Intraindividual, intraspecific, and interspecific variation shapes natural selection and its detection in two convergently-evolved lizard species
Bibliographic record
Abstract
Much of our understanding of how natural selection operates comes from studies of highly heritable traits presumed to vary little within individuals. Here we show that intraindividual (within-individual) phenotypic variation is an important source of intraspecific variation, shaping both natural selection and its detection in wild, open populations. We employed a multi-year capture-mark-recapture (CMR) study of two lizard species (Sceloporus cowlesi and Holbrookia maculata) at the ecotone between the white gypsum dunes at White Sands National Park and the surrounding dark Chihuahuan desert soils. Unlike many CMR studies examining selection on morphology, we measured individuals' traits at each capture. We found that our inferences into which traits were under selection depended on which measurement instance we used (first, last, or median measurement of all measurements of a given trait), and, therefore, the degree of intraindividual variation within each trait. We present a contingency analysis to facilitate assessing when traits are under selection, when they are not, and when intraindividual variation complicates these inferences. Beyond these conceptual advances, our work has implications for the White Sands system, a model system for repeated evolution. In particular, both lizard species experience different selection regimes within the same ecotonal habitat, despite both showing convergent evolution in dorsal blanching on White Sands.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".