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确立复杂性目标以增强人工礁设计。

Establishing complexity targets to enhance artificial reef designs.

机构信息

Université Côte d'Azur, CNRS, ECOSEAS, Parc Valrose, 06108, Nice Cedex 02, France.

Muséum National d'Histoire Naturelle, UMR 8067 BOREA, MNHN-SU-CNRS-UCN-UA-IRD, Station Marine de Concarneau, Concarneau, France.

出版信息

Sci Rep. 2024 Sep 27;14(1):22060. doi: 10.1038/s41598-024-72227-z.

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.

摘要

人工鱼礁(AR)是鱼类管理、生态恢复和修复工作的重要工具,需要使用无污染材料和复杂的设计来模拟自然栖息地。尽管它们具有三维复杂性,但目前的设计仍然依赖于缺乏标准化预浸泡评估的经验方法。为了提高生态系统的整合性,我们建议使用受功能生态学原理启发的方法来评估三维计算机辅助设计(3D CAD)模型。基于现有的指标,我们评估几何形状(C-凸度、P-堆积、D-分形维数)和信息复杂性(R-特有丰富度、H-多样性、J-均匀度)。将这些指标应用于为保护栖息地、增加生物量和生物模拟而建造的不同人工鱼礁,我们确定潜在的复杂目标点(CTP)。这种方法为提高人工鱼礁设计的有效性提供了一个框架,允许调整结构特性。这些 CTP 是增强 AR 设计的第一步。我们可以通过评估来自自然栖息地 3D 重建的复杂性指标来改进它们,以推进生物模拟工作。在现场,通过探索人工鱼礁尺度上的复杂性-多样性或复杂性-物种分布关系,对特定感兴趣物种进行的后浸泡研究可以帮助使 CTP 更加具体。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18e8/11436664/c2c8365234f8/41598_2024_72227_Fig1_HTML.jpg

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