Cape ground squirrels as ecosystem engineers: modifying habitat for plants, small mammals and beetles in Namib Desert grasslands
Bibliographic record
Abstract
Abstract Burrowing and foraging of semi‐fossorial rodents can affect species distribution and composition. Ground squirrels dig large burrow systems for refuge from predators and temperature extremes. Burrowing and foraging around burrows by squirrels may affect habitat and resource distributions for other organisms. We examined the impact of Cape ground squirrels ( Xerus inauris ) on vegetation, small mammals and beetles during winter and summer in grasslands on the edge of the Namib Desert. At each burrow system and paired control site without burrows, we estimated plant cover and height using quadrats (N = 8 paired sites), small mammal abundance and species richness using mark‐recapture techniques (N = 8 paired sites) and beetle abundance and species richness using pitfall traps (N = 6 paired sites, winter only). Squirrel burrowing and foraging activities resulted in lower plant cover and height, higher small mammal abundance and lower beetle abundance and species richness. Squirrels also reduced more plant cover in winter compared to summer, but had no effect on small mammal species richness. Furthermore, plant cover and height were higher in summer, whereas small mammal abundance and species richness were higher in winter. Our results suggest that Cape ground squirrels are important ecosystem engineers that influence plant and animal communities in the Namib Desert grasslands.
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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 | 0.000 |
| 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 | 0.000 |
| 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".