Disparities in the analysis of morphological disparity

Abstract

Analyses of morphological disparity have been used to characterize and investigate the evolution of variation in the anatomy, function and ecology of organisms since the 1980s. While a diversity of methods have been employed, it is unclear whether they provide equivalent insights. Here, we review the most commonly used approaches for characterizing and analysing morphological disparity, all of which have associated limitations that, if ignored, can lead to misinterpretation. We propose best practice guidelines for disparity analyses, while noting that there can be no 'one-size-fits-all' approach. The available tools should always be used in the context of a specific biological question that will determine data and method selection at every stage of the analysis.

Keywords: disparity; ecology; morphology; multidimensionality; palaeobiology; variance/variation.

Figure 1.

Major routes to obtain morphological data for disparity analyses. Data can be collected as discrete trait observations (e.g. presence–absence data) or as continuous data. Continuous data can be collected by various methods including linear measurements and landmark coordinates or contours (curves). These measurements can then be mathematically transformed (logarithmic transformations, scaling, Procrustes superimposition, elliptic Fourier transforms, etc.). Regardless of the method, data collection produces a trait matrix where the observed traits constitute columns and the studied elements (generally taxa or OTUs) the rows.

Figure 2.

Illustration of the relationships between the different morphospaces and visualization of the same dataset (the ‘penguin’ dataset of [44,45]). Morphospaces: different mathematical representations of a morphospace. A trait matrix can be an ordinated matrix (e.g. in [20]) or transformed into a distance matrix (e.g. in [31], not represented here). Here, we consider all these matrices as being morphospaces, i.e. objects containing all the combinations of traits and observations (albeit transformed differently). Visualization: different ways to represent the morphospace in 2D. Visualizations can use either trait plots (directly from the trait matrix); or ordination axis plots (directly from the ordinated matrix). Note that in 2D representations, it is good practice to plot both axes on the same scale to avoid visually distorting the importance of one axis.