Observing bees and wasps: Why surveys and monitoring programs are critical and how they can improve our understanding of these beneficial hymenopterans
Bibliographic record
Abstract
Flower-visiting bees and wasps (Hymenoptera: Apoidea, Pompiloidea, Scolioidea, Tiphioidea, and Vespoidea) provide essential services in agricultural and urban systems, and ecological functions in natural ecosystems. Understanding the population trends, resource requirements and preferences, ecological challenges, and how to manage these species better requires increased surveys and standardized monitoring efforts for both groups. A monitoring program performed at various scales that provides ecological data is a prerequisite to managing either bees or wasps for conservation or crop pollination purposes. Methods to survey and monitor bees and wasps can be accomplished by a variety of means, depending on the researchers’ aims and goals. Herein, we discuss the importance of 1) evaluating populations of threatened and endangered bee and wasp species, 2) detecting and identifying pollinators of crops, 3) identifying and managing wasp species for use as biological control agents, 4) surveying the ranges of non-native bees and wasps, and 5) utilizing bees and wasps as biological indicators. We also discuss strategies for the selection of surveying and monitoring tools and methodologies best suited to specific goals and situations in beneficial Hymenoptera research. Our hope is that this review will lead to additional bee/wasp survey and monitoring programs and assist researchers with selecting tools and methodologies for the purpose of better understanding these beneficial insects.
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.
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".