Balancing Number of Locations with Number of Individuals in Telemetry Studies
Bibliographic record
Abstract
The study of habitat selection usually compares assessments of habitat use to habitat availability. To investigate habitat selection of large mammals today, researchers must choose between a few very expensive Global Positioning System (GPS) telemetry collars that can provide many locations and several inexpensive very high frequency telemetry collars that will provide few numbers of locations (unless substantial resources are spent in the field). We investigated the effects of number of locations and sampled animals on the outcome of habitat-selection analyses. We evaluated whether tracking frequency and sample size of individuals influenced our ability to detect habitat selection. We used data obtained from adult female moose fitted with GPS collars to generate data sets simulating various sampling frequencies and sample sizes of individuals. Tracking schedules conformed to those commonly used in ungulate telemetry studies (1 location every 14, 7, or 3 d and 1 or 3 locations per d) as did animal sample sizes (between 8 and 20 individuals). We determined habitat use and availability at the landscape and home-range scales during summer–autumn and winter. Precision of habitat use and availability estimates did not improve markedly with increasing tracking frequency. Only results obtained with the least-intensive tracking schedule (1 location every 14 d) differed from those obtained with the other schedules and only in 25% of the cases. Above this threshold in tracking frequency, number of sampled animals was clearly more important than number of locations in detection of habitat selection. Our results indicated that habitat-selection analyses were more sensitive to inter- than intra-individual variability. Depending on study objectives, it may be more profitable to prioritize number of sampled individuals rather than number of locations per individual. We suggest methods allowing researchers to assess inter-individual variability while studying habitat selection.
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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 | 0.045 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".