Spatial and seasonal dietary patterns of the desert horned lizard (Phrynosoma platyrhinos): harvester ant specialist or generalist ant feeder?
Bibliographic record
Abstract
A suite of factors including prey availability and prey defenses influence prey selection by consumers and ultimately define an animal’s feeding strategy (e.g., generalist or specialist). Here we examined the relationship between availability and selection of ants by the desert horned lizard ( Phrynosoma platyrhinos Girard, 1852) to test the hypothesis that P. platyrhinos are specialist predators on harvester ants, and to investigate which factors influence the prey preference of lizards. Variation in ant availability and lizard diets was assessed using 83 plots established along a Great Basin shrub–steppe bajada in northwestern Utah, USA. Across the study site, 14 of 20 ant species were represented in the lizard diet. However, 70% of that diet consisted of just two species. In contrast to previous studies, P. platyrhinos did not show a disproportionate preference for harvester ants. Instead, larger ants of any species (including harvester ants) were preferentially selected over medium-sized and smaller ants. These findings suggest that desert horned lizards demonstrate an opportunistic feeding strategy in which lizards select ants based on their body size and abundance. More generally, these results emphasize the importance of assessing predator response across naturally varying gradients in prey availability to evaluate feeding specialization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".