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What are the methods for detecting anagenetic versus cladogenetic change?

What are the methods for detecting anagenetic versus cladogenetic change?



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I'm new to the study of phylogenetics and am wondering if there are ways of identifying evolutionary changes that associate with cladogenetic events versus those that arise via anagenesis.


Unifying fossils and phylogenies for comparative analyses of diversification and trait evolution

Macroevolution is evolutionary change occurring at or above the species level (Stanley 1979 ). As implied by this broad definition, the study of macroevolution encompasses a range of evolutionary processes, including phenotypic change through time in a single lineage, speciation and extinction patterns in clades, and modes of phenotypic evolution during adaptive radiations. For many years, studies of macroevolution have lived in two distinct realms. Palaeontologists have used direct evidence from fossils to uncover long-term patterns in trait evolution and species diversification over geologic time-scales. At the same time, neontologists have used phylogenetic trees and statistical comparative methods to ask similar questions about the tempo and mode of trait evolution and diversification through time. Although there has always been some cross-talk between these two subfields (discussed below), the methodologies and some of the core questions addressed by palaeontologists and neontologists often differ. These differences have impeded progress in understanding the pattern and process of evolution over very long time-scales.

A few studies have successfully bridged the gap between macroevolutionary studies that use fossils and those that use phylogenetic trees. One approach is to apply statistical comparative methods to data that includes fossil taxa. This approach has a long history (e.g. Gingerich 1983 , 1993 Cheetham 1986 , 1987 Alroy 1998 , 1998 1999 Hunt 2006 ), but can be difficult, especially since most modern comparative methods require phylogenetic trees with branch lengths and good sampling at the species level. Another approach is to include fossil information in comparative analyses across phylogenetic trees of living species (Finarelli & Flynn 2006 Albert et al. 2009 Pyron & Burbrink 2012 Slater et al. 2012 ). Both of these approaches have great potential to add to our understanding of macroevolution in a way that spans both living and extinct taxa.

In this Special Feature, we have gathered a set of papers that seek to continue the merger of phylogenetic comparative methods and palaeontology. These papers are drawn primarily from palaeontologists and comprise a mixture of methodological and empirical studies. All are united by a common theme however: harnessing the power that comes from using phylogenetic approaches together with fossils to understand macroevolution.


Abstract

Adaptive radiation is a common mode of speciation among plants endemic to oceanic islands. This pattern is one of cladogenesis, or splitting of the founder population, into diverse lineages in divergent habitats. In contrast, endemic species have also evolved primarily by simple transformations from progenitors in source regions. This is anagenesis, whereby the founding population changes genetically and morphologically over time primarily through mutation and recombination. Gene flow among populations is maintained in a homogeneous environment with no splitting events. Genetic consequences of these modes of speciation have been examined in the Juan Fernández Archipelago, which contains two principal islands of differing geological ages. This article summarizes population genetic results (nearly 4000 analyses) from examination of 15 endemic species, involving 1716 and 1870 individuals in 162 and 163 populations (with amplified fragment length polymorphisms and simple sequence repeats, respectively) in the following genera: Drimys (Winteraceae), Myrceugenia (Myrtaceae), Rhaphithamnus (Verbenaceae), Robinsonia (Asteraceae, Senecioneae) and Erigeron (Asteraceae, Astereae). The results indicate that species originating anagenetically show high levels of genetic variation within the island population and no geographic genetic partitioning. This contrasts with cladogenetic species that show less genetic diversity within and among populations. Species that have been derived anagenetically on the younger island (1–2 Ma) contain less genetic variation than those that have anagenetically speciated on the older island (4 Ma). Genetic distinctness among cladogenetically derived species on the older island is greater than among similarly derived species on the younger island. An important point is that the total genetic variation within each genus analysed is comparable, regardless of whether adaptive divergence occurs.


