Assessing the relative influences of abiotic and biotic factors on American eel <i>Anguilla rostrata</i> distribution using hydrologic, physical habitat, and functional trait data
Bibliographic record
Abstract
Species’ distributions are influenced by abiotic and biotic factors but direct comparison of their relative importance is difficult, particularly when working with complex, multi‐species datasets. Here, we compare the relative effects of hydrology, physical habitat, and co‐occurring fish functional traits on the contemporary (1950–1990) distribution of the American eel Anguilla rostrata in six Mid‐Atlantic (USA) rivers. To do so, we implement a null model approach that compares conditions at sites of known American eel presence to a random sample of sites throughout a broader landscape, allowing us to identify variables that may have the strongest influences on American eel distribution. Results suggest that, within this subset of the American eel's geographic range, the functional characteristics of locally co‐occurring fishes and habitat fragmentation by dams may have the strongest influences on American eel distribution, compared to other predictor variables included in the analysis. Given the widespread distribution and complex biology of this species, we caution that our results may not apply to all American eel subpopulations or life stages. Nonetheless, the observed importance of co‐occurring fish functional traits may inform American eel conservation and, more generally, provide a means to incorporate biotic influences in research on species’ distributions.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".