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Tropics, trophics and taxonomy: the determinants of parasite‐associated host mortality

2010· article· en· W2000629988 on OpenAlexaff
Nicholas Robar, Gary Burness, Dennis L. Murray

Bibliographic record

VenueOikos · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEvolution and Genetic Dynamics
Canadian institutionsTrent University
Fundersnot available
KeywordsBiologyParasite hostingHost (biology)EcologyZoology

Abstract

fetched live from OpenAlex

Empirical studies often reveal deleterious effects of parasites on host survival, but the ecological and environmental processes modulating parasite‐associated host mortality are not well understood. We conducted meta‐analysis of experimental studies assessing parasite‐associated mortality (n = 52) to evaluate broad‐scale patterns in host mortality risk relative to host or parasite taxon, parasite life cycle, or local environmental conditions. Overall, likelihood of host mortality was ∼2.6 times higher among infected individuals when compared with hosts that either lacked parasites or had experimentally‐reduced parasite burdens. Parasites with complex life cycles reliant on predation‐mediated transmission generally were associated with higher mortality risk than those exploiting other transmission strategies. We also detected a negative relationship between parasite‐associated host mortality and latitude; host mortality risk declined by ∼2.6% with each degree increase in latitude. This result indicated the likely importance of abiotic factors in determining parasite effects. Host taxonomy further influenced parasite‐associated mortality risk, with amphibian, fish, and mollusc hosts generally having higher hazard than arthropod, mammal, and bird hosts. Our results suggest patterns that conform to the predicted link between host mortality and parasite transmissibility, and pathogenicity. The relationship between host mortality and latitude in particular may portend marked shifts in host–parasite relationships pursuant to ongoing and projected global climate change.

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 score0.188
Threshold uncertainty score0.238

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 head0.014
GPT teacher head0.266
Teacher spread0.252 · 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

Citations55
Published2010
Admission routes1
Has abstractyes

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