Seasonal influence on detection probabilities for multiple aquatic invasive species using environmental DNA
2023-12-14
Loading...
Persistent link to this item
Statistics
View StatisticsCollection period
2021-04-01
2022-11-01
2022-11-01
Date completed
2023-07-01
Date updated
Time period coverage
Geographic coverage
Source information
Journal Title
Journal ISSN
Volume Title
Title
Seasonal influence on detection probabilities for multiple aquatic invasive species using environmental DNA
Published Date
2023-12-14
Author Contact
Rounds, Christopher
round060@umn.edu
round060@umn.edu
Type
Dataset
Field Study Data
Observational Data
Field Study Data
Observational Data
Abstract
Aquatic invasive species (AIS) are a threat to freshwater ecosystems. Documenting AIS prevalence is critical to effective management and early detection. However, conventional monitoring for AIS is time and resource intensive and is rarely applied at the resolution and scale required for effective management. Monitoring using environmental DNA (eDNA) of AIS has the potential to enable surveillance at a fraction of the cost of conventional methods, but key questions remain related to how eDNA detection probability varies among environments, seasons, and multiple species with different life histories. To quantify spatiotemporal variation in the detection probability of AIS using eDNA sampling, we surveyed 20 lakes with known populations of four aquatic invasive species: Common Carp (Cyprinus carpio), Rusty Crayfish (Faxonius rusticus), Spiny Waterflea (Bythotrephes longimanus), and Zebra Mussels (Dreissena polymorpha). We collected water samples at 10 locations per lake, five times throughout the open water season. Quantitative PCR was used with species-specific assays to determine the presence of species DNA in water samples. Using Bayesian occupancy models, we quantified the effects of lake and site characteristics and sampling season on eDNA detection probability. These results provide critical information for decision makers interested in using eDNA as a multispecies monitoring tool and highlight the importance of sampling when species are in DNA releasing life history stages.
Description
The files contain data and code to analyze detection probability of four common Aquatic Invasive species using Environmental DNA in Minnesota. The analysis, model and results come from the R markdown file model_code.rmd. This file reads in platewise_4species.csv in the "data" folder and outputs a file named modelW4species.gof.rds in the "models" folder. Additionally this markdown document outputs figures in the "figures" folder. The model is run using the R package jagsUI and data manipulation is done with the R package dplyr from the tidyverse.
Referenced by
Rounds, C. I., Arnold, T. W., Chun, C. L., Dumke, J., Totsch, A., Keppers, A., Edblad, K., García, S. M., Larson, E. R., Nelson, J. K. R., & Hansen, G. J. A. (2024). Aquatic invasive species exhibit contrasting seasonal detectability patterns based on environmental DNA: Implications for monitoring. Freshwater Biology, 00, 1–15. https://doi.org/10.1111/fwb.14320
Related to
Replaces
item.page.isreplacedby
Publisher
Funding information
Funding for this project was provided by the Minnesota Environment and Natural Resources Trust Fund as recommended by the Minnesota Aquatic Invasive Species Research Center (MAISRC) and the Legislative-Citizen Commission on Minnesota Resources (LCCMR
item.page.sponsorshipfunderid
item.page.sponsorshipfundingagency
item.page.sponsorshipgrant
Previously Published Citation
Other identifiers
Suggested citation
Rounds, Christopher; Arnold, Todd W; Chun, Chan Lan; Dumke, Josh; Totsch, Anna; Keppers, Adelle; Edbald, Katarina; García, Samantha M; Larson, Eric R; Nelson, Jenna KR; Hansen, Gretchen JA. (2023). Seasonal influence on detection probabilities for multiple aquatic invasive species using environmental DNA. Retrieved from the Data Repository for the University of Minnesota (DRUM), https://doi.org/10.13020/xvb3-2672.
View/Download File
File View/Open
Description
Size
MAISRC_eDNA_analysis.Rproj
R project for organizing R workspace
(218 B)
model_code.Rmd
R markdown file used for reading in the data and running the model
(33.8 KB)
data.zip
Data used to run occupancy model and determine detection probabilities
(58.56 KB)
models.zip
Model output from R markdown document
(431.62 MB)
Readme_eDNA.txt
Descriptions of the data and code
(13.95 KB)
eDNA_occ.txt
Text file containing the occupancy model coded in JAGS
(3.17 KB)
Content distributed via the University Digital Conservancy may be subject to additional license and use restrictions applied by the depositor. By using these files, users agree to the Terms of Use. Materials in the UDC may contain content that is disturbing and/or harmful. For more information, please see our statement on harmful content in digital repositories.