Evidence map›Paper›PMID 40190264›Full record

ArticleEcological applications : a publication of the Ecological Society of America2025

Going with the flow: Leveraging reef-scale hydrodynamics for upscaling larval-based restoration.

Marine Gouezo, Clothilde Langlais, Jack Beardsley, George Roff, Peter L Harrison, Damian P Thomson, Christopher Doropoulos

Abstract read
In one paragraph

Article in Ecological applications : a publication of the Ecological Society of America, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Larval seedboxes: A modular and effective tool for scaling coral reef restoration.Ecological applications : a publication of the Ecological Society of America · 2025
    Article
  5. Going with the flow: Leveraging reef-scale hydrodynamics for upscaling larval-based restoration.Ecological applications : a publication of the Ecological Society of America · 2025
    Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Marine GouezoFaculty of Science and Engineering, Southern Cross University, East Lismore, New South Wales, Australia.ORCID https://orcid.org/0000-0002-8288-0085
Clothilde LanglaisCSIRO Environment, St Lucia, Queensland, Australia.
Jack BeardsleyCSIRO Environment, St Lucia, Queensland, Australia.
George RoffCSIRO Environment, St Lucia, Queensland, Australia.ORCID https://orcid.org/0000-0002-2823-0575
Peter L HarrisonFaculty of Science and Engineering, Southern Cross University, East Lismore, New South Wales, Australia.
Damian P ThomsonCSIRO Environment, St Lucia, Queensland, Australia.
Christopher DoropoulosCSIRO Environment, St Lucia, Queensland, Australia.ORCID https://orcid.org/0000-0001-8038-2771

Funding

Australian Government's Reef TrustGreat Barrier Reef FoundationReef Restoration and Adaptation Program (RRAP)
6 · The paper itself

Abstract

Anthropogenic pressures are impacting coastal marine ecosystems, necessitating large-scale interventions to accelerate recovery. Propagule-based restoration holds the potential for restoring shallow coastal systems at hectare scales by harnessing natural dispersal. However, predicting propagule dispersal remains challenging due to the complex hydrodynamic nature of coastal marine ecosystems and the complex behaviors of marine propagules. To improve predictions of fine-scale larval dispersal patterns, we developed a 3D reef-scale (~30-m resolution) dispersal model for Lizard Island, Australia, with the aim to predict the effect of island-scale hydrodynamics on the distribution of coral spawn slicks and larvae. Using in situ field observations and dispersal simulations, we assessed the model's capability to (1) forecast hydrodynamic conditions, (2) predict coral spawn slick convergence zones for collection efforts, and (3) identify optimal locations and timeframes where high particle residence time may enhance local settlement following larval delivery to damaged reefs. Predictions of convergence zones in the upper water column aligned well with field observations of coral spawn slicks. At the reef benthos, the model captured variability in current speed and direction at ~58% of studied locations. At other locations, the model did not resolve hydrodynamic conditions due to sheltering effects and associated hydrodynamic processes occurring at a scale below 50 m. At locations where the model performed well, propagules could remain within a 1-ha area around the delivery site for 5-15 h depending on locations and the timing of larval release. These high retention conditions were infrequent but occurred at least once at 15 of the 25 studied sites. Observations of local currents a posteriori confirmed model predictions, showing periods of little water movement lasting from 6.5 to 15 h. Overall, our study highlights fine-scale dispersal modeling as a key tool for scaling up larval-based reef restoration, while also acknowledging the need for better predictions of local conditions in complex, shallow environments. Applications of fine-scale modeling, coupled with local knowledge of reproductive timing and larval behavioral ecology, assist with the mass collection of propagules upon release and in identifying areas and times of optimal larval deployment to achieve the greatest impact.

Indexed as

AnthozoaConservation of Natural ResourcesCoral ReefsHydrodynamicsWater MovementsAnimal DistributionAnimalsAustraliaLarvaModels, Biologicalcoral larvaecoral reefscoral spawn slickshydrodynamic modelinglarvae‐based restorationlarval transportresidencyretentionshallow marine habitat

Identifiers

PMID40190264
PMCPMC11973625

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.