Spatiotemporal railway use by grizzly bears in Canada's Rocky Mountains
Bibliographic record
Abstract
ABSTRACT Railway networks contribute to the direct mortality of wildlife through collisions with trains, which can threaten vulnerable wildlife populations even in protected areas, including grizzly bears ( Ursus arctos ) in Banff and Yoho National Parks, Canada. Mitigation to reduce bear‐train collisions requires information about how grizzly bears use the railway spatially and temporally and how particular types of use might increase collision vulnerability. We used data from 27 grizzly bears fitted with global positioning system (GPS) collars between 2000 and 2016 to relate railway use by bears via resource selection functions to variables that described land cover, human use, and topography. We used the same suite of explanatory variables to distinguish pairs of 4 types of steps, in which 3 successive GPS points (with 2‐hr fix rates) included ≥1 within 30 m of the rail (hereafter on) and 2 others that defined locations where bears effectively entered the railway (first fix off rail, next 2 on), crossed it (only the middle fix on the rail), continued along the railway (all 3 fixes on), or exited the railway corridor (first 2 on, last off). We compared both sites of higher use and each of these 4 step types to the relative frequency of bear‐train collisions, predicting a positive correlation for continue step types. Relative to available locations, bears were more likely to use the railway close to railroad sidings (sections of twinned track where trains sometimes stop), at intermediate distances from human‐use features (e.g., town sites, highways, trails), in areas with lower values of the compound topographic index (a proxy for wetness; within 500 m), and within 90 m of rugged terrain. Seasonally, bears made greater use of the railway in spring and fall. Among 1,515 sequences of 3 steps, crossing locations comprised >50% and were most distinct from continue locations (about 20%), which occurred in areas with more rugged terrain (within 300 m), closer to railway sidings, in spring and fall, and with steps that were 60% shorter. Contrary to our prediction, past reports of bear‐train collisions were negatively correlated with continue locations and unrelated to overall use or any other movement type. Our results suggest that railway use by bears increased where it provided increased forage or easier travel, particularly in spring and fall, but more work will be needed to determine the mechanistic basis of bear‐train collisions. Meanwhile, mitigation efforts such as habitat alteration or warning systems might target locations where past strikes are concentrated for grizzly bears or other sensitive populations. © 2019 The Wildlife Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| 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 teacher head, 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".