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24 results for “character mapping”
Fig. 5. Combined tree from Fig. 4 with mapped morphological characters. Numbers above branches represent characters from Table 3. Mapped using WinClada ver. 1.61 in A revision of Scipopus Enderlein including the subgenera Scipopus s. str., Phaeopterina Frey and Parascipopus subgen. nov. (Diptera, Micropezidae, Taeniapterinae)
Fig. 5. Combined tree from Fig. 4 with mapped morphological characters. Numbers above branches represent characters from Table 3. Mapped using WinClada ver. 1.61 (Nixon 1999–2002) with unambiguous characters only.
Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria
<p>Cyanobacteria are the only prokaryotes to have evolved oxygenic photosynthesis paving the way for complex life. Studying the evolution and ecological niche of cyanobacteria and their ancestors is crucial for understanding the intricate dynamics of biosphere evolution. These organisms frequently deal with environmental stressors such as salinity and drought, and they employ compatible solutes as a mechanism to cope with these challenges. Compatible solutes are small molecules that help maintain cellular osmotic balance in high-salinity environments, such as marine waters. Their production plays a crucial role in salt tolerance, which, in turn, influences habitat preference. Among the five known compatible solutes produced by cyanobacteria (sucrose, trehalose, glucosylglycerol, glucosylglycerate, and glycine betaine), their synthesis varies between individual strains. In this study, we work in a Bayesian stochastic mapping framework, integrating multiple sources of information about compatible solute biosynthesis in order to predict the ancestral habitat preference of Cyanobacteria. Through extensive model selection analyses and statistical tests for correlation, we identify glucosylglycerol and glucosylglycerate as the most significantly correlated with habitat preference, while trehalose exhibits the weakest correlation. Additionally, glucosylglycerol, glucosylglycerate, and glycine betaine show high loss/gain rate ratios, indicating their potential role in adaptability, while sucrose and trehalose are less likely to be lost due to their additional cellular functions. Contrary to previous findings, our analyses predict that the last common ancestor of Cyanobacteria (living at around 3180 Ma) had a 97% probability of a high salinity habitat preference and was likely able to synthesize glucosylglycerol and glucosylglycerate. Nevertheless, cyanobacteria likely colonized low-salinity environments shortly after their origin, with an 89% probability of the first cyanobacterium with low-salinity habitat preference arising prior to the Great Oxygenation Event (2460 Ma). Stochastic mapping analyses provide evidence of cyanobacteria inhabiting early marine habitats, aiding in the interpretation of the geological record. Our age estimate of ~2590 Ma for the divergence of two major cyanobacterial clades (Macro- and Microcyanobacteria) suggests that these were likely significant contributors to primary productivity in marine habitats in the lead-up to the Great Oxygenation Event, and thus played a pivotal role in triggering the sudden increase in atmospheric oxygen.</p>
Figure 1 in Character mapping and cladogram comparison versus the requirement of total evidence: does it matter for polychaete systematics?
Figure 1. Example of the error of cladogram comparisons. A, phylogenetic hypotheses inferred from separate sets of premises. Letters on cladogram 'nodes' indicate population-splitting events relevant to the various hypotheses of character origin/fixation within ancestral populations. The requirement of total evidence precludes such a comparison of cladogram topologies because explanations of characters 1(1)–5(1) by population-splitting events A–C (left cladogram) contradict explanations of 6(1)–8(1) by population-splitting events D–F. See text for further discussion. B, explaining observations in accordance with the requirement of total evidence, correcting the problem in 'A'.
Figure 2 in Character mapping and cladogram comparison versus the requirement of total evidence: does it matter for polychaete systematics?
Figure 2. Example of the error of character mapping. A, phylogenetic hypotheses are inferred for a set of characters. Numbers on cladogram 'nodes' indicate population-splitting events relevant to the various hypotheses of character origin/fixation within ancestral populations (not shown; cf. fig. 1). B, a different set of characters are 'mapped' onto the branches of the cladogram in 'A'. C, the 'mapped' characters in 'B' actually refer to phylogenetic hypotheses inferred separately from the hypotheses implied by the cladogram in 'A' and 'B'. D, explaining observations in accordance with the requirement of total evidence, correcting the problem in 'B' and 'C'. See text for further discussion.
Fig. 3. Character state maps for four traits. A in Phylogenetic revision of Dennstaedtioideae (Dennstaedtiaceae: Polypodiales) with description of Mucura, gen. nov.
Fig. 3. Character state maps for four traits. A, Spore shape: monolete (blue), trilete (yellow); B, Sorus position: abaxial (blue), marginal (yellow). C, Chromosome base number: 43 or 86 (light blue), 34 (blue), 46 or 47 (light green), 29 (green), 44 (pink), 30 (red), 31 (light orange), 32 (orange), 48 (light purple), 28 (purple), 56 (yellow), 26 (brown), 52 (magenta), 38 (aquamarine); D, Perpispore morphology: prominent ridges (light blue), prominent ridges and verrucae (blue), prominent ridges, verrucae + irregular reticles (light green), verrucae (orange), verrucae and ridges (green), regular reticles (pink), regular reticles + tubercles (red), echinae (purple), baculae (light yellow), ornamented verrucae (brown), rodlets (yellow), tubercles (aquamarine), rugulae (light brown). White circles indicate taxa for which character state data is missing.