The methods

The diversitree package implements a series of methods for detecting associations between species traits and rates of speciation and/or extinction, given a phylogeny and trait data, including the BiSSE method ( Maddison, Midford & Otto 2007 ). Under BiSSE, speciation and extinction follow a birth–death process, where the rate of speciation and extinction may vary with a binary trait, itself evolving following a continuous-time Markov process. BiSSE has been used to look at the associations between many different traits and speciation or extinction, including migration in warblers ( Winger, Lovette & Winker 2012 ), fruiting body morphology in fungi ( Wilson, Binder & Hibbett 2011 ) and recombination in plants ( Johnson et al. 2011 ).

In its original formulation, BiSSE assumes that character change occurs only along branches (anagenetic change), using the same model of character evolution as used in the ‘discrete’ ( Pagel 1994 ) or ‘Mk’ models ( Lewis 2001 ). This may not always be a reasonable assumption, and we might expect some characters to show considerable change during speciation (cladogenetic change). One such example is geographic range while geographic ranges are expected to change anagenetically, allopatric speciation should also alter range sizes. The Geographic SSE (GeoSSE Goldberg, Lancaster & Ree 2011 ) method allows speciation rates to vary depending on a species’ presence in two different geographic regions, allowing within- and between-region speciation. This has been used to examine diversification in plants endemic to serpentine regions ( Anacker et al. 2010 ). More recently, the BiSSE-ness (BiSSE-node enhanced state shift Magnuson-Ford & Otto 2012 ) and Cladogenetic SSE (ClaSSE Goldberg & Igić in press ) models have been developed to allow both anagenetic and cladogenetic character evolution, such as that expected for traits involved in ecological speciation ( Schluter 2009 ). Importantly, with extinction or incomplete taxonomic sampling, not all speciation events will appear as nodes in a phylogeny these missing nodes must be modelled to accurately estimate the rate of cladogenetic trait change ( Nee, May & Harvey 1994 and Bokma 2008 , and note that the placement of these missing nodes is nonlinear in time).

Diversitree also includes methods for nonbinary traits. Quantitative SSE (QuaSSE FitzJohn 2010 ) allows speciation and extinction rates to be modelled as any user-supplied function of a continuously varying trait, which itself evolves under Brownian motion. This has been used to test for associations between diversification rates and body size in snakes ( Burbrink, Ruane & Pyron 2012 ) and dispersal ability in birds ( Claramunt et al. 2012 ). Finally, MuSSE extends BiSSE to multistate traits or combinations of binary traits.

Diversitree includes variants that relax some of the original assumptions of the included methods. Birth–death-based speciation/extinction models will give biased parameter estimates unless all extant taxa in the focal clade are present in a phylogeny. For cases where not all extant species are included in a phylogeny, diversitree includes methods for where species are included randomly or where all species are represented in ‘unresolved clades’ ( FitzJohn, Maddison & Otto 2009 ). Rates of speciation, extinction or character change can be set to vary as any user-supplied function of time. Similar approaches have been used elsewhere to model slowdowns in speciation or diversification over time ( Rabosky & Glor 2010 ).

Rates of speciation, extinction and character change may also be allowed to vary in different regions of a tree. This is similar to Medusa (modelling evolutionary diversification under stepwise AIC: Alfaro et al. 2009 ) for diversification and Auteur ( Eastman et al. 2011 ) for continuous character evolution. Such methods can be used to test whether membership of a clade that has undergone a shift in diversification rates is misleading BiSSE or other methods. For example, if particular trait values are concentrated in a highly diverse clade, BiSSE may detect an association when none exists (see applications in Johnson et al. 2011 and FitzJohn 2010 , the diversitree tutorial for a worked example, and further discussion in Read & Nee 1995 ).

In the above models, if speciation and extinction do not vary with character state, the models converge on classical models of character evolution ( Pagel, 1994 ) and state-independent speciation and extinction ( Nee, May & Harvey 1994 ). For completeness, these models are also included. However, when comparing models to determine whether traits are associated with speciation or extinction using likelihood ratio tests, comparisons must involve only nested models to be valid. For example, BiSSE and Mk2 are not directly comparable, but BiSSE can be compared with a constrained version of BiSSE that disallows state-dependent diversification. See Table 1 for a summary of included methods.

