Early life drought predicts components of adult body size in wild female baboons
Bibliographic record
Abstract
OBJECTIVES: In many taxa, adverse early-life environments are associated with reduced growth and smaller body size in adulthood. However, in wild primates, we know very little about whether, where, and to what degree trajectories are influenced by early adversity, or which types of early adversity matter most. Here, we use parallel-laser photogrammetry to assess inter-individual predictors of three measures of body size (leg length, forearm length, and shoulder-rump length) in a population of wild female baboons studied since birth. MATERIALS AND METHODS: Using >2000 photogrammetric measurements of 127 females, we present a cross-sectional growth curve of wild female baboons (Papio cynocephalus) from juvenescence through adulthood. We then test whether females exposed to several important sources of early-life adversity-drought, maternal loss, low maternal rank, or a cumulative measure of adversity-were smaller for their age than females who experienced less adversity. Using the "animal model," we also test whether body size is heritable in this study population. RESULTS: Prolonged early-life drought predicted shorter limbs but not shorter torsos (i.e., shoulder-rump lengths). Our other measures of early-life adversity did not predict variation in body size. Heritability estimates for body size measures were 36%-67%. Maternal effects accounted for 13%-17% of the variance in leg and forearm length, but no variance in torso length. DISCUSSION: Our results suggest that baboon limbs, but not torsos, grow plastically in response to maternal effects and energetic early-life stress. Our results also reveal considerable heritability for all three body size measures in this study population.
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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 | 0.001 |
| 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".