Phytoplankton in Boreal SubArctic Lakes Following Enhanced Phosphorus Loading from Forest Fire: Impacts on Species Richness, Nitrogen and Light Limitation
Bibliographic record
Abstract
ABSTRACT Forest fire in peatland environments can cause enhanced loading of coloured compounds and of phosphorus relative to nitrogen resulting in reduced light penetration and nitrogen to phosphorus ratios in lake water. To determine the potential impacts of forest fire in peatland dominated catchments, we tested nitrogen (N), phosphorus (P) and light limitation of pelagic phytoplankton with in situ microcosms in three lakes from a Boreal SubArctic ecozone. To assess if phytoplankton assemblages were influenced by water chemistry changes following fire, phytoplankton species were identified from 10 lakes in unburnt and 10 lakes in burnt catchments. In the microcosm study, P limitation and concurrent N + P limitation of phytoplankton biomass were apparent (P « 0.01) in the two lake waters representing the range of N and P concentrations for lakes in unburnt catchments. In the lake with water representative of lakes in burnt catchments, nitrogen limitation was observed (P « 0.01). Light limitation of phytoplankton biomass was observed in microcosms from one lake in a burnt and one lake from an unburnt catchment likely due to high water colour in both lake waters (> 200 mg/L [Pt]). For the 20 surveyed lakes, phytoplankton species richness was 36% lower (P « 0.01) in lakes from burnt compared to unburnt catchments. Phytoplankton communities in all lakes in this study were dominated by cyanobacteria. Phytoplankton communities in boreal forest lakes may be particularly sensitive to catchment disturbances such as fire because changes in phosphorus and carbon loading from peatlands enhance nitrogen and light limitation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 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".