FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2102886673> ?p ?o ?g. }

• W2102886673 endingPage "173" @default.
• W2102886673 startingPage "167" @default.
• W2102886673 abstract "Meristic characters, or counts of discrete serially ho-mologous structures, are a distinctive and ubiquitousclass of quantitative organismal variation. Meristic char-acters share many properties with morphometric char-acters (i.e., measurements, proportions, etc.): they arereadily described numerically, they usually vary withinand among taxa, and they often appear to follow similarunderlying frequency distributions (e.g., Burbrink, 2001;Allsteadt et al., 2006). Based on these similarities, meris-tic characters are generally lumped with morphometriccharactersintothebroadercategoryof“quantitativecon-tinuouscharacters”forphylogenyreconstruction.Meris-tic characters also exhibit properties that differ subtly,butperhapsimportantly,frommorphometriccharacters.It has been increasingly recognized that morphologicalsystematists often code intrinsically quantitative char-acters as qualitative by artificially compartmentalizingvariation into relatively few ordered states (e.g., inter-clavicle,medianprocess:0=normallength;1=reduced;Etheridge and de Queiroz, 1988). This practice alwaysproduces arbitrary character states with morphometricdata. In contrast, meristic characters may be viewed asdiscrete traits that, depending on the range of variation,showacontinuumfrombinarytransformations,throughmultistate polymorphic characters, to quasicontinu-ous variation analogous to morphometric data (Wiens,2001).Arguments both for and against the inclusion ofquantitative continuous characters are prevalent in thesystematics literature and are beyond the scope of thisarticle (see Rae, 1998; Swiderski et al., 1998; Thiele,1993). Regardless, various authors have shown thatsuch characters do provide substantial phylogenetic in-formation despite the potential for increased levels ofhomoplasy, and thus remain relevant to empirical sys-tematics (e.g., Campbell and Frost, 1993; Wiens, 1995;WiensandServedio,1998).Severalcodingmethodshavebeen developed for incorporating meristic characters inphylogenetic analysis and dealing with the problem ofpartially overlapping character states across taxa. For bi-nary characters, frequency bins have most often beenused; whereas polymorphic multistate characters havebeen analyzed using majority methods, segment cod-ing, gap coding, gap weighting, step matrices, and var-ious statistical similarity analyses (e.g., Colless, 1980;Mabee and Humphries, 1993; Mickevich and Johnson,1976; Swiderski et al., 1998; Thiele, 1993; Wiens, 1993).Wiens (1998, 2000) and Wiens and Servedio (1997, 1998)evaluated several classes of coding methods and con-cluded that frequency methods were generally most ef-fective. In recent years, two methods that augment andimprove on previous approaches have become widelyused for coding meristic (and other quantitatively de-fined) characters for phylogenetic analysis: step-matrixgap-weighting (SMGW; Wiens, 2001) and general-ized frequency coding (GFC; Smith and Gutberlet,2001).SMGW is an application of step matrices (Wiens,1995) to the gap-weighting method introduced by Thiele(1993). In gap weighting, taxa are assigned states basedon range-standardized mean values of a trait, with thenumber of possible states scaled to the maximum num-ber allowed by the software used to infer phylogenies(e.g.,32statesforPAUP*;Swofford,1993).Gapsbetweenmeans are weighted based on the magnitude of theirdifferences so that larger differences in trait means be-tween taxa translate into larger weights. A limitation ofthe step-matrix approach is that the number of distinctstates is potentially restricted by the software used tobuild phylogenies (e.g., PAUP* only allows 32 states, sodata sets with over 32 taxa would likely not be amenableto SMGW). The purported advantage of SMGW is thatstep matrices allow more fine-grained weighting thansimple gap weighting by increasing the trait range from32 states to 1000 states (the maximum cost betweenstatesinastepmatrixusingPAUP*).Thus,charactersaretreated as approximations of a continuous scale (Wiens,2001).GFC can be viewed as a method that combines ele-ments of both gap weighting (as implemented by Thiele,1993) and the frequency bins method of Wiens (1993).In GFC, each quantitative character is divided into sub-charactersthatcorrespondwitheachcharacterstate.Thefrequency of specimens falling into a given subcharac-ter is described with frequency bins; the overall effectis that cumulative frequency distributions of characterstates per taxa are constructed for each character. A po-tential advantage of GFC is that frequency distributionsare simply translated into phlyogenetically analyzabledata, maximizing information content while eliminat-ing the need for further data manipulation (Smith andGutberlet, 2001). The primary operational difference be-tween these methods is that character states within taxaare coded using estimates of cumulative frequencies of" @default.
• W2102886673 created "2016-06-24" @default.
• W2102886673 creator A5003895914 @default.
• W2102886673 creator A5067934116 @default.
• W2102886673 creator A5072804367 @default.
• W2102886673 date "2008-02-01" @default.
• W2102886673 modified "2023-09-25" @default.
• W2102886673 title "Coding Meristic Characters for Phylogenetic Analysis: A Comparison of Step-Matrix Gap-Weighting and Generalized Frequency Coding" @default.
• W2102886673 cites W1552303814 @default.
