DOI: 10.5061/dryad.rn8pk0pnp
title: Data from: Macroevolutionary divergence along allometric lines of least resistance in frog hindlimb traits and its effect on locomotor evolution

description: Understanding whether and why microevolutionary patterns of trait
 covariation match macroevolutionary divergence is essential for linking
 evolution at different timescales. However, recent work has focused on
 developmental constraints for alignment between intraspecific variation
 and divergence, neglecting a potential role of natural selection on
 function to connect these scales. Here, we compare the support for the
 selection and constraint hypotheses to explain both phenotypic trait
 covariation and species divergence. To test these hypotheses, we collected
 data on hindlimb and jumping performance traits within and across species
 of two frog genera. We compared patterns of within-species phenotypic
 variation (the P-matrix) with divergence and selective covariance
 matrices, from which we could extract the major axes of the realized
 adaptive landscape (AL), the directions in which adaptive peaks shifted
 the most over evolutionary time. We also tested whether the major axes of
 the AL were related to selection on jumping performance. We found high
 alignment between patterns of variation across scales. Most divergence
 occurred in allometric size, defined as the first eigenvector of the
 P-matrix. However, jumping performance gradients were unaligned with the
 major axes of the AL and the P-matrix. Across species, however, evolution
 of maximum acceleration showed a strong negative relationship with changes
 in allometric size. We infer that the jumping peak evolved under
 fluctuating selection, and species have tracked the peak along the
 direction of most within-species variation, allometric size. We conclude
 that long-term hindlimb divergence was constrained by developmental
 interactions among traits associated with growth and not net directional
 selection. Nonetheless, divergence on size indirectly influenced jumping
 evolution.
description: The dataset was collected using a force plate and the software
 BioWare to measure jumping performance on live frogs collected in French
 Guiana, a caliper to measure hindlimb dimensions and a scale to measure
 body mass, both in live frogs and museum specimens. 
 The force traces were processed using a MATLAB script to extract
 acceleration and velocity for each jumping peak analyzed. Then, peak
 jumping performance values were extracted for each specimen using R
 programming environment. Morphological and jumping
 performance data were subsequently analyzed to contruct phenotypic
 covariance matrices and divergence matrices (rate matrices), and test
 whether variation within-species matched divergence across species. We
 also tested the role of selection on hindlimb morphology associated with
 jumping performance and on promoting divergence across species. All
 analyses were performed in R programming environment.
description: # Data from: Macroevolutionary divergence along allometric lines of least
 resistance in frog hindlimb traits and its effect on locomotor evolution
 [https://doi.org/10.5061/dryad.rn8pk0pnp](https://doi.org/10.5061/dryad.rn8pk0pnp) ## Description of the data and file structure Jumping performance measured using a force plate and BioWare software. Hindlimb morphology measured using a caliper and body mass measured with a scale. ### Files and variables #### File: vel\_complete.2.csv **Description:** Maximum acceleration and velocity values for all jumping peaks analyzed with MATLAB ##### Variables * ID: Identification number of field specimens caught in French Guyane * species: Species names (not updated for recent taxonomy) * max.vel: Peak jumping velocity in m/s * max.acc: Peak jumping acceleration in m/s2 * cont.time: Contact time for the jumping peak in s * exp.vel: Expected peak velocity in m/s calculated from the MATLAB script #### File: diverg\_list.Rdata **Description:** Divergence matrices for Boana and Leptodactylus to load directly into R Global Environment in 'R_script_3.R' #### File: suppl\_data.csv **Description:** Extra information for all specimens used, including updated taxonomy Note: The NAs in this file indicate not available information, localities for which GPS data could not be gathered at the time of collection. The NAs at the 'taxonomic_data' column is for one species for which we could not find a recent paper to support its taxonomic status. ##### Variables * spec_num: Record number of specimens (identification of museum or field series) * species_at_collection: Species names when they were caught and/or measured * species_2024: Species names after updating with recent taxonomy literature (see details in main text) * 2024_name_in_PortikEA2023: yes=species present in phylogeny; blank=species not present in phylogeny * taxonomic_data: Papers used to update the taxonomy of species * locality: Localities where specimens were collected * GPS: Geographic coordinates of where specimens where collected #### File: TreePL-Rooted\_Anura\_bestTree\_PortikEA2023.tre **Description:** Phylogenetic tree extracted from Portik et al. 2023 #### File: force\_traces\_AmNat\_Jan2025.zip **Description:** Contains hundreds of .txt files describing force traces for each jumping peak extracted from BioWare Each .txt file has the following 4 columns abs time (s): Absolute time in s Fx: Force values on the horizontal direction (x-axis) in Newtons Fy: Force values on the horizontal direction (y-axis) in Newtons Fz: Force values on the