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Macro‐Tidal Salt Marsh Ecosystem Response to Culvert Expansion

2009· article· en· W2001476272 on OpenAlexaff
Tony Bowron, Nancy Neatt, Danika van Proosdij, Jeremy Lundholm, Jennie Graham

Bibliographic record

VenueRestoration Ecology · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsSalt marshMarshCulvertNektonRestoration ecologyVegetation (pathology)Environmental scienceHabitatSpecies richnessWetlandEcologyHydrology (agriculture)GeographyGeology

Abstract

fetched live from OpenAlex

The purpose of this paper was to examine the vegetative, sedimentary, nekton and hydrologic conditions pre‐restoration and the initial 2 years post‐restoration at a partially restricted macro‐tidal salt marsh site. Replacement of the culvert increased tidal flow by 88%. This was instrumental in altering the geomorphology of the site, facilitating the creation of new salt marsh pannes, expansion of existing pannes in the mid and high marsh zones, and expansion of the tidal creek network by incorporating relict agricultural ditches. In addition, the increase in area flooded resulted in a significant increase in nekton use, fulfilling the mandate of a federal habitat compensation program to increase and improve the overall availability and accessibility of fish habitat. The restoration of a more natural hydrological regime also resulted in the die‐off of freshwater and terrestrial vegetation along the upland edge of the marsh. Two years post‐restoration, Salicornia europea (glasswort) and Atriplex glabriuscula (marsh orache), were observed growing in these die‐back areas. Similar changes in the vegetation community structure were not observed at the reference site; however, the latter did contain higher species richness. This study represents the first comprehensive, quantitative analysis of ecological response to culvert replacement in a hypertidal ecosystem. These data will contribute to the development of long‐term data sets of pre‐ and post‐restoration, and reference marsh conditions to determine if a marsh is proceeding as expected, and to help with models that are aimed at predicting the response of marshes to tidal restoration at the upper end of the tidal spectrum.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.619
Threshold uncertainty score0.997

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.004

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 head0.006
GPT teacher head0.226
Teacher spread0.220 · 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

Citations39
Published2009
Admission routes1
Has abstractyes

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