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Record W2810630583 · doi:10.3354/meps12683

Beta diversity changes in estuarine fish communities due to environmental change

2018· article· en· W2810630583 on OpenAlexaboutno aff
J. Linke, Mathieu Boudreau, MH Thériault, S. Courtenay, Roland Cormier, MJ Fortin

Bibliographic record

VenueMarine Ecology Progress Series · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
Fundersnot available
KeywordsBeta diversityGeographyEcologyEstuaryGobyBiodiversityFish <Actinopterygii>FisheryBiology

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 603:161-173 (2018) - DOI: https://doi.org/10.3354/meps12683 Beta diversity changes in estuarine fish communities due to environmental change Andrew T. M. Chin1,*, Julia Linke2, Monica Boudreau3, Marie-Hélène Thériault3, Simon C. Courtenay4, Roland Cormier5, Marie-Josée Fortin1 1Department of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks Street, Toronto, Ontario M5S 3B2, Canada 2Department of Geography, University of Calgary, 2500 University Drive NW, Calgary, Alberta T2N 1N4, Canada 3Fisheries and Oceans Canada, 343 Université Avenue, Moncton, New Brunswick E1C 9B6, Canada 4School of Environment, Resources and Sustainability, Canadian Rivers Institute, University of Waterloo, 200 University Avenue W., Waterloo, Ontario N2L 3G1, Canada 5Centre for Materials and Coastal Research, Helmholtz-Zentrum Geesthacht, 1 Max-Planck-Straße, 21502 Geesthacht, Germany *Corresponding author: atm.chin@mail.utoronto.ca ABSTRACT: Estuarine ecosystems are intrinsically resilient to the dynamic fluctuations of environmental conditions. Yet, it is unknown how the changes in environmental variability associated with climate change will affect fish communities. We assessed how species turnover over space and time in estuaries is influenced by changes in environmental conditions over years. We used fish abundances and water quality sampled at 42 stations among 7 estuaries in New Brunswick (Canada) from 2005 to 2012 to estimate (1) spatial turnover between stations based on the local contribution to beta diversity (LCBD) index, and (2) temporal turnover from year to year based on the β-Sørensen index. We found that beta diversity was potentially structured (i) over space due to inherent within-year differences in each estuary and (ii) over time related to the environmental condition of the previous year which led to changes in salinity, dissolved oxygen, and water temperature at sampling stations. Species contribution to spatial beta diversity (SCBD) was attributed across all years to 4 key species which were sensitive to dissolved oxygen. The current environmental condition of dissolved oxygen, temperature, salinity, and eelgrass Zostera marina affected temporal year-to-year turnover. When each year is analyzed separately, the estuaries with the greatest annual summer temperature fluctuations within a station contribute the most to spatial beta diversity between estuaries. Understanding how fish community structure responds to changes in environmental conditions can help inform the management of estuarine resources in the face of a rapidly changing environment. KEY WORDS: Fish · Environmental change · Community ecology · Salinity · Temperature · Dissolved oxygen · Spatial beta diversity · Temporal beta diversity Full text in pdf format Supplementary material PreviousNextCite this article as: Chin ATM, Linke J, Boudreau M, Thériault MH, Courtenay SC, Cormier R, Fortin MJ (2018) Beta diversity changes in estuarine fish communities due to environmental change. Mar Ecol Prog Ser 603:161-173. https://doi.org/10.3354/meps12683 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in MEPS Vol. 603. Online publication date: September 17, 2018 Print ISSN: 0171-8630; Online ISSN: 1616-1599 Copyright © 2018 Inter-Research.

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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 categoriesOpen science, Insufficient 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 score0.214
Threshold uncertainty score0.996

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.012
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0310.000

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 head0.027
GPT teacher head0.243
Teacher spread0.216 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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