Historical evidence for context-dependent assessment of Erigeron canadensis invasions in an 18th-century European landscape
Bibliographic record
Abstract
Understanding the historical roots of invasion science provides insights into early perceptions of invasive species, allows us to trace the evolution of the discipline over time, and helps contextualize modern research. This paper analyzes work by Christian Ludwig Krause, published 250 years ago, on the invasion of an 18th-century European landscape by Erigeron [Conyza] canadensis (Canadian horseweed), one of the most common invasive species today and a widespread agricultural weed. Here an analysis is conducted of the ecological consequences and underlying mechanisms Krause described, how he evaluated E. canadensis invasions in different land-use systems and how his insights align with existing knowledge. Krause identified copious seed production and long-distance dispersal by wind as key mechanisms for the formation of dominant stands on degraded sandy soils. He recognized various ecosystem services associated with population establishment, such as erosion control, increased soil fertility, and the facilitation of other species. While Krause highlighted the benefits of E. canadensis invasions for the recovery of degraded grasslands and fields, he also acknowledged this introduced species as a troublesome weed in gardens. Thus, Krause’s work is not only an early report on the invasion of a cultural landscape subject to wind erosion but also an early example of a context-dependent invasion assessment, illustrating both positive and negative impacts of the same species in different environments. Krause’s perspective may encourage current assessments of E. canadensis not solely based on its presence or frequency, but on documented ecological and socioeconomic effects and their associated benefits or harms. As Krause impressively demonstrated 250 years ago, these effects can differ starkly in different environments, necessitating multiple responses to the same species.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".