Cascading ecological effects from local extirpation of an ecosystem engineer in the Arava desert
Bibliographic record
Abstract
The extinction of a single species from a local community may carry little cost in terms of species diversity, yet its loss eliminates its biotic and abiotic interactions. We describe such a scenario in the Arava desert, where different cultural and law enforcement practices exclude Dorcas gazelles (Gazella dorcas (Linnaeus, 1758)) from the Jordanian side of the border while protecting their populations on the Israeli side. We found that gazelles break the soil crust, formed in desert systems after annual flooding, thereby creating patches of loose and cooler sand that are used by pit-building antlions (Neuroptera: Myrmeleontidae). When we artificially broke the soil crust on both sides of the border, we found a significant increase in antlion density in these patches, but only on the Israeli side. On the Jordanian side, where no gazelles have been observed since the early 1980s, no antlions colonized either control or manipulated plots. Additional choice/no-choice feeding experiments, in which we offered antlions to lizards and birds, revealed that the effect of humans on gazelles cascades farther, as antlions serve as a palatable food source for both groups. Thus, the human-mediated loss of nontrophic interactions between gazelles and antlions cascades to the loss of trophic interactions between antlions and their predators.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".