Stoichiometric traits of stickleback: Effects of genetic background, rearing environment, and ontogeny
Bibliographic record
Abstract
Phenotypes can both evolve in response to, and affect, ecosystem change, but few examples of diverging ecosystem-effect traits have been investigated. Bony armor traits of fish are good candidates for this because they evolve rapidly in some freshwater fish populations, and bone is phosphorus rich and likely to affect nutrient recycling in aquatic ecosystems. Here, we explore how ontogeny, rearing environment, and bone allocation among body parts affect the stoichiometric phenotype (i.e., stoichiometric composition of bodies and excretion) of threespine stickleback. We use two populations from distinct freshwater lineages with contrasting lateral plating phenotypes (full vs. low plating) and their hybrids, which are mostly fully plated. We found that ontogeny, rearing environment, and body condition were the most important predictors of organismal stoichiometry. Although elemental composition was similar between both populations and their hybrids, we found significant divergence in phosphorus allocation among body parts and in phosphorus excretion rates. Overall, body armor differences did not explain variation in whole body phosphorus, phosphorus allocation, or phosphorus excretion. Evolutionary divergence between these lineages in both allocation and excretion is likely to have important direct consequences for ecosystems, but may be mediated by evolution of multiple morphological or physiological traits beyond plating phenotype.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".