The paradox of increasing long-term carbon sequestration in lake ecosystems despite reoligotrophication : the case of four large French perialpine lakes
Bibliographic record
Abstract
Although lakes only represent a small fraction of the surface of the earth, a growing number of studies have shown that they play a critical role in the global carbon cycle ([i],[ii],[iii]), mediating carbon transfer from land to the atmosphere, and burying organic carbon in their sediments. The magnitude and temporal variability of carbon burial is, however, poorly constrained, and the degree to which lake productivity has influenced lake carbon cycling has not been systematically assessed ([iv]). Here, trends in total organic carbon (TOC) sequestration and primary production are reconstructed from sediment records for the last 300 years in four perialpine deep lakes. We rely on High Performance Liquid Chromatography (HPLC) and geochemical proxies to investigate changes in algal communities. Then, we evaluate the temporal contribution of algal assemblages to the variability of lake primary production, as well as the potential effects on carbon sequestration magnitude. Other contributors to carbon sequestration derived from the IPER RETRO project (2009-2013) are also investigated, such as past oxygen conditions, lake thermal structure or allochthonous supplies of carbon. Our results suggest that despite reoligotrophication of all lakes (e.g., decrease in dissolved phosphorus concentration in water column and relative restoration of diatoms communities ([v])) over the last 3 decades, TOC in lakes sediments is still increasing in the sediment. The study of algal pigments suggests that changes in algal assemblages and oxygen conditions could be responsible of this persistent increase in carbon burial. Future development (e.g., DNA analysis) should provide more detail on algal communities to validate these results. [i] J. J. Cole et al., ‘Plumbing the Global Carbon Cycle: Integrating Inland Waters into the Terrestrial Carbon Budget’, Ecosystems 10, no. 1 (May 2007): 172–85, https://doi.org/10.1007/s10021-006-9013-8. [ii] Tom J. Battin et al., ‘Biophysical Controls on Organic Carbon Fluxes in Fluvial Networks’, Nature Geoscience 1, no. 2 (February 2008): 95–100, https://doi.org/10.1038/ngeo101. [iii] Lars J. Tranvik et al., ‘Lakes and Reservoirs as Regulators of Carbon Cycling and Climate’, Limnology and Oceanography 54, no. 6part2 (November 2009): 2298–2314, https://doi.org/10.4319/lo.2009.54.6_part_2.2298. [iv] N. J. Anderson et al., ‘Anthropogenic Alteration of Nutrient Supply Increases the Global Freshwater Carbon Sink’, Science Advances 6, no. 16 (April 2020): eaaw2145, https://doi.org/10.1126/sciadv.aaw2145. [v] Vincent Berthon et al., ‘Trophic History of French Sub-Alpine Lakes over the Last 150 Years: Phosphorus Reconstruction and Assessment of Taphonomic Biases’, Journal of Limnology 72, no. 3 (September 2013): 34, https://doi.org/10.4081/jlimnol.2013.e34.
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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 | 0.001 | 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 | 0.001 |
| 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".