Constructing a multiple-part morphospace using a multiblock method

Simple item page
dc.citation.issue1
dc.citation.volume14
dc.contributor.authorThomas DB
dc.contributor.authorHarmer AMT
dc.contributor.authorGiovanardi S
dc.contributor.authorHolvast EJ
dc.contributor.authorMcGoverin CM
dc.contributor.authorTenenhaus A
dc.contributor.editorGraham L
dc.date.accessioned2024-01-11T19:30:21Z
dc.date.accessioned2024-07-25T06:35:44Z
dc.date.available2021-12-08
dc.date.available2024-01-11T19:30:21Z
dc.date.available2024-07-25T06:35:44Z
dc.date.issued2023-01-06
dc.description.abstract1. Popular current methods for quantifying variation in biological shape are well-suited to analyses of isolated parts (e.g. the same bone from the skeletons of many individuals). An analytical challenge exists for quantifying variation between the shapes of multiple-part objects where each part has a different position, rotation or scale (e.g. partial or whole articulated skeletons). We investigated regularised consensus principal component analysis (RCPCA) as a multiblock method for quantifying variation in the shape of multiple-part objects. Multiblock methods are routinely used in other big data research fields such as bioinformatics/medicine, marketing and food research, but have not been widely embraced for evolutionary biology research. 2. We have created the new package morphoBlocks for the r programming language to make RCPCA more accessible for shape evolution research. morphoBlocks provides a complete workflow for formatting, analysing and visualising the variation between multiple-part objects by integrating functions from a diverse range of other packages. In particular, global components produced by RCPCA provide a consensus space that we present here as a morphospace for multiple-part objects. 3. morphoBlocks is demonstrated with a case study of manually placed landmarks and automatically placed pseudolandmarks from the partial wing skeletons of 15 extant penguin species and five fossil penguin species. Our case study provides quantitative support for a historical hypothesis about the magnitude and mode of morphological change across the evolutionary history of penguins. 4. RCPCA can be used to analyse two- or three-dimensional datasets with 10s of landmarks, or 100s to 1,000s of semilandmarks or pseudolandmarks, from 10s to 100s of specimens comprised of two or more parts. We use morphoBlocks on a small three-bone case study and provide a framework for applying this method to much larger studies investigating the ecological or evolutionary significance of multiple-part objects.
dc.description.confidentialfalse
dc.edition.editionJanuary 2023
dc.format.pagination65-76
dc.identifier.author-urlhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000731695400001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fef
dc.identifier.citationThomas DB, Harmer AMT, Giovanardi S, Holvast EJ, McGoverin CM, Tenenhaus A. (2023). Constructing a multiple-part morphospace using a multiblock method. Methods in Ecology and Evolution. 14. 1. (pp. 65-76).
dc.identifier.doi10.1111/2041-210X.13781
dc.identifier.eissn2041-210X
dc.identifier.elements-typejournal-article
dc.identifier.issn2041-210X
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/70511
dc.languageEnglish
dc.publisherJohn Wiley and Sons Ltd on behalf of British Ecological Society
dc.publisher.urihttps://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13781
dc.relation.isPartOfMethods in Ecology and Evolution
dc.subjectbone
dc.subjectgeneralised Procrustes surface analysis
dc.subjectgeometric morphometrics
dc.subjectmorphoBlocks
dc.subjectpenguin
dc.subjectregularised consensus principal component analysis
dc.subjectregularised generalised canonical correlation analysis
dc.subjectshape
dc.titleConstructing a multiple-part morphospace using a multiblock method
dc.typeJournal article
pubs.elements-id450169
pubs.organisational-groupOther
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
Evidence
Size:
329.98 KB
Format:
Microsoft Word XML
Description:
Download
Thumbnail Image
Name:
Published version
Size:
10.59 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Journal Articles