phylogeny usedProperties for of reconstruction analytical methods MP MP X X X X NA MP X X X X NA X MP NA X X NA X X BI NA X X NA X BI NA X X X X NA X BI X X X X NA X X BI NA NA X X X X NA NA X X X X maximum value for species with corolla tubes less than 20 mm in length, and minimum value for species with corolla tubes Table 1. Scoring Scheme MP, maximum parsimony; BI, Bayesian inference. MIN, minimum value for each species; MEAN, mean value for each species; MAX, maximum value for each spe species; ± 1/2 RANGE, range/2) ± half of2002b). the range; NA, not applicable. However, the use of only a single point does © 2012 The Linnean Society of London, Biological Journal of the Linnean Society, 2012, ••, ••–•• EVOLUTION OF COROLLA TUBE LENGTH 3 A. B. C. D. E. F. G. Figure 1. Flowers of species of Lithospermum.A,Lithospermum leonotis, corolla tubes 49–75 mm long. B, Lithospermum macromeria, corolla tubes 47–70 mm long. C, Lithospermum exsertum, corolla tubes 47–60 mm long. D, Lithospermum matamorense, corolla tubes 1–2 mm long. E, Lithospermum nelsonii, corolla tubes 11–14 mm long. F, Lithosper- mum multiflorum, corolla tubes 6–13 mm long. G, Lithospermum trinervium, corolla tubes 11–16 mm long. Lithospermum leonotis (A) and L. nelsonii (E) are sisters, and so are L. macromeria (B) and L. multiflorum (F). not capture the variation of a quantitative character 1985; Kittelson & Handler, 2006). Therefore, a change (Stevens, 1991; Gift & Stevens, 1997; Swiderski in the length of the corolla may be associated with a et al., 1998). To include all variation, analyses utiliz- shift in the type of pollinator that visits the flower. By ing either a range of values or multiple single points placing, in a phylogenetic framework, corolla tube should be undertaken (Swiderski et al., 1998; Goloboff length, it is possible to hypothesize patterns of evo- et al., 2006). In the present study, six different scoring lution and rates of change for corolla tube length, as schemes and eight different types of analytic methods well as identify locations on the phylogenetic tree (as implemented in four software packages) are where pollinator shifts may have occurred. utilized. These different coding schemes are needed because most of the available software for ancestral character state reconstruction only accepts single MATERIAL AND METHODS point values, and not
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