Temperature driven density gradients of two congeneric felids reveal contrasting responses to climate change at a range margin
Bibliographic record
Abstract
Climate change causes divergent range shifts in cold versus warm-tolerant species, potentially reshuffling biotic interactions at range margins. Yet, outside of coarse distributional metrics, little information exists regarding the ecology of species along range peripheries. Here, we use camera traps and spatially-explicit-capture-recapture (secr) modeling to examine how climatic gradients influence current and future patterns of density, abundance, and density overlap between two congeneric felids - cold-adapted Canada lynx (Lynx canadensis) and warm-adapted bobcats (Lynx rufus) - at a range margin in Washington, United States. Temperature drove density patterns along the range margin, with lynx densities declining and bobcat densities increasing as a function of temperature. Future abundances, obtained via projection of current-day models onto future climate scenarios, declined for lynx but were stable for bobcats, with both species experiencing upward elevational shifts. Areas of the landscape with high-lynx and low-bobcat densities declined in the future, but areas with low-lynx and high-bobcat densities increased, with only limited high-elevation refugia for lynx from expanding bobcat populations. Our approach reveals how temperature gradients shape density patterns of cold and warm-tolerant mammals and could be applied to other species and montane systems to better understand mammalian population trajectories and spatial associations at range edges.
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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 | 0.000 | 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 | 0.000 |
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".