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Record W2337561254 · doi:10.3354/meps11674

Linking eelgrass decline and impacts on associated fish communities to European green crab Carcinus maenas invasion

2016· article· en· W2337561254 on OpenAlexaboutno aff
Kyle Matheson, CH McKenzie, R. S. Gregory, DA Robichaud, IR Bradbury, PVR Snelgrove, GA Rose

Bibliographic record

VenueMarine Ecology Progress Series · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicCrustacean biology and ecology
Canadian institutionsnot available
Fundersnot available
KeywordsCarcinus maenasZostera marinaFisherySeagrassMarine ecosystemGeographyBiologyEcosystemEcologyDecapodaCrustacean

Abstract

fetched live from OpenAlex

MEPS Marine Ecology Progress Series Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsTheme Sections MEPS 548:31-45 (2016) - DOI: https://doi.org/10.3354/meps11674 Linking eelgrass decline and impacts on associated fish communities to European green crab Carcinus maenas invasion K. Matheson1,*, C. H. McKenzie1, R. S. Gregory1, D. A. Robichaud2, I. R. Bradbury1, P. V. R. Snelgrove3, G. A. Rose4 1Fisheries and Oceans Canada, Ecological Sciences Section, Northwest Atlantic Fisheries Centre, St. John’s, Newfoundland and Labrador A1C 5X1, Canada 2LGL Limited, Sidney, British Columbia V8L 3Y8, Canada 3Department of Ocean Sciences and Biology Department, Memorial University of Newfoundland, St. John’s, Newfoundland and Labrador A1C 5S7, Canada 4Centre for Fisheries Ecosystems Research, Fisheries and Marine Institute of Memorial University of Newfoundland, St. John’s, Newfoundland and Labrador A1C 5R3, Canada *Corresponding author: kyle.matheson@dfo-mpo.gc.ca ABSTRACT: Following their first detection in Newfoundland in 2007, populations of invasive European green crabs Carcinus maenas (Linnaeus, 1758) have increased and spread throughout eelgrass Zostera marina meadows. Green crabs can reduce eelgrass biomass by damaging rhizomes and plant shoots when burrowing for shelter and digging for prey. Empirically demonstrating large spatial-scale impacts of green crabs on eelgrass and subsequent cascading effects on the ecosystem has proven difficult because of the general absence of effective baseline studies prior to an invasion of green crabs. We conducted surveys in Placentia and Bonavista bays, Newfoundland (20 sites) to compare eelgrass and associated fish communities before and after an invasion of green crabs. We analyzed eelgrass surveys from 1998 and 1999 (before green crab) and again in 2012 (after green crab) using a Before-After-Control-Impact (BACI) study design in order to isolate effects of crab-induced eelgrass loss from effects independent of green crabs. Underwater video sampling evaluated eelgrass change over time and indicated a 50% decline in eelgrass percent cover since 1998 at sites with green crabs, and eelgrass declines up to 100% at sites with highest abundances and longest established presence of green crabs. Beach seining showed a sharp decline in abundance and biomass of fish (~10-fold between sites with and without green crabs) and indicated changes in fish community structure after green crab arrival at a site. Our results suggest cascading effects on fish communities and substantial potential impacts in coastal ecosystems occur following green crab invasion. KEY WORDS: Invasive species · Green crab · Eelgrass · Fish community · Before-After-Control-Impact · BACI Full text in pdf format PreviousNextCite this article as: Matheson K, McKenzie CH, Gregory RS, Robichaud DA, Bradbury IR, Snelgrove PVR, Rose GA (2016) Linking eelgrass decline and impacts on associated fish communities to European green crab Carcinus maenas invasion. Mar Ecol Prog Ser 548:31-45. https://doi.org/10.3354/meps11674 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 548. Online publication date: April 21, 2016 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2016 Inter-Research.

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)0.001
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 score0.057
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.002
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 head0.015
GPT teacher head0.229
Teacher spread0.213 · 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

Citations96
Published2016
Admission routes1
Has abstractyes

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