Combining experimental evolution and field population assays to study the evolution of host range breadth
Bibliographic record
Abstract
Adapting to specific hosts often involves trade-offs that limit performance on other hosts. These constraints may either lead to narrow host ranges (i.e. specialists, able to exploit only one host type) or wide host ranges often leading to lower performance on each host (i.e. generalists). Here, we combined laboratory experiments on field populations with experimental evolution to investigate the impact of adaptation to the host on host range evolution and associated performance over this range. We used the two-spotted spider mite, Tetranychus urticae, a model organism for studies on the evolution of specialization. Field mite populations were sampled on three host plant species: tomato, citrus tree and rosebay (Nerium oleander). Testing these populations in the laboratory revealed that tomato populations of mites could exploit tomato only, citrus populations could exploit citrus and tomato whereas Nerium populations could exploit all three hosts. Besides, the wider niche ranges of citrus and Nerium populations came at the cost of low performance on their non-native hosts. Experimental lines selected to live on the same three host species exhibited similar patterns of host range and relative performance. This result suggests that adaptation to a new host species may lead to wider host ranges but at the expense of decreased performance on other hosts. We conclude that experimental evolution may reliably inform on evolution in the field.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".