THE INFLATIONARY EFFECTS OF ENVIRONMENTAL FLUCTUATIONS ENSURE THE PERSISTENCE OF SINK METAPOPULATIONS
Bibliographic record
Abstract
Under current rates of environmental change many populations may be found in habitats of low quality and low conservation value, creating population sinks. We test recent theory that suggests, surprisingly, that stochastic environmental variability may enhance the long-term persistence of sink metapopulations. Using experimental populations of Paramecium aurelia we show that it is possible for a metapopulation comprised entirely of sink populations to persist for many generations in a random environment. In accordance with the theory, we show that positive temporal autocorrelation and low spatial correlation in the environment can ensure the long-term persistence and enhance the mean and maximum abundance of sink metapopulations. High levels of spatial correlation in the environment created strong population synchrony and limited the persistence time of the sink metapopulations. These results have important implications for the development of a theory underlying the synergistic effects of habitat fragmentation and environmental change on population persistence.
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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".