Resilience of Epilithic Algal Assemblages in Atmospherically and Experimentally Acidified Boreal Lakes
Bibliographic record
Abstract
Algal assemblages can be highly responsive to environmental changes in recovering acidified lakes. We compared epilithic algal assemblages in boreal lakes during chemical recovery from atmospheric (Killarney Park, Ontario) and experimental (Lake 302S, Experimental Lakes Area, Ontario) acidification to assess the impact of spatial and temporal scale of severe acidification on taxonomic resilience (i.e. recovery rate). Resilience was measured as the distance traveled by lakes in ordination space during pH recovery based on canonical correspondence analysis. Resilience was relatively negligible in the Killarney lakes, suggesting that eight years of experimental acidification in Lake 302S had less impact on biological recovery than did decades of regional acidification. Increases in dissolved organic carbon, dissolved inorganic carbon, and calcium best explained temporal variance of epilithic species abundances in the recovering acidified lakes. In Lake 302S, contrasting trajectories of taxonomic resilience and resistance, i.e. displacement from reference conditions following a perturbation, indicated that ecological factors affecting epilithon differed at corresponding pH levels during recovery and acidification. Our findings reveal that modeling of ecosystem recovery from severe acidification must account for the spatial and temporal scale of the perturbation, and biological delay responses that result in differences between recovery and acidification trajectories.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".