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基于真实世界的底栖综合多营养水产养殖废物扩散模型。

Real-world waste dispersion modelling for benthic integrated multi-trophic aquaculture.

机构信息

Institute of Aquaculture, University of Stirling, Stirling, Scotland.

MFF Ltd., Marsaxlokk, Malta.

出版信息

PLoS One. 2024 May 23;19(5):e0303538. doi: 10.1371/journal.pone.0303538. eCollection 2024.

Abstract

In real-world situations, marine fish farms accommodate multiple fish species and cohorts within the farm, leading to diverse farm layouts influenced by cage dimensions, configurations, and intricate arrangements. These cage management practices are essential to meet production demands, however, farm-level complexities can impact model predictions of waste deposition and benthic impact near fish cages. This is of particular importance when the cages are used for integrated multi-trophic aquaculture (IMTA) with benthic feeders, where this waste not only affects environmental conditions but also provides a potential food source. The Cage Aquaculture Particulate Output and Transport (CAPOT) model incorporated multiple species, cohorts, and cage arrangements to estimate waste distribution from a commercial fish farm in the Mediterranean between October 2018 and July 2019. This spreadsheet model estimated dispersion for individual fish cages using a grid resolution of 5 m x 5 m. The study categorized discrete production periods for each fish cage every month, aligning with intermittent changes in biomass and food inputs due to different cage management practices throughout production. This approach facilitated the use of detailed input data and enhanced model representativeness by considering variations in cage biomass, food types, settling velocities, and configurations. Model outputs, represented in contour plots, indicated higher deposition directly below fish cages that varied monthly throughout fish production cycles. Deposition footprints reflected changes in cage biomass, food inputs, and farm-level practices reflecting this real-world scenario where aquaculture does not follow a production continuum. Moreover, cohort dynamics and cage movements associated with the cage management practices of the fish farm influenced the quantity and fate of wastes distributed around fish cages, revealing variability in deposition footprints. Clearly, these findings have important implications for the design of benthic IMTA systems, with species such as sea cucumber and polychaetes. Variability in waste deposition creates challenges in identifying where the benthic organisms should be placed to allow optimal uptake of waste to meet their food requirements and increase survivability. Evidently, models have an important role to play and this study emphasizes the need for representative input data to describe actual food inputs, cage biomass changes, and management practices for more representative farm-scale modelling and essentially to improve particulate waste management. To effectively mitigate benthic impacts through IMTA, models must quantify and resolve particulate waste distribution and impact around fish farms to maintain a balanced system with net removal of wastes. Resolving farm-level complexities provides vital information about the variability of food availability and quality for extractive organisms that helps improve recycling of organic wastes in integrated systems, demanding a more representative modelling approach.

摘要

在实际情况下,海水养殖场在农场中容纳多种鱼类和养殖批次,导致受网箱尺寸、配置和复杂布置影响的多样化农场布局。这些网箱管理实践对于满足生产需求至关重要,但是,农场级别的复杂性会影响模型对网箱附近废物沉积和底栖影响的预测。当网箱用于具有底栖饲料的综合多营养水产养殖 (IMTA) 时,这一点尤其重要,因为这些废物不仅会影响环境条件,还会提供潜在的食物来源。Cage Aquaculture Particulate Output and Transport (CAPOT) 模型结合了多种鱼类、养殖批次和网箱布置,以估算 2018 年 10 月至 2019 年 7 月期间地中海商业鱼类养殖场的废物分布情况。这个电子表格模型使用 5 m x 5 m 的网格分辨率估算单个网箱的扩散情况。该研究将每个网箱的离散生产期归类为每月一次,这与由于生产过程中不同的网箱管理实践而导致的生物量和食物输入的间歇性变化相吻合。这种方法通过考虑网箱生物量、食物类型、沉降速度和配置的变化,促进了详细输入数据的使用并提高了模型的代表性。模型输出以等高线图表示,表明在整个鱼类生产周期中,每月都有更高的废物直接沉积在鱼类网箱下方。沉积足迹反映了网箱生物量、食物投入和农场级实践的变化,反映了水产养殖没有遵循生产连续性的现实情况。此外,与鱼类养殖场的网箱管理实践相关的养殖批次动态和网箱移动,影响了废物在鱼类网箱周围的分布和命运,揭示了沉积足迹的可变性。显然,这些发现对设计底栖 IMTA 系统具有重要意义,特别是对于海参和多毛类等物种。废物沉积的可变性给确定底栖生物应放置的位置带来了挑战,以便最大限度地吸收废物以满足其食物需求并提高存活率。显然,模型具有重要作用,本研究强调需要有代表性的输入数据来描述实际食物投入、网箱生物量变化和管理实践,以实现更具代表性的农场规模建模,并从本质上改善颗粒状废物管理。为了通过 IMTA 有效减轻底栖影响,模型必须量化和解决鱼类养殖场周围的颗粒状废物分布和影响,以维持一个具有废物净去除能力的平衡系统。解决农场级别的复杂性为提取生物提供了有关食物可利用性和质量的重要信息,有助于提高综合系统中有机废物的循环利用,需要更具代表性的建模方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7b9b/11115333/7d0f6d98067b/pone.0303538.g001.jpg

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