Capture Rate Declines of Northern Myotis in the Canadian Maritimes
Bibliographic record
Abstract
ABSTRACT The disease white‐nose syndrome (WNS) has caused widespread decline of North American bat species. Species like little brown myotis ( Myotis lucifugus ) have received more attention than others, such as northern myotis ( Myotis septentrionalis ). We were concerned that management decisions based on the demographic condition of little brown myotis may be inappropriate for northern myotis due to the potential for variation in species‐specific responses to WNS. We therefore compiled capture data from Canada's Maritime provinces collected between 2003 and 2019 to identify if disparate population trends exist between the 2 species. We identified a decline in northern myotis capture‐per‐unit‐effort (CPUE) after the detection of WNS (hereafter, post‐WNS), in each study region and a divergence from the historic CPUE ratio between the 2 species. Whereas 380 northern myotis were captured pre‐WNS, only 4 were captured post‐WNS. The pre‐WNS ratio of northern myotis to little brown myotis CPUE in New Brunswick, Prince Edward Island, and Nova Scotia declined from 0.541, 0.273, and 0.291 to 0.046, 0, and 0 respectively, post‐WNS. Our results indicate that northern myotis populations in the Maritimes have experienced a serious decline. Standardized and systematic capture surveys should be conducted at summer roosting areas and swarming sites in combination with counts in hibernacula to clarify the current distribution, population size, and resource selection patterns of northern myotis. © 2021 The Wildlife Society.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".