The Neighbourhood Bat Watch project reveals that rapid declines of bats after white-nose syndrome are exacerbated by a high rate of colony exclusion
Bibliographic record
Abstract
Long-term and large-scale monitoring of wildlife populations is fundamental to answer questions relevant to conservation. Participatory (or “citizen”) science has become a popular tool to collect additional data and for monitoring trends across larger scales. As white-nose syndrome (WNS), a disease caused by a fungal pathogen, spreads throughout North American bat populations, a participatory science project was initiated in 2012, asking the public to help monitor bat maternity colonies in Quebec, Canada. Using historical data from 1997 to 2011 and the Neighbourhood Bat Watch database from 2012 to 2022, we examined variation in the distribution and size of maternity colonies in relation to WNS invasion and maternity colony exclusions from buildings in Québec. Based on 580 emergence counts from 144 colonies, colony size of little brown bats ( Myotis lucifugus (LeConte, 1831)) declined from 77% to 85% during the WNS invasion period, while trends during the epidemic and established periods varied depending on the species and geographical region. Out of the 287 colonies with long-term monitoring, 102 (36%) have subsequently been excluded from buildings. The probability of exclusion was higher in the north than the south, and in houses than in outbuildings. The Neighbourhood Bat Watch allowed us to estimate trends in bat populations and rate of colony exclusions, therefore informing where conservation efforts are most needed.
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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 | 0.001 |
| 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 | 0.000 |
| 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".