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Record W6948282750 · doi:10.5061/dryad.3r2280gtm

Species' traits modulate rapid changes in flight time in high-Arctic muscid flies under climate change

2025· dataset· en· W6948282750 on OpenAlexaff

Bibliographic record

VenueDRYAD · 2025
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCell Image Analysis Techniques
Canadian institutionsBishop's UniversityFisheries and Oceans Canada
FundersInnovationsfonden
KeywordsPhenologyInterspecific competitionClimate changeContext (archaeology)PopulationInsect flightHabitatAdaptation (eye)

Abstract

fetched live from OpenAlex

Insects are experiencing notable phenological shifts due to climate change, with substantial interspecific variability. However, our understanding is limited by a shortage of long-term studies, beyond Lepidoptera. This study presents a hierarchical modeling framework to analyze the phenological distribution of eleven muscid fly species across three vegetation types over 18 years (1996 - 2014) in Zackenberg, Northeast Greenland. We examined species-specific changes in phenology and assessed ecological traits for explaining interspecific variation. Additionally, we investigated the associations between phenological shifts and timing of snowmelt and temperature. We found consistent trends of earlier flight activity and interspecific variation in responses, with smaller species shifting their end of the season activity at faster rates than larger species. Flight activity was strongly associated with the timing of snowmelt, while warming was linked to an earlier end of the flight season. Late-active species exhibited more pronounced shifts in response to climate variations than early-active species. This study highlights the species-specific climate sensitivity of high-Arctic muscid flies potentially having demographic effects if temporal overlaps among interacting species change. We advocate for prioritizing species-specific insect population studies, ideally analyzed within the context of interacting species, to understand better and address disparities in responses to 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2025
Admission routes1
Has abstractyes

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