Impacts of forestry planting on primary production in upland lakes from north‐west Ireland
Bibliographic record
Abstract
Planted forests are increasing in many upland regions worldwide, but knowledge about their potential effects on algal communities of catchment lakes is relatively unknown. Here, the effects of afforestation were investigated using palaeolimnology at six upland lake sites in the north-west of Ireland subject to different extents of forest plantation cover (4-64% of catchment area). (210)Pb-dated sediment cores were analysed for carotenoid pigments from algae, stable isotopes of bulk carbon (δ(13)C) and nitrogen (δ(15)N), and C/N ratios. In lakes with >50% of their catchment area covered by plantations, there were two- to sixfold increases in pigments from cryptophytes (alloxanthin) and significant but lower increases (39-116%) in those from colonial cyanobacteria (canthaxanthin), but no response from biomarkers of total algal abundance (β-carotene). In contrast, lakes in catchments with <20% afforestation exhibited no consistent response to forestry practices, although all lakes exhibited fluctuations in pigments and geochemical variables due to peat cutting and upland grazing prior to forest plantation. Taken together, patterns suggest that increases in cyanobacteria and cryptophyte abundance reflect a combination of mineral and nutrient enrichment associated with forest fertilization and organic matter influx which may have facilitated growth of mixotrophic taxa. This study demonstrates that planted forests can alter the abundance and community structure of algae in upland humic lakes of Ireland and Northern Ireland, despite long histories of prior catchment disturbance.
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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 | 0.000 |
| 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 | 0.001 | 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".