Where have all the flowers gone? A systematic evaluation of factors driving native terrestrial plant decline in North America
Bibliographic record
Abstract
Prior to the arrival of Europeans in North America, forest and grasslands individually covered a 3rd of the conterminous United States; however, following the colonial and pioneer periods, respectively, these land cover categories were reduced to 70% and 50% of their original prominence. The dominant driving force for native land conversion was agriculture, which expanded exponentially from the Atlantic to the Pacific, comprising over half the total land area of America at its peak in 1950. However, farmland area has subsequently declined by 25%, so what has been driving native plant declines north of the 30th latitudinal parallel over the past 75 years? Analysis of recovery plans issued by the U.S. Fish and Wildlife Service indicates that of the over 900 plant species "listed" as threatened and endangered the primary driver of decline was invasive species, followed by habitat alteration, and development, which collectively accounted for 93.2% of the primary drivers for listed species. In Canada, these three drivers of decline were the primary drivers for 81% of listed species. Comparatively, herbicides were identified as the primary or secondary driver in 13 out of 1124 cases (1.2%). Given that agricultural land area is contracting in the U.S. and Canada, there appears to be a misconception that agrochemicals are the seminal cause of native plant decline. Here, we explore the individual contribution of drivers relative to the historical events of North America to provide context and perspective as well as focus and prioritize conservation efforts accordingly.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".