Name Trait a Missing taxa b Extensions c Description and reference
bd Sk, Un Sp, Tv Constant-rate birth–death ( Nee, May & Harvey 1994 )
mk2 , mkn B,M Sp, Tv Markov discrete character evolution ( Pagel, 1994 Lewis, 2001 )
bisse B Sk, Un Sp, Tv Binary State Speciation and Extinction ( Maddison, Midford & Otto 2007 FitzJohn, Maddison & Otto 2009 )
bisseness B Sk, Un BiSSE-ness ( Magnuson-Ford & Otto 2012 )
geosse T Sk Geographic state speciation and extinction ( Goldberg, Lancaster & Ree 2011 )
musse M Sk, Un Sp, Tv Multistate speciation and extinction
classe M Sk Clade-state speciation and extinction ( Goldberg & Igić in press )
bm Q Brownian motion
ou Q Ornstein–Uhlenbeck
quasse Q Sk Sp Quantitative state speciation and extinction ( FitzJohn 2010 )
  • a Trait type key: B = binary (0/1), T = ternary (three combinations of presence/ absence in two regions), M = multistate (1, 2, 3, …), Q = quantitative (real-valued). b Missing taxa support: Sk = ‘skeleton tree’ (random sampling) correction, Un = ‘unresolved clade’. c Extensions: Sp = ‘split tree’ (allows Medusa -style different rate classes in different areas of the tree), Tv = time-varying rates.

In addition to the likelihood calculations, tree simulation routines are implemented for birth–death models, BiSSE, MuSSE and QuaSSE. Simulating character evolution on a given tree is possible for discrete (binary or multistate) characters and continuous characters under Brownian motion and Ornstein–Uhlenbeck processes. Ancestral state reconstruction ( Schluter et al. 1997 ) and stochastic character mapping ( Bollback 2006 ) are implemented for discrete characters.


What are the methods for detecting anagenetic versus cladogenetic change? - Biology

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Phylogeny, ancestors, and anagenesis in the hominin fossil record

Caroline Parins-Fukuchi />https://orcid.org/0000-0003-0084-2323 , 1 Elliot Greiner, 2 Laura M. MacLatchy, 3 Daniel C. Fisher 4

1 Caroline Parins-Fukuchi. Department of Ecology and Evolutionary Biology, University of Michigan, Ann
2 Elliot Greiner. Department of Anthropology, University of Michigan, Ann Arbor, Michigan 48109, U.S.A
3 Laura M. MacLatchy. Department of Anthropology and Museum of Paleontology, University of Michigan, A
4 Daniel C. Fisher. Museum of Paleontology, Department of Earth and Environmental Sciences, and Depart

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Probabilistic approaches to phylogenetic inference have recently gained traction in paleontological studies. Because they directly model processes of evolutionary change, probabilistic methods facilitate a deeper assessment of variability in evolutionary patterns by weighing evidence for competing models. Although phylogenetic methods used in paleontological studies have generally assumed that evolution proceeds by splitting cladogenesis, extensions to previous models help explore the potential for morphological and temporal data to provide differential support for contrasting modes of evolutionary divergence. Recent methodological developments have integrated ancestral relationships into probabilistic phylogenetic methods. These new approaches rely on parameter-rich models and sophisticated inferential methods, potentially obscuring the respective contributions of data and models. In this study, we describe a simple likelihoodist approach that combines probabilistic models of morphological evolution and fossil preservation to reconstruct both cladogenetic and anagenetic relationships. By applying this approach to a data set of fossil hominins, we demonstrate the capability of existing models to unveil evidence for anagenesis presented by morphological and temporal data. This evidence was previously recognized by qualitative assessments, but largely ignored by quantitative phylogenetic analyses. For example, we find support for directly ancestral relationships in multiple lineages: Sahelanthropus is ancestral to later hominins Australopithecus anamensis is ancestral to Australopithecus afarensis Australopithecus garhi is ancestral to Homo Homo antecessor is ancestral to Homo heidelbergensis, which in turn is ancestral to both Homo sapiens and Homo neanderthalensis. By accommodating direct ancestry in phylogenetics, quantitative results align more closely with previous qualitative expectations.


