Prey deficit for reintroduced Golden Eagles (Aquila chrysaetos) in Ireland
Bibliographic record
Abstract
Golden Eagles (Aquila chrysaetos) were reintroduced to Ireland in 2001 following prey availability and habitat feasibility studies undertaken in the 1990s which deemed northwest Ireland as suitable for their ecological requirements. However, they have failed to reach the predicted population growth necessary to sustain a viable population in the long-term. It has been suggested that inadequate prey biomass may have led to the poor reproductive performance. To determine prey availability within the core range of reintroduced Golden Eagles in Ireland, pre-release density estimates for Irish Hare (Lepus timidus) and Red Grouse (Lagopus lagopus) - their main prey - were compared with post-release estimates derived from transect and camera trap surveys. Camera trapping suggested 0.2 hare detections/km2 in upland areas in Cloghernagore Bog and Glenveagh National Park Species Area of Conservation (SAC) indicating that the hare population is very low, corroborated by the local results in the National Hare Survey 2017-2019 which detected zero hares in the vicinity of Glenveagh. Tracks and signs on walked transects suggested 1.6 grouse males/km2 which was comparable to the 1.2 males/ km2 reported during 2006/2008. The availability of hares and grouse as sources of live prey was estimated at 1.5-2.4 kg prey/km2/year, which was 74-83 % lower than the 9.2 kg prey/km2/year estimated as required to maintain Golden Eagle productivity in Scotland. Thus, prey deficit may explain the poor performance of reintroduced Golden Eagles in Ireland. Land management or site interventions could potentially increase hare and grouse numbers to improve eagle productivity. As such, integrated landscape management interventions may be necessary to ensure the success of species reintroduction programmes.
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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".