Datasets, Annotated R code files, and Metadata for Analysis and Figures
Creators: name: Valiya Parambil, Gokul
Publisher: name: figshare
Year: 2025
Subjects: classificationCode: 310307
subject: Population ecology
classificationCode: 310399
subject: Ecology not elsewhere classified
classificationCode: 310405
subject: Evolutionary ecology
classificationCode: 310406
subject: Evolutionary impacts of climate change
classificationCode: 310408
subject: Life histories
classificationCode: 310499
subject: Evolutionary biology not elsewhere classified

description: The folder contains datasets and the annotated R code files used for analysis and figures in the manuscript.1 Dataset for trait allometry.csv :The dataset comprises morphometric measurements of adults Psammophilus dorsalis from April to November for 2011 to 2021. We performed standardised major axis regression on the trait dataset to understand trait allometric scaling for head width and interlimb length in males and females.2 Dataset for analysis of the effect of predictors on trait allometry.csv :The dataset comprises morphometric measurements of adults in a wild lizard population from 2011 to 2021. Ecology conditions are extracted from daily measurements in a weather station within the study site. Demographic conditions are estimated using around-the-year life-history census data taken for lizards across multiple sheetrock at our study site once a week.We performed linear mixed-effect model regression on this dataset to examine the effect of different ecological and demographic conditions on trait allometry across years and sheetrock.3 Dataset with parameter values for males.csv :4 Dataset with parameter values for females.csv :A summary of ecological and demographic conditions experienced by males and females during the 11-year period of our study. Q3 and Q1 or the 75th quantile and 25th quantile values are used to describe the effect of high and low values of predictor on trait allometry. The predictor dataset was calculated separately for males and females.Code files:5 Code for analysis and figure for estimating trait allometry across sexes.R :Here, we plot Figure 2 in the manuscript using dataset 1 containing morphometric measurements of individuals pooled over 11 years. Full annotated code for analysis and figure is provided within the RCode and with metadata file for dataset 1.6 Code for analysis investigating the effect of predictors on trait allometry.R :Here, we perform linear mixed effect model analysis to investigate the effect of different ecological and demographic predictors on trait allometry, i.e. how, head width and interlimb length allometry in males and females. Detailed information on the analysis is provided in the methods and results section of the manuscript.7 Code for figures showing the effect of significant predictors on trait allometry.R :The model results from the analysis performed in 6 are used to plot figures 3 and 5 in the manuscript. Figures 3 and 5 are plots that show trait scaling against body size(SVL) for high and low levels of predictors that significantly affected the allometry (model results showing significant fixed interaction effects).8 Code for figures showing the partial slope trait allometry with environmental predictors varying across years.R :The model results from the analysis performed in 6 are used to calculate partial slopes of trait allometry indicating change in allometry with change in environmental predictors across the 11-year period of our study. Figures 4 and 6 in the manuscript show partial slope of trait scaling against body size(SVL) for ecological and demographic predictors that significantly affected the allometry varying across years (model results showing significant fixed interaction effects).9 Code for correlation in head width and interlimb length allometry.R :Using linear regression with year as grouping factor, we calculated disproportionate investment in head width and interlimb length as difference in absolute trait size with predicted trait size from isometric scaling (slope = 1). Correlation between disproportionate investment in head width and interlimb length in males and females were calculated separately to check for presence of trade-off or correlation in positive allometry for the two traits.<br>Metadata files in .txt:Appropriate metadata files for each dataset are provided. Kindly refer to the metadata text file for information on dataset and column information with units of measurements.10 Metadata for Dataset for trait allometry:Metadata for dataset 1.11 Metadata for Dataset for analysis of the effect of predictors on trait allometry:Metadata for dataset 2.12 Metadata for Dataset with parameter values for figuresMetadata for dataset 3.