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Record W2507554845 · doi:10.1139/cjfas-2015-0507

A once in 10 year drought alters the magnitude and quality of a floodplain prey subsidy to coastal river fishes

2016· article· en· W2507554845 on OpenAlexvenueno aff
Ross E. Boucek, Mariajesus Soula, Felipe Tamayo, Jennifer S. Rehage

Bibliographic record

VenueCanadian Journal of Fisheries and Aquatic Sciences · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
FundersU.S. Army Corps of EngineersU.S. Geological SurveySouth Florida Water Management DistrictNational Science Foundation
KeywordsFloodplainPredationFood webEcologyHabitatFisheryEnvironmental scienceBiology

Abstract

fetched live from OpenAlex

Disturbances that alter cross-habitat food web linkages can lead to whole-scale changes to aquatic systems. In coastal rivers of the Everglades (Florida, U.S.A.), increases in rainfall inundate adjacent floodplains, providing habitat for floodplain fish and macroinvertebrate species. In the dry season, rainfall decreases and floodplains dry, forcing floodplain prey into these river systems. These prey provide a prey subsidy for an estuarine predator, the common snook (Centropomus undecimalis). In 2011, severe drought impacted the region, likely affecting this prey subsidy. In this study, we ask (i) did the 2011 drought affect the magnitude and composition of floodplain prey subsidies to the common snook? and (ii) if species composition changed, were there energetic differences between the pre- and post-disturbance prey species? Results showed that 1 year after the drought, subsidies to the common snook decreased by 75%. On top of that decrease in overall flux, diet composition of the common snook switched from floodplain fishes to drought-tolerant floodplain macroinvertebrates. Lastly, energetic analyses showed that these postdrought macroinvertebrate prey subsidies had 43% less calories than floodplain fishes. Our findings illustrate the importance of considering not only the biomass that transfers from one food web to the next, but also how the species composition of the subsidy may affect incorporation into recipient food webs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.021
GPT teacher head0.228
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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

Citations31
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Fisheries and Aquatic Sciences→Same topicFish Ecology and Management Studies→French-language works237,207→