Long chain n-alkanes in lake sediment track differences in adjacent land vegetation.
Bibliographic record
Abstract
• We measured sediment biomarkers from 19 lakes spanning 4 ecoregions in Canada. • We correlated n -alkanes in sediment to land cover type (herbaceous/woody plants). • Chlorophyll- a in lake water was not correlated with n -alkane composition. • C/N ratio & δ 13 C in sediment did not differ among ecoregions or land cover types. We conducted an analysis of n -alkanes, lignin-derived phenols, and other sediment markers from 19 lakes across four ecoregions in Saskatchewan, Canada, spanning from Prairie Grassland to Boreal Upland. Our goal was to establish whether these biomarkers relate to different ecoregions and land cover types (herbaceous plants vs trees) in the catchments of these lakes. Our findings revealed a significant inverse correlation between the proportion of herbaceous plants to trees in a lake’s catchment and the proportion of aquatic n -alkanes P aq (C 23 + C 25 )/(C 23 + C 25 + C 29 + C 31 ) indicating that aquatic plants contributed proportionally more to sedimentary n -alkanes when the catchments were mostly in Boreal forest. We also observed significant positive correlations between the proportion of herbaceous plants to trees in a lake’s catchment and the n -alkane composition ratios C 31 /(C 27 + C 31 ) and C 31 /(C 27 + C 29 + C 31 ), reflecting higher relative inputs of C 31 from herbaceous vegetation. These findings suggest that these ratios could potentially be utilized to infer historical land cover composition based on dated sediment records. Moreover, variations in the C 31 /(C 27 + C 31 ) alkane ratio were observed among ecoregions, particularly between Prairie Grassland and the forest-dominated areas. We found no correlations between chlorophyll- a concentrations in lake water and the above-mentioned n -alkane ratios in sediment, suggesting that these ratios primarily reflect land cover composition rather than autochthonous production in the lakes. Additionally, the C/N ratio and δ 13 C were not effective in distinguishing ecoregions or land cover composition, likely due to influences from algal production and perhaps agricultural activities in surrounding farmland. In contrast, lignin-derived phenols in sediments showed relatively little association with their respective ecoregions and appeared to be influenced by decomposition as evidenced by high ratios of carboxylic acids relative to aldehydes (Ad/Al). Overall, our research highlights the potential of n -alkanes as biomarkers for tracking distinct land cover types due to their strong associations with the proportion of grasses and trees.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".