Ecology and conservation of cavity-nesting birds in the Neotropics: Recent advances, future directions, and contributions to ornithology
Bibliographic record
Abstract
Abstract About 35% of tree-cavity-nesting bird species inhabit the Neotropics, a region crucial to understanding their breeding ecology, conservation, and roles in social-ecological systems. Sixteen years ago, Cornelius et al. (2008) reviewed published knowledge and identified research priorities for Neotropical cavity-nesting birds. Advances since 2008 have not been synthesized and many remain excluded from dominant ornithology because of barriers that disproportionately affect people and ideas from the Global South. Here, we review recent advances in knowledge about Neotropical cavity-nesting birds, introduce the Special Feature series “Ecology and conservation of cavity nesters in the Neotropics,” and outline possible directions for future research. Research in the Neotropics has advanced knowledge of breeding biology, demonstrated that nest sites are limited and birds compete for cavities (mainly in humid forests), identified non-excavated cavities (formed by wood decay) as the main source of cavities and demonstrated the importance of understanding Indigenous and local community relationships to birds. With field studies across the Neotropics, the Special Feature series shows how environment, people’s common imaginaries, vegetation management, and behavior of avian excavators can interact to influence cavity availability, with ecological consequences for many cavity-using organisms. In the future, researchers should center ethno-knowledge and natural history to create an accurate list of cavity-nesting birds in the Neotropics, and integrate this knowledge into studies of population and community ecology. It is also important to study factors that influence cavity dynamics, especially using a social-ecological systems framework and especially in arid and semi-arid regions. We recommend expanding the concept of nest webs (ecological networks of cavity nesters) to incorporate additional cavity substrates (e.g., termitaria, cliffs), cavity alternatives (e.g., bulky enclosed stick nests of many Furnariidae), and cavity-using taxa beyond birds and mammals (e.g., social insects, snakes), which abound in the Neotropics but were not contemplated in the original nest web formulation. Translated versions of this article are available in Supplementary Material 1 (Spanish) and Supplementary Material 2 (Portuguese).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".