Pursuing best practices for minimizing wild bee captures to support biological research
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
Abstract Bees are important pollinators of wild and domesticated flowering plant species. Over the last 30 years, an increasing number of scientific articles have been published on the ecology and conservation of wild bees. To achieve research goals, many studies have pursued the lethal take of wild bees. Although the impact of lethal take for scientific pursuits is likely negligible compared to the negative impacts of human‐mediated phenomena such as climate change, urbanization, and agricultural intensification, it is important to evaluate the history of lethal take on scientific endeavors. In our study, we evaluated a random sample of 30 years of scientific publications on wild bees. Across 1426 surveyed publications, 536 reported the lethal take of wild bees. We found that 61% of these studies lethally captured wild bees primarily for species identification. Furthermore, we determined passive sampling of wild bees resulted in substantially more lethal collections than active methods per study. However, combined approaches of passive and active collection resulted in the greatest lethal take of wild bees per study. Finally, we determined that 64% of the studies did not provide deposition information for their samples, hindering additional research that could be done with them. The increasing availability of video and photographic devices and artificial intelligence approaches to identification, the development of low and noninvasive molecular methods, and the ease of sharing information, allow for a timely discussion on alternative routes and potentially new best practices in bee research. We focus our discussion on alternative methods for minimizing lethal captures for identification purposes and through passive methods, and for maximizing the utility of the data collected. Finally, we provide a framework for continued engagement among researchers and managers to develop strategies that can contribute to reducing our impact on wild bee communities and making the most of collected specimens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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