Depth and temperature drive patterns of spatial overlap among fish thermal guilds in lakes across Ontario, Canada
Bibliographic record
Abstract
Abstract Aim The spatial distribution of ectotherms is strongly dependent on the temperature of their environments. In temperate lakes, fishes with different thermal optima can become spatially segregated during summer stratification. This habitat partitioning, or niche complementarity, may play a role in the coexistence of trophically similar species; however, the extent of partitioning is dependent on the resources available within each habitat. Although habitat partitioning of fish thermal guilds has been studied in individual lakes, broad‐scale patterns of spatial overlap and segregation are not yet understood. In this study, we explore the patterns and drivers of spatial overlap among thermal guilds (cold‐, cool‐, and warm‐water) at a broad scale. Location Ontario, Canada. Methods We built a multivariate regression tree to explore patterns and environmental drivers of spatial overlap in freshwater fishes across three thermal guilds from 438 lakes. Results We identified five clusters of lakes exhibiting different patterns of spatial overlap among the three thermal guilds. Temperature (growing degree days) and maximum lake depth were strong drivers of the spatial overlap patterns. Main Conclusions These findings provide a better understanding of broad‐scale patterns of spatial overlap and allow us to predict how spatial overlap, and ultimately species interactions and competition, may change under a warming 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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".