DOI: 10.5061/dryad.5tb2rbpck
title: Data from: Intraspecific diversity is critical to population-level risk assessments

description: Environmental risk assessment (ERA) is a critical tool for protecting life
 and its effectiveness is predicated on predicting how natural populations
 respond to contaminants. Yet, routine toxicity testing typically examines
 only one genotype from surrogate species, which may render risk
 assessments inaccurate as populations are most often composed of
 genetically distinct individuals. To determine the importance of
 intraspecific variation in the translation of toxicity testing to
 populations, we quantified the magnitude of phenotypic variation within 20
 Daphnia magna clones derived from one lake. We repeated these assays
 across two exposure levels of microcystins, a cosmopolitan and lethal
 aquatic contaminant produced by harmful algal blooms. We found
 considerable intraspecific genetic variation in survival, growth, and
 reproduction, which was amplified by microcystins exposure. Using
 simulations, we demonstrate that the common practice of employing a single
 genotype to calculate toxicity tolerance failed to produce an estimate
 within the 95% confidence interval over half of the time. Finally, we
 conducted whole genome sequencing of all 20 clones to test whether
 differences in toxicological responses were associated with overall
 genomic divergence or divergence at candidate loci based on prior gene
 expression work. We find no overall correlations, suggesting that clonal
 variation, but not variation at candidate genes, is an important predictor
 of population-level responses to toxic insults. These results illuminate
 the importance of incorporating overall intraspecific genetic variation,
 without focusing specifically on variation in candidate genes, into ERAs
 to reliably predict how natural populations will respond to contaminants.
description: # Data from: Intraspecific diversity is critical to population-level risk
 assessments This dataset is associated with the manuscript:
 **Intraspecific diversity is critical to population-level risk
 assessments** DOI:
 [https://doi.org/10.5061/dryad.5tb2rbpck](https://doi.org/10.5061/dryad.5tb2rbpck) ## Change log April 22, 2025: In this version, the authors have uploaded the variant call format (VCF) file along with its associated index file that allows users to download the full set of variant sites identified across the whole genomes of all 20 *Daphnia magna* clones used in the study. ## General Description This dataset contains the raw and processed data, the variant call format (VCF) file, as well as the R scripts necessary to reproduce the statistical analyses and figures presented in the manuscript. The data pertain to phenotypic and genomic variation in *Daphnia*, analyzed under different experimental treatments. ## File Structure The dataset includes: * **Eight CSV files** containing phenotypic and genomic data * **Two R script files** used for statistical analyses and figure generation * **One VCF file** storing the full set of variant sites identified across the whole genomes of all 20 *Daphnia magna* clones used in the study * **One VCF index file** included alongside the compressed VCF file to enable rapid access to variant data by genomic position, facilitating efficient downstream analyses * **This README file** describing the dataset and its contents ## Data Files and Descriptions Each CSV file is structured in a tabular format with proper headings, including relevant variables such as clone ID, treatment conditions, and measured phenotypes. Below is a detailed breakdown of each file: ### Phenotypic Data 1. **Survival_210129.csv** * Contains survival data across experimental treatments. * Variables: * `Clone`: Unique identifier for each clone * `Treatment`: Experimental condition applied * `Replicate`: Replicate number * `Day X`: Survival on Day 0, 7, and 14 (1 = alive, 0 = dead) 2. **Neonates_210226.csv** * Contains reproductive output data. * Variables: * `Clone`: Unique identifier for each clone * `Treatment`: Experimental condition applied * `Replicate`: Replicate number * `Neonates`: Number of offspring produced (unit: count) * `Fbrood`: Number of neonates produced in the first brood recorded per replicate * `Tbroods`: Total number of broods recorded throughout the entire experiment per replicate * **Missing Values:** Some values in `Fbrood`, `Tbroods`, and `neonates` columns are missing. These reflect cases where Daphnia mothers never reproduced or other constraints (e.g., the Daphnia replicate died). 3. **DaphniaGrow_ResponseMeans_210407.csv** * Summarized response means for various phenotypic traits. * Variables: * `Clone`: Unique identifier for each clone * `Treatment`: Experimental condition applied * `Trait`: Phenotypic trait measured * `Mean_Response`: Mean value of the measured trait (e.g., mean survival on Day 7, mean length on Day 14, etc.) ### Genomic Data 1. **DaphniaBenchtop_FunctionSNPDifferences.csv** * Contains SNP functional annotations and their differences among groups. * Variables: * `Clone`: Unique identifier for each clone * **Missing Values:** Some SNPs have missing effect annotations, indicating regions where functional effects were not determined. 2. **DaphniaBenchtop_MCGeneVariantCounts_230817.csv** * Contains gene-level variant counts from sequencing data. * Variables: * `ID`: Unique sample identifier * `TotalVariant`: Total number of variants detected in the gene * `nRefHom`: Number of homozygous reference alleles in the sample * `nNonRefHom`: Number of homozygous non-reference alleles in the sample * `nHets`: Number of heterozygous alleles in the sample * `nIndels`: Number of insertions and deletions detected in the sample 3. **DaphBenchtopSNPmatrixDifferences.csv** * SNP matrix indicating presence/absence or frequency differences across experimental groups. * Variables: * `Clone`: Identifier for each sample * **Missing Values:** Some SNPs have missing allele counts, likely due to sequencing depth limitations. 4. **GenomicPCAOutput.csv** * Principal component analysis (PCA) results from genomic data. * Variables: * `Clone`: Unique identifier for each clone * `PC1`, `PC2`, `PC3`: Principal component scores 5. **DMOrderLetters.csv** * Statistical comparison output, containing grouping information from post-hoc tests. * Variables: * `Comparison`: Pairwise comparison identifier * `Order_Letter`: Letter groupings from statistical analysis ## Code Files 1. **DaphniaRiskAssessment_RCode_240606.R** * This script performs all statistical analyses and generates figures found in the manuscript. * Dependencies: Requires all CSV files for data input. 2. **Daphnia_Bootstrap_230826.R** * This script conducts bootstrap resampling analyses for certain statistical comparisons. * Dependencies: Uses phenotypic data CSV files. ## Variant Call Format Files 1. **Shahmohamadlooetal_2024_daphnia_geneticvariance.vcf.gz** * This file contains the full set of variant sites identified across the whole genomes of all 20 *Daphnia magna *clones used in the study, enabling population-level comparisons of genomic variation and facilitating genotype–phenotype association analyses under toxin exposure. * Dependencies: All VCF files for data input. 2. **Shahmohamadlooetal_2024_daphnia_geneticvariance_index** * This file is included alongside the compressed VCF file to enable rapid access to variant data by genomic position, facilitating efficient downstream analyses. * Dependencies: All VCF files for data input. ## Software and Reproducibility * All statistical analyses were performed using **R version 4.2.2**. * Required R packages: (Ensure these are installed before running scripts) * `ggplot2` for visualization * `lme4` for mixed models * `dplyr` for data manipulation * `vegan` for multivariate analyses * Ensure all CSV files are in the working directory before running the scripts. ## Handling of Missing Data Some variables in the dataset contain missing values due to experimental constraints or data collection limitations. These missing values have been left as empty cells to ensure compatibility with analysis scripts. Users should handle these appropriately during their analyses. The specific files and columns with missing values are documented in the file descriptions above.