Ecological factors influencing the spatial pattern of Canada lynx relative to its southern range edge in Alberta, Canada
Bibliographic record
Abstract
We examined the spatial pattern of Canada lynx ( Lynx canadensis Kerr, 1792) relative to its southern range edge at the boreal plains – prairie ecotone in Alberta, Canada. Relative to the original distribution of boreal forest in our study area, lynx range seems to have contracted up to 22%. In 100 km2 sampling areas, lynx occupancy rate increased 1.93 times every 100 km farther (north) from the range edge that we sampled. An information–theoretic approach was used to evaluate 31 models to see which environmental factors were the best predictors of this spatial pattern. Lynx were strongly correlated with track counts of their primary prey, the snowshoe hare ( Lepus americanus Erxleben, 1777), but this did not explain the observed increase in occupancy farther north. Rather, lynx occupancy was lower in areas with higher road densities and this effect was magnified in areas where coyote ( Canis latrans Say, 1823) activity was highest. The inclusion of these effects rendered the south–north pattern no longer significant. The rapid pace of road building and associated development in Alberta’s boreal forest seems to be reducing habitat quality for Canada lynx, particularly at the southern edge of its range. This may be leading to range contractions for lynx in Alberta, much like has happened elsewhere in North America.
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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) | 0.001 | 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".