Using indicators and models for an ecosystem approach to fisheries and aquaculture management: the anchovy fishery and Pacific oyster culture in Chile: case studies

Silva, Claudio; Barbieri, María Ángela; Yáñez, Eleuterio; Gutiérrez‐Estrada, Juan Carlos; DelValls, T.À.

doi:10.3856/vol40-issue4-fulltext-12

Cited by 12 publications

(9 citation statements)

References 48 publications

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“…Only few countries apply the ECC approach (e.g., Norway) and regulations from the majority are aimed at favoring farmers' production (e.g., feed efficiency and absence of anoxic sediments) and regulate food standards (FAO, 2009;Weitzman and Filgueira, 2019). Determination of ECC of an aquatic ecosystem is a complex process generally based on mathematical models, making difficult to assess and implement standards (Silva et al, 2012;Bueno et al, 2017).…”

Section: Environmental Monitoring Plansmentioning

confidence: 99%

Towards Environmental Sustainability in Marine Finfish Aquaculture

et al. 2021

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Aquaculture is one of the fastest growing food production sectors and has great potential for food security and livelihoods. However, it generates concerning consequences for the environment, including chemical and biological pollution, disease outbreaks, unsustainable feeds and competition for coastal space. Recent investigations are focusing on sustainable techniques (e.g., polyculture, offshore facilities) to improve the relationship between the industry, environment and society. This review provides an overview of the main factors of ecological concern within marine finfish aquaculture, their interactions with the environment, and highlights sustainable alternatives that are currently in use or development. Adequate environmental monitoring and location of farms, the reduction and exploitation of wastes and chemicals being used is crucial to ensure the growth and continuity of aquaculture production.

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Section: Environmental Monitoring Plansmentioning

confidence: 99%

Towards Environmental Sustainability in Marine Finfish Aquaculture

et al. 2021

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“…Predicting fishing grounds has been a great challenge for Chile and Latin America, due to which many efforts have made [36][37][38][39][40]. Although Chilean efforts have shown satisfactory results [37,39,40], they used oceanographic satellite information, which are instantaneous moments of the ocean…”

Section: Discussionmentioning

confidence: 99%

“…The use of a neural network model to predict PFGs of E. ringens in Northern Chile proved to be an interesting method with practical applications. Predicting fishing grounds has been a great challenge for Chile and Latin America, due to which many efforts have made [36][37][38][39][40]. Although Chilean efforts have shown satisfactory results [37,39,40], they used oceanographic satellite information, which are instantaneous moments of the ocean state that do not allow temporal projection of these conditions in the short term.…”

Section: Discussionmentioning

confidence: 99%

Identification and Forecast of Potential Fishing Grounds for Anchovy (Engraulis ringens) in Northern Chile Using Neural Networks Modeling

Armas

Arancibia

Neira

2022

Fishes

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Engraulis ringens (E. ringens) is a small pelagic fish of which the geographic and bathymetric distribution is conditioned by fluctuations in oceanographic conditions at different time scales (daily, weekly, monthly, annually, supra-annually, and longer) and by fishing. Understanding the organism−environment interactions and predicting the spatial distribution of its schools can improve conservation actions and fishery management, along with the operation of the fleets targeting E. ringens. There is an important fishery of E. ringens in Northern Chile (18°21′ S–26°00′ S), which provides about 80% of the purse seine catch. To identify and predict potential fishing grounds for E. ringens in this system, we implemented a predictive model of fishing grounds based on neural networks, which was trained with the georeferenced data of daily catches by industrial purse sein ships from 2003 to 2020 and information on oceanographic variables (sea surface temperature, salinity, depth of the mixed layer, sea height, and currents) obtained from the Copernicus Marine Enviroment Monitoring Service (CMEMS program). The neural network model had a very good performance (86%). Longitude (23%) was the most relevant variable for identifying potential fishing grounds, followed by the mixed layer depth (18%), latitude (15%), sea surface temperature (12%), month (12%), sea height (9%), salinity (9%), and the zonal and meridional components of the current velocity (2%). The neural network model classified correctly the majority of the areas with and without fishing potential; thus, its use is recommended to predict fishing grounds for E. ringens in the study area. Its application could increase by 88% of the probability of capture anchovy by the purse seine fleet of Northern Chile.

