DOI: 10.5061/dryad.c866t1gdb
title: Data from: Inferring the locomotor ecology of two of the oldest fossil squirrels: Influence of operationalisation, trait, body size, and machine learning method

description: Correlations between morphology and lifestyle of extant taxa are useful
 for predicting lifestyles of extinct relatives. Here, we infer the
 locomotor behaviour of Palaeosciurus goti from the middle Oligocene and P.
 feignouxi from the lower Miocene of France using their femoral morphology
 and different machine learning methods. We used two ways to operationalise
 morphology, in the form of a geometric morphometric shape dataset and a
 multivariate dataset of eleven femoral traits. The predictive models were
 built and tested using more than half (180) of the extant species of
 squirrel relatives. The neural network model had the greatest predictive
 power, sometimes outperforming more traditional methods such as linear
 discriminant analysis. However, the predictive power also depended on the
 operationalisation and the femoral traits used to build the model. We also
 found that predictive power tended to slightly improve with increasing
 body size. Contrary to previous suggestions, the older species, P. goti,
 was most likely arboreal, whereas P. feignouxi was more likely
 terrestrial. This provides further evidence that arboreality was already
 the most common locomotor ecology among the earliest squirrels, while a
 predominantly terrestrial locomotor behaviour evolved shortly afterwards,
 before the vast establishment of grasslands in Europe.
description: # R data and code
 [https://doi.org/10.5061/dryad.c866t1gdb](https://doi.org/10.5061/dryad.c866t1gdb) **Inferring the locomotor ecology of two of the oldest fossil squirrels: influence of operationalisation, trait, body size, and machine learning method** Jan Wölfer1* & Lionel Hautier2 1Humboldt-Universität zu Berlin, Philippstraße 13, 10115 Berlin 2Institut des Sciences de l’Évolution de Montpellier, UMR 5554, Univ de Montpellier, CNRS, IRD, Montpellier Cedex 5, France *corresponding author: [jan.woelfer@gmx.de](mailto:jan.woelfer@gmx.de) Article DOI: 10.1098/rspb.2024.0743 --- Here you find the R code and data together presented as an R project. It contains all data necessary to reproduce the geometric morphometric analysis of the landmark data, the generation of the multivariate dataset, the machine learning methods for classification/testing/prediction, and the generation of the phylogeney with ancestral character estimation. After unzipping the folder, you will find the following subfolders and main files: * ply subfolder: contains all bone surface files as .ply * pts subfolder: contains all landmarks stored in the .pts format * Template subfolder: contains the .ply and .pts files of the template for projecting the surfice sliding semi-landmarks onto the target specimens * Ident_fossils.xlsx: grouping variables in terms of lifestyle (see the R script for more information), sex, body mass [g] and an indicator variable of the fossils. * linear_data_fossils.xlsx: variables which where measured directly on surface files in Geomagic (patellar grove width, anteroposterior and mediolateral midshaft diameters) with three measurements each. * sciuromorpha_phylogeny.nex: Phylogenetic information in nexus format * Femur.Rproj: R project file * .RData: The R objects generated during the final analysis * resamplingsV2.R: R code used to downsample the curve sliding semi-landmarks. This file is based on a previous version (kindly provided by Anne-Claire Fabre), which was published as supplementary material to the following publication: Botton-Divet, Léo, et al. "Morphological analysis of long bones in semi-aquatic mustelids and their terrestrial relatives." *Integrative and Comparative Biology* 56.6 (2016): 1298-1309. * Script: R script for analysis In addition, you find various .R objects which store interim results: atlas.R, final_specimens_fossils_all.R, linear_df_fossils_all.R, Proc_fit_fossils_all.R, Proc_fit2_fossils_all.R, slid_specimens_fossils_all.R. When going through the script, these are loaded by default into R to ensure that the results of the analysis remain the same despite potential changes in the R functions since the generation of these files. --- To reproduce all results and raw figures, open the Femur.Rproj file and the R script called 'Script' (if not loaded automatically). The script is annotated and contains all steps of the analysis from top to bottom.