Constructing Standard Invasion Curves from Herbarium Data—Toward Increased Predictability of Plant Invasions
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
Prevention, early detection, rapid response, and prioritization are essential components of effective and cost-efficient invasive plant management. However, successfully implementing these strategies requires the ability to accurately predict the temporal and spatial dynamics of newly/recently detected nonnative species. Why some nonnative species become invasive and the source of variation in lag time between arrival and the onset of invasive expansion are poorly understood. One tool to fill these knowledge gaps is the “invasion curve,” which tracks nonnative species abundance (i.e., area invaded) over time after arrival in a new area. Since invasive species curves rely primarily on records from herbarium collections, we propose that these collections can be used as a springboard to develop a standardized approach to building invasion curves. This would allow researchers to compare the trajectories of nonnative species, improving risk assessment and our ability to recognize potential invasive species and factors contributing to both invasibility and invasiveness. While there have been admirable efforts to produce invasion curves, several barriers exist to their reliable production and standardization. In this paper, we explore the challenges related to the efficient production of these curves for plants using herbarium data and suggest ways in which progress could occur. It is our hope that this will better position herbaria and researchers to aid natural resource managers to prioritize needs, make effective management decisions, and develop targeted prevention and monitoring programs by taking advantage of lag times to implement timely responses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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