Dataset DOI: 10.5061/dryad.2bvq83c2d

Description of the data and file structure

Principal Investigator Contact Information

Name: Elise D. Snyder

Institution: University of Illinois at Urbana-Champaign

Email: edsnyde2@illinois.edu

Data overview:

This dataset contains the data required to replicate analyses in Snyder et al. “Warming increases environmental DNA (eDNA) removal rates in flowing waters” (accepted for publication), wherein we explored two questions: 1) Do higher water temperatures accelerate eDNA removal from the water column in flowing waters? and 2) Do higher water temperatures impact eDNA removal differently based on particle size? To assess eDNA removal under a range of water temperatures (20, 23, and 26°C), we used n=12 recirculating, stream mesocosms, which we conditioned at their respective temperatures for 23 days. We then conducted pulse releases of eDNA from Common Carp (Cyprinus carpio) and Steelhead Trout (Oncorhynchus mykiss), measured the decline in eDNA concentration across 24 hours, and used vacuum filtration through 1.0 and 0.2-μm polycarbonate membrane filters to partition different sizes of eDNA particles. These data include the eDNA concentration measurements used to calculate eDNA removal rates for each mesocosm and the accompanying environmental metrics measured in each mesocosm during the experiment. We found that eDNA removal from the water column in stream mesocosms was faster under higher temperatures, particularly for small particles. We observed that, across our temperature treatments over 24 hours, eDNA removal patterns differed between larger (1.0 μm) and smaller (0.2 μm) eDNA particle sizes. Removal rates for smaller eDNA particles were faster in warmer mesocosms (23 and 26°C), while the two sizes displayed similar removal rates in the 20°C mesocosms. As a result, the proportion of smaller-sized particles decreased over time under elevated temperatures, with larger particles persisting for longer in the water column.

Data analysis methods:

To estimate removal rates for total eDNA and for each size class, we calculated eDNA mass in each mesocosm for both Common Carp and Steelhead Trout using the eDNA concentrations and water volume at each sampling time, and derived the eDNA removal (k; hr−1) from the decline in natural-log transformed mass in each mesocosm. We also converted k to depositional velocity (Vdep; mm s-1) as Vdep = (k*d) where v is mesocosm velocity and d is depth.

We employed linear mixed models (LMMs) to compare eDNA k across temperature treatments for both total eDNA (i.e. the sum of both size classes), and for each size class, using the “lmer” function in R with the following equations: with the following equations: ln(eDNA Mass) ~ Time:Temperature + (1+Time|Mesocosm:Target) and ln(eDNA Mass) ~ Time:Temperature x Size + (1+Time|Mesocosm:Target). The first model regressed ln(total eDNA mass) with the interaction of time and temperature and included random intercepts and slopes (in time), with each eDNA release (mesocosm and target) as the random unit. The second model employed the same random structure, but included size as an additional interacting fixed effect. Additionally, we ran the same models a second time with average measured temperature during the 24-hour release period (as opposed to temperature as a categorical variable) to estimate the numeric relationship between temperature and time.  We used the “emtrends” function within the R package “emmeans” for post-hoc pairwise comparisons among treatments and eDNA sizes. To assess whether eDNA PSD changed over time at our three temperatures, we used a Beta family generalized linear model (BLM) within the R package “betareg” with the following equation: eDNA Proportion ~ Time. Finally, we compared environmental metrics with each other and with eDNA removal rates, using pairwise Pearson correlation tests within the R package “Hmisc.” For strong correlations between an environmental metric and eDNA removal rates (p<0.05), we modeled the relationship via an LMM containing a random effect for mesocosm to account for the lack of independence between measurements for the two targets within a given mesocosm. We conducted all statistical analyses in R version 4.3.0.

Files and variables

File: Data_and_metadata_for_covariables_and_eDNA_removal_rates_for_Warming_increases_environmental_DNA_(eDNA)_removal_rates_in_flowing_waters.xlsx

Description: A .xlsx file containing the data and metadata for the eDNA concentration measurements for the experiment described in the manuscript. This data was generated using digital droplet PCR and includes the droplet counts and thresholding information for each eDNA sample, as well as eDNA concentration and mass calculated from these measurements, the water volumes used to calculate the concentration/mass, and the identifying information for each sample.

File: Data_and_metadata_for_ddPCR_eDNA_concentrations_for_Warming_increases_environmental_DNA_(eDNA)_removal_rates_in_flowing_waters.xlsx

Description: A .xlsx file with the data and metadata for the covariables and eDNA removal rates (calculated using the above dataset) for the experiment described in the manuscript. These environmental covariables measured in each mesocosm include temperature, community respiration (mg/m^{2/h), gross primary productivity (mg/m}2/h), chlorophyll-a (μg/cm^2/h), water column ammonium concentration (μg/L), water column soluble reactive phosphorus concentration (μg/L), water column nitrate concentration (μg/L), water column turbidity (NTU), water column pH, and water velocity (m/s).

This data was derived from samples and measurements collected by the authors.