Fig. 2. Character state maps for four traits. A in Phylogenetic revision of Dennstaedtioideae (Dennstaedtiaceae: Polypodiales) with description of Mucura, gen. nov.
Fig. 2. Character state maps for four traits. A, Abaxial indusium present (blue) vs. absent (yellow); B, Adaxial indusium present (blue) vs. absent (yellow); C, Epipetiolar buds present (blue) vs. absent (yellow); D, Leaf buds present (blue) vs. absent (yellow). White circles indicate taxa for which character state data is missing.
Map 1 in New Taxa And Cryptic Species Of Neotropical Snakes (Xenodontinae), With Commentary On Hemipenes As Generic And Specific Characters
Map 1. Locality records for three species of Eutrachelophis, new genus, in western and middle Amazonia.
Fig. 60. Unambiguous character changes mapped onto topology from figure 57A. Each circle represents a in Morphology And Relationships Of Apternodus And Other Extinct, Zalambdodont, Placental Mammals
Fig. 60. Unambiguous character changes mapped onto topology from figure 57A. Each circle represents a character state change, with the character number above and state below (corresponding to the list given in the text and in table 5). Changes represented by solid circles show less homoplasy than those represented by open circles.
Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria
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Buildings indicating the multicultural character of Balat on the Pervitich Maps
<p>Buildings indicating the multicultural character of Balat on the Pervitich Maps</p> <p>includes: (i) geo-referenced tiff images of the base maps (Pervititch Maps), tiff images of the 1946-aerial photos, a shapefile data on monuments and attributes, and a readme.txt file which explains the attributes.</p>
An evaluation of the usefulness of morphological characters to infer higher-level relationships in birds by mapping them to a molecular phylogeny
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FIGURE 16. Distribution map for 11 in A revision of the Brazilian species of Lysmata Risso, 1816 (Decapoda: Caridea Lysmatidae), with discussion of the morphological characters used in their identification
FIGURE 16. Distribution map for 11 species of Lysmata Risso, 1816 currently known from the Brazilian coast and offshore islands. Abbreviations: Alagoas (AL), Amapá (AP), Atol das Rocas (AR), Bahia (BA), Ceará (CE), Espírito Santo (ES), Maranhão (MA), Pará (PA), Paraíba (PB), Paraná (PR), Pernambuco (PE), Piauí (PI), Rio Grande do Norte (RN), Rio Grande do Sul (RS), Rio de Janeiro (RJ), Santa Catarina (SC), São Paulo (SP), São Pedro e São Paulo (SPSP), Sergipe (SE), Trindade e Martim Vaz (TMV).
Data from: Range-wide spatial mapping reveals convergent character displacement of bird song
A long-held view in evolutionary biology is that character displacement generates divergent phenotypes in closely related coexisting species to avoid the costs of hybridisation or ecological competition, whereas an alternative possibility is that signals of dominance or aggression may instead converge to facilitate coexistence among ecological competitors. Although this counter-intuitive process⎯termed convergent agonistic character displacement⎯is supported by recent theoretical and empirical studies, the extent to which it drives spatial patterns of trait evolution at continental scales remains unclear. By modeling variation in song structure of two ecologically similar species of Hypocnemis antbird across Western Amazonia, we show that their territorial signals converge such that trait similarity peaks in the sympatric zone, where intense interspecific territoriality between these taxa has previously been demonstrated. We also use remote sensing data to show that signal convergence is not explained by environmental gradients and is thus unlikely to evolve by sensory drive (i.e. acoustic adaptation to the sound transmission properties of habitats). Our results suggest that agonistic character displacement driven by interspecific competition can generate spatial patterns opposite to those predicted by classic character displacement theory, and highlight the potential role of social selection in shaping geographical variation in signal phenotypes of ecological competitors.
