Experimental removal of invasive <scp>Africanized</scp> honey bees increased breeding population size of the endangered <scp>Lear's</scp> macaw
Bibliographic record
Abstract
BACKGROUND: Invasive Africanized honey bees potentially compete with cavity-nesting birds in South America. However, the impacts of this competition and its conservation consequences to threatened species are poorly known. We quantified the presence of these bees and assessed their competition for cliff cavities used by nesting Lear's macaws Anodorhynchus leari, a globally endangered parrot endemic to the Caatinga biome of Brazil. We treated beehives with permethrin by shooting them with a crossbow bolt that distributed the compound upon impact. When feasible, we removed the comb and applied an insecticide (fipronil) to deter bee recolonization. We subsequently surveyed the macaw breeding population to verify whether our treatment allowed for nest recruitment in cavities previously occupied by bees. RESULTS: We recorded > 100 beehives in the nesting cliffs. Hives outnumbered macaw nests tenfold in two areas recently recolonized by macaws. Cavities occupied by bees were significantly higher than those occupied by macaws, suggesting that macaws may be forced to breed in lower cavities. None of the untreated cavities (n = 50) were occupied by nesting macaws, whereas 15% of treated cavities (n = 52) were occupied within 2 years post treatment. Treated cavities occupied by macaws were significantly higher than those not occupied. Hive management increased macaw breeding population by 71% of the macaw breeding population increase. CONCLUSION: Experimental hive treatments were effective in restoring nesting resources lost due to bee infestation. An intensive and continued eradication program is recommended to enhance macaw habitat restoration, facilitating its expansion into historical areas. © 2020 Society of Chemical Industry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".