Diet composition of omnivorous Mesopotamian spiny‐tailed lizards (<i>Saara loricata</i>) in arid human‐altered landscapes of Southwest Iran
Bibliographic record
Abstract
Abstract The Mesopotamian spiny‐tailed lizard, Saara loricata, is one of the largest lizard species in the Middle East. Here, we report on the diet of the lizard and their potential role in seed dispersal in Southwestern Iran. We analyzed lizard fecal pellet groups (n = 124) for their food item composition and seed content. We calculated the relative frequency of occurrence (FO%), relative volume (V%), and importance value (IV%) for each food item. Moreover, the number of seeds of each plant food item was counted. Our findings reveal the first solid evidence of omnivorous behavior in the lizard. In total, 16 plant food items and 14 animal food items were identified. Herbaceous plants (IV = 110.2%) and invertebrates (4.8%) were the most important food groups. The plant food items with the highest FO% were Poaceae (56.4%), Centaurea sp. (43.5%), and Medicago polymorpha (27.4%); and the V% for these items were 53.6%, 30.9%, and 13.1%, respectively. Most of the seeds that were consumed by lizards were from Poaceae (547 seeds; 47.81%) and Fabaceae (285 seeds; 24.91%). We also found that each individual lizard could play an equal role in the seed dispersal of all plant families identified. Previous studies show that plant species density and richness are important features for the burrow site selection of Mesopotamian spiny‐tailed lizard. This study highlights the potential role of lizards in influencing the vegetation communities around their burrows through seed dispersal.
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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".