Interspecific allometric scaling in <scp>eDNA</scp> production among northwestern Atlantic bony fishes reflects physiological allometric scaling
Bibliographic record
Abstract
Abstract Relating environmental DNA (eDNA) signal strength to organism abundance requires a fundamental understanding of eDNA production. A number of studies have demonstrated that eDNA production may scale allometrically—that is, larger organisms tend to exhibit lower mass‐specific eDNA production rates, likely due to allometric scaling in key processes related to eDNA production (e.g., surface area, excretion/egestion). While most previous studies have examined intraspecific allometry, physiological rates and organism surface area also scale allometrically across species. We therefore hypothesize that eDNA production will similarly exhibit interspecific allometric scaling. To evaluate this hypothesis, we reanalyzed previously published eDNA data from Stoeckle et al. (ICES Journal of Marine Science, 78(1), 293–304, 2021) which compared metabarcoding read count to organism count and biomass data obtained from trawl surveys off the New Jersey coast. Using a Bayesian model, we empirically estimated the value of the allometric scaling coefficient (“b”) for Northwestern Atlantic bony fishes to be 0.77 (credible interval = 0.64–0.92), although our model failed to converge for Chondrichthyan species. We found that integrating allometry significantly improved correlations between organism abundance and metabarcoding read count relative to traditional metrics of abundance (density and biomass) for bony fishes. Although substantial unexplained variation remains in the relationship between read count and organism abundance, our study provides evidence that eDNA production may scale allometrically across species in some contexts. Future studies investigating the relationship between eDNA signal strength and metrics of fish abundance could potentially be improved by accounting for allometry; to this end, we developed an online tool that can facilitate the integration of allometry in eDNA/abundance relationships.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".