Anthropogenic development over the last ~200 years leaves physical and geochemical imprints in lake sediment records
Bibliographic record
Abstract
Lakes, and the environment in general, have been subjected to important modifications when considering the last 200 years. While local case studies are informative about the specific modification brought to their environment, generalizing changes over large geographic areas is insightful to define the magnitude, scale and frequency of environmental change more clearly. My PhD thesis aims to: (1) identify the spatial and temporal patterns of lake sedimentation rates over the past ~200 years; and (2) quantify the magnitude and direction of geochemical change recorded in lake sediments and identify predictors associated with their distribution and dynamics across the landscape. In my first chapter, I focused on the global variation in lake sedimentation rates using published records and considered both the spatial and temporal distribution of lake sedimentation rates. In this chapter, I was able to shed light on the acceleration of lake sedimentation rates around the world and to demonstrate a significant association between increasing sedimentation rates and anthropogenic land use metrics such as cropland and urban cover. In my second chapter, I narrowed in on Eastern Canada, a region which spans thousands of square kilometers and comprises a wide range of land use and climate using a network of 37 sediment cores. In this chapter, after having developed a reproducible framework for producing 210Pb-based chronologies, I found that predictors of lake sedimentation rate at a global scale were also significant when considering this smaller geographical extent. These findings reinforced the idea that anthropogenic contributions were prominent predictors of lake sedimentation rate patterns. In my third chapter, using the same network of sediment cores, I characterized the specific sediment geochemical constituents responsible for observed differences across Eastern Canada, which was primarily a gradient of organic to inorganic content. Another finding of this chapter was the detection of a temporal gradient in sedimentary metals such as lead and zinc. Watershed land-use, measured as human population and cropland cover were key predictors of sedimentary composition, with metals co-varying primarily with human population, while watershed geology accounted for a lot of variation in both the organic and inorganic content of the sediment. Finally, in my fourth chapter, I considered the effect of lake acidification and eutrophication, key environmental stressors, on the concentration and accumulation of lead (Pb) in lake sediments by collecting sediment cores from the Experimental Lake Area (IISD-ELA), where whole-lake manipulations had been carried out during the late 1960s – present. I hypothesized that Pb accumulation rates would be muted in acidified lakes relative to reference lakes, whereas eutrophied lakes would show accelerated Pb accumulation rates, due to changes in Pb solubility and retention. My results aligned with my hypothesis and demonstrated the positive influence of dissolved organic carbon (DOC) with Pb accumulation in lake sediments across the intervention period. Overall, my PhD thesis demonstrated the profound impact of human activities on the cycling of numerous sedimentary properties, including sedimentation rates and the distribution of geochemical constituents across multiple scales. The time series and model results developed not only inform site specific landscape management, but also contribute key data for more global modelling efforts
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".