Invaders induce coordinated isotopic niche shifts in native fish species
Bibliographic record
Abstract
Food-web investigations inform management strategies by exposing potential interactions between native and nonnative species and anticipating likely outcomes associated with species removal efforts. We leveraged a natural gradient of compositional turnover from native-only to nonnative-only fish assemblages, combined with an intensive removal effort, to investigate underlying food-web changes in response to invasive species expansion in a Lower Colorado River tributary. Nonnative fishes caused coordinated isotopic niche displacement in native fishes by inducing resource shifts toward lower trophic positions and enriched carbon sources. By contrast, nonnative fishes did not experience reciprocal shifts when native fishes were present. Asymmetrical outcomes between native and nonnative fishes indicated species displacement may result from competitive or consumptive interactions. Native species’ isotopic niches returned to higher trophic levels after nonnative green sunfish (Lepomis cyanellus) removal, indicating removal efforts can support trophic recovery of native fishes like desert suckers (Catostomus clarkii) and roundtail chub (Gila robusta). Using stable isotope analysis in preremoval assessments provides opportunities to identify asymmetric interactions, whereas postremoval assessments could identify unintended consequences, like mesopredator release, as part of adaptive decision making to recover native fishes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".