Dispersal and spatial heterogeneity allow coexistence between enemies and protective mutualists
Bibliographic record
Abstract
Protective mutualisms, where a symbiont reduces the negative effects of another species on a shared host, represent a common type of species interaction in natural communities, yet it is still unclear what ecological conditions might favor their emergence. Studies suggest that the initial evolution of protective mutualists might involve closely related pathogenic variants with similar life histories, but different competitive abilities and impacts on host fitness. We derive a model to evaluate this hypothesis and show that, in general, a protective variant cannot spread from rarity or exclude a more pathogenic strain. While the conditions allowing mutualist invasion are more likely with increased environmental productivity, they still depend on initial densities in the invaded patch exceeding a threshold, highlighting the likely importance of spatial structure and demographic stochasticity. Using a numerical simulation approach, we show that regional coexistence is in fact possible in an explicitly spatial system and that, under some circumstances, the mutualist population can exclude the enemy. More broadly, the establishment of protective mutualists may be favored when there are other life-history differences from more pathogenic symbionts, such as vertical transmission or additional direct benefits to hosts.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".