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Record W4409190872 · doi:10.1002/edn3.70089

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

2025· article· en· W4409190872 on OpenAlexaffabout
Monika Goralczyk, Arnaud Lyet, Robin Naidoo, A. Cole Burton, Loïc Pellissier, Luca Carraro

Bibliographic record

VenueEnvironmental DNA · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsUniversity of British Columbia
FundersHorizon 2020 Framework ProgrammeMinistry of EnvironmentSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungEuropean Commission
KeywordsTrap (plumbing)Environmental DNAEnvironmental scienceHydrology (agriculture)EcologyGeologyBiologyBiodiversityEnvironmental engineeringGeotechnical engineering

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.244
Teacher spread0.221 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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