Results

Species Differences

The eight traits we included in our analyses are correlated with species differences (Table 2), and more than 65% of the traits differ significantly across the five ancestor-descendant species pairs we analyzed when standardized by the ancestral mean (mean calculated on the basis of all populations across all time intervals where the ancestor is present, both before and after the hypothesized descendant linage is present in the fossil record) for each ancestor-descendant pair. The number of significant trait differences ranged from eight (between M. auriculatum and M. boldi) to one (between M. tainorum and M. jungi). All traits differed across at least one ancestor-descendant species pair, while the length of the shorter avicularium (LAVL) differed significantly across four of five ancestor-descendant species pairs.

Trait Dynamics and Rates of Evolution during Anagenesis and Cladogenesis

The multivariate distribution of cladogenetic and anagenetic changes is illustrated through a 3D phylomorphospace based on the average discriminant score for each lineage in each time interval where it is present (fig. 4A see the supplemental material for a rotating 3D version of this figure). Discriminant functions 1 and 2 explain 67.3% and 19.4% of the variation, respectively. Lineages show large fluctuations in multivariate trait space and tend to occupy different parts of the morphospace at different time points. All lineages occupy overlapping ranges in this multivariate morphospace except M. tainorum and M. jungi, which are located in a separate part of the multivariate morphospace. The original presentation of multivariate evolution within Metrarabdotos (from Cheetham et al. 1994) is shown in figure 4B for comparison. Figure 4.

Comparison of multivariate phylomorphospaces of Metrarabdotos lineages. A, The phylomorphospace calculated on the basis of a discriminant function analysis of the curated set of eight traits analyzed in the current study shows how the lineages evolve through time. Points represent the average scores for each species at each sampling time point. Black dashed lines represent cladogenetic events. The first discriminant function describes 67.3% of the variation, while the second function describes 19.4%. A rotating 3D version of this figure is available in the supplemental material. B, Original illustration of the phylomorphospace of Metrarabdotos (Cheetham 1986 Cheetham et al. 1994). Colors are comparable across the two plots, allowing a direct comparison of the illustrated dynamics for each lineage. The morphology axis represents a composite variable based on canonical axes of variation from a discriminant function analysis. Differences between species that are not part of a particular ancestor-descendant pair are not comparable, as they are on an arbitrary scale.

Second, we compared the rates of evolution during cladogenetic and anagenetic events using mean-standardized measurements of trait evolution following Hansen and Houle (2008). We find no evidence for differences in rates of cladogenetic versus anagenetic change (fig. 3A Table 4). The range of observed rates is larger for the full anagenetic evolution data set, but this may be due to the fact that the number of rates for possible cladogenetic events ( N = 8 ) is smaller than the number of rates for observed anagenetic events ( N = 99 ). To more directly compare rates of anagenetic and cladogenetic evolution, we therefore calculated rates of anagenetic evolution between the population in the ancestral lineage that is older but closest in time to the first appearance of the descendant population and all older populations in the ancestral lineage (see the diagram in fig. 3B for details). These anagenetic rates are therefore calculated on the basis of time intervals that are very similar (most often identical) to the ones used for the rates calculated during cladogenetic events, but these are also indistinguishable from the cladogenetic rates (fig. 3A Table 4). In the supplemental PDF (table S2), we provide the calculated rates of evolution between every possible ancestor-descendant sample in each of the five cladogenetic events, along with rates during “comparable” anagenetic evolution. There is no systematic trend in the rates of evolution regarding which sample that is treated as ancestral in a cladogenetic event, but note that the number of possible ancestral samples for each cladogenetic event is small and never larger than three.