Historical biogeography and character-mapping of Hiptage (Malpighiaceae) corroborate Indochina's rainforests as one of the main sources of plant diversity in Southeastern Asia
<p>In Malpighiaceae,<b> </b><i>Hiptage</i> represents one of the seven past dispersal events from the Neotropics to the Paleotropical region, being by far the most widely diversified and distributed genus of Paleotropical Malpighiaceae. In this study, we tested the monophyly of the current infrageneric classification of <i>Hiptage</i> with a dated and calibrated molecular phylogeny. We also reconstructed ancestral areas to elucidate which route led to the colonization of Southeast Asia by the most recent common ancestor (MRCA) of this genus (Mainland or Indian routes). The pre-existing infrageneric classification of <i>Hiptage</i> was recovered as non-monophyletic due to being solely based on homoplasic morphological characters, such as the presence and number of sepal nectar glands. Regarding its biogeography, the MRCA of <i>Hiptage</i> arose in the rainforests of southeast Asia ca. 24.0 Mya and greatly diversified in this region. Few lineages have dispersed eastward to the pacific islands or westwards to India. Based on our results, we hypothesize that the MRCA of <i>Hiptage</i> did not take the Indian route to reach Southeastern Asia. Instead, it reached this region by past mainland forest connections between North America-Europe (Boreotropical hypothesis) and southeast Asia. Nonetheless, distribution ranges for the species of <i>Hiptage</i> must be carefully revised, and the five species of <i>Hiptage</i> endemic to India must also be sampled so we can properly test which route led the MRCA of <i>Hiptage</i> to reach Southeast Asia in the early Miocene.</p>
FIGURE 2. Morphological characters mapped onto a 95 in Revised circumscription of Nothofagus and recognition of the segregate genera Fuscospora, Lophozonia, and Trisyngyne (Nothofagaceae)
FIGURE 2. Morphological characters mapped onto a 95% bootstrap consensus tree, derived from Sauquet et al. (2012) (Analysis 2) for Fuscospora (Nothofagus subgenus Fuscospora), Lophozonia (Nothofagus subgenus Lophozonia), Nothofagus (Nothofagus subgenus Nothofagus), and Trisyngyne (Nothofagus subgenus Brassospora).
Figure 6. Cyclostome protoecial pseudopore patterns mapped onto a in Ancestrular morphology in cyclostome bryozoans and the quest for phylogenetically informative skeletal characters
Figure 6. Cyclostome protoecial pseudopore patterns mapped onto a molecular phylogeny. Numbers correspond to the patterns defined in the text (no species of Pattern 2 have been sequenced to date). Brackets signify that the pattern has been observed in a congener of the sequenced species and 'iw' an interior walled protoecium, which cannot have pseudopores. Diagrammatic figures of the patterns are shown next to the pattern numbers. Pattern 1 has been assigned to sequenced Crisia spp. based upon observations of a congener (Crisia eburnea) from the literature (Nielsen 1970; Silén 1977). The outgroup taxon for this phylogeny, based on Taylor, Waeschenbach, et al. (2015), is the phylactolaemate Pectinatella magnifica which lacks a mineralized skeleton and therefore a protoecium. Tree topology generated from Bayesian analysis of a concatenated lsrDNA and ssrDNA dataset. All nodes with <0.95 posterior probability were collapsed. See Taylor, Waeschenbach, et al. (2015) for full details.
Figure 10. Reference topology with mapped dermal sculpture characters showing a phylogenetic signal, continued. A, character 10. B, character 11. C, character 12. For character 12 in Sculpture and vascularization of dermal bones, and the implications for the physiology of basal tetrapods
Figure 10. Reference topology with mapped dermal sculpture characters showing a phylogenetic signal, continued. A, character 10. B, character 11. C, character 12. For character 12, the coloration is similar to that in Figure 9, whereas for character 10 (four character states) and 11 (three character states), the lightest shading refers to character state 1, and the increasingly darker shadings refer to the ascending character states. For definition of characters, see Appendix 2.
Historical biogeography and character-mapping of Hiptage (Malpighiaceae) corroborate Indochina's rainforests as one of the main sources of plant diversity in Southeastern Asia
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Data from: Range-wide spatial mapping reveals convergent character displacement of bird song
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Data from: A comment on the use of stochastic character maps to estimate evolutionary rate variation in a continuously valued trait
Phylogenetic comparative biology has progressed considerably in recent years. One of the most important developments has been the application of likelihood-based methods to fit alternative models for trait evolution in a phylogenetic tree with branch lengths proportional to time. An important example of this type of method is O'Meara et al.'s (2006) "noncensored" test for variation in the evolutionary rate for a continuously valued character trait through time or across the branches of a phylogenetic tree. According to this method, we first hypothesize evolutionary rate regimes on the tree (called "painting" in Butler and King, 2004); and then we fit an evolutionary model, specifically the popular Brownian model, in which the instantaneous variance of the Brownian random diffusion process has different values in different parts of the phylogeny. The authors suggest that to test a hypothesis that the state of a discrete character influenced the rate of a continuous character, one could use the approach of Neilsen (2002) to first stochastically map the discretely valued trait, and then "test to see whether the portions of the tree with one state for the discrete character have a different rate of evolution for the continuous character than portions of the tree to which the other discrete state has been mapped" (O'Meara et al., 2006, p. 931). Indeed, this has become common practice for this and other closely related methods. Here, I examine this practice. In particular, I show that evolutionary rates estimated this way (i.e., by using maximum likelihood [ML] to fit a multirate model on each stochastically mapped tree; and then averaging across trees) are systematically biased to be more similar to each other than are the underlying generating parameters. My analysis also reveals that this effect is dependent on the rate of evolution for the discrete trait. Specifically, if the rate of evolution for the discrete character is low then the difference between the true history and any stochastically mapped 1 history is generally small. This results in evolutionary rates for the continuous trait that are estimated with little bias. Conversely, if the rate of evolution for the discrete character is very high, then the true and hypothesized character histories are often extremely dissimilar, evolutionary rate estimates are biased to be more similar to each other than their underlying generating values, and we lose power to distinguish evolutionary rates on the tree.
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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