Elevated <scp>δ<sup>15</sup>N</scp> Linked to Inhibited Nitrification Coupled to Ammonia Volatilization in Sediments of Shallow Alkaline‐Hypersaline Lakes
Bibliographic record
Abstract
ABSTRACT Alkaline lakes are among the most bioproductive aquatic ecosystems on Earth. The factors that ultimately limit productivity in these systems can vary, but nitrogen (N) cycling in particular has been shown to be adversely affected by high salinity, evidently due to the inhibition of nitrifying bacteria (i.e., those that convert ammonic species to nitrogen oxides). The coastal plain of Coorong National Park in South Australia, which hosts several alkaline lakes along 130 km of coastline, provides an ideal natural laboratory for examining how fine‐scale differences in the geochemistry of such environments can lead to broad variations in nitrogen cycling through time, as manifest in sedimentary δ 15 N. Moreover, the lakes provide a gradient of aqueous conditions that allows us to assess the effects of pH, salinity, and carbonate chemistry on the sedimentary record. We report a wide range of δ 15 N values (3.8‰–18.6‰) measured in the sediments (0–35 cm depth) of five lakes of the Coorong region. Additional data include major element abundances, carbonate δ 13 C and δ 18 O values, and the results of principal component analyses. Stable nitrogen isotopes and wt% sodium (Na) display positive correlation ( R 2 = 0.59, p < 0.001) across all lake systems. Principal component analyses further support the notion that salinity has historically impacted nitrogen cycling. We propose that the inhibition of nitrification at elevated salinity may lead to the accumulation of ammonic species, which, when exposed to the water column, are prone to ammonia volatilization facilitated by intervals of elevated pH. This process is accompanied by a significant isotope fractionation effect, isotopically enriching the nitrogen that remains in the lake water. This nitrogen is eventually buried in the sediments, preserving a record of these combined processes. Analogous enrichments in the rock record may provide important constraints on past chemical conditions and their associated microbial ecologies. Specifically, ancient terrestrial aquatic systems with high δ 15 N values attributed to denitrification and thus oxygen deficiency may warrant re‐evaluation within the framework of this alternative. Constraints on pH as provided by elevated δ 15 N via ammonia volatilization may also inform critical aspects of closed‐basin paleoenvironments and their suitability for a de novo origin of life.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 0.001 |
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".