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Record W2943379602 · doi:10.1098/rspb.2019.0260

Community disassembly and disease: realistic—but not randomized—biodiversity losses enhance parasite transmission

2019· article· en· W2943379602 on OpenAlexaff
Pieter T. J. Johnson, Dana M. Calhoun, Tawni B. Riepe, Travis McDevitt‐Galles, Janet Koprivnikar

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsToronto Metropolitan University
FundersDivision of Environmental BiologyNational Institute of General Medical SciencesNational Institutes of HealthDavid and Lucile Packard Foundation
KeywordsSpecies richnessBiologyHost (biology)BiodiversityEcologyTransmission (telecommunications)Community structureSpecies diversity

Abstract

fetched live from OpenAlex

Debates over the relationship between biodiversity and disease dynamics underscore the need for a more mechanistic understanding of how changes in host community composition influence parasite transmission. Focusing on interactions between larval amphibians and trematode parasites, we experimentally contrasted the effects of host richness and species composition to identify the individual and joint contributions of both parameters on the infection levels of three trematode species. By combining experimental approaches with field surveys from 147 ponds, we further evaluated how richness effects differed between randomized and realistic patterns of species loss (i.e. community disassembly). Our results indicated that community-level changes in infection levels were owing to host species composition, rather than richness. However, when composition patterns mirrored empirical observations along a natural assembly gradient, each added host species reduced infection success by 12-55%. No such effects occurred when assemblages were randomized. Mechanistically, these patterns were due to non-random host species assembly/disassembly: while highly competent species predominated in low diversity systems, less susceptible hosts became progressively more common as richness increased. These findings highlight the potential for combining information on host traits and assembly patterns to forecast diversity-mediated changes in multi-host disease systems.

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)0.001
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 score0.298
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.013
GPT teacher head0.254
Teacher spread0.241 · 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
Published2019
Admission routes1
Has abstractyes

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