Differential space use inferred from live trapping versus telemetry: northern flying squirrels and fine spatial grain
Bibliographic record
Abstract
Small mammal space use is inferred from live-capture data or various methods of tracking, with differences between these methods potentially affecting the input and subsequent inferential abilities of resulting wildlife-habitat models. Unlike tracking via radio telemetry, live trapping employs use of bait, which is known to change proximate animal density as evident in many food addition studies (the ‘pantry effect’), and conceivably bias individuals’ space use, particularly if measured over small spatial extents in heterogeneous areas. The present study analysed both trapping and telemetry data from northern flying squirrels (Glaucomys sabrinus) to assess whether different habitat associations could be generated based on methods alone. Conditional on sampling method, two different space-use patterns were identified from the same group of squirrels and two significantly different sets of habitat model input were associated with each. Trap areas were not used post capture; once enumerated, animals on average (n = 34) spent over 80% of their time from 100 to 200+ m, upwards of 800 m, away from trap areas. Using telemetry and fine-grained habitat structure data, this study found 33% of sampled squirrels used areas not identified via habitat-stratified trap effort (specifically black spruce habitat). It is concluded that wildlife-habitat investigations dealing with fine spatial grain are likely to acquire different results using trapping versus telemetry, especially if animals are relatively mobile and habitat structure is relatively heterogeneous.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".