2022
DOI: 10.1071/am21047
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Morphological variation in skull shape and size across extinct and extant populations of the greater stick-nest rat (

Abstract: Within-species morphological variation is often observed across spatial and climatic gradients. Understanding this variation is important to conservation planning, as specialised adaptations may influence a population’s persistence following translocation. However, knowing whether local adaptations are prevalent within a species can be challenging when the species has undergone range contractions. Here, we used museum specimens to study size and shape variation of the greater stick-nest rat (Leporillus condito… Show more

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Cited by 2 publications
(1 citation statement)
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“…In morphological datasets, a high proportion of the variance explained by PC1 is usually due to size (Jolicoeur 1963;Berner 2011) and accounting for the effect of size has been shown to help distinguish between morphologically cryptic dasyurid species as it allows for a comparison on skull shape (viacava et al 2023). to account for size effects (specifically caused by isometric scaling) between species, we used log-shape ratios by calculating the geometric mean of all variables, per individual, as a proxy for skull size, dividing each variable by the geometric mean then log-transforming this ratio (Mosimann 1970;onley et al 2022;Pavónvázquez et al 2022;viacava et al 2023). we then re-ran the PCA on the 'size-corrected' dataset.…”
Section: Methodsmentioning
confidence: 99%
“…In morphological datasets, a high proportion of the variance explained by PC1 is usually due to size (Jolicoeur 1963;Berner 2011) and accounting for the effect of size has been shown to help distinguish between morphologically cryptic dasyurid species as it allows for a comparison on skull shape (viacava et al 2023). to account for size effects (specifically caused by isometric scaling) between species, we used log-shape ratios by calculating the geometric mean of all variables, per individual, as a proxy for skull size, dividing each variable by the geometric mean then log-transforming this ratio (Mosimann 1970;onley et al 2022;Pavónvázquez et al 2022;viacava et al 2023). we then re-ran the PCA on the 'size-corrected' dataset.…”
Section: Methodsmentioning
confidence: 99%