Variance and Rate‐of‐Change as Early Warning Signals for a Critical Transition in an Aquatic Ecosystem State: A Test Case From Tasmania, Australia
Bibliographic record
Abstract
Abstract Critical transitions in ecosystem states are often sudden and unpredictable. Consequently, there is a concerted effort to identify measurable early warning signals (EWS) for these important events. Aquatic ecosystems provide an opportunity to observe critical transitions due to their high sensitivity and rapid response times. Using palaeoecological techniques, we can measure properties of time series data to determine if critical transitions are preceded by any measurable ecosystem metrics, that is, identify EWS. Using a suite of palaeoenvironmental data spanning the last 2,400 years (diatoms, pollen, geochemistry, and charcoal influx), we assess whether a critical transition in diatom community structure was preceded by measurable EWS. Lake Vera, in the temperate rain forest of western Tasmania, Australia, has a diatom community dominated by Discostella stelligera and undergoes an abrupt compositional shift at ca. 820 cal yr BP that is concomitant with increased fire disturbance of the local vegetation. This shift is manifest as a transition from less oligotrophic acidic diatom flora (Achnanthidium minutissimum, Brachysira styriaca, and Fragilaria capucina) to more oligotrophic acidic taxa (Frustulia elongatissima, Eunotia diodon, and Gomphonema multiforme). We observe a marked increase in compositional variance and rate‐of‐change prior to this critical transition, revealing these metrics are useful EWS in this system. Interestingly, vegetation remains complacent to fire disturbance until after the shift in the diatom community. Disturbance taxa invade and the vegetation system experiences an increase in both compositional variance and rate‐of‐change. These trends imply an approaching critical transition in the vegetation and the probable collapse of the local rain forest system.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".