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These are the Supplementary Material, R scripts and numerical results accompanying Bartoszek, Fuentes Gonzalez, Mitov, Pienaar, Piwczyński, Puchałka, Spalik and Voje "Model Selection Performance in Phylogenetic Comparative Methods under multivariate Ornstein–Uhlenbeck Models of Trait Evolution". The four data files concern two datasets. Ungulates: measurements of muzzle width, unworn lower third molar crown height, unworn lower third molar crown width and feeding style and their phylogeny; Ferula: measurements of ratio of canals, periderm thickness, wing area, wing thickness, and fruit mass, and their phylogeny. Methods Ungulates The compiled ungulate dataset involves two key components: phenotypic data (Data.csv) and phylogenetic tree (Tree.tre), which consist on the following (full references for the citations presented below are provided in the paper linked to this repository, which also provides further details on the compiled dataset):The phenotypic data includes three continuous variables and one categorical variable. The continuous variables (MZW: muzzle width; HM3: unworn lower third molar crown height; WM3: unworn lower third molar crown width), measured in cm, come from Mendoza et al. (2002; J. Zool.). The categorical variable (FS, i.e. feeding style: B=browsers, G=grazers, M=mixed feeders) is based on Pérez–Barbería and Gordon (2001; Proc. R. Soc. B: Biol. Sci.). Taxonomic mismatches between these two sources were resolved based on Wilson and Reeder (2005; Johns Hopkins University Press). Only taxa with full entries for all these variables were included (i.e. no missing data allowed). The phylogenetic tree is pruned from the unsmoothed mammalian timetree of Hedges et al. (2015; MBE) to only include the 104 ungulate species for which there is complete phenotypic data available. Wilson and Reeder (2005; Johns Hopkins University Press) was used again to resolve taxonomic mismatches with the phenotypic data. The branch lengths of the tree are scaled to unit, Usage note:any text file editor and RFunding:Swedish Research Council, Award: 2017–04951NSF CAREER, Award: 2225683Polish National Science Centre, Award: 2015/18/E/NZ8/00716ERC–2020–STG, Award: 948465ELLIIT, Award: ELLIIT Call CStiftelsen for Vetenskaplig Forskning och Utbildning i MatematikLicense:This work is licensed under a CC0 1.0 Universal (CC0 1.0) Public Domain Dedication license.

The advent of fast computational algorithms for phylogenetic comparative methods allows for considering multiple hypotheses concerning the co-adaptation of traits and also for studying if it is possible to distinguish between such models based on contemporary species measurements. Here we demonstrate how one can perform a study with multiple competing hypotheses using mvSLOUCH by analyzing two data sets, one concerning feeding styles and oral morphology in ungulates, and the other concerning fruit evolution in Ferula (Apiaceae). We also perform simulations to determine if it is possible to distinguish between various adaptive hypotheses. We find that Akaikes information criterion corrected for small sample size has the ability to distinguish between most pairs of considered models. However, in some cases there seems to be bias towards Brownian motion or simpler Ornstein-Uhlenbeck models. We also find that measurement error and forcing the sign of the diagonal of the drift matrix for an Ornstein-Uhlenbeck process influences identifiability capabilities. It is a cliche that some models, despite being imperfect, are more useful than others. Nonetheless, having a much larger repertoire of models will surely lead to a better understanding of the natural world, as it will allow for dissecting in what ways they are wrong. [Adaptation; AICc; model selection; multivariate Ornstein-Uhlenbeck process; multivariate phylogenetic comparative methods; mvSLOUCH.], Funding: Swedish Research Council (Vetenskapsradet) [2017-04951]; ELLIIT Call C grant; Stiftelsen for Vetenskaplig Forskning och Utbildning i Matematik (Foundation for Scientific Research and Education in Mathematics); National Science Foundation [2225683]; National Science Center [2015/18/E/NZ8/00716]; ERC-2020-STG [948465]; Swedish Research Council [2017-04951] Funding Source: Swedish Research Council

Adams and Collyer argue that contemporary multivariate (Gaussian) phylogenetic comparative methods are prone to favouring more complex models of evolution and sometimes rotation invariance can be an issue. Here we dissect the concept of rotation invariance and point out that, depending on the understanding, this can be an issue with any method that relies on numerical instead of analytical estimation approaches. We relate this to the ongoing discussion concerning phylogenetic principal component analysis. Contrary to what Adams and Collyer found, we do not observe a bias against the simpler Brownian motion process in simulations when we use the new, improved, likelihood evaluation algorithm employed by mvSLOUCH, which allows for studying much larger phylogenies and more complex model setups., Funding Agencies|Swedish Research Council (Vetenskapsradet) [2017-04951]; ELLIIT Call C grant; Stiftelsen foer Vetenskaplig Forskning och Utbildning i Matematik (Foundation for Scientific Research and Education in Mathematics); National Science Foundation [2225683]; National Science Center [2015/18/E/NZ8/00716]; ERC-2020-STG [948465]

Phylogenetic comparative methods have been limited in the way they model adaptation. Although some progress has been made, there are still no methods that can fully account for coadaptationbetween traits. Based on Ornstein-Uhlenbeck (OU) models of adaptive evolution, we present a method,with R implementation, in which multiple traits evolve both in response to each other and, as inprevious OU models, to fixed or randomly evolving predictor variables. We present the interpretation ofthe model parameters in terms of evolutionary and optimal regressions enabling the study of allometric and adaptive relationships between traits. To illustrate the method we reanalyze a data set of antlerand body-size evolution in deer (Cervidae).