Pervasive changes in algal indicators since pre-industrial times: A paleolimnological study of changes in primary production and diatom assemblages from ~200 Canadian lakes
Bibliographic record
Abstract
Anthropogenic stressors affect lakes around the world, ranging in scale from catchment-specific pollutants to the global impacts of climate change. Canada has a large number and diversity of lakes, yet it is not well understood how, where, and when human impacts have affected these lakes at a national scale. The NSERC Canadian Lake Pulse Network sought to create the first nationwide database of Canadian lake health, undertaking a multi-year survey of 664 lakes spanning 12 ecozones across Canada. A key objective of the network is to determine where, by how much, and why have Canadian lakes changed during the Anthropocene. To address this objective, we compared sedimentary chlorophyll a and diatoms from modern and pre-industrial sediment intervals of ~200 lakes. The lakes spanned a range of sizes, ecozones, and degrees of within-catchment land use change. We inferred the quantity of chlorophyll a, its isomers and main diagenetic products using visible reflectance spectroscopy. We found widespread increases in primary production since pre-industrial times. Primary production increased, on average, across all ecozones, human impact classes, and stratification classes. Likewise, an increase in planktonic diatom taxa over time was detected in the majority of sampled lakes, likely due to recent climate warming. However, regional factors (ecozones) explained the most variation in modern diatom species assemblages as well as their temporal turnover. Furthermore, lakes with high human impact (i.e., higher weighted proportions of human land use in the catchment) exhibited greater taxonomic turnover than lakes with a low human impact class. The greatest diatom turnover was found in the agriculture-rich Prairies and the lowest in the sparsely populated Boreal Shield and Taiga Cordillera ecozones. Overall, our study highlights that drivers operating at different geographic scales (i.e., climatic and land-use changes) have led to significant alterations in algal indicators since pre-industrial times across the country.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".