A meta‐analysis of shrub density as a predictor of animal abundance
Bibliographic record
Abstract
Facilitative interactions between shrub and animal species influence the structure and composition of communities. The benefits associated with woody shrub species can critically influence local animal populations, in particular. Here, we tested the relative importance of the density of shrub species on the local abundance of animal populations using a meta‐analysis. Full‐text review for shrub density, animal abundance or density and sampling effort, resulted in a total of 113 independent observations that reported both shrub density and animal abundance. A meta‐regression of shrub density on animal density with feeding functional group of the animal species as a moderator was used to test the predictive capacity of this simple vegetation measure on animal populations. Shrub density positively predicted animal abundance in these studies – particularly in deserts and grasslands. Shrub and woody plant density can thus be used as a potential rapid proxy for habitat in predicting local animal abundances. This method can support restoration and conservation of resident animal species in impacted ecosystems structured by woody shrubs globally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".