Current status of biological control of introduced Phragmites in Canada: Insights from initial years of post-release monitoring and a larval density release experiment
Bibliographic record
Abstract
Introduced Phragmites (Phragmites australis australis (Cav.) Trin. Ex Steud.) is one of the most invasive plants in North America. To supplement existing management tools, a classical biological control program began in Canada in 2019 using two host-specific stem-boring moths, Archanara neurica (Hübner) and Lenisa geminipuncta (Haworth) (Lepidoptera: Noctuidae). In this article, we summarize the first three years of monitoring data for L. geminipuncta and A. neurica as biological control agents for introduced Phragmites. First, we assess agent presence and activity in the initial years post-release based on feeding damage from long-term monitoring data across 30 release sites initiated between 2019 and 2023. Second, we investigate the within-site distribution of agent feeding damage to improve future monitoring and agent collection from nurse sites. Third, we report the results of an experiment to determine optimal release densities of A. neurica larvae. We found agent feeding damage at 92% of initial release sites in the first year and agent activity persisted at all of these sites into years two and three post-release. Patterns of agent feeding damage suggest that the agents disperse quickly through the patch following release, favouring the interior area over the edges of introduced Phragmites stands. Finally, releasing intermediate densities of 40 A. neurica larvae per release point was more efficient than releasing either units of 20 or 80 larvae. The results of the first three years of monitoring are highly encouraging for the introduced Phragmites biological control program. Insights from these early monitoring results will be used to refine optimal release strategies, improve our ability to locate egg-bearing stems at nurse sites to facilitate the collection and redistribution of agents to new release locations, and inform protocols for longer-term monitoring of impacts on the target weed once agents are established.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".