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Record W2773817173 · doi:10.1111/ecog.03514

Compositional turnover in host and parasite communities does not change network structure

2017· article· en· W2773817173 on OpenAlexaff
Tad Dallas, Timothée Poisot

Bibliographic record

VenueEcography · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsGeographical distanceHost (biology)Parasite hostingDistance decayEcologySimilarity (geometry)BiologyComputer sciencePopulationDemography

Abstract

fetched live from OpenAlex

Decreasing similarity between ecological communities with increasing geographic distance (i.e. distance‐decay ) is a common biogeographical observation in free‐living communities, and a slightly less common observation for parasite communities. Ecological networks of interacting species may adhere to a similar pattern of decreasing interaction similarity with increasing geographic distance, especially if species interactions are maintained across space. We extend this further, examining if host–parasite networks – independent of host and parasite species identities – become more structurally dissimilar with increasing geographic distance. Utilizing a global database of helminth parasite occurrence records, we find evidence for distance‐decay relationships in host and parasite communities at both regional and global scales, but fail to detect similar relationships in network structural similarity. Host and parasite community similarity were strongly related, and both decayed rapidly with increasing geographic distance, typically resulting in complete dissimilarity after approximately 2500 km. Our failure to detect a decay in network structural similarity suggests the possibility that different host and parasite species are filling the same functional roles in interaction networks, or that variation in network similarity may be better explained by other geographic variables or aspects of host and parasite ecology.

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 score0.076
Threshold uncertainty score0.940

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 studies0.0010.000
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 head0.053
GPT teacher head0.225
Teacher spread0.172 · 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

Citations33
Published2017
Admission routes1
Has abstractyes

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