Tipping points and multiple drivers in changing aquatic ecosystems: A review of experimental studies
Bibliographic record
Abstract
Abstract Natural ecosystems are experiencing unprecedented rates of change due to anthropogenic activities and global change, leading to either gradual changes in a given response or tipping points. While the tipping point concept has been tested in an array of habitats since the 1960s, the spatiotemporal superposition of multiple drivers in different ecosystems needs to be considered when investigating the response of species, communities, populations, and ecosystems along environmental gradients. Here, we (1) develop a historical and current perspective of tipping point studies in terrestrial, freshwater, and marine ecological systems; (2) portray the research effort in different freshwater and marine habitats; and (3) explore the results of experimental studies focusing on tipping points measured at the individual, communities, ecosystem level, as well as ecosystem functions and services in a context of single and multiple stressors. The number of studies mentioning the concept of tipping points increases every year, but very few studies have specific objective to identify them. Even fewer studies consider how the addition of another stressor into an ecosystem may alter a tipping point. In addition, many studies investigated multiple responses, but only one‐fourth (7 out of 28) of them concentrate their effort on multiple biological or ecological levels of complexity. This review allowed us to identify shortcomings in this research field and propose ways to make this ecological concept anew.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".