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Record W1974781330 · doi:10.1111/jeb.12362

Combining experimental evolution and field population assays to study the evolution of host range breadth

2014· article· en· W1974781330 on OpenAlexfundno aff
Simon Fellous, Gersende Angot, Marion Orsucci, Alain Migeon, Philippe Auger, Isabelle Olivieri, Maria Navajas

Bibliographic record

VenueJournal of Evolutionary Biology · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsnot available
FundersAgence Nationale de la RechercheOntario Genomics InstituteGenome Canada
KeywordsBiologyHost (biology)Range (aeronautics)Generalist and specialist speciesAdaptation (eye)Host adaptationSpider miteExperimental evolutionPopulationMiteEvolutionary biologyEcologyHabitatGenetics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score0.215

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.250
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations46
Published2014
Admission routes1
Has abstractyes

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