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Predicting the similarity of parasite communities in freshwater fishes using the phylogeny, ecology and proximity of hosts

2012· article· en· W2034772955 on OpenAlexaffabout
Sean A. Locke, John McLaughlin, David J. Marcogliese

Bibliographic record

VenueOikos · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsEnvironment and Climate Change Canada
Fundersnot available
KeywordsBiologyEcologyDNA barcodingHost (biology)Parasite hostingTrophic levelPhylogeneticsAbundance (ecology)Phylogenetic treeDistance decayZoologyGeographical distanceSpatial ecologyPopulationGene

Abstract

fetched live from OpenAlex

Parasite communities tend to be dissimilar in hosts that are geographically, phylogenetically, ecologically and developmentally distant from one another. The decay of community similarity is a powerful and increasingly common method of studying parasite beta diversity, but most studies have examined only a single type of distance. Here, we evaluate distances based on the phylogeny, ecology, spatial proximity and size of hosts, as predictors of the similarity of parasite communities in individual hosts, host populations and host species. We surveyed parasites in six species of fish collected simultaneously from six localities in the St. Lawrence River, Canada, and species in a common group of larval parasites were discriminated using DNA sequences from barcode region of cytochrome c oxidase I. Distances based on the habitat use patterns of host species were good predictors of short‐term, ecological similarity of parasite communities, such as that operating at the scale of the individual host. The genetic distance between host species was associated with almost all types of similarity at all scales, particularly qualitative and phylogenetic similarity of parasite communities at the level of populations and meta‐populations of hosts. The trophic level, diet, spatial proximity and size of hosts were poor predictors of parasite community similarity. The increased taxonomic resolution provided by molecular data increased the explanatory power of regression models, and different factors were implicated when parasite species were distinguished with DNA barcodes than when larval parasites were lumped into morphospecies, as is commonly practiced.

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 score0.008
Threshold uncertainty score0.620

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)0.0000.000

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 head0.033
GPT teacher head0.311
Teacher spread0.278 · 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

Citations65
Published2012
Admission routes2
Has abstractyes

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