Assessing Historical Occupancy Trends in North American Flower Flies (Diptera: Syrphidae)
Bibliographic record
Abstract
Given the declines occurring in many insect populations around the world, there is an urgent need to assess large-scale trends in important groups like pollinators. Museum data and biological collections present unique opportunities to assess population trends over long historical timescales. Here, I use a Bayesian multi-season, multi-species occupancy model to estimate long-term, range-wide occupancy changes for 318 North American syrphids, which are the most common pollinators of major crops after bees. Syrphids as a whole declined in occupancy by 10.5% between the periods of 1960–1990 and 1991–2020, but no overall trend was observed from an earlier baseline of 1900–1930. Species-specific declines outnumbered increases and were associated with smaller body size, predatory larvae, and the subfamily Syrphinae. I propose next steps to improve the reliability of this approach and address remaining knowledge gaps. Ultimately, these declines warrant further efforts to monitor and conserve syrphid populations.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".