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Spiders and subsidies: results from the riparian zone of a coastal temperate rainforest

2007· article· en· W2059661178 on OpenAlexaff
Laurie B. Marczak, John S. Richardson

Bibliographic record

VenueJournal of Animal Ecology · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAquatic insectEcologyRiparian zoneAbundance (ecology)BiologyHabitatRainforest

Abstract

fetched live from OpenAlex

1. Aquatic insects emerging from streams can provide an important energy subsidy to recipient consumers such as riparian web-building spiders. This subsidy has been hypothesized to be of little importance where the primary productivity of the recipient habitat exceeds that of the donor habitat. 2. To test this hypothesis, we manipulated emerging stream insect abundance in a productive riparian rainforest in a replicated design using greenhouse-type exclosures, contrasted with unmanipulated stream reaches (four exclosures on two streams). 3. Experimental exclosures resulted in a 62.9% decrease in aquatic insect abundance in exclusion reaches compared with control reaches. The overall density of riparian spiders was significantly positively correlated with aquatic insect abundances. Horizontal orb weavers (Tetragnathidae) showed a strong response to aquatic insect reduction - abundance at exclosure sites was 57% lower than at control sites. Several spider families that have not been associated with tracking aquatic insect subsidies also showed significantly decreased abundance when aquatic insects were reduced. 4. This result is contrary to predictions of weak subsidy effects where recipient net primary productivity is high. These results suggest that predicting the importance of resource subsidies for food webs requires a focus on the relative abundance of subsidy materials in recipient and donor habitats and not simply on the total flux of energy between systems.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.038
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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

Citations127
Published2007
Admission routes1
Has abstractyes

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