Folders: ROOT > ScienceBase Catalog > Upper Midwest Environmental Sciences Center (UMESC) > Upper Midwest Environmental Sciences Center Data > Environmental DNA (eDNA) ( Show all descendants )4 results (6ms)
This data was collected by the US Fish and Wildlife Service to see if environmental DNA (eDNA) varied across pools and within pools in the Illinois River basin. The data was collected in 2015 from three different habitat types: shoreline, main channel, and bays. The resulting data were then analyzed using an occupancy model.
Environmental DNA metabarcoding as a tool for biodiversity assessment and monitoring: Reconstructing established fish communities of north-temperate lakes and rivers
To evaluate the ability of precipitation-based environmental DNA (eDNA) sample collection and mitochondrial 12S metabarcoding sequencing to reconstruct well-studied fish communities in lakes and rivers. Specific objectives were to 1) determine correlations between eDNA species detections and known community composition based on traditional field sampling, 2) compare efficiency of eDNA to detect fish biodiversity among systems with variable morphologies and trophic states, and 3) determine if species habitat preferences predicts eDNA detection. Fish community composition was estimated for seven lakes and two MIssissippi River navigation pools using sequence data from the mitochonrial 12S gene amplified from 10 to...
This data set was collected to provide examples and aid in developing a standardized way of determining LOD and LOQ for eDNA assays and has 3 data files. GEDWG_LOD_DATA3.csv is raw qPCR data from multiple labs running multiple standards of known concentration for eDNA assays they regularly use. Comparison-Data.csv is the merged data output from running a generic LOD/LOQ calculator script multiple times with different LOD model settings. The generic LOD/LOQ calculator script is available at: https://github.com/cmerkes/qPCR_LOD_Calc, and details about the multiple settings used are commented in the analysis script available at: https://github.com/cmerkes/LOD_Analysis
Resource managers conduct landscape-level monitoring using environmental DNA (eDNA). These managers must contend with imperfect detection in samples and sub-samples (i.e., molecular analyses). This imperfect detection impacts their ability to both detect species and estimate occurrence. Although occurrence (synonymously occupancy) models can estimate these probabilities, most models and guidance for their application do not consider three levels. This simulated dataset assumes sites are occupied (probably psi =1, Z = 1 ) and simulates sample (probability theta, A = 0,1) and subsample (probability p, Y = 0, 1) occurrence probabilies and detections (1)/non-detections (0).