Metapopulations, the inflationary effect, and consequences for public health
Bibliographic record
Abstract
Abstract The metapopulation perspective is an important conceptual framework in ecology and evolutionary ecology. Metapopulations are spatially distributed populations linked by dispersal. Both metapopulation models and their community and ecosystem level analogues, metacommunity and meta-ecosystem models, tend to be more stable regionally than locally and display enhanced abundance because of the interplay of spatiotemporal heterogeneity and dispersal (an effect that has been called the “inflationary effect”). We highlight the essential role of spatiotemporal heterogeneity in metapopulation biology, sketch empirical demonstrations of the inflationary effect, and provide a mechanistic interpretation of how the inflationary effect arises and impacts population growth and abundance. We illustrate the effect with examples from the spread of infectious disease. Namely, failure to recognize the full possible effects of spatiotemporal heterogeneity likely enhanced the spread of COVID-19, a failure based on lack of understanding of emergent population processes at large scales which may hamper control and eradication of other infectious diseases. We finish by noting how the effects of spatiotemporal heterogeneity have implicitly played roles in the history of ecology, ranging across subdisciplines as diverse as natural enemy-victim dynamics, species coexistence, and conservation biology. Seriously confronting the complexity of spatiotemporal heterogeneity could push many of these subdisciplines forward.
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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 | 0.007 | 0.030 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".