How specialists can be generalists: resolving the “parasite paradox” and implications for emerging infectious disease
Bibliographic record
Abstract
The parasite paradox arises from the dual observations that parasites (broadly construed, including phytophagous insects) are resource specialists with restricted host ranges, and yet shifts onto relatively unrelated hosts are common in the phylogenetic diversification of parasite lineages and directly observable in ecological time. We synthesize the emerging solution to this paradox: phenotypic flexibility and phylogenetic conservatism in traits related to resource use, grouped under the term ecological fitting, provide substantial opportunities for rapid host switching in changing environments, in the absence of the evolution of novel host-utilization capabilities. We discuss mechanisms behind ecological fitting, its implications for defining specialists and generalists, and briefly review empirical examples of host shifts in the context of ecological fitting. We conclude that host shifts via ecological fitting provide the fuel for the expansion phase of the recently proposed oscillation hypothesis of host range and speciation, and, more generally, the generation of novel combinations of interacting species within the geographic mosaic theory of coevolution. Finally, we conclude that taxon pulses, driven by climate change and large-scale ecological perturbation are drivers of biotic mixing and resultant ecological fitting, which leads to increased rates of rapid host switching, including the agents of Emerging Infectious Disease.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".