Reconstructing the spread of invasive plants: taking into account biases associated with herbarium specimens
Bibliographic record
Abstract
Abstract Aim To reconstruct the spread of invasive wetland species using herbarium specimens and to develop a method that accounts for the biases associated with this type of historical record. Location Southern Québec. Methods The temporal and spatial distribution of herbarium specimens of vascular plants was examined. Six invasive species that are mainly found in wetlands were compared with five native, non‐expanding hydrophytes. The cumulative number of locations was plotted against time to construct invasion curves. For native species, this ‘invasion curve’ indicates the spatiotemporal distribution of the sampling for herbarium specimens of wetland plants. It also represents the history of the state of knowledge of the distribution of native species. Deviation from the native species invasion curve can be used to describe the spread of invasive species. For every year on record, the cumulative number of locations of each exotic species was divided by the cumulative number of locations of all native species, and the proportions obtained were plotted against time. Periods of invasiveness were then delineated. Results During the twentieth century, two periods of intensive herbarium specimen collection in Québec can be clearly distinguished, i.e. from 1930 to 1940 and from 1950 to 1985. Several periods of invasiveness were delineated for Butomus umbellatus L. (1922–35), Hydrocharis morsus‐ranae L. (1957–96), Lycopus europaeus L. (1963–2000), Lythrum salicaria L. (1890–1905, 1923–46), Phragmites australis (Cav.) Trin. ex Steudel (1963–84) and Rorippa amphibia (L.) Bess. (1929–34, 1943–52, 1971–79). Main conclusions Accounting for sampling biases associated with herbarium specimens is important in order to objectively delineate periods of invasiveness for exotic species. The spread of wetland vascular plant species can be reconstructed using herbarium specimens, even when the phenomenon is rapid, but the reconstruction is certainly more accurate when the invasion occurred during an active sampling period. The highly variable sampling effort for herbarium specimens in Québec and in other regions strongly cautions against using herbarium data without correction procedures.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".