Variable strength of predator-mediated effects on species occurrence in an arctic terrestrial vertebrate community
Bibliographic record
Abstract
Indirect effects resulting from species sharing the same enemy can shape spatio-temporal variations in species occurrence. The strength of such effects remains poorly known in natural communities composed of species from different trophic levels interacting in heterogeneous landscapes. Benefiting from a well-known arctic vertebrate community and marked spatio-temporal variations in the density of key prey species, we examined the effects of direct predator-prey and indirect predator-mediated effects on species occurrence in the landscape. We found both positive effects of one prey (lemmings), as well as negative indirect effects of another prey (colonial nesting snow geese) on the occurrence of species (ground-nesting birds) belonging to different guilds and trophic levels but sharing a common predator (arctic fox). However, species using prey refuges available in the landscape were not or less affected by predator-mediated effects. Similarly, the smallest (a passerine) and the largest and most dangerous species (an owl) for the shared predator were not affected by these effects. Our study provides one of the rare empirical evidence of predator-mediated effects ascending the food web (i.e., negative indirect effect of an herbivore on avian predators) and underlines how habitat structure and species traits can modulate the strength of indirect effects in natural communities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".