Evaluation of a decade of management of a North American aquatic invasive species (<i>Nitellopsis obtusa</i>) highlights scale-dependent effectiveness and monitoring gaps
Bibliographic record
Abstract
Nitellopsis obtusa (starry stonewort) is an invasive macroalga subject to substantial control efforts in the Midwestern United States; however, there has not been systematic evaluation of treatment effectiveness. We synthesized management approaches and outcomes using monitoring performed over a decade-long period across 38 lakes in Indiana, Wisconsin, and Minnesota. Copper-based algaecide treatments were the primary means of control, followed by physical removal methods or combination treatments. Control efforts and associated monitoring data varied by spatial scale, as did surveyors’ N. obtusa sampling methods. At the largest (whole-lake) scale, we found no evidence that algaecide treatments were slowing expansion or reducing abundance of N. obtusa within infested lakes. At smaller, within-lake scales, we found that algaecide and physical treatments could reduce N. obtusa frequency and biomass, but outcomes were highly variable. At the smallest scales, hand pulling was an effective containment strategy for small, localized populations that were detected early. These results highlight the need to set realistic goals for N. obtusa control and develop improved management techniques. There were also critical gaps in monitoring that limited our ability to evaluate treatment effectiveness. In particular, increased monitoring of unmanaged reference lakes and untreated areas within managed lakes is needed.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".