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Record W4321166785 · doi:10.1002/ecy.4001

Environmental refuges from disease in host–parasite interactions under global change

2023· review· en· W4321166785 on OpenAlexaff
Alena S. Gsell, Arjen Biere, Wietse de Boer, Irene de Bruijn, Götz Eichhorn, Thijs Frenken, Stefan Geisen, Henk P. van der Jeugd, Kyle Mason‐Jones, Annelein Meisner, Madhav P. Thakur, Ellen van Donk, Mark P. Zwart, Dedmer B. Van de Waal

Bibliographic record

VenueEcology · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicParasite Biology and Host Interactions
Canadian institutionsUniversity of Windsor
FundersInstitut National Du CancerVetenskapsrådetHORIZON EUROPE Marie Sklodowska-Curie ActionsNederlandse Organisatie voor Wetenschappelijk OnderzoekDeutsche Forschungsgemeinschaft
KeywordsHost (biology)EcologyEnvironmental changeParasite hostingBiologyGeographyClimate change

Abstract

fetched live from OpenAlex

The physiological performance of organisms depends on their environmental context, resulting in performance-response curves along environmental gradients. Parasite performance-response curves are generally expected to be broader than those of their hosts due to shorter generation times and hence faster adaptation. However, certain environmental conditions may limit parasite performance more than that of the host, thereby providing an environmental refuge from disease. Thermal disease refuges have been extensively studied in response to climate warming, but other environmental factors may also provide environmental disease refuges which, in turn, respond to global change. Here, we (1) showcase laboratory and natural examples of refuges from parasites along various environmental gradients, and (2) provide hypotheses on how global environmental change may affect these refuges. We strive to synthesize knowledge on potential environmental disease refuges along different environmental gradients including salinity and nutrients, in both natural and food-production systems. Although scaling up from single host-parasite relationships along one environmental gradient to their interaction outcome in the full complexity of natural environments remains difficult, integrating host and parasite performance-response can serve to formulate testable hypotheses about the variability in parasitism outcomes and the occurrence of environmental disease refuges under current and future environmental conditions.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.537
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.036

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 head0.068
GPT teacher head0.404
Teacher spread0.336 · 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
GenreReview

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

Citations35
Published2023
Admission routes1
Has abstractyes

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