Spatially‐explicit foraging by an apex predator linked to nearshore prey and their accessibility in lakes
Bibliographic record
Abstract
Habitat coupling – where mobile predators forage over broad spatial scales and, in doing so, link food webs from semi‐discrete habitat patches – has emerged as a major structuring force in lake ecosystems. For the cold‐water apex predator lake trout Salvelinus namaycush , food‐web structure and morphometry‐driven accessibility to nearshore areas in summer strongly determine the degree of littoral–pelagic habitat coupling across lakes. Much of the evidence for habitat coupling, however, is based on stable isotopes of carbon to estimate littoral energy acquisition, whereas spatial data directly linking fish movements and foraging behaviour in lakes – on which this theory is based – are limited. Here we estimated nearshore prey abundance at sites of different thermal accessibility and collected stomach content data, which we combined with three‐dimensional acoustic telemetry positioning and acceleration data to directly measure the spatial location of summer foraging movements and habitat coupling by lake trout in lakes with and without an offshore prey fish. Both study lakes contained higher abundances of nearshore prey fish at the most thermally accessible (i.e. steep) sites monitored. Nearshore occupancy accounted for a small proportion of lake trout positions in both lakes (<5%), although prey fish were present in most (72%) diets sampled. High acceleration events indicative of foraging were concentrated in steep, thermally accessible nearshore areas in the lake where offshore forage fish were absent, but located further offshore in the lake with offshore prey fish. We directly demonstrate that habitat coupling by a wild, apex predator is driven by habitat and prey accessibility.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".