Lake morphometry predicts the degree of habitat coupling by a mobile predator
Bibliographic record
Abstract
Habitat coupling is an ecosystem process whereby semi‐discontinuous habitats are connected through the movement of energy and nutrients by chemical, physical or biological processes. One oft‐cited example is that of littoral–pelagic coupling in lakes. Theory has argued that such habitat coupling may be critical to food web dynamics, yet there have been few empirical studies that have quantified ecological factors that affect the degree of habitat coupling in ecosystems. Specifically, the degree to which habitat coupling occurs across important physical gradients has largely been ignored. To address this, we investigate the degree of littoral habitat coupling (i.e. the degree to which a top predator lake trout, Salvelinus namaycush, derives energy from the littoral zone) along a gradient of lake shape, where lake shape modifies the relative quantity of coupled epilimnetic benthic and pelagic habitats within each lake. Herein we demonstrate that littoral habitat coupling is intensified in simple circular lakes compared to their reticulate counterparts in seven Canadian Shield lakes. Although the more reticulate lakes had larger areas of epilimnetic benthic habitat, littoral food sources comprised 11% compared to 24% of lake trout diet in reticulate and circular lakes, respectively. This heightened interaction in circular lakes also appears to translate into increased omnivory in more circular lakes compared to reticulate lakes such that lake trout of circular lakes have a significantly lower trophic position than lake trout of reticulate lakes (F 1,5 =6.71 p=0.05). These results suggest that it is the accessibility of littoral production via thermal refugia, and not the amount of littoral production, that determines the degree to which lake trout couple littoral and pelagic habitats in lakes.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".