Comparing rates of evolution during anagenesis and cladogenesis

Note. Shown are results from the random permutation procedure aimed at testing whether the rates of evolution and/or evolvability statistics in the direction of evolution are different during anagenesis (“comparable anagenetic” and “anagenesis sequential” see fig. 3) and cladogenesis.

Genetic Drift, Selection Gradients, and Patterns of Evolvability

The estimated (broad-sense) G matrices for five species of Metrarabdotos indicate that genetic variation accounts, on average, for 50.2% of the total variation in the zooid morphology. The five species seem to have above-average trait evolvability, as the average trait evolvability and conditional evolvability were 1.18% and 0.49%, respectively, across all five G matrices, both larger than the median evolvability of size traits reported in Hansen et al. (2011). We note that a potential reason for this high evolvability is the fact that the estimated G matrices contain not only additive genetic variances but also dominance and epistatic variance components, colony-specific environmental effects, and potential temporal variance caused by anagenetic evolution and plasticity. Most of the variation in each of the estimated lineage-specific G matrices is concentrated among the first four principal components, which account for more than 90% of the total variation in each G (fig. 5A). We do not find evidence of any differences among the five G matrices, as measured by random skewers (Marroig and Cheverud 2001), that cannot be explained by sampling error alone (fig. 5B). This suggests that differences in the patterns of genetic association within species are sufficiently small not to be detected given the sample sizes we have available. Phenotypic and genetic variation is distributed unevenly in morphospace (fig. 5C, 5D). Figure 5.

Genetic variation is concentrated in a few axes of the morphospace. A, Percent variance explained by each principal component of each of the five species-specific G matrices. Each line represents the distribution of genetic variation for each of the five species for which we were able to estimate a broad-sense G matrix. B, Pairwise similarity, measured through random skewers, between all five G matrices (red points) compared with the similarity that would be expected due to sampling (black points). C, D, Phenotypic (C) and genetic (D) correlation matrix for the eight morphological traits in the species with highest sample size (Metrarabdotos auriculatum). Note that we are using correlation matrices solely to illustrate the overall similarities in trait associations between P and G. All calculations in the main text were done using species-specific mean-standardized variance-covariance matrices. Trait abbreviations are defined and described in Table 3.

Using Lande’s (1979) equation of multivariate evolution under drift, we find that the observed morphological differentiation within lineages and at speciation events are compatible with a purely stochastic evolutionary process as revealed by the regression tests (95% confidence intervals of slopes all include 1 Table 5). Drift can therefore not be excluded as the mechanism explaining both within-lineage evolution and morphological divergence during speciation. However, the 95% confidence intervals of each slope estimate are large (the sample size is small for each regression analysis). Also, finding that the slope is not statistically significantly different from 1 does not exclude other evolutionary mechanisms as potential drivers of the evolutionary changes.

Note. Shown are results from the regression of between-population variances on within-population variances aimed at testing whether genetic drift alone can explain the patterns of evolutionary diversification within lineages and at speciation events. Slope estimate are regression coefficients. CI = confidence interval.

Under the assumption that directional selection drove the observed trait dynamics, we used Lande’s (1979) equation of multivariate evolution to reconstruct mean-standardized directional selection gradients (βμ) based on differences between successive time points in time series (anagenetic selection gradients) and changes in morphology associated with lineage splits (cladogenetic selection gradients). We find no difference in the strength of selection during anagenesis compared with cladogenesis (fig. 6 Table 6). We also find that evolution happened almost exclusively in directions with higher-than-average evolvability and conditional evolvability in multivariate space (fig. 7 Table 7) compared with random directions in morphospace ( P < .001 ). Evolvability and conditional evolvability are not different in directions traveled during anagenetic and cladogenetic evolution (Table 4). Figure 6.

