Mammal-rich diet associated with reproductive success of Saker Falcons in Mongolia
Bibliographic record
Abstract
Understanding how diet influences the breeding success of Saker Falcons ( Falco cherrug ) is key to assessing the role of food supply in population dynamics and informing conservation strategies. Through pellet analysis, we evaluated the influence of small mammal prey—present in 95% of pellets—on the reproductive performance of sakers. In Mongolia, three species comprised 95% of the identified small mammals: Mongolian Gerbil ( Meriones unguiculatus , 39%), Brandt’s Vole ( Lasiopodomys brandtii , 49%) and Daurian Pika ( Ochotona dauurica , 7%). We found a strong positive association between the proportion of small mammals in the diet and key breeding parameters of sakers. Clutch size, fledged brood sizes, and nest success all increased with a mammal-rich diet. Earlier laying was also linked to higher small mammal intake and was independently associated with improved breeding outcomes. These findings highlight a critical role of small mammal prey in shaping the reproductive success of sakers. Conserving and restoring grassland habitats that support abundant prey populations is essential for sustaining saker populations and achieving long-term conservation goals.
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".