Recovery of African wild dogs suppresses prey but does not trigger a trophic cascade
Bibliographic record
Abstract
Increasingly, the restoration of large carnivores is proposed as a means through which to restore community structure and ecosystem function via trophic cascades. After a decades-long absence, African wild dogs (Lycaon pictus) recolonized the Laikipia Plateau in central Kenya, which we hypothesized would trigger a trophic cascade via suppression of their primary prey (dik-dik, Madoqua guentheri) and the subsequent relaxation of browsing pressure on trees. We tested the trophic-cascade hypothesis using (1) a 14-year time series of wild dog abundance; (2) surveys of dik-dik population densities conducted before and after wild dog recovery; and (3) two separate, replicated, herbivore-exclusion experiments initiated before and after wild dog recovery. The dik-dik population declined by 33% following wild dog recovery, which is best explained by wild dog predation. Dik-dik browsing suppressed tree abundance, but the strength of suppression did not differ between before and after wild dog recovery. Despite strong, top-down limitation between adjacent trophic levels (carnivore-herbivore and herbivore-plant), a trophic cascade did not occur, possibly because of a time lag in indirect effects, variation in rainfall, and foraging by herbivores other than dik-dik. Our ability to reject the trophic-cascade hypothesis required two important approaches: (1) temporally replicated herbivore exclusions, separately established before and after wild dog recovery; and (2) evaluating multiple drivers of variation in the abundance of dik-dik and trees. While the restoration of large carnivores is often a conservation priority, our results suggest that indirect effects are mediated by ecological context, and that trophic cascades are not a foregone conclusion of such recoveries.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".