vertical direction in Newtons #### File: all\_data.final.csv **Description:** Dataset analyzed with R scripts 1,2 and 3. ##### Variables * spec_num: Record number of specimens (identification of museum or field series) * species_phylo: Species names following recent taxonomy * sex: Sex of specimens (M=male; F=female) * SVL: Snout-to-vent length in mm * FEMUR: Length of right femur in mm * TIBL: Length of right tiobiofibula in mm * TARS: Length of right tarsus in mm * FOOT: Length of right foot in mm * UpLeg.dim.1: Largest width of right upper leg muscles in mm * UpLeg.dim.2: Width perpendicular to UpLeg.Dim.1 of right upper leg in mm * LowLeg.dim.1: Largest width of right lower leg muscles in mm * LowLeg.dim.2: Width perpendicualr to LowLeg.Dim.1 of right lower leg in mm * max.vel.jump: Peak jumping velocity for each specimen in m/s * max.acc.jump: Peak jumping acceleration for each specimen in m/s2 * ID: Identification number of field specimens caught in French Guyane #### File: compare\_mass.csv **Description:**  To be used to add mass values in the MATLAB script ##### Variables * ID: Identification number of field specimens caught in French Guyane * mass_trace: Mass of specimens in grams measured from force traces * mass_scale: Mass of specimens in grams measured in scale on the field * species: Species names (not updated for recent taxonomy) ## Code/software The data are in .csv format (can be viewed with Excel) or in .txt format (can e viewed with Notepad). Below are descriptions of the scripts used to analyzed the data, staring with the force traces (MATLAB version R2020a) then analyses of morphology and jumping performance using R programming environment (R version 4.3.2 (2023-10-31)). 1\. Matlab_script_AmNat_2025 Interactive script to read individual force traces from French Guiana specimens and perform the following steps: Smooth the force curves using a moving average filter Zero the force curves in x, y and z directions Calculate acceleration and velocity profiles from the resultant force curve Calculate contact time in s for the selected jumping peak 2\. R_script_0: Extraction of peak acceleration and velocity values for each specimen measured in French Guiana Elimination of acceleration and velocity data associated with too high contact times and too high velocity values Extraction of final peak acceleration and velocity values for each specimen 3\. R_script_1: Main analyses of the paper using the full morphological data set (6 hindlimb traits) and the two jumping performance variables. Estimation of 6 x 6 species P-matrices, pooled P-matrices, divergence matrices and selection covariance matrices (W-matrices) for each frog genus. Calculation of allometric vectors and test for isometry. Comparisons of eigenvectors of these matrices and calculation of 95% confidence intervals using bootstrapped distributions of matrices. Drift tests and Brownian Motion simulations to calculate significance of regression slopes. Estimation of performance gradients and their vector correlations with eigenvectors of pooled P, divergence, and W-matrices (significance tests using bootstrapped distributions). 4\. R_script_2: Simpler analyses using only two hindlimb traits (leg length and muscle CSA) and the two jumping performance variables. Estimation of 2 x 2 species P-matrices, pooled P-matrices, divergence matrices and selection covariance matrices (W-matrices) for each frog genus. Estimation of 95% confidence intervals for elements of each matrix using bootstrapped distributions. Estimation of performance gradients within- and between-species. 5\. R_script_3: Comparison of models for the evolution of jumping performance. Estimation of evolutionary parameters using maximum likelihood framework and SLOUCH R package. Model comparison using AICc for models assuming Brownian Motion or Ornstein-Uhlenbeck processes, with or without allometric size as a predictor. Estimation of multivariate models for the joint evolution of jumping velcocity and acceleration using mvSLOUCH R package. 6\. R_functions_jumping.R: Collection of 12 custom-made functions to run the analyses of the other three R scripts This script is needed to run R scripts 1, 2 and 3. The functions are captured in each R script by using source('R_functions_jumping.R'). Package versions: evolqg 0.3-4 corrplot 0.92 ggplot2 3.4.4 cowplot 1.1.3 ape 5.7-1 phytools 2.1-1 geiger 2.0.11 phylolm 2.6.2 slouch 2.1.4 mvSLOUCH 2.7.6 PCMBaseCpp 0.1.9 ## Access information Other publicly accessible locations of the data: * Data from literature: [http://dx.doi.org/10.5061/dryad.8vv63](http://dx.doi.org/10.5061/dryad.8vv63)  - Phylogeny: [https://osf.io/3cbsh/](https://osf.io/3cbsh/) Data was derived from the following sources: * Data from literature (see Moen et al. 2013; 2016): Boana hobbsi (n=16), Boana lanciformis (n=10), Boana punctata (n=12), Leptodactylus leptodactyloides (n=13), Leptodactylus rhodomystax (n=11). Moen D.S., D.J. Irschick, and J.J. Wiens. 2013. Evolutionary conservatism and convergence both lead to striking similarity in ecology, morphology and performance across continents in frogs. Proceedings of the Royal Society B: Biological Sciences 280:20132156. Moen D.S., H. Morlon, and J.J. Wiens. 2016. Testing Convergence Versus History: Convergence Dominates Phenotypic Evolution for over 150 Million Years in Frogs. Systematic Biology 65:146–160. * Phylogeny: Portik D.M., J.W. Streicher, and J.J. Wiens. 2023. Frog phylogeny: a time-calibrated, species-level tree based on hundreds of loci and 5,242 species. Molecular Phylogenetics and Evolution 188:107907.