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Record W4220904687 · doi:10.1111/gcb.16134

Influence of offshore oil and gas structures on seascape ecological connectivity

2022· review· en· W4220904687 on OpenAlexafffund
Dianne McLean, Luciana C. Ferreira, Jessica A. Benthuysen, Karen J. Miller, Marie‐Lise Schläppy, Matthew J. Ajemian, Oliver Berry, Silvana N.R. Birchenough, Todd Bond, Fabio Boschetti, Ann Scarborough Bull, Jeremy T. Claisse, Scott A. Condie, Pierpaolo Consoli, Joop W.P. Coolen, Michael Elliott, Irene Fortune, Ashley M. Fowler, Bronwyn M. Gillanders, Hugo B. Harrison, Kristen M. Hart, Lea‐Anne Henry, Chad L. Hewitt, Natalie Hicks, Karlo Hock, Kieran Hyder, Milton S. Love, Peter I. Macreadie, Robert J. Miller, William A. Montevecchi, Mary M. Nishimoto, Henry M. Page, David M. Paterson, Charitha Pattiaratchi, GT Pecl, Joanne S. Porter, David B. Reeves, Cynthia Riginos, Sally Rouse, Debbie J. F. Russell, Craig D. H. Sherman, Jonas Teilmann, Victoria L. G. Todd, Eric A. Treml, David H. Williamson, Michele Thums

Bibliographic record

VenueGlobal Change Biology · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaNational Drug Research InstituteScottish Funding CouncilSight Research UKMarine Alliance for Science and Technology for ScotlandUK Research and InnovationNatural Environment Research CouncilAustralian GovernmentCentre for Environment, Fisheries and Aquaculture Science
KeywordsSeascapeNuclear decommissioningBiological dispersalEcologyEnvironmental resource managementHabitatGeographyEnvironmental scienceBiologyEngineering

Abstract

fetched live from OpenAlex

Offshore platforms, subsea pipelines, wells and related fixed structures supporting the oil and gas (O&G) industry are prevalent in oceans across the globe, with many approaching the end of their operational life and requiring decommissioning. Although structures can possess high ecological diversity and productivity, information on how they interact with broader ecological processes remains unclear. Here, we review the current state of knowledge on the role of O&G infrastructure in maintaining, altering or enhancing ecological connectivity with natural marine habitats. There is a paucity of studies on the subject with only 33 papers specifically targeting connectivity and O&G structures, although other studies provide important related information. Evidence for O&G structures facilitating vertical and horizontal seascape connectivity exists for larvae and mobile adult invertebrates, fish and megafauna; including threatened and commercially important species. The degree to which these structures represent a beneficial or detrimental net impact remains unclear, is complex and ultimately needs more research to determine the extent to which natural connectivity networks are conserved, enhanced or disrupted. We discuss the potential impacts of different decommissioning approaches on seascape connectivity and identify, through expert elicitation, critical knowledge gaps that, if addressed, may further inform decision making for the life cycle of O&G infrastructure, with relevance for other industries (e.g. renewables). The most highly ranked critical knowledge gap was a need to understand how O&G structures modify and influence the movement patterns of mobile species and dispersal stages of sessile marine species. Understanding how different decommissioning options affect species survival and movement was also highly ranked, as was understanding the extent to which O&G structures contribute to extending species distributions by providing rest stops, foraging habitat, and stepping stones. These questions could be addressed with further dedicated studies of animal movement in relation to structures using telemetry, molecular techniques and movement models. Our review and these priority questions provide a roadmap for advancing research needed to support evidence-based decision making for decommissioning O&G infrastructure.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.987
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.064
GPT teacher head0.308
Teacher spread0.244 · 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 designOther design
Domainnot available
GenreReview

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

Citations81
Published2022
Admission routes2
Has abstractyes

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