Resource partitioning among top-level piscivores in a sub-Arctic lake during thermal stratification
Bibliographic record
Abstract
In systems with multiple piscivores, co-occurrence is dependent on resource partitioning. This is pronounced in oligotrophic northern lakes, which have simple food webs and short open-water seasons. We used acoustic telemetry and stable isotopes to quantify habitat and dietary partitioning during thermal stratification among three piscivores that commonly co-occur in Canadian sub-Arctic lakes—burbot (Lota lota), lake trout (Salvelinus namaycush), and northern pike (Esox lucius). Spatial core areas and core habitat niches (space and depth) did not significantly overlap among species. Although burbot and lake trout occupied similar mean daily depths (16.2 m and 13.4 m, respectively), and water temperatures (5.4 °C and 6.9 °C, respectively), they were spatially segregated. Burbot were closely associated with the lake bottom on steep drop-offs between the offshore and nearshore zone with moderate substrate complexity, whereas lake trout were located over deep offshore basins and suspended above the lake bottom. Northern pike occupied shallow depths (5.3 m) and warmer water (16.5 °C) within the nearshore region and were closely associated with bottom substrate of highest complexity. Some significant overlap among spatial home ranges and broad habitat niches indicated that these species interact. However, dietary niches did not significantly overlap at either the core or broad levels, suggesting that species were utilizing spatially diverse food sources. Our results highlight the importance of including depth and space when quantifying resource partitioning among fishes and provide insight into the mechanisms that promote piscivore co-occurrence in northern lakes.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".