Reconstructing macrophyte biomass dynamics in temperate lakes of northeastern North America using paleolimnology
Bibliographic record
Abstract
Submerged macrophytes are known to influence the structure and function of lake ecosystems. Despite their importance, long term monitoring records of macrophytes are rare and thus relatively little is know regarding how human activities have altered macrophyte abundance in lakes. Paleolimnological reconstructions may provide the best approach for examining long-term trends in macrophyte abundance. This thesis evaluates the potential of sedimentary diatoms as indicators of macrophyte abundance and examines the long-term effects of human activities on ecosystem stability and macrophyte abundance. A novel analysis of an existing dataset showed that there were significant differences in the sedimentary diatom assemblage of lakes with either high or low macrophyte cover, and that these differences in the diatom assemblages can be used to infer the macrophyte cover of lakes. I further examined the influence of macrophytes on diatoms using a continuous measure of whole-lake macrophyte biomass in 41 lakes located in southern Quebec, Canada, and showed that diatoms can be used to detect substantial changes in macrophyte abundance through time. An analysis of the effects of external phosphorus (P) inputs to the lake showed that increasing P inputs resulted in greater diatom dissimilarity through time, providing empirical evidence for the idea that P inputs increase ecosystem variability. Finally, I show a widespread trend of a relative reduction in benthic diatoms and an inferred decline in macrophyte abundance between pre-1850 and present-day conditions, which is significantly related to modern land use. Together, this thesis advances our ability to track long-term changes in macrophyte abundance and demonstrates that human activities over the past ~150 years have altered ecosystem stability and reduced macrophyte abundance in southern Quebec lakes.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".