Wood hardness drives nest-site selection in woodpeckers of the humid Chaco
Bibliographic record
Abstract
ABSTRACT Avian excavators (woodpeckers and other species) select nest sites based on the characteristics of the nest patch, nest tree, and substrate. These characteristics could increase foraging opportunities or reduce the risk of predation, but there is also a potentially important role for wood hardness in restricting nest-site selection, a role that has been little explored and is expected to vary among species according to their ability to excavate. We examined patterns of nest-site selection in 8 woodpecker species in the humid Chaco of South America, where the dominant trees have extremely hard wood. We hypothesized that (1) wood hardness is the main factor driving the selection of excavation sites, and (2) interspecific variation in body size and foraging behavior (traits frequently related to excavation ability) explain interspecific variation in the wood hardness of excavated nest substrates. From 2016 to 2019 in well-preserved forests of the Argentine Chaco, we compared nesting cavities excavated in wood (cases; n = 42) vs. potential wood substrates without cavities (matched controls) and made 187 focal observations of foraging woodpeckers. Woodpeckers selected nesting substrates with softer heartwood than potential substrates, regardless of any other characteristics of the tree or nest patch. Wood hardness around nest cavities increased with body size and the prevalence of chiseling during foraging, traits that were positively correlated. Woodpeckers often excavated in Prosopis spp. (Algarrobos) but rarely in Schinopsis balansae (Quebracho), a tree with exceptionally hard internal wood, in which cavity adopters frequently nest in non-excavated cavities. Wood hardness is critical to assessing the role of woodpeckers as cavity facilitators, understanding the costs and benefits of excavation, and interpreting excavation patterns across species and forests globally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.009 | 0.001 |
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 teacher head, 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".