Fitness at the Expanding Front: An Exploration-Exploitation Trade-off in Phenotypic Switching
Bibliographic record
Abstract
Phenotypic switching is a key bet-hedging strategy for navigating the exploration-exploitation trade-off in fluctuating environments, yet its interplay with spatial population dynamics during range expansions remains poorly understood. We use an individual-based spatial simulation to investigate how switching strategies shape fitness (expansion speed) in heterogeneous landscapes. Maximizing expansion speed requires balancing exploration and exploitation, leading to an optimal intermediate switching rate in these spatial settings. Critically, we incorporate a phenotypic switching lag, representing the biophysical cost of adaptation. We demonstrate this lag imposes a hard fitness constraint, forcing the optimal strategy toward slower switching rates as lag duration increases and reducing maximum expansion speed. Contrasting with models focusing on irreversible mutations, we show reversible switching enhances resilience by allowing recovery from maladaptation, influencing specialist persistence boundaries. Spatial structure also generates emergent phenomena: we find clustering provides collective protection, enhancing the survival of disadvantaged phenotypes. Additionally, our simulations show that conditioning on lineage survival alone is sufficient to generate the apparent stagnancy of deleterious sectors reported experimentally, revealing this observational bias as a crucial factor when characterizing selection effects at expanding fronts. This work integrates bethedging theory with adaptation costs and spatial dynamics, offering a quantitative framework for fitness at expanding fronts.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".