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Record W2100129479 · doi:10.1603/an10047

Tachinid (Diptera: Tachinidae) Parasitoid Diversity and Temporal Abundance at a Single Site in the Northeastern United States

2011· article· en· W2100129479 on OpenAlexfundno aff
Diego J. Inclán, John O. Stireman

Bibliographic record

VenueAnnals of the Entomological Society of America · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicDiptera species taxonomy and behavior
Canadian institutionsnot available
FundersAgriculture and Agri-Food CanadaNational Museum of Natural HistoryDirectorate for Biological SciencesNational Science Foundation
KeywordsTachinidaeSpecies richnessBiologyParasitoidEcologyRarefaction (ecology)Abundance (ecology)Species diversityHymenoptera

Abstract

fetched live from OpenAlex

Abstract Although tachinids are one of the most diverse families of Diptera and represent the largest group of nonhymenopteran parasitoids, their local diversity and distribution patterns of most species in the family are poorly known. In this study, 2 yr of samples from a Malaise trap were used to analyze the local richness and temporal distribution of a tachinid community in southwestern Ohio. In total, 883 tachinid specimens were collected, consisting of 117 species belonging to 69 genera. The majority of the specimens were collected in fall, followed by summer and spring, with 67, 22, and 11%, respectively. Estimated rarefaction curves indicate a predicted species richness of 217 species and suggest that we have sampled only a fraction of the diversity of Tachinidae occurring at this particular site. The species recorded in this study represent 16 and 19% of the species that are likely to occur in the northeastern United States and Ohio, respectively. In North America, there have been few, if any, previous attempts to quantify the diversity of tachinids on a local scale. Our results provide a baseline for understanding the temporal and spatial diversity of these ecologically and agronomically important parasitoids.

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 score0.018
Threshold uncertainty score0.846

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.092
GPT teacher head0.253
Teacher spread0.160 · 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

Citations9
Published2011
Admission routes1
Has abstractyes

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