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Record W2917893885 · doi:10.1038/s42003-019-0364-z

Localised anthropogenic wake generates a predictable foraging hotspot for top predators

2019· article· en· W2917893885 on OpenAlexfundno aff
Lilian Lieber, W. Alex M. Nimmo‐Smith, James J. Waggitt, Louise Kregting

Bibliographic record

VenueCommunications Biology · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine and fisheries research
Canadian institutionsnot available
FundersNatural Environment Research CouncilEuropean CommissionQueen's UniversityDepartment for Environment, Food and Rural Affairs, UK GovernmentQueen's University BelfastSight Research UK
KeywordsForagingSeabirdPredationWakeHotspot (geology)Environmental scienceEcologyTransectPredatorOceanographyGeologyBiologyEngineering

Abstract

fetched live from OpenAlex

With rapid expansion of offshore renewables, a broader perspective on their ecological implications is timely to predict marine predator responses to environmental change. Strong currents interacting with man-made structures can generate complex three-dimensional wakes that can make prey more accessible. Whether localised wakes from man-made structures can generate predictable foraging hotspots for top predators is unknown. Here we address this question by quantifying the relative use of an anthropogenically-generated wake by surface foraging seabirds, verified using drone transects and hydroacoustics. We show that the wake of a tidal energy structure promotes a localised and persistent foraging hotspot, with seabird numbers greatly exceeding those at adjacent natural wake features. The wake mixes material throughout the water column, potentially acting like a prey conveyer belt. Our findings highlight the importance of identifying the physical scales and mechanisms underlying predator hotspot formation when assessing the ecological consequences of installing or removing anthropogenic structures.

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 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 score0.950
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.030
GPT teacher head0.309
Teacher spread0.279 · 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

Citations46
Published2019
Admission routes1
Has abstractyes

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