Identification of molecular determinants of antibiotic resistance in some fish farms of Ghana.

Antimicrobial resistance is a global health challenge caused by the ability of microorganisms including bacteria, fungi, protozoans and viruses to survive the effects of drugs that hitherto were effective against them. This study sought to investigate the presence of antibiotic-resistant bacteria and their corresponding molecular determinants in fish farms of the Central and Western Regions of Ghana. Management practices and antibiotic use at the fish farms were obtained through the administration of a questionnaire. Coliform and Gram-positive bacterial loads of catfish (Clarias gariepinus), tilapia (Oreochromis niloticus) intestinal microbiota, and pond water samples recovered on MacConkey Agar and Mannitol Salt Agar were determined. Bacterial isolates were identified using various biochemical assays. Antibiotic resistance profiles and possible responsible genes of bacterial isolates were determined using the disc diffusion and Polymerase Chain Reaction (PCR) methods respectively. The study revealed that none of the fish farm managers admitted using antibiotics for prevention and treatment of diseases and no major disease outbreak had ever been recorded. Bacterial loads of pond water exceeded the acceptable level of ≤100 E. coli and <10 coliforms per mL for wastewater recommended for use in fish farming. In all, 145 bacterial isolates comprising 99 Gram negative and 46 Gram-positive bacteria were stored and identified. Most isolates were resistant to at least an antibiotic. Both Gram-negative and Gram-positive bacteria were highly resistant to beta-lactam antibiotics with a corresponding high percentage detection of the bla TEM gene compared to other classes of antibiotics. This study has revealed the presence of various molecular determinants of antibiotic resistance including bla TEM, cmIA, qnrS, tetB and bla CTX-M, in multidrug-resistant bacteria at some fish farms in Ghana. There is the need to increase awareness about risks associated with the misuse and overuse of antibiotics by humans and the potential risk of spread of multi-drug resistant-bacteria in the environment.

Physical distancing and risk of COVID-19 in small-scale fisheries: a remote sensing assessment in coastal Ghana.

The novel coronavirus is predicted to have dire implications on global food systems including fisheries value chains due to restrictions imposed on human movements in many countries. In Ghana, food production, both agriculture and fisheries, is exempted from restrictions as an essential service. The enforcement of COVID-19 prevention protocols, particularly social distancing, has been widely reported in Ghana's agricultural markets whereas casual observations and media reports on fish landing sites suggest no such enforcements are in place. This study aimed to provide sound scientific evidence as a basis for informed policy direction and intervention for the artisanal fishing sector in these challenging times. We employed an unmanned aerial vehicle in assessing the risk of artisanal fishers to the pandemic using physical distancing as a proxy. From analysis of cumulative distribution function (G-function) of the nearest-neighbour distances, this study underscored crowding at all surveyed fish landing beaches, and identified potential "hotspots" for disease transmission. Aerial measurements taken at times of peak landing beach activity indicated that the highest proportion of people, representing 56%, 48%, 39% and 78% in Elmina, Winneba, Apam and Mumford respectively, were located at distances of less than one metre from their nearest neighbour. Risk of crowding was independent of the population at the landing beaches, suggesting that all categories of fish landing sites along the coast would require equal urgency and measured attention towards preventing and mitigating the spread of the disease.

Pollen-mediated gene flow and seed exchange in small-scale Zambian maize farming, implications for biosafety assessment.

Gene flow in agricultural crops is important for risk assessment of genetically modified (GM) crops, particularly in countries with a large informal agricultural sector of subsistence cultivation. We present a pollen flow model for maize (Zea mays), a major staple crop in Africa. We use spatial properties of fields (size, position) in three small-scale maize farming communities in Zambia and estimate rates of cross-fertilisation between fields sown with different maize varieties (e.g. conventional and transgene). As an additional factor contributing to gene flow, we present data on seed saving and sharing among farmers that live in the same communities. Our results show that: i) maize fields were small and located in immediate vicinity of neighboring fields; ii) a majority of farmers saved and shared seed; iii) modeled rates of pollen-mediated gene flow showed extensive mixing of germplasm between fields and farms and iv) as a result, segregation of GM and non-GM varieties is not likely to be an option in these systems. We conclude that the overall genetic composition of maize, in this and similar agricultural contexts, will be strongly influenced both by self-organised ecological factors (pollen flow), and by socially mediated intervention (seed recycling and sharing).