Patterns and drivers of intraspecific variation in avian life history along elevational gradients: a meta‐analysis
Bibliographic record
Abstract
Elevational gradients provide powerful natural systems for testing hypotheses regarding the role of environmental variation in the evolution of life-history strategies. Case studies have revealed shifts towards slower life histories in organisms living at high elevations yet no synthetic analyses exist of elevational variation in life-history traits for major vertebrate clades. We examined (i) how life-history traits change with elevation in paired populations of bird species worldwide, and (ii) which biotic and abiotic factors drive elevational shifts in life history. Using three analytical methods, we found that fecundity declined at higher elevations due to smaller clutches and fewer reproductive attempts per year. By contrast, elevational differences in traits associated with parental investment or survival varied among studies. High-elevation populations had shorter and later breeding seasons, but longer developmental periods implying that temporal constraints contribute to reduced fecundity. Analyses of clutch size data, the trait for which we had the largest number of population comparisons, indicated no evidence that phylogenetic history constrained species-level plasticity in trait variation associated with elevational gradients. The magnitude of elevational shifts in life-history traits were largely unrelated to geographic (altitude, latitude), intrinsic (body mass, migratory status), or habitat covariates. Meta-population structure, methodological issues associated with estimating survival, or processes shaping range boundaries could potentially explain the nature of elevational shifts in life-history traits evident in this data set. We identify a new risk factor for montane populations in changing climates: low fecundity will result in lower reproductive potential to recover from perturbations, especially as fewer than half of the species experienced higher survival at higher elevations.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.014 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".