Selection gradients during anagenetic and cladogenetic evolution. Permutation tests revealed no significant differences between selection gradients calculated for anagenetic and cladogenetic evolution (Table 6). Selection gradients for sequential anagenetic changes are computed between consecutive ancestor-descendant population pairs within a lineage (see fig. 3B). Cladogenetic selection gradients are estimated on the basis of evolutionary changes between the first population (oldest fossil sample) in the descendant lineage and all populations in the ancestral lineage that are older than the descendant population. Selection gradients for “Anagenesis-Comparable” are computed between the population in the ancestral lineage that is older but closest in time to the first appearance of the descendant population and all older populations in the ancestral lineage they are therefore more or less directly comparable to the selection gradients for the cladogenetic events, as these rates are estimated across time intervals that are similar (often identical) to those used for the rates calculated during cladogenetic events.

Comparisons of selection gradients and evolvabilities during anagenesis and cladogenesis

Note. Shown are results from the random permutation procedure aimed at testing whether the strength of selection and/or evolvability in the direction of selection are different during anagenesis (“comparable anagenetic” see fig. 3) and cladogenesis.

Anagenetic and cladogenetic changes occur in directions of higher than average evolvability. A, B, Comparison of evolvability (A) and conditional evolvability (B) along directions of anagenetic (red), cladogenetic (green), and random (blue) directions in the five lineage-specific G matrices. Note that for cladogenesis, the selected G matrix is the one calculated for the ancestral species. Both anagenetic and cladogenetic changes happen in directions of above-average evolvability. Asterisks represent statistically significant differences (Table 7).

Comparing evolvability in directions of observed evolution to random directions in morphospace

Note. Shown are results from the random permutation procedure aimed at testing whether the unconditional and conditional evolvabilities of empirical G matrices in directions of anagenesis and/or cladogenesis are significantly different from the evolvability of G in random directions.


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High-resolution transcriptome analysis with long-read RNA sequencing


High-resolution transcriptome analysis with long-read RNA sequencing
Hyunghoon Cho, Joe Davis, Xin Li, Kevin S. Smith, Alexis Battle, Stephen B. Montgomery
Comments: 29 pages, 8 figures, 11 supplementary figures
Subjects: Genomics (q-bio.GN)

RNA sequencing (RNA-seq) enables characterization and quantification of individual transcriptomes as well as detection of patterns of allelic expression and alternative splicing. Current RNA-seq protocols depend on high-throughput short-read sequencing of cDNA. However, as ongoing advances are rapidly yielding increasing read lengths, a technical hurdle remains in identifying the degree to which differences in read length influence various transcriptome analyses. In this study, we generated two paired-end RNA-seq datasets of differing read lengths (2呇 bp and 2𴣞 bp) for lymphoblastoid cell line GM12878 and compared the effect of read length on transcriptome analyses, including read-mapping performance, gene and transcript quantification, and detection of allele-specific expression (ASE) and allele-specific alternative splicing (ASAS) patterns. Our results indicate that, while the current long-read protocol is considerably more expensive than short-read sequencing, there are important benefits that can only be achieved with longer read length, including lower mapping bias and reduced ambiguity in assigning reads to genomic elements, such as mRNA transcript. We show that these benefits ultimately lead to improved detection of cis-acting regulatory and splicing variation effects within individuals.


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Acknowledgements

Many thanks are due to two referees for their constructive comments on the manuscript. J.P. and A.V. gratefully acknowledge financial support from the Belgian Funds for Scientific Research (FNRS) (grants 1.5036.11 and 2.4557.11) and the University of Liège (grant C 11/32). J.P. also acknowledges support from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement GB-TAF-1801 (SYNTHESYS).

Please note: Wiley Blackwell are not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing material) should be directed to the New Phytologist Central Office.

Fig. S1 Habitat partitioning by vegetation zones of single- and multiple-species endemics in the bryophyte, pteridophyte and seed plant floras endemic to a single Macaronesian archipelago, including the Azores, Canaries and Madeira.

Table S1 Literature sources used to document patterns of endemicity in the bryophyte, pteridophyte and seed plant floras of nine oceanic archipelagos

Table S2 Geographical features of the nine studied archipelagos

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