Reduced intraspecific variation in lake trout food webs under warmer temperatures and smaller ecosystem sizes
Bibliographic record
Abstract
Food web theory has illustrated that mobile top predators, such as lake trout (Salvelinus namaycush), can be potent stabilizers of food webs due to their ability to shift foraging behaviors in response to changing conditions. Consistent with this, research has demonstrated that mean lake trout food web attributes (i.e., trophic position and nearshore coupling) structurally change across environmental gradients; however, intraspecific variation in these attributes across gradients has not been fully explored. Here, we used stable isotope-based food web metrics to investigate how both mean and intraspecific variation in trophic structure changes in Canadian boreal shield lakes across gradients in ecosystem size, temperature, and competition. Consistent with earlier findings, we find nearshore coupling decreases and trophic position increases with warmer summer climate. In contrast to previous findings, increasing lake area predicted increased nearshore coupling and was not associated with lake trout trophic position. Our results show that warmer temperatures and smaller ecosystem sizes reduce the expression of intraspecific variation in food web structures. Specifically, larger lakes increased variation in nearshore coupling and trophic position, resulting in larger niche areas, and warmer lakes reduced variation in nearshore coupling and tended to generate smaller niche areas. Interestingly, we found little evidence for the relative abundance of lake trout or other predator taxa (surrogates of intra- and interspecific competition) influencing mean and variance in lake trout trophic structure. Intraspecific variation can promote ecosystem resilience by enabling diverse individual responses that help buffer populations against environmental change. Therefore, reduction in intraspecific variation in smaller, warmer lakes may have undesirable consequences for lake trout and the biota in these Canadian boreal shield lakes, leaving these ecosystems less able to adjust to future perturbations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".