The importance of native shrubs on the distribution and diversity of reptiles and amphibians in the central deserts of California
Bibliographic record
Abstract
Abstract Conservation and management of drylands is a global challenge. Key attributes of these ecosystems, such as dominant vegetation including shrubs, can provide a crucial mechanism to inform conservation strategies. The shrub species Ephedra californica and Larrea tridentata are common native shrub species within the deserts of California and frequently benefit other plant and animal species. Here, we tested the hypothesis that shrubs support reptile and amphibian communities through relative increases in available habitat, estimated through increasing shrub densities at the site level. Reported occurrence data from the Global Biodiversity Information Facility (GBIF) and high-resolution satellite images were used to test for local-to-regional patterns in reptile and amphibian distribution and diversity by shrub densities at sites. A total of 71 reptile and amphibian species were present regionally. Increases in shrub density across sites positively influenced the relative abundance and richness of reptiles and amphibians observed. Moreover, increasing shrub density also had a positive influence on species evenness. Aridity did not significantly influence the relationship between shrub density and herptiles suggesting the relationship is robust. This study highlights the importance of foundational shrub species in supporting reptile and amphibian communities in arid and semi-arid regions supporting better understanding of large-scale patterns of biodiversity supported by positive ecological interactions in the context of global change.
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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 | 0.001 | 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".