The influence of dietary and whole-body nutrient content on the excretion of a vertebrate consumer
Bibliographic record
Abstract
In many contexts, nutrient excretion by consumers can impact ecosystems by altering the availability of limiting nutrients. Variation in nutrient excretion can be predicted by mass balance models, most of which are premised on two key ideas: (1) consumers maintain fixed whole-body nutrient content (i.e., %N and %P), so-called fixed homeostasis; (2) if dietary nutrients are not matched to whole-body nutrients, excesses of any nutrient are released as excretion to maintain fixed homeostasis. Mass balance models thus predict that consumer excretion should be positively correlated with diet nutrients and negatively correlated with whole-body nutrients. Recent meta-analyses and field studies, however, have often failed to find these expected patterns, potentially because of a confounding influence-flexibility in whole-body nutrient content with diet quality (flexible homeostasis). Here, we explore the impact of flexible homeostasis on nutrient excretion by comparing the N and P excretion of four genetically diverged Trinidadian guppy (Poecilia reticulata) populations when reared on diets of variable P content. As predicted by mass balance, P excretion increased on the high-P diet, but, contrary to the notion of fixed homeostasis, guppy whole-body %P also increased on the high-P diet. While there was no overall correlation between excretion nutrients and whole-body nutrients, when the effect of diet on both whole-body and excretion nutrients was included, we detected the expected negative correlation between whole-body N:P and excretion N:P. This last result suggests that mass balance can predict excretion rates within species, but only if dietary effects on whole-body nutrient content are controlled. Flexible homeostasis can obscure patterns predicted by mass balance, creating an imperative to accurately capture an organism's diet quality in predicting its excretion rate.
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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 | 0.001 |
| 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".