Evidence for large-scale effects of competition: niche displacement in Canada lynx and bobcat
Bibliographic record
Abstract
Determining the patterns, causes and consequences of character displacement is central to our understanding of competition in ecological communities. However, the majority of competition research has occurred over small spatial extents or focused on fine-scale differences in morphology or behaviour. The effects of competition on broad-scale distribution and niche characteristics of species remain poorly understood but critically important. Using range-wide species distribution models, we evaluated whether Canada lynx (Lynx canadensis) or bobcat (Lynx rufus) were displaced in regions of sympatry. Consistent with our prediction, we found that lynx niches were less similar to those of bobcat in areas of sympatry versus allopatry, with a stronger reliance on snow cover driving lynx niche divergence in the sympatric zone. By contrast, bobcat increased niche breadth in zones of sympatry, and bobcat niches were equally similar to those of lynx in zones of sympatry and allopatry. These findings suggest that competitively disadvantaged species avoid competition at large scales by restricting their niche to highly suitable conditions, while superior competitors expand the diversity of environments used. Our results indicate that competition can manifest within climatic niche space across species' ranges, highlighting the importance of biotic interactions occurring at large spatial scales on niche dynamics.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".