Juvenile socio-ecological environment shapes material technology in nest-building birds
Bibliographic record
Abstract
Abstract Variation in animal material technology, such as tool use and nest construction, is thought to be caused, in part, by differences in the early-life socio-ecological environment—that is, who and what is around—but this developmental hypothesis remains unconfirmed. We used a tightly controlled developmental paradigm to determine whether adult and/or raw-material access in early life shape first-time nest construction in laboratory-bred zebra finches Taeniopygia guttata at sexual maturity. We found that juvenile access to both an unrelated adult and raw material of one color led to a majority preference (75%) by novice builders for this color of material over that for either natal-nest or novel-colored material, whereas a lack of juvenile access to both an unrelated adult and raw material led to a 4- and nearly 3-fold reduction in the speed at which novice builders initiated and completed nest construction, respectively. Contrary to expectation, neither the amount of time juveniles nor their adult groupmate spent handling the raw material appear to drive these early-life effects on zebra finches’ first-time nest construction, suggesting that adult presence might be sufficient to drive the development of animal material technology. Together these data show that the juvenile socio-ecological environment can trigger variation in at least two critical aspects of animal material technology (material preference and construction speed), revealing a potentially powerful developmental window for technological advancement. Thus, to understand selection on animal material technology, the early-life environment must be considered.
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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".