Investigating Stoichiometric Controls of Nutrient Recycling in Rivers Using the Threespine Stickleback (<i>Gasterosteus aculeatus</i>)
Bibliographic record
Abstract
In freshwater ecosystems, fish play a critical role in regulating the standing stock and turnover rates of biologically important elements such as nitrogen (N) and phosphorus (P). They do so by assimilating dietary nutrients, storing them, and recycling a subset as liquid (excreta) or solid (egesta) waste. The elemental composition of fish diets, bodies and waste varies considerably both within and between species. However, the mechanistic link between intraspecific variation in particular traits, such as bone investment, and variation in waste elemental composition and release rates remains poorly understood. Using the framework of Ecological Stoichiometry, we investigate how phenotypic traits impact nutrient storage and recycling. The threespine stickleback serves as an ideal model because it has undergone significant phenotypic diversification following its relatively recent colonisation of freshwater environments. Specifically, we examine variation in P-rich bony armour along a river continuum, where estuarine sticklebacks are typically heavily armoured (fully plated), and freshwater sticklebacks have less armour (low plated). We collected sticklebacks from five sites along the Sooke River, ranging from the estuary to the upper reaches, and measured their excretion rates in the field. We quantified the P content of their bodies, diet, egesta and excreted wastes, and the N:P of their bodies and excreta. Our results revealed substantial variation in body P content (2.2%-5.9%), with fully plated fish exhibiting higher body %P and lower body N:P. Dietary P was highly variable, with fully plated fish showing marginally higher dietary %P. Notably, P excretion rates were positively correlated with body %P but not diet %P, suggesting that contrary to predictions, bone content may decrease P demand. This study demonstrates that differences in stickleback bone investment have led to meaningful differences in nutrient storage and recycling.
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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) | 0.000 | 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 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".