Identifying peaks in bat activity: a new application of SaTScan’s space–time scan statistic
Bibliographic record
Abstract
Context Identifying key spatio–temporal periods of an organism’s activity is an important focus of many ecological studies. Bat activity, as assessed by passive acoustic monitoring, can be extremely variable and currently there exists no agreed-upon method for identifying periods of high activity. Aims We proposed a new application for the space–time scan statistic (SaTScan) as an objective technique for identifying peak periods of bat activity. We aimed to test the validity of SaTScan as a method for identifying peaks in bat activity and demonstrate its use for assessing species-specific temporal patterns of activity. Methods To evaluate the effectiveness of SaTScan for detecting peaks in activity, we compared SaTScan to peaks identified with percentile thresholds. We evaluated peaks in activity across three scales: within nights; among nights at a site; and among sites. We applied SaTScan to demonstrate analysis of species-specific activity as further use of this technique. Key results SaTScan was effective at identifying peak time periods that included the majority of the high activity minutes. Timing of peaks of activity was most consistent for Myotis lucifugus during swarming and Lasiurus borealis during migration. All other activity for five species at three sites was indicative of foraging activity, with peaks in the first 5 h after sunset. Conclusions SaTScan can be a valuable tool for quickly identifying peaks in acoustic activity of bats, with an objective, replicable and statistically sound method that can be applied at many temporal and spatial scales. Implications This tool has the potential for many more applications in ecology. Beyond acoustic analyses of bat activity, this technique could easily identify peaks in a research or management context, such as study activity among habitats, commercial developments or years at a single site.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".