• W2102886673 cites W1965485136 @default.
• W2102886673 cites W1997887791 @default.
• W2102886673 cites W2013216824 @default.
• W2102886673 cites W2026793430 @default.
• W2102886673 cites W2046701650 @default.
• W2102886673 cites W2077186234 @default.
• W2102886673 cites W2079999580 @default.
• W2102886673 cites W2095796640 @default.
• W2102886673 cites W2099074956 @default.
• W2102886673 cites W2102163163 @default.
• W2102886673 cites W2107584607 @default.
• W2102886673 cites W2110224102 @default.
• W2102886673 cites W2122082385 @default.
• W2102886673 cites W2131197564 @default.
• W2102886673 cites W2141264884 @default.
• W2102886673 cites W2142038331 @default.
• W2102886673 cites W2159895675 @default.
• W2102886673 cites W2176372109 @default.
• W2102886673 cites W2179984775 @default.
• W2102886673 cites W2195504228 @default.
• W2102886673 cites W2271737123 @default.
• W2102886673 cites W2334343197 @default.
• W2102886673 cites W2479347895 @default.
• W2102886673 doi "https://doi.org/10.1080/10635150801898938" @default.
• W2102886673 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/18300030" @default.
• W2102886673 hasPublicationYear "2008" @default.
• W2102886673 type Work @default.
• W2102886673 sameAs 2102886673 @default.
• W2102886673 citedByCount "20" @default.
• W2102886673 countsByYear W21028866732012 @default.
• W2102886673 countsByYear W21028866732014 @default.
• W2102886673 countsByYear W21028866732015 @default.
• W2102886673 countsByYear W21028866732016 @default.
• W2102886673 countsByYear W21028866732017 @default.
• W2102886673 countsByYear W21028866732018 @default.
• W2102886673 countsByYear W21028866732019 @default.
• W2102886673 countsByYear W21028866732020 @default.
• W2102886673 countsByYear W21028866732022 @default.
• W2102886673 countsByYear W21028866732023 @default.
• W2102886673 crossrefType "journal-article" @default.
• W2102886673 hasAuthorship W2102886673A5003895914 @default.
• W2102886673 hasAuthorship W2102886673A5067934116 @default.
• W2102886673 hasAuthorship W2102886673A5072804367 @default.
• W2102886673 hasBestOaLocation W21028866731 @default.
• W2102886673 hasConcept C104317684 @default.
• W2102886673 hasConcept C105795698 @default.
• W2102886673 hasConcept C126838900 @default.
• W2102886673 hasConcept C179518139 @default.
• W2102886673 hasConcept C183115368 @default.
• W2102886673 hasConcept C193252679 @default.
• W2102886673 hasConcept C33923547 @default.
• W2102886673 hasConcept C54355233 @default.
• W2102886673 hasConcept C59821588 @default.
• W2102886673 hasConcept C70721500 @default.
• W2102886673 hasConcept C71924100 @default.
• W2102886673 hasConcept C78458016 @default.
• W2102886673 hasConcept C86803240 @default.
• W2102886673 hasConcept C90856448 @default.
• W2102886673 hasConceptScore W2102886673C104317684 @default.
• W2102886673 hasConceptScore W2102886673C105795698 @default.
• W2102886673 hasConceptScore W2102886673C126838900 @default.
• W2102886673 hasConceptScore W2102886673C179518139 @default.
• W2102886673 hasConceptScore W2102886673C183115368 @default.
• W2102886673 hasConceptScore W2102886673C193252679 @default.
• W2102886673 hasConceptScore W2102886673C33923547 @default.
• W2102886673 hasConceptScore W2102886673C54355233 @default.
• W2102886673 hasConceptScore W2102886673C59821588 @default.
• W2102886673 hasConceptScore W2102886673C70721500 @default.
• W2102886673 hasConceptScore W2102886673C71924100 @default.
• W2102886673 hasConceptScore W2102886673C78458016 @default.
• W2102886673 hasConceptScore W2102886673C86803240 @default.
• W2102886673 hasConceptScore W2102886673C90856448 @default.
• W2102886673 hasIssue "1" @default.
• W2102886673 hasLocation W21028866731 @default.
• W2102886673 hasLocation W21028866732 @default.
• W2102886673 hasLocation W21028866733 @default.
• W2102886673 hasOpenAccess W2102886673 @default.
• W2102886673 hasPrimaryLocation W21028866731 @default.
• W2102886673 hasRelatedWork W1967807546 @default.
• W2102886673 hasRelatedWork W2011825632 @default.
• W2102886673 hasRelatedWork W2086940246 @default.
• W2102886673 hasRelatedWork W2091298617 @default.
• W2102886673 hasRelatedWork W2132619240 @default.
• W2102886673 hasRelatedWork W2417800113 @default.
• W2102886673 hasRelatedWork W2484229304 @default.
• W2102886673 hasRelatedWork W3126668599 @default.
• W2102886673 hasRelatedWork W3127473566 @default.
• W2102886673 hasRelatedWork W2028477758 @default.
• W2102886673 hasVolume "57" @default.

• next