Sampling period, size and duration influence measures of bat species richness from acoustic surveys
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.002 |
| 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 it