DOI: 10.5061/dryad.0rxwdbs8b
title: Data from: A novel approach to quantifying mammal locomotor repertoires using scoring and cluster analysis

description: Describing behaviour using qualitative categories is a staple of studies
 on tetrapod functional morphology, ecology, and evolution. However, such
 categorisation has several issues, primarily subjectivity and the loss of
 important behavioural repertoire information. Here, we propose a novel
 method for quantifying behaviour, using mammal locomotion as a case study
 to demonstrate its utility and efficacy. Species are scored from 0-4 on
 their proficiency in five locomotor modes (swimming, climbing, digging,
 running and aerial movement), then Ward’s hierarchical clustering is used
 on the resulting data matrix to group species into biologically
 informative categories (the number of which can be optimised using
 clustering validation methods), thus producing a mathematically defined
 categorical variable. The method is demonstrated on a dataset of 250
 mammal species, representing every extant mammal family. We show that this
 approach successfully quantifies mammal locomotion, producing both a data
 matrix that can be used as a set of covariates in multivariate analyses
 and a categorical variable. This method introduces a replicable technique
 for quantifying animal behaviour and subsequently deriving a categorical
 variable, which is highly versatile and can be tailored to analyse a
 variety of other behaviours and taxonomic groups for widespread use across
 evolutionary and ecological research.
description: 1. Sample A total of 250 extant mammal species
 were included in this study. The species were selected to represent one
 species per subfamily, or family where such divisions have not been
 defined. Decisions regarding which particular species within their
 subfamilies would be scored were largely based on the availability of
 locomotor information within the literature to avoid introducing missing
 data into the scoring matrix. In the case of
 Chiroptera, not all subspecies are included, regardless of the presence of
 many subfamilies within the order, because the Chiroptera broadly
 represent a single behavioural repertoire which would be overrepresented
 if all subfamilies were included. Similarly, not all rodent subfamilies
 were included as it would risk overrepresenting this very large order.
 Note, however, that the reduced coverage still represents the whole
 locomotor variation within Rodentia. 2.
 Scoring Each species was given a score from 0-4 in each
 of five locomotor modes: swimming, climbing, digging, running, and aerial
 movement. A score of 0 represents an animal being physically unable to
 engage in a behaviour (e.g. a whale cannot run), while a score of 4
 represents an animal being able to perform this behaviour highly
 proficiently (e.g. whales are extremely good swimmers).
 The criteria for scoring are given in Table 1 of the associated
 manuscript; these were determined prior to scoring and based on function
 rather than frequency (i.e., an animal’s ability to perform a behaviour
 informs its score in the column, not the frequency of use of that
 behaviour). Strict adherence to these criteria is essential, and thus the
 wording of the criteria is intended to give as little room for ambiguity
 as possible in order to reduce subjectivity, and example taxa are provided
 to further clarify and differentiate the scoring levels.
 Scoring was based on information from literature, with full
 reference list provided in File 2.
description: # Data from: A novel approach to quantifying mammal locomotor repertoires
 using scoring and cluster analysis 
 
 Data for: Sophia C. Anderson, Philip G. Cox, Laura C. Fitton, Karl T.
 Bates, Eloy Gálvez-López (2025) A novel approach to quantifying mammal
 locomotor repertoires using scoring and cluster analysis. Royal Society
 Proceedings B. DOI 10.1098/rspb.2025.2515 This README.md file was
 generated on 10/12/2025 
 
 ## GENERAL INFORMATION 1\. Title of dataset: ‘Data for: A novel approach
 to quantifying mammal locomotor repertoires using scoring and cluster
 analysis’ 2\. Date of data collection: 2023-2025 3\. Keywords: cluster
 analysis, Ward's hierarchical clustering, mammal locomotion,
 behaviour quantification 4\. Language: English (UK) ## DATA AND FILE
 OVERVIEW 1\. Description of dataset This is a dataset containing raw data
 in which 250 extant mammals have been scored from 0-4 on their ability to
 engage in five types of locomotion (swimming, climbing, digging, running,
 and aerial movement), where 0 = physically unable; 4 = extremely able).
