English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Bivariate morphological scaling relationships describe how the sizes of two traits co-vary among adults in a population. In as much as body shape is reflected by the relative size of various traits within the body, morphological scaling relationships capture how body shape varies with size, and therefore have been used widely as descriptors of morphological variation within and among species. Despite their extensive use, there is continuing discussion over which line-fitting method should be used to describe linear morphological scaling relationships. Here I argue that the “best” line-fitting method is the one that most accurately captures the proximate developmental mechanisms that generate scaling relationships. Using mathematical modeling, I show that the “best” line-fitting method depends on the pattern of variation among individuals in the developmental mechanisms that regulate trait size. For Drosophila traits, this pattern of variation indicates that major axis regression is the best line-fitting method. For morphological traits in other animals, however, other line-fitting methods may be more accurate. I provide a simple web-based application for researchers to explore how different line-fitting methods perform on their own morphological data.

Which Line to Follow? The Utility of Different Line-Fitting Methods to Capture the Mechanism of Morphological Scaling