Landscape and site factors drive invasive <scp><i>Phragmites</i></scp> management and native plant recovery across Chesapeake Bay wetlands
Bibliographic record
Abstract
Abstract Successful invasive plant management—where invaders are sufficiently reduced and diverse native plant communities recover—remains an elusive goal for land managers. The site‐ and landscape‐scale drivers of variable management outcomes and vegetation recovery are poorly understood due to a lack of rigorous experiments that characterize longer term vegetation trends across contexts. We present the results of a five‐year experiment across eight subestuaries of Chesapeake Bay, representing a gradient of watersheds with differing dominant land‐use types and anthropogenic impacts, to evaluate invasive and native plant response to herbicide management. The focal invader, Phragmites australis (common reed), is one of the most aggressive and pervasive invasive plants in North American wetlands. We found that with multiyear herbicide treatments, it was possible to greatly reduce Phragmites across an array of subestuaries while increasing the cover and quality of native plant communities. Yet, by the end of the study, plant community composition in all Phragmites‐managed sites remained distinct from, even if composition was shifting toward, reference sites. There was also large inter‐site variation in the vegetation responses related to site environmental conditions and subestuary vegetation conditions. We uncovered specific aspects of the surrounding landscape that were linked to improved vegetation recovery—the species richness and conservation value of nearby wetlands. Results from this five‐year experiment conducted at multiple sites in Chesapeake Bay inform what is possible for management, particularly in more degraded landscapes and sites where setting realistic expectations and pragmatic goals will be essential. Assessing environmental and vegetation conditions of the site and surrounding landscape prior to commencing invasive species management is critical to predict the time and effort required to achieve restoration goals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".