Increased ecosystem variability and reduced predictability following fertilisation: Evidence from palaeolimnology
Bibliographic record
Abstract
We tested the hypothesis that fertilisation increases community and ecosystem variability while reducing predictability using annual fossil records from fertilised Lake 227, Experimental Lakes Area, Ontario, Canada. Comparison of fossil pigments from unperturbed and eutrophied periods using a median‐log Levene’s test demonstrated that variability increased significantly during enrichment for total algae (chlorophyll a, sum of carotenoids), cyanobacteria (aphanizophyll, lutein‐zeaxanthin), chlorophytes (pheophytin b, lutein‐zeaxanthin), and cryptophytes (alloxanthin), but not for other algal taxa (chrysophytes, dinoflagellates) or herbivory (pheophorbides). Dynamic linear models (DLMs) of individual time series showed that forecast accuracy declined during enrichment for taxa which showed increased variability, while forecast uncertainty increased for all fossil pigments. DLMs of simulated data identified a strong inverse relationship between variability and predictability, suggesting that predictability will decline whenever variability increases. These findings imply that anthropogenic eutrophication of ecosystems may destabilise lakes, obscure impacts of global change, and reduce the sensitivity of whole‐ecosystem experiments.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".