Invasion by a non‐native ecosystem engineer alters distribution of a native predator
Bibliographic record
Abstract
Abstract Aim Shifts in diet composition, abundance or distribution of native predators can occur as a result of exotic prey introductions. We examined effects of non‐native earthworms and anthropogenic landscape disturbance on habitat selection by the American robin (Turdus migratorius), a generalist predator, at landscape and local levels. We also investigated whether robins could act as vectors of spread for earthworm cocoons (egg cases). Location Boreal forest of Alberta, Canada. Methods We conducted robin and earthworm surveys at campgrounds, well pads, roads, pipelines, seismic lines and forest interiors across northern Alberta. At a subset of paired locations that had similar habitats and anthropogenic disturbance levels, we sampled both robins and earthworms. Results Both groups were most likely to occur at campgrounds, well pads and roads. Furthermore, robins were more likely to occur at locations where earthworms were present in our paired local‐level surveys. This correlation between robin and earthworm distributions could be due to robins acting as a vector for earthworm spread, rather than robins’ use of earthworms as prey. However, in tests using captive robins, earthworm cocoons did not survive digestion. Main conclusions Robin and earthworm distributions were correlated, likely due to robins’ use of earthworms as prey. These results suggest exotic prey can strongly influence native predators at both landscape and local levels, with shifts in native predator distributions occurring as a result of spatial variability in exotic prey distributions. Although the impacts of ecosystem engineering by earthworms have been previously demonstrated, our study provides evidence that effects of earthworms can also cascade upwards via trophic interactions.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".