Thermal habitat fragmentation in stratified lakes induces resource waves that brook charr track across seasons
Bibliographic record
Abstract
The spatial configuration of thermal habitats constrains the thermoregulatory performance of ectotherms. Thermal landscapes also vary through time, which is particularly relevant in seasonal environments such as temperate lakes. Indeed, elevated temperatures in the epilimnion of dimictic lakes during summer could substantially reduce the use of this habitat by cold‐stenothermic fish during the stratified period. The main objective of this study was to evaluate whether thermal habitat fragmentation in stratified lakes modulates accessibility to resources that brook charr, Salvelinus fontinalis , which is a mobile consumer, can track across seasons. More specifically, we hypothesize that reduced access to the littoral habitat during summer enhances foraging opportunities in this habitat during winter. We used an automatic acoustic telemetry system offering full coverage of the lake to continuously record brook charr locations across seasons, and we estimated zoobenthos abundances in the littoral habitat using image processing and semi‐automatic classification. While brook charr concentrate in the metalimnion of the pelagic habitat in summer, most individuals in winter shift to a shallow bay that is unexploited in summer due to thermal constraints. In this habitat, zoobenthos abundance is more than twice as high at the end of the summer compared to littoral habitats close to the thermal refuge in the pelagic habitat. Surprisingly, brook charr showed strong within‐lake site fidelity between two consecutive summers, which suggests that spatial memory could be a key driver of seasonal habitat use in this lacustrine population. Overall, our results suggest that thermal barriers create fragmentation between littoral and pelagic habitats that in turn produces resource opportunities that brook charr can track across seasons.
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) | 0.001 | 0.001 |
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".