Evidence map›Paper›PMID 39333629›Full record

ArticleScientific reports2024

Establishing complexity targets to enhance artificial reef designs.

Elisabeth Riera, Benjamin Mauroy, Patrice Francour, Cédric Hubas

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
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

4 authors.

Elisabeth RieraUniversité Côte d'Azur, CNRS, ECOSEAS, Parc Valrose, 06108, Nice Cedex 02, France. riera.elisabeth13@gmail.com.
Benjamin MauroyUniversité Côte d'Azur, CNRS, UMR 7351 LJAD, Parc Valrose, 06108, Nice Cedex 02, France.
Patrice FrancourUniversité Côte d'Azur, CNRS, ECOSEAS, Parc Valrose, 06108, Nice Cedex 02, France.
Cédric HubasMuséum National d'Histoire Naturelle, UMR 8067 BOREA, MNHN-SU-CNRS-UCN-UA-IRD, Station Marine de Concarneau, Concarneau, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial reefs (AR), which are integral tools for fish management, ecological reconciliation and restoration efforts, require non-polluting materials and intricate designs that mimic natural habitats. Despite their three-dimensional complexity, current designs nowadays rely on empirical methods that lack standardised pre-immersion assessment. To improve ecosystem integration, we propose to evaluate 3-dimensional Computer-aided Design (3D CAD) models using a method inspired by functional ecology principles. Based on existing metrics, we assess geometric (C-convexity, P-packing, D-fractal dimension) and informational complexity (R-specific richness, H- diversity, J-evenness). Applying these metrics to different reefs constructed for habitat protection, biomass production and bio-mimicry purposes, we identify potential complexity target points (CTPs). This method provides a framework for improving the effectiveness of artificial reef design by allowing for the adjustment of structural properties. These CTPs represent the first step in enhancing AR designs. We can refine them by evaluating complexity metrics derived from 3D reconstructions of natural habitats to advance bio-mimicry efforts. In situ, post-immersion studies can help make the CTPs more specific for certain species of interest by exploring complexity-diversity or complexity-species distribution relationships at the artificial reef scale.

Indexed as

Coral ReefsEcosystemAnimalsBiodiversityBiomassComputer-Aided DesignConservation of Natural ResourcesFishes3-Dimensional computer-aided design modelArtificial reefHabitat complexity

Identifiers

PMID39333629
PMCPMC11436664

What OpenQuestion holds

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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.