Recovery of acid damaged zooplankton communities: measurement, extent, and limiting factors
Bibliographic record
Abstract
Anthropogenic acidification has affected biota in thousands of lakes in eastern North America and Europe. To measure the degree and extent of biological recovery following pH recovery in acidified lakes, many studies have assessed changes occurring in acid-damaged zooplankton communities. In this review we synthesize studies of zooplankton recovery from regions severely affected by acidification. In doing so, we provide a critical overview of: (1) the design of studies used to detect recovery; (2) the status of communities in acidified regions; and (3) our current understanding of the factors that limit recovery. The design of most studies assessing zooplankton recovery fall into three categories based on their selection of data to be used for recovery benchmarks: (1) historical; (2) reference-lakes; and (3) temporal. Within these study designs, the most commonly used metrics include species richness, indicator species, and relative species abundances. Many studies have used species richness as the sole indicator of recovery; however, we argue that additional metrics should be considered in analyses to make conclusions more robust. Studies conducted in eastern North America and Northern Europe have demonstrated significant, though often incomplete, recovery of zooplankton communities in lakes that reach a pH > 6.0. Data collected in central Europe indicate little recovery in the heavily affected Bohemian Forest lakes, but complete recovery of species richness in the moderately acidified Tatra Mountain lakes. Factors limiting biological recovery, including slow chemical recovery, dispersal limitation, and community resistance, vary in importance among and within regions, suggesting that region- and lake-specific management approaches may be required.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".