DOI: 10.5061/dryad.wpzgmsc06
title: Data from: A framework to unify the relationship between numerical abundance, biomass, and environmental DNA

description: Does environmental DNA (eDNA) concentration correlate with numerical
 abundance (N) or biomass in aquatic organisms? We hypothesize that eDNA
 can be adjusted to simultaneously reflect both. Building on frameworks
 developed from the Metabolic Theory of Ecology, we derive two equations to
 adjust eDNA data to simultaneously reflect both N and biomass using
 population size structure data and allometric scaling coefficients. We
 also demonstrate that these equations share model parameters,
 necessitating the joint estimation of regressions between adjusted eDNA,
 N, and biomass. Furthermore, our framework can be extended to model how
 other variables (temperature, taxa, diet, trophic level, etc.) might
 impact relationships between eDNA, N, and biomass in natural ecosystems.
 We applied our framework to data from two previously published studies
 correlating eDNA to Brook Trout (Salvelinus fontinalis) N and biomass. In
 both case studies, point estimates of the scaling coefficient (b)
 reflected allometric processes (b = 0.51 and 0.37 for Case Study 1 and 2,
 respectively), with credible intervals indicating that b likely differed
 from zero (i.e., eDNA scales with N) and one (i.e., eDNA scales with
 biomass). Directly estimating the value of b improved estimates of N and
 biomass relative to assuming b equals 0, which particularly affected the
 capacity to estimate biomass. However, models assuming eDNA production
 scaled with biomass (i.e., b = 1) were largely similar to estimating b,
 implying that assuming eDNA scales linearly with biomass might be a
 sufficient approximation for some systems. Nevertheless, the framework
 demonstrates that correlating eDNA directly with either N or biomass (as
 is commonly done in many studies) inherently necessitates an adjustment to
 infer the other metric if populations exhibit size structure variation.
 Collectively, we demonstrate that quantitative eDNA data is unlikely to
 correspond exactly to either population N or biomass but can be adjusted
 to simultaneously reflect both.
description: The data contained herein were collected from two previous
 studies:  Yates et al. 2021, <em>Mol
 Ecol</em>: https://doi.org/10.1111/mec.15543 Gaudet-Boulay et al. 2023, <em>Env DNA</em>: https://doi.org/10.1002/edn3.341
description: # A framework to unify the relationship between numerical abundance,
 biomass, and environmental DNA
 [https://doi.org/10.5061/dryad.wpzgmsc06](https://doi.org/10.5061/dryad.wpzgmsc06) ## Description of the data and file structure These data were collected as a part of two studies comparing the relationship between abundance, biomass, and eDNA concentrations in lakes inhabited by Brook Trout populations. Data were obtained from Yates et al. 2021, *Mol Ecol* ([https://doi.org/10.1111/mec.15543](https://doi.org/10.1111/mec.15543)) and Gaudet-Boulay et al. 202e, *Env DNA* ([https://doi.org/10.1002/edn3.341](https://doi.org/10.1002/edn3.341)). Also included are the R code files to create the joint models simultaneously relating eDNA to both abundance and biomass - modelling was performed using the RJAGS package, so for each study includes a script file for modelling in R as well as a script file to generate a .txt file with the model inputs for JAGS. ### Files and variables #### File: BT\_example\_census\_file.csv **Description:** This file contains 7 variables characterizing data from 9 populations of Brook char in the Rocky Mountains, CA. Data are derived from Yates et al. (2021), *Mol Ecol* ##### Variables * Population: The population (lake) from which the data were collected * eDNA: The average concentration of eDNA observed in the study lake, derived from 4 samples collected in the littoral zone and 4 samples collected in the pelagic zone, with the final mean weighted by the proportion surface area represented by each zone in the study lake * N: The number of fish per hectare inhabiting each lake, estimated through mark-recapture. * copy.per.fish: The number of copies of eDNA per L of water per fish/ha * Mean.Mass: The average size of fish present in each lake, estimated from index-netting * Biomass: The total biomass/ha of fish in each lake * copy.per.gram: The number of copies of eDNA per L of water per gram of fish/ha #### File: BT\_individual\_mass\_data.csv **Description:** Size structure data for the populations of Brook char studies in Yates et al (2021), *Mol Ecol*. Data were collected using mixed-mesh index netting. ##### Variables * Population: The study lake from which the individual was captured. * Mass: The body mass (in grams) of the captured individual. #### File: GB\_eDNA\_BT\_data\_file.csv **Description:** This file contains 5 variables characterizing data from 15 populations of Brook char in Quebec, CA. Data are from Gaudet-Boulay et al. (2023), *Env DNA. * ##### Variables * Lake: The lake/population from which the data are derived * Station: The station from which the eDNA estimate was obtained * eDNA: The concentration of eDNA (Copies/L) from the station sampled * density.ha: The density of fish harvested from each lake (i.e., fish/ha harvested) * mean.mass.g: The average body mass (in grams) of the fish harvested from the lake ## Code/software **File: Yates_et_al_study_code.R** **Description:** This file conducts the joint-modelling analysis used in the manuscript for the Yates et al. (2021) dataset. It relies on the 'BT_example_census_file.csv' and 'BT_individual_mass_data.csv', which must be selected using the read.csv(file.choose()) function at the appropriate input. Alternatively, the R script could be rewritten to include the filepath to these datafiles or use the names of these datafiles if they are in the same working directory. Additionally, it requires the 'Yates_et_al_models.R' (see below), which contain the model parameters used as inputs for JAGS. **Language and Environment** R Environment for Statistical Computing **Version** R 4.4.2 **Dependencies** * rjags * jagsUI * MCMCvis * dplyr * reshape2 * ggplot2 * ggpubr * ggridges **File: Yates_et_al_models.R** **Description:** This model contains the model parameters for the three models used for the joint-modelling approach on the data from Yates et al. (2021), in which b is: (i) estimated; (ii) fixed at 0 (i.e., eDNA production scales linearly with numerical abundance); and (iii) fixed at 1 (i.e., eDNA production scales linearly with biomass). **File: Gaudet_Boulay_et_al_code.R** **Description:** This file conducts the joint-modelling analysis used in the manuscript for the Gaudet-Boulay et al. (2023) dataset. It relies on the 'GB_eDNA_BT_data_file.csv', which must be selected using the read.csv(file.choose()) function at the appropriate input. Alternatively, the R script could be rewritten to include the filepath to these datafiles or use the names of these datafiles if they are in the same working directory. Additionally, it requires the 'Gaudet_Boulay_et_al_models.R' (see below), which contain the model parameters used as inputs for JAGS. **Language and Environment** R Environment for Statistical Computing **Version** R 4.4.2 **Dependencies** * rjags * jagsUI * MCMCvis * dplyr * reshape2 * ggplot2 * ggpubr * ggridges **File: Gaudet_Boulay_et_al_models.R** **Description:** This model contains the model parameters for the three models used for the joint-modelling approach on the data from Gaudet-Boulay et al. (2023), in which b is: (i) estimated; (ii) fixed at 0 (i.e., eDNA production scales linearly with numerical abundance); and (iii) fixed at 1 (i.e., eDNA production scales linearly with biomass). ## Access information Data were derived from the following sources: * [https://doi.org/10.1111/mec.15543](https://doi.org/10.1111/mec.15543)(CC BY 4.0) * [https://doi.org/10.1002/edn3.341](https://doi.org/10.1002/edn3.341)(CC BY 4.0)