Water Temperature and Prey Size Effects on the Rate of Digestion of Larval and Early Juvenile Fish
Bibliographic record
Abstract
Abstract While predation is widely accepted as a major cause of mortality for fish larvae, its extent is largely unknown because few studies have been able to identify larvae in the stomach contents of predatory fish. Rapid digestion rates probably explain why fish larvae are rarely found in stomach contents, yet quantification of digestion rates of fish larvae is generally lacking, especially in freshwater systems. Using a series of laboratory experiments, we quantified the effects of temperature and larval fish (prey) size on digestion rate. We also evaluated whether species type (both predator and prey) influences digestion rate and described the morphological breakdown of fish larvae during digestion. Bluegills Lepomis macrochirus and yellow perch Perca flavescens were force‐fed the larvae of guppies Poecilia spp., rainbow trout Oncorhynchus mykiss, and yellow perch at a range of temperatures (7–22°C), and digestion rates were measured using prey mass before and after digestion (i.e., proportional loss of prey mass after ingestion). As expected, digestion rates increased with water temperature and decreased with prey body mass but were unaffected by species of predator. A confounding effect of prey type (fresh versus frozen) prevented a thorough evaluation of prey species, although yellow perch and rainbow trout (both previously flash‐frozen) were digested at similar rates. The complete breakdown of larvae in predator stomachs and the loss of morphological characters needed to identify larvae occurred rapidly, confirming the challenges of evaluating predation mortality based on stomach contents of field‐collected predators. Ultimately, our findings can be used to help researchers quantify the likelihood of detecting larval fish in the stomachs of field‐caught predators when using conventional stomach content analyses.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".