Sampling period, size and duration influence measures of bat species richness from acoustic surveys
Bibliographic record
Abstract
Summary 1. Understanding animal ecology depends on an ability to accurately inventory species. However, there are few quantitative data available, which allow for an assessment of the effectiveness of acoustic sampling methods for determining bat species richness. 2. We assessed inventory efficiency, defined as the percentage of species detected per survey effort, using data from 7 to 9 Anabat bat detectors deployed concurrently between June 2008 and August 2009 at fixed locations. We examined sampling period and time of night to calculate the minimum duration of sampling effort required to detect the greatest percentage of species. 3. In all cases, multiple survey nights at multiple sampling locations were necessary to detect higher levels of species richness using acoustic detectors. Additionally, continuous sampling throughout the night was important for detecting more species, especially during summer, fall and spring months. 4. Species accumulation curves indicated that relatively few nights were needed to detect ‘common’ species at various sampling locations (2–5 nights on average); however, longer sample periods (>45 nights) were necessary to detect ‘rare’ species at some sampling locations. Accumulation curves indicated that the number of detector locations positively influenced the number of species detected during surveys periods. 5. A priori knowledge of sampling effort is fundamental for designing biologically robust inventories. We make recommendations for improving the efficiency of acoustic surveys using analytical methods that are broadly applicable to a range of survey methods and taxa.
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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 | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".