Food for all: designing sustainable and secure future seafood systems

Food from the sea can make a larger contribution to healthy and sustainable diets, and to addressing hunger and malnutrition, through improvements in production, distribution and equitable access to wild harvest and mariculture resources and products. The supply and consumption of seafood is influenced by a range of 'drivers' including ecosystem change and ocean regulation, the influence of corporations and evolving consumer demand, as well as the growing focus on the importance of seafood for meeting nutritional needs. These drivers need to be examined in a holistic way to develop an informed understanding of the needs, potential impacts and solutions that align seafood production and consumption with relevant 2030 Sustainable Development Goals (SDGs). This paper uses an evidence-based narrative approach to examine how the anticipated global trends for seafood might be experienced by people in different social, geographical and economic situations over the next ten years. Key drivers influencing seafood within the global food system are identified and used to construct a future scenario based on our current trajectory (Business-as-usual 2030). Descriptive pathways and actions are then presented for a more sustainable future scenario that strives towards achieving the SDGs as far as technically possible (More sustainable 2030). Prioritising actions that not only sustainably produce more seafood, but consider aspects of access and utilisation, particularly for people affected by food insecurity and malnutrition, is an essential part of designing sustainable and secure future seafood systems.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11160-021-09663-x.

Food for all: designing sustainable and secure future seafood systems

Food from the sea can make a larger contribution to healthy and sustainable diets, and to addressing hunger and malnutrition, through improvements in production, distribution and equitable access to wild harvest and mariculture resources and products. The supply and consumption of seafood is influenced by a range of 'drivers' including ecosystem change and ocean regulation, the influence of corporations and evolving consumer demand, as well as the growing focus on the importance of seafood for meeting nutritional needs. These drivers need to be examined in a holistic way to develop an informed understanding of the needs, potential impacts and solutions that align seafood production and consumption with relevant 2030 Sustainable Development Goals (SDGs). This paper uses an evidence-based narrative approach to examine how the anticipated global trends for seafood might be experienced by people in different social, geographical and economic situations over the next ten years. Key drivers influencing seafood within the global food system are identified and used to construct a future scenario based on our current trajectory (Business-as-usual 2030). Descriptive pathways and actions are then presented for a more sustainable future scenario that strives towards achieving the SDGs as far as technically possible (More sustainable 2030). Prioritising actions that not only sustainably produce more seafood, but consider aspects of access and utilisation, particularly for people affected by food insecurity and malnutrition, is an essential part of designing sustainable and secure future seafood systems.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11160-021-09663-x.

Spatial modeling of the schistosomiasis mansoni in Minas Gerais State, Brazil using spatial regression

Schistosomiasis is a transmissible parasitic disease caused by the etiologic agent Schistosoma mansoni, whose intermediate hosts are snails of the genus Biomphalaria. The main goal of this paper is to estimate the prevalence of schistosomiasis in Minas Gerais State in Brazil using spatial disease information derived from the state transportation network of roads and rivers. The spatial information was incorporated in two ways: by introducing new variables that carry spatial neighborhood information and by using spatial regression models. Climate, socioeconomic and environmental variables were also used as co-variables to build models and use them to estimate a risk map for the whole state of Minas Gerais. The results show that the models constructed from the spatial regression produced a better fit, providing smaller root mean square error (RMSE) values. When no spatial information was used, the RMSE for the whole state of Minas Gerais reached 9.5%; with spatial regression, the RMSE reaches 8.8% (when the new variables are added to the model) and 8.5% (with the use of spatial regression). Variables representing vegetation, temperature, precipitation, topography, sanitation and human development indexes were important in explaining the spread of disease and identified certain conditions that are favorable for disease development. The use of spatial regression for the network of roads and rivers produced meaningful results for health management procedures and directing activities, enabling better detection of disease risk areas.