Seasonality can affect ecological interactions between fishes of different thermal guilds
Bibliographic record
Abstract
Seasonality could play a crucial role in structuring species interactions. For example, many ectotherms alter their activity, habitat, and diet in response to seasonal temperature variation. Species also vary widely in physiological traits, like thermal preference, which may mediate their response to seasonal variation. How behavioral responses to seasonality differ between competing species and alter their overlap along multiple niche axes in space and time, remains understudied. Here, we used bulk carbon and nitrogen stable isotopes combined with stomach content analysis to determine the seasonal diet overlap between a native cold-water species [lake trout (Salvelinus namaycush)] and a range-expanding warm-water species [smallmouth bass (Micropterus dolomieu)] in two north-temperate lakes over 2 years. We coupled these analyses with fine-scale acoustic telemetry from one of the lakes to determine seasonal overlap in habitat use and activity levels. We found that dietary niche overlap was higher in the spring, when both species were active and using more littoral resources, compared to the summer, when the cold-water lake trout increased their reliance on pelagic resources. Telemetry data revealed that activity rates diverged in the winter, when lake trout remained active, but the warm-water smallmouth bass reduced their activity. Combining stable isotopes and stomach contents with acoustic telemetry was a powerful approach for demonstrating that species interactions are temporally and spatially dynamic. In our case, the study species diverged in their diet, habitat, and activity more strongly during certain times of the year than others, in ways that were consistent with their thermal preferences. Despite large differences in thermal preference, however, there were times of year when both species were active and sharing a common habitat and prey source (i.e., resource overlap was greater in spring than summer). Based on our findings, important ecological processes are occurring during all seasons, which would be missed by summer sampling alone. Our study stresses that quantifying multiple niche axes in both space and time is important for understanding the possible outcomes of altered seasonal conditions, including shorter winters, already arising under a changing climate.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".