Gauging recovery of zooplankton from historical acid and metal contamination: the influence of temporal changes in restoration targets
Bibliographic record
Abstract
Summary Clearly defined restoration targets are necessary to judge the effectiveness of management actions in restoring damaged ecosystems. However, the identification of appropriate targets is difficult in a rapidly changing world. Historical reference conditions commonly provide recovery targets, but they may not be appropriate if present‐day environments have shifted in response to regional or global drivers. Such shifts may need to be incorporated into restoration targets to avoid erroneous conclusions about the recovery of ecosystems damaged by localized stressors. No previous study has examined whether the selection of historical vs. present‐day reference conditions alters judgments of the recovery of historically damaged ecosystems. We examined 35‐year trends in the zooplankton communities of four lakes polluted by smelter emissions in Sudbury, Ontario, Canada. Recovery was assessed by comparing the Sudbury lakes to both historical (1983–1984) and present‐day (2004–2006) conditions in a set of minimally impacted reference lakes in south‐central Ontario. Sudbury zooplankton communities improved substantially over time when compared with both the historical and present‐day recovery targets. However, recovery occurred later, and improvements differed quantitatively when judged against the present‐day vs. historical targets. These differences were attributable to regional shifts in zooplankton communities that happened after the historical sampling period but were reflected in the present‐day data. Species richness in two Sudbury lakes met recovery targets and communities in all four lakes became more similar to those in the reference lakes. However, the continued absence of many daphniids, cyclopoids and large calanoids indicated that the lakes had not fully recovered and further monitoring is needed. Synthesis and applications. Our results show that the choice of reference condition can alter recovery assessments. This finding emphasizes the importance of establishing clearly defined restoration goals to ensure appropriate choice of reference conditions. Restoration is unlikely to be judged as successful if an historical reference point is used to guide management actions meant to restore an ecosystem to present‐day regional conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".