Assessing the effects of <scp>guppy</scp> life history evolution on nutrient recycling: from experiments to the field
Bibliographic record
Abstract
Summary Trait evolution can occur in response to anthropogenic alterations to ecosystems and can occur on timescales similar to those of ecological processes suggesting that it could alter ecosystem function. In this study, we characterise the effects of life history evolution on nutrient recycling using the Trinidadian guppy (Poecilia reticulata) as a model system. Guppy life history traits evolve in response to predation pressure. When predation pressure is removed, guppy population density and average body size of the population also increase. Therefore, the evolution of guppy life histories involves changes in individual traits and demographic characteristics, both of which can alter nutrient recycling. The relative contributions of these variables to guppy‐driven nutrient recycling are unknown. We synthesise data from published experiments to disentangle how differences in individual traits, population characteristics and environmental conditions contribute to differences in guppy excretion rates. Individual guppies adapted to the absence of predators [low‐predation (LP) guppies] have lower nitrogen and phosphorus excretion rates than individual guppies adapted to predators [high‐predation (HP) guppies]. However, LP guppy populations excrete twice as much nitrogen as HP populations because of their larger average body size and higher population densities. We compare these findings to guppy excretion data collected from HP and LP sites in four rivers in Trinidad. Phenotypic and population differences in excretion rates are consistent with those observed in the experiments. Our study demonstrates that life history evolution can alter nutrient recycling in freshwater ecosystems. Characterizing the combined effects of traits and demographics is essential for understanding the effects of life history evolution on ecosystem processes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".