Investigating the distribution of Nitellopsis obtusa in Ontario Lakes and its role as an ecosystem engineer
Bibliographic record
Abstract
Nitellopsis obtusa is a non-native macrophyte introduced into the Laurentian Great Lakes in 1974. Over the last decade, increasing reports emerged for N. obtusa within lakes in the Great Lakes Basin. Despite N. obtusa being in North America since the 1970s, little is known about the ecosystem- and community-level impacts upon establishment. My thesis aimed to elucidate these potential effects by focusing on both within-lake and regional scale N. obtusa populations. I conducted a comprehensive investigation into ecosystem impacts of N. obtusa across 12 sites in Lake Scugog, Ontario, Canada, from spring to fall over four years (2016-2019). Upon N. obtusa establishment, diversity across lower aquatic food web (LAFW) communities significantly (p-value < 0.05, multiple linear regression) decreased. Despite community diversity decreased across the LAFW, Microcystis spp. blooms emerged where they were previously unreported. To assess drivers of bloom development, a structural equation model demonstrated that N. obtusa had a direct faciliatory (p-value < 0.05) role in Microcystis spp. blooms. In addition, N. obtusa biomass was a moderately strong negative predictor of near-bed oxygen concentration (R2 = 0.59, p-value < 0.001), which likely promoted internal loading of phosphorus. Supporting this conclusion was a strong predictive model showing that N. obtusa biomass explained 90% of pore water soluble reactive phosphorus concentrations (R2 = 0.90, p-value < 0.001). During a narrow sampling window (3 weeks) in 2019, I sampled 60 lakes across a geological transition zone in Ontario, Canada. Calcium is for N. obtusa, thus a strong response related to calcium availability was expected. Nitellopsis obtusa was documented at 37 sites in 19 of the study lakes. Interestingly, an ecological niche model demonstrated that depth and the cations potassium, magnesium, and sodium were better predictors of N. obtusa presence than calcium. Strong positive associations between N. obtusa and native Characeae members were revealed through general linear latent variable modelling, in addition to strong negative associations with other macrophyte taxa such as Myriophyllum spicatum. In combination, these findings indicate that N. obtusa may be an ecosystem engineer in invaded lakes by affecting key lake elements and altering community composition throughout the LAFW community.
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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 | 0.001 |
| 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".