Variability in numbers of long‐tailed bats <i>(Chalinolobus tuberculatus)</i> roosting in Grand Canyon Cave, New Zealand: Implications for monitoring population trends
Bibliographic record
Abstract
Abstract Counts of roosting bats undertaken within caves are used frequently as indicators of population size, long‐term indices of population trends, and as measures of response to management. Numbers of New Zealand long‐tailed bats (Chalinolobus tuberculatus Forster 1844) using Grand Canyon Cave were monitored over 8 years. Grand Canyon Cave is a focal point for one of the largest known populations. Its has been used by long‐tailed bats consistently for >40 years. The aims of this study were to examine the utility of cave‐roost counts for long‐term monitoring of population trends in this threatened species, and to establish a baseline for future monitoring. Two population indices, number of bats counted roosting inside the cave during the day and net number emerging at dusk, were not significantly different. Monthly and daily counts were characterised by high variability. Indices varied significantly through the year but not between years. Distribution of bats within the cave was not random. Bats avoided roosting within 30 m of each entrance and larger groups were always concentrated along two 50 m stretches of cave ceiling. Large groups occurred when cave temperatures ranged from 10–13°C, suggesting an optimum temperature range. Maximum counts of 250 (day roosting) and 358 (night roosting) bats confirm that Grand Canyon Cave is significant as a site for C. tuberculatus . Counts can be used as a baseline against which to judge future trends in the population of C. tuberculatus at Grand Canyon Cave and contribute to national monitoring of bat populations. Effects of variation can be overcome with standardisation of repeat counts, adopting a sampling frequency that provides sufficient power to detect changes, and use of statistical models that separate sampling effects from variance in bat activity. Recommendations for future monitoring are made.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".