Foraging ecology of Eurasian lynx populations in southwest Asia: Conservation implications for a diet specialist
Bibliographic record
Abstract
Abstract Intraspecific variation in key traits of widespread species can be hard to predict, if populations have been very little studied in most of the distribution range. Asian populations of the Eurasian lynx ( Lynx lynx ), one of the most widespread felids worldwide, are such a case in point. We investigated the diet of Eurasian lynx from feces collected Mediterranean, mixed forest‐steppe, and subalpine ecosystems of Turkey. We studied prey preferences and functional responses using prey densities obtained from Random Encounter Modelling. Our analysis revealed that the main prey was brown hare ( Lepus europaeus ) in all three areas (78%–99% of biomass consumed) and lynx showed a strong preference for brown hare (Chesson's selectivity index, α = 0.90–0.99). Cannibalism contributed at least 5% in two study areas. The type II functional response of lynx populations in Turkey was similar to the Canada lynx ( Lynx canadensis ) and daily food intake in grams per lynx matched that of Canada lynx and Iberian lynx ( Lynx pardinus ), both lagomorph specialists, rather than those of Eurasian lynx from Europe. Therefore, lynx in Turkey may be better described as a lagomorph specialist even though it coexists with ungulate prey. We suggest that ungulate‐based foraging ecology of Eurasian lynx in Europe may be a recent adjustment to the availability of high densities of ungulates and cannot be representative for other regions like Turkey. The status of lagomorphs should become an essential component of conservation activities targeted at Eurasian lynx or when using this species as a flagship species for landscape preservation.
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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 | 0.000 | 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".