 Scoring follows strictly laid out criteria available in the associated
 manuscript submitted to Proceedings of the Royal Society B and is based on
 literature review (full reference list for scoring is provided in File 2).
 The scoring matrix dataset was then used as the input for Ward's
 hierarchical cluster analysis and subsequent cluster number optimisation.
 The output of this process was an optimal cluster number of 9, and each
 species is subsequently assigned to one of the 9 clusters accordingly. The
 annotated R code used to carry out analyses for the associated manuscript
 is provided here in File 1. Additionally, interobserver repeatability of
 the scoring process was investigated on a subset of 30 species from the
 original 250 which were independently scored by 8 individuals. Results are
 provided in Files 2 & 4. **2. File list** ### File 1:
 Anderson_Annotated_R-Script_ESM1.txt (Zenodo, Software) Text file
 containing annotated R code used to perform all the analyses in the
 associated manuscript, using the datasets provided here. ### File 2:
 Anderson_Additional_Data_and_Results_ESM2.xlsx This workbook contains 8
 sheets: 'Locomotor scores', 'Reference list',
 'Polychoric PC scores', 'Interobserver scoring
 matrices', 'Interobserver Medians ± QDs', 'Participant
 information', 'P1-P8 results', and 'Interobserver
 LMs'. #### 'Locomotor scores' sheet Table containing 250
 rows (no. of species in dataset) and 12 columns (5 of which contain
 scoring data, and 7 of which contain additional information). The rows are
 ordered alphabetically by species binomial. Variables: * Binomial –
 binomial species name following VertLife (Upham et al. 2019) * Order -
 taxonomic order * Family - taxonomic family * Subfamily - taxonomic
 subfamily where such designation exists * Common Name - common name for
 species following Animal Diversity Web (University of Michigan, 2020) *
 Cluster - simplified descriptor for cluster assigned in cluster analysis,
 where cluster number = 9 * Swimming - score for swimming ability (0-4) for
 each species * Climbing - score for climbing ability (0-4) for each
 species * Digging - score for digging ability (0-4) for each species *
 Running - score for running ability (0-4) for each species * Aerial -
 score for aerial ability (0-4) for each species * References - numerical
 references for scoring, referring to full reference list in
 'Reference list' sheet #### 'Reference list' sheet
 Table containing 250 rows (no. species in dataset) and 3 columns.
 Variables: * Citation number - number referring to the references in the
 'Locomotor scores' sheet * Short citation - in the form of an
 in-text citation * Full citation - complete reference ####
 'Polychoric PC scores' Table containing 250 rows (no. species in
 dataset) and 6 columns. Variables: * Binomial – binomial species name
 following VertLife (Upham et al. 2019) * PC1 to PC5 - principal component
 scores arising from transforming the scoring matirx via polychoric
 correlation and PCA #### 'Interobserver scoring matrices' sheet
 A table containing 240 rows (30 species scored by 8 participants) and 9
 columns (5 of which are scoring data). Variables: * Binomial – binomial
 species name following VertLife (Upham et al. 2019) * Participant -
 numerical ID for each participant * Swimming, Climbing, Digging, Running
 and Aerial - ability scores (0-4) for each species for each participant *
 k9 - numerical ID of cluster assignment when k = 9 * Designation -
 categorical descriptor for cluster assignment #### 'Interobserver
 Medians ± QDs' sheet A table containing 30 rows (species subset for
 repeatability analyses) and 6 columns. Variables: * Binomial – binomial
 species name following VertLife (Upham et al. 2019) * Swimming, Climbing,
 Digging, Running and Aerial - median score ± quartile deviation (QD)
 across the 8 participants. Cells are coloured according to QD value, with
 a key provided. #### 'Participant information' sheet A table
 containing 8 rows (no. participants) and 13 columns. Variables: *
 Participant - numerical ID for each participant * Background - indicates
 if the participant's background includes the study of locomotion
 behaviour * Career stage - indicates if the participant is an early career
 researcher (ECR) or principal investigator (PI) * Cladistic XP - indicates
 if the participant has previous experience of cladistic scoring. 1 = yes,
 0 = no. * *Mamm Spp* - indicates if participants used articles from the
 journal Mammalian Species as reference for scoring. 1 = yes, 0 = no. *
 Books - indicates if participants used books as reference for scoring. 1 =
 yes, 0 = no. * Papers - indicates if participants used published papers as
 reference for scoring. 1 = yes, 0 = no. * Google - indicates if
 participants used Google search as reference for scoring. 1 = yes, 0 = no.
