Anthropogenic deposition of heavy metals and phosphorus may reduce biological N2 fixation in boreal forest mosses
Bibliographic record
Abstract
A study was undertaken to test the effects of molybdenum (Mo) and phosphorus (P) amendments on biological nitrogen (N) fixation (BNF) by boreal forest moss-associated cyanobacteria. Feather moss (Pleurozium schreberi) samples were collected on five sites, on two dates and at different roadside distances (0-100m) corresponding to an assumed gradient of reactive N deposition. Potential BNF of Mo and P amended moss samples was measured using the acetylene reduction assay. Total N, P and heavy metal concentrations of mosses collected at 0 and 100m from roadsides were also measured. Likewise, the needles from Norway spruce trees (Picea abies) at different roadside distances were collected in late summer and analyzed for total N, P and heavy metals. There was a significant increase in BNF with roadside distance on 7-of-10 individual Site×Date combinations. We found no clear evidence of an N gradient across roadside distances. Elemental analyses of feather moss and Norway spruce needle tissues suggested decreasing deposition of heavy metals (Mo-Co-Cr-Ni-V-Pb-Ag-Cu) as well as P with increasing distance from the roadside. The effects of Mo and P amendments on BNF were infrequent and inconsistent across roadside distances and across sites. One particular site, however, displayed greater concentrations of heavy metals near the roadside, as well as a steeper P fertility gradient with roadside distance, than the other sites. Here, BNF increased with roadside distance only when moss samples were amended with P. Also at this site, BNF across all roadside distances was higher when mosses were amended with both Mo and P, suggesting a co-limitation of these two nutrients in controlling BNF. In summary, our study showed a potential for car emissions to increase heavy metals and P along roadsides and underscored the putative roles of these anthropogenic pollutants on BNF in northern latitudes.
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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) | 0.000 | 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".