Prior exposure to stress allows the maintenance of an ecosystem cycle following severe acidification
Bibliographic record
Abstract
Ecosystem processes vary temporally due to environmental fluctuations, such as when variation in solar energy causes diurnal cycles in primary production. This normal variation in ecosystem functioning may be disrupted and even lost if taxa contributing to functioning go extinct due to environmental stress. However, when communities are exposed to the stress at sub‐lethal levels over several generations, they may be able to develop community‐level stress tolerance via ecological (e.g. species sorting) or evolutionary (e.g. selection for tolerant genotypes) mechanisms and thus avoid the loss of stability, as defined by the resistance of a process. Community tolerance to a novel stressor is expected to increase the resistance of key processes in stressed ecosystems. In freshwater communities we tested whether prolonged prior exposure to an environmental stressor, i.e. acidification, could increase ecosystem stability when the communities were exposed to a subsequent press perturbation of more severe acidification. As a measure of ecosystem stability, we quantified the diurnal variation in dissolved oxygen (DO), and the resistance of the DO cycle and phytoplankton biomass. High‐frequency data from oxygen loggers deployed in 12 mesocosms showed that severe acidification with sulfuric acid to pH 3 could cause a temporary (i.e. two‐week long) loss of diurnal variation in dissolved oxygen concentration. The loss of diurnal variation was accompanied by a strong reduction in phytoplankton biomass. However, the pre‐exposure to acidification for several weeks resulted in the maintenance of the diurnal cycle and higher levels of phytoplankton biomass, though they did not return to as rapidly to pre‐exposure functioning as non‐exposed mesocosms. These results suggest that ecosystem stability is intrinsically linked to community‐wide stress tolerance, and that a history of exposure to the stressor may increase resistance to it, though at the cost of some resilience.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".