 * YouTube - indicates if participants used YouTube videos as reference for
 scoring. 1 = yes, 0 = no. * Personal XP - indicates if participants used
 personal experience and knowledge as reference for scoring. 1 = yes, 0 =
 no. * Other sources - indicates if participants used other, unspecified
 sources as reference for scoring. 1 = yes, 0 = no. * Source diversity -
 indicates the number of source types utilised by participants * Time
 (minutes) - time taken by participant to complete scoring of the 30
 species in minutes #### 'P1-P8 results' sheet Table containing
 72 rows (9 clusters for each of 8 participants) and 14 columns. Variables:
 * Participant - numerical identity of participant, 1 to 8 * Cluster -
 numerical ID of cluster where k = 9 * N - number of species assigned to
 each cluster * Swim.median - median swimming score within each cluster *
 Swim.QD - quartile deviation (QD) in swimming score in each cluster *
 Climb.median - median climbing score within each cluster * Climb.QD -
 quartile deviation (QD) in climbing score in each cluster * Dig.median -
 median digging score within each cluster * Dig.QD - quartile deviation
 (QD) in digging score in each cluster * Run.median - median running score
 within each cluster * Run.QD - quartile deviation (QD) in running score in
 each cluster * Aer.median - median aerial score within each cluster -
 Aer.QD - quartile deviation (QD) in aerial score in each cluster - Cluster
 descriptor - short descriptor of cluster #### 'Interobserver
 LMs' sheet 12 results tables for linear models. These models assess
 the influence of 12 variables provided in 'Participant
 information' on: * mean MSE - mean mean squared error. We calculated
 the mean square error (MSE) of each participant by subtracting their
 scores from those of each other participant and calculating an averaged
 sum of those values squared. These pairwise interobserver MSEs were then
 averaged for each participant to calculate a general mean - the mean MSE.
 This is provided for each level of the independent variable. E.g. within
 the career stage model, there is a mean MSE for ECRs and a mean MSE for
 PIs. * cluster diffs. - mean number of species which differ in cluster
 assignment between participants. This is provided for each level of the
 independent variable. Each table provides: * R squared - value of R
 squared (coefficient of determination) for mean MSE and cluster diffs. in
 each model * F - value of F statistic (comparing variances) for mean MSE
 and cluster diffs. in each model * p - p-value for mean MSE and cluster
 diffs. in each model ### File 3: Anderson_Labelled_Dendrogram_ESM3.png
 (Zenodo, Supplemental Information) A .png image of the dendrogram produced
 via Ward's hierarchical clustering, with tips labelled with species
 binomials. ### File 4:
 Anderson_Interobserver_repeatability_analyses_ESM4.docx (Zenodo,
 Supplemental Information) A Word document containing a full description of
 interobserver repeatability analyses carried out as part of this study,
 referencing data and results available in File
 2: Anderson_Additional_Data_and_Results_ESM2.xlsx ## SHARING/ACCESS
 INFORMATION These data are used in the associated manuscript. ##
 CODE/SOFTWARE All analyses using these data were carried out in The R
 statistical environment v. 4.2.2. (R Core Team, 2024). The code is
 provided here in File 1 and is annotated throughout. ## Human subjects
 data Explicit consent from all human participants was obtained to publish
 de-identified data. Information regarding career stage and relevant
 educational background are included in these data, but all participants
 are referred to across all materials by a number for anonymity.