Effects of fish predation on density and size spectra of prey fish communities in lakes
Bibliographic record
Abstract
Planktivorous and benthivorous fish have been documented to influence the density and size structure of their prey communities in lakes. We hypothesized that piscivorous fish modify their prey fish communities in the same way and sought to find evidence for such predation effects from a comparison across 356 lakes located in nine European ecoregions. We categorized individual fish as being piscivore, nonpiscivore, or prey of piscivores, depending on species and individual size. We calculated piscivore, nonpiscivore, and piscivore prey densities, respectively, and fit linear abundance size spectra (SS) on lake-specific piscivore, nonpiscivore, and piscivore-prey size distributions. Multiple linear regressions were calculated to quantify the effect of piscivore density and SS slopes on nonpiscivore and piscivore-prey densities and SS slopes by accounting for potentially confounding factors arising from lake morphometry, productivity, and local air temperature. Piscivore density correlated positively with piscivore-prey density but was uncorrelated with density of nonpiscivores. Across a subset of 76 lakes for which SS slopes of piscivores were statistically significant, SS slopes of piscivores were uncorrelated with SS slopes of either nonpiscivores or piscivore prey. However, densities of piscivores, nonpiscivores, or piscivore prey were a significant negative predictor of SS slopes of the respective groups. Our analyses suggest that direct predation effects by piscivorous fish on density and size structure of prey fish communities are weak in European lakes, likely caused by low predator–prey size ratios and the resulting size refuges for prey fish. In contrast, competition may substantially contribute to between-lake variability in fish density and size.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".