Data from: Macroevolutionary divergence along allometric lines of least resistance in frog hindlimb traits and its effect on locomotor evolution
Data files
Mar 11, 2025 version files 2.22 MB
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all_data.final.csv
46.22 KB
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compare_mass.csv
17.56 KB
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diverg_list.Rdata
611 B
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force_traces_AmNat_Jan2025.zip
1.71 MB
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README.md
8.31 KB
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suppl_data.csv
189.72 KB
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TreePL-Rooted_Anura_bestTree_PortikEA2023.tre
223.28 KB
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vel_complete.2.csv
24.86 KB
Abstract
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.
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
- Phylogeny: https://osf.io/3cbsh/
Data was derived from the following sources:
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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.
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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.
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.