Targeted hybridization capture enables comprehensive detection of freshwater bioassessment invertebrates from environmental DNA
Craine, J. M.; Darcy, J. L.; Devitt, J.; Leopold, D.; Miller, G. W.; Ralson, M.; Schulte, N.; Fierer, N.
Show abstract
Freshwater bioassessment relies on assessing aquatic assemblages to infer ecological conditions, yet conventional surveys require extensive field sampling, specimen processing, and specialized taxonomic expertise. Existing environmental DNA (eDNA) methods have not yet provided a practical alternative to conventional macroinvertebrate assays in part because current approaches cannot feasibly recover broad taxonomic diversity at sufficient taxonomic resolution. Here, we evaluated targeted hybridization capture of mitochondrial cytochrome oxidase I (COI) target sequences as a unified molecular approach for cross-phylum freshwater bioassessment. Environmental DNA was collected at 18 sites along 63 km of Boulder Creek spanning nearly 1,500 m of elevation from forested headwaters to agricultural plains. COI targets were enriched using custom RNA bait panels designed to target regional freshwater arthropods, annelids, and molluscs. Hybridization capture increased recovery of COI sequences [~]1,760-fold relative to unenriched shotgun libraries, generating Folmer-region COI contigs that averaged [~]400 bp. Across the watershed, we recovered sequences for approximately 450 macroinvertebrate genera across 8 phyla. Detected macroinvertebrate richness averaged 56 genera per site and increased down Boulder Canyon before declining downstream of the city. Macroinvertebrate assemblage composition from hybridization capture paralleled patterns observed with past conventional bioassessment. These results demonstrate that targeted hybridization capture enables robust, cross-phylum detection of species used for freshwater bioassessment from environmental DNA.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Applying environmental DNA metabarcoding to calculate an index of biotic integrity for freshwater fish 95%
- Limited dispersion and quick degradation of environmental DNA in fish ponds inferred by metabarcoding 95%
- Metabarcoding unsorted kick-samples facilitates macroinvertebrate-based biomonitoring with increased taxonomic resolution, while outperforming environmental DNA 94%
Similar papers in this journal
- Fast, Flexible, Feasible: A Transparent Framework for Evaluating eDNA Workflow Trade-offs in Resource-Limited Settings 94%
- Assessing the utility of marine filter feeders for environmental DNA (eDNA) biodiversity monitoring 93%
- Estimation of Species Abundance Based on the Number of Segregating Sites using Environmental DNA (eDNA) 93%
Similar papers in this journal
- Watered-down biodiversity? A comparison of metabarcoding results from DNA extracted from matched water and bulk tissue biomonitoring samples 97%
- Evaluation of nanopore sequencing for increasing accessibility of eDNA studies in biodiverse countries 95%
- Environmental DNA monitoring of waterfowl reveals community changes during migration 94%
Similar papers in this journal
- Fishing for mammals: landscape-level monitoring of terrestrial and semi-aquatic communities using eDNA from lotic ecosystems 93%
- Proving a negative; estimating species 'Confidence in Absence for Decision-Making' (CIADM) using environmental DNA monitoring 93%
- Biomarkers of recovery: characterizing trophic flow following ecological restoration 91%
Similar papers in this journal
- A Multi-Taxa Approach to Estuarine Biomonitoring: Assessing Vertebrate Biodiversity and Ecological Continuity using Environmental DNA Metabarcoding in the Rance River (Brittany, France) 95%
- Considerations for metabarcoding-based port biological baseline surveys aimed at marine non-indigenous species monitoring and risk-assessments 93%
- A breath of fresh air: comparative evaluation of passive versus active airborne eDNA sampling strategies 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.