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“…GIS applications in this main issue category are carried out as a prelude to aquaculture development. The application of GIS-based MCE has developed worldwide and has reached many achievements in aquaculture, particularly in bivalve farming and oyster culture towards long-term sustainability [2,4,[5][6][7][8][9][10][11][12][13]. In this work, we attempt to apply GIS based MCE for identifying suitable sites for Pacific oyster (Crassostrea gigas) farming in the coastal waters of Central Vietnam.…”

Section: Introduction mentioning

confidence: 99%

GIS-Based Multi-Criteria Evaluation Models for Selection of Suitable Sites for Pacific Oyster (Crassostrea gigas) Aquaculture in the Central Region of Vietnam

Tùng¹,

Son²

2019

JESE-A

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Oysters are nutritious food organisms, rich in protein, minerals and vitamins. Production from Pacific oyster (Crassostrea gigas) aquaculture is increasing and supports the livelihoods of coastal communities. To ensure both success and long-term sustainability of oyster production, the determination of suitable sites is an important step in any aquaculture operation. This study applied GIS (Geographic Information System) based MCE (Multi-Criteria Evaluation) for locating suitable sites for Pacific oyster (Crassostrea gigas) farms in coastal regions in Central Vietnam. Remote sensing data were obtained viaMODIS (Moderate Resolution Imaging Spectroradiometer) and high resolution imagery from the Google Earth Engine, while oceanic data were obtained from HYCOM-NCODA (Hybrid Coordinate Ocean Model-Navy Coupled Ocean Data Assimilation) coupled with local hydrodynamic FEM (Finite Element Model) Hydrographic charts and GPS (Global Positioning System) data were used to extract required information layers for GIS based MCE for the suitable site selection of oyster farms. Six thematic layers of biophysical parameters were used to analysis MCE non-constraints including the depth, temperature, salinity, Chl-a (Chlorophyll-a) content, suspended matter concentration and velocity of sea current. Then, an MCE analysis with constraint was used to exclude the areas from suitability maps where oyster aquaculture could not be developed. They were conducted in two subgroups, biophysical subgroup (including unsuitable depth, unsuitable substratum and shore line types, potential regions prone to strong impact of natural disasters and environmental risks, sensitive habitats in MPAs (Marine Protection Areas), and social-infrastructural subgroups (including human settlement in urban area, wastewater system, industrial zones, piers, harbors etc.). A series of GIS models was developed to identify the most suitable areas for oyster culture using MCE with the estimating of factor score (X i) based on expert knowledge as well as calculating of Weighting (W i) based on Saaty's matrix relevant to the importance level of assessed factors in comparison. Suitability scores were ranked on a scale from 1 (least suitable) to 8 (most suitable). The best suitable sites for oyster aquaculture in coastal areas of the central region of Vietnam have been found. These study results confirmed that a GIS based MCE model is a powerful tool that supports site selection decision-making in aquaculture.

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Using indicators and models for an ecosystem approach to fisheries and aquaculture management: the anchovy fishery and Pacific oyster culture in Chile: case studies

Cited by 12 publications

References 48 publications

Towards Environmental Sustainability in Marine Finfish Aquaculture

Towards Environmental Sustainability in Marine Finfish Aquaculture

Identification and Forecast of Potential Fishing Grounds for Anchovy (Engraulis ringens) in Northern Chile Using Neural Networks Modeling

GIS-Based Multi-Criteria Evaluation Models for Selection of Suitable Sites for Pacific Oyster (Crassostrea gigas) Aquaculture in the Central Region of Vietnam

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