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Insect community organisation in estuaries: the role of the physical environment

2002· article· en· W2121500343 on OpenAlexafffund
Tanya Hamm

Bibliographic record

VenueEcography · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMarine Biology and Ecology Research
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEstuaryAquatic insectSpecies richnessEcologyBenthic zoneHabitatCommunity structureSalt marshFaunaBiology

Abstract

fetched live from OpenAlex

Insects are reportedly uncommon in marine habitats and, from a spatial/temporal intercomparison perspective, estuaries are among the least studied. We examined the natural variability seen among insect community organisation in estuaries on both sides of the North Atlantic, and evaluated the role of their physical environments. Community composition was found to be strongly influenced by three physical factors: estuary size, the degree of inundation by incoming tides, and substrate size/stability. Insects formed a significant proportion (17–54%, by numbers) of the benthic community of coarse‐grained‐substratum estuaries, and species richness increased with estuary size. Nymphs/larvae of mayflies, stoneflies, caddisflies, elmid beetles and chironomids dominated channel sites inundated by up to 25% of all incoming tides, but a gradual loss in species richness occurred downstream. However, even the most seaward sites supported high insect densities (up to 25 016 and 5433 m −2 , supporting 26 and 4 species, at sites inundated by 75 and 100% of all incoming tides, respectively). Sites covered by tides for between 3 and 5 h twice daily were dominated by orthocladine chironomids, especially of the genus Orthocladius . Chironomid larvae contribute significantly to the diets of some coastal fish species, particularly juvenile flounder and sticklebacks. We present a schematic model summarising the relationships between estuary size, degree of inundation by salt water and insect community structure.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.998

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)0.0030.000

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 head0.019
GPT teacher head0.188
Teacher spread0.170 · 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 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

Citations27
Published2002
Admission routes2
Has abstractyes

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