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Record W2396969315 · doi:10.1002/ecy.1437

Exploited and excreting: parasite type affects host nutrient recycling

2016· article· en· W2396969315 on OpenAlexaff
Charlotte F. Narr, Paul C. Frost

Bibliographic record

VenueEcology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsTrent University
Fundersnot available
KeywordsBiologyNutrientDaphniaHost (biology)FecundityParasite hostingEcologyZoologyNutrient cycleCrustaceanPopulation

Abstract

fetched live from OpenAlex

Parasite-induced changes in the nutrient balance of hosts could alter the availability of nutrients in ecosystems by changing consumer-driven nutrient recycling. While these effects on host nutrient use are mediated by host physiology, they likely depend on characteristics of the parasite and host diet quality. We examined this possibility by measuring nutrient release rates of uninfected Daphnia and conspecifics infected by two microparasites (the bacterium Pasteuria ramosa and the microsporidium Hamiltosporidium tvaerminnensis) from daphnid hosts fed food that varied in phosphorus content. We found that infection type and diet affected host nutrient release rates, but the strength of these effects varied among parasite treatments. To improve our understanding of these effects, we examined whether two separate aspects of host exploitation (parasite-induced reductions in host fecundity and parasite load) could account for variation in Daphnia nutrient release, ingestion, and elemental ratios caused by our infection and diet treatments. Regardless of whether we compared individuals across infection type or diet treatment, Daphnia fecundity described variation in multiple aspects of host nutrient use better than infection, diet, or spore load. Our results suggest that parasite-induced changes in host nutrient use are both parasite and diet specific, and that host fecundity could be a useful parameter for predicting the magnitude and direction of these changes.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.999

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)0.0030.002

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 head0.016
GPT teacher head0.304
Teacher spread0.288 · 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; both teacher heads agree on what is shown here.

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

Citations20
Published2016
Admission routes1
Has abstractyes

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