Can lizard richness be driven by termite diversity? Insights from the Brazilian Cerrado
Bibliographic record
Abstract
We test predictions of the Morton and James hypothesis, which states that high termite diversity promotes high lizard diversity. We explore consumption of termites by lizards in the Brazilian Cerrado, a system that shares many similarites with arid Australia whose fauna formed the basis for the original hypothesis. We found that Cerrado lizards prey heavily on termites. Several species had >40% of their diet consisting of termites, some species reached up to 80%. However, lizards prey on termites independently of their diversity in the environment and do not show niche segregation in relation to termite resource. Hence, our results in the Cerrado do not support the hypothesis that termite diversity can promote lizard diversity. The diets of Cerrado lizards have a high proportion of termites; however, the diets of desert lizards from the Australian and the Kalahari deserts have a much higher proportion of termites when compared with those from the Cerrado and the Amazon. Differences in termite consumption by lizards across ecosystems do not seem to be related to local termite diversity. We hypothesize that overall prey availability can explain this pattern. Several arthropod groups are abundant in the Cerrado and the Amazon. In deserts, other prey types may be less abundant; therefore, termites may be the best available resource.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".