Climate change: the potential for latitudinal effects on algal biomass in aquatic ecosystems
Bibliographic record
Abstract
Arctic aquatic systems are considered to be highly susceptible to climate change. Both increases in temperature and nutrient input would be anticipated to alter primary production within these lakes. Consequently, understanding the current relationship between nutrients and productivity is crucial for predicting the effects of climate change. In this paper, we synthesize published data on algal biomass, total phosphorus, total nitrogen, maximum depth, altitude, longitude, and latitude to determine whether average algal biomass differs for temperate and arctic lakes. A total of 57 sources were used, resulting in data for 433 lake-years, ranging in latitudes from 41 to 79°N. Average algal biomass observed during the ice-free season increased significantly with phosphorous levels, but the latitude of the system had a significant negative impact on algal biomass. We briefly outline two major hypotheses, based on existing empirical evidence, for the lower algal yield found in higher latitude systems. The first hypothesis discusses bottom-up control and the influence of abiotic factors on algal biomass. The second hypothesis relates to food chain composition and top-down influences. The latitudinal effect on algal yield suggests that arctic lakes could dramatically increase in productivity if these systems experience increases in temperature and nutrient concentrations as predicted by climate change models.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".