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Record W4200199666 · doi:10.1017/s0031182021002122

Host specificity and the reproductive strategies of parasites

2021· article· en· W4200199666 on OpenAlexfundno aff

Bibliographic record

VenueParasitology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorUniversity of Otago
KeywordsGeneralist and specialist speciesHost (biology)Host specificityTraitEvolutionarily stable strategyReproductive successAvian clutch sizeNiche

Abstract

fetched live from OpenAlex

Environmental stability can have profound impacts on life history trait evolution in organisms, especially with respect to development and reproduction. In theory, free-living species, when subjected to relatively stable and predictable conditions over many generations, should evolve narrow niche breadths and become more specialized. In parasitic organisms, this level of specialization is reflected by their host specificity. Here, we tested how host specificity impacts the reproductive strategies of parasites, a subject seldomly addressed for this group. Through an extensive review of the literature, we collated a worldwide dataset to predict, through Bayesian multilevel modelling, the effect of host specificity on the reproductive strategies of parasitic copepods of fishes or corals. We found that copepods of fishes with low host specificity (generalists) invest more into reproductive output with larger clutch sizes, whereas generalist copepods of corals invest less into reproductive output with smaller clutch sizes. The differences in host turnover rates through an evolutionary timescale could explain the contrasting strategies across species observed here, which should still favour the odds of parasites encountering and infecting a host. Ultimately, the differences found in this study reflect the unique evolutionary history that parasites share both intrinsically and extrinsically with their hosts.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.334
Teacher spread0.320 · 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.

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

Citations9
Published2021
Admission routes1
Has abstractyes

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