Ecosystem impacts of an invasive charophyte (<i>Nitellopsis obtusa</i>) interpreted in a multiple stressor context using paleolimnology
Bibliographic record
Abstract
Korosi JB, Adano RC, Do PHP, Hall RI, Jeyarajah J, Stewart EM, Wiklund JA, Thienpont JR. 2024. Ecosystem impacts of an invasive charophyte (Nitellopsis obtusa) interpreted in a multiple stressor context using paleolimnology. Lake Reserv Manage. 40:132–144. Proliferation of Nitellopsis obtusa (starry stonewort), an aquatic invasive macroalga, is an emerging water quality concern in North America, but disentangling its effects is complicated by multiple stressors and a lack of long-term data. In this study, we investigated the potential impacts of N. obtusa on benthic oxygen depletion and the lower food web of Lake Scugog (southern Ontario, Canada) in a multiple-stressor context through a paleolimnological assessment of ecosystem changes up to the invasion and proliferation of N. obtusa. Results reveal that myxoxanthophyll, a pigment produced by colonial cyanobacteria, was no longer detected in the sediments after the early 1990s despite being prevalent throughout the earlier history of Lake Scugog. This indicates that recently documented blooms of Microcystis, which have been hypothesized to be unprecedented and facilitated by N. obtusa, are not a new phenomenon but instead may represent a resurgence in cyanobacteria production following a period of water quality improvement. Sediment cores also documented increased relative abundance of Chironomidae taxa associated with low benthic oxygen availability after about 1920, while changes in subfossil diatom assemblage provided evidence of increased frequency and/or duration of thermal stratification indicative of climate warming effects. This suggests that N. obtusa may be acting synergistically with existing stressors on Lake Scugog to exacerbate challenges with benthic hypoxia.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".