Systematic deviations from linear size spectra of lake fish communities are correlated with predator–prey interactions and lake‐use intensity
Bibliographic record
Abstract
Size structure of organisms at logarithmic scale (i.e. size spectrum) can often be described by a linear function with a negative slope; however, substantial deviations from linearity have often been found in natural systems. Theoretical studies suggest that greater nonlinearity in community size spectrum is associated with high predator–prey size ratios but low predator–prey abundance ratios; however, empirical evaluation of the effects of predator–prey interactions on nonlinear structures remains scarce. Here, we aim to empirically explore the pattern of the size‐specific residuals (i.e. deviations from the linear regression between the logarithmic fish abundance and the logarithmic mean fish size) by using size spectra of fish communities in 74 German lakes. We found that nonlinearity was strong in lakes with high predator–prey abundance ratios but at low predator–prey size ratios. More specifically, our results suggest that only large predators, even if occurring in low abundances, can control the density of prey fishes in a broad range of size classes in a community and thus promote linearity in the size spectrum. In turn, the lack of large predator fishes may cause high abundances of fish in intermediate size classes, resulting in nonlinear size spectra in these lakes. Moreover, these lakes were characterized by a more intense human use including high fishing pressure and high total phosphorus concentrations, which have negative impacts on the abundance of large, predatory fish. Our findings indicate that nonlinear size spectra may reflect dynamical processes potentially caused by predator–prey interactions. This opens a new perspective in the research on size spectrum, and can be relevant to further quantify the efficiency of energy transfer in aquatic food webs.
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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 | 0.002 |
| 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".