Combining <scp>eDNA</scp> Metabarcoding, Hydrology‐Based Modeling and Camera Trap Datasets to Assess the Potential of River <scp>eDNA</scp> in Monitoring Terrestrial Mammals
Bibliographic record
Abstract
ABSTRACT Global efforts aimed at safeguarding and restoring biodiversity require methods to monitor progress towards conservation objectives. Such methods should provide a systematic and robust assessment of biodiversity for the lowest cost. River environmental DNA (eDNA) metabarcoding has been successfully applied to measure biodiversity in dendritic riverine habitats and is increasingly used to describe communities of terrestrial vertebrates in ecosystems that are challenging to survey using traditional methods. However, interpreting eDNA surveys in riverine habitats requires an understanding of the influence of eDNA transport, decay, and production on the distribution of eDNA. To this end, the hydrology‐based eDITH (eDNA Integrating Transport and Hydrology) model incorporates such factors and can recover reliable spatial biodiversity patterns for aquatic taxa, but its potential to successfully model terrestrial taxa is so far unexplored. Here, we applied eDITH to eDNA metabarcoding data for terrestrial mammals collected over two mountainous catchments (575 and 745 km 2 ) in British Columbia, Canada. We assessed prediction transferability between neighboring catchments and compared model predictions with observations from camera trapping. We found that for 9 out of 15 taxa detected by both eDNA and camera traps, predicted distributions predominantly matched observations from camera trap surveys, illustrating that eDITH can uncover patterns of mammal distribution in mountainous catchments. While lacking knowledge of actual taxon density prevents us from determining whether discrepancies stem from data limitations or complex eDNA production‐density relationships, good transferability of predictions to the neighboring catchments suggests that eDNA distribution of some terrestrial and semi‐aquatic mammals is partly determined by habitat preference and hydrology. Downstream sampling can recover most biodiversity across the catchment, but the inclusion of upstream samples can aid in detecting elusive species. This study underscores the broader applications of river eDNA beyond aquatic species and illustrates its potential use in addressing terrestrial mammal biodiversity monitoring objectives with tailored sampling approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".