Search for: All records

Fisheries are often characterized by high heterogeneity in the spatial distribution of habitat quality, as well as fishing effort. However, in several fisheries, the objective of achieving a sustainable yield is addressed by limiting Total Allowable Catch (TAC), set as a fraction of the overall population, regardless of the population's spatial distribution and of fishing effort. Here, we use an integral projection model to investigate how stock abundance and catch in the green abalone fishery in Isla Natividad, Mexico, are affected by the interaction of heterogeneity in habitat quality and fishing effort, and whether these interactions change with Allee effects—reproductive failure in a low-density population. We found that high-quality areas are under-exploited when fishing pressure is homogeneous but habitat is heterogeneous. However, this leads to different fishery outcomes depending on the stock's exploitation status, namely: sub-optimal exploitation when the TAC is set to maximum sustainable yield, and stability against collapses when the fishery is overexploited. Concentration of fishing effort in productive areas can compensate for this effect, which, similarly, has opposite consequences in both scenarios: fishery performance increases if the TAC is sustainable but decreases in overexploited fisheries. These results only hold when Allee effects are included.

Humans live in complex socio-ecological systems where we interact with parasites and pathogens that spend time in abiotic and biotic environmental reservoirs (e.g., water, air, soil, other vertebrate hosts, vectors, intermediate hosts). Through a synthesis of published literature, we reviewed the life cycles and environmental persistence of 150 parasites and pathogens tracked by the World Health Organization's Global Burden of Disease study. We used those data to derive the time spent in each component of a pathogen's life cycle, including total time spent in humans versus all environmental stages. We found that nearly all infectious organisms were “environmentally mediated” to some degree, meaning that they spend time in reservoirs and can be transmitted from those reservoirs to human hosts. Correspondingly, many infectious diseases were primarily controlled through environmental interventions (e.g., vector control, water sanitation), whereas few (14%) were primarily controlled by integrated methods (i.e., combining medical and environmental interventions). Data on critical life history attributes for most of the 150 parasites and pathogens were difficult to find and often uncertain, potentially hampering efforts to predict disease dynamics and model interactions between life cycle time scales and infection control strategies. We hope that this synthetic review and associated database serve asmore »a resource for understanding both common patterns among parasites and pathogens and important variability and uncertainty regarding particular infectious diseases. These insights can be used to improve systems-based approaches for controlling environmentally mediated diseases of humans in an era where the environment is rapidly changing.« less

The development of water management infrastructures, such as dams and canals, are important components of society’s response to feed a growing human population and to fight climate change. Yet, these changes in land use can also increase the transmission risk for waterborne diseases. Transmission risk associated with artificial reservoirs has been extensively documented for schistosomiasis, a parasitic disease of poverty that infects more than 240 million people worldwide. Over 90% of these cases are in sub-Saharan Africa, a region that is being steadily reshaped by climate change. Controlling the parasite’s obligate intermediate host snail is key to reducing transmission of this disease. Using commercial aquaculture to farm marketable species which predate upon these snails in vulnerable regions can have multiple positive effects, including the improved socioeconomic and nutritional health of surrounding communities. Here the authors assessed the viability of using the aquaculture of snail predators to simultaneously control schistosomiasis infection rates while alleviating economic and/or nutritional poverty in endemic regions of sub-Saharan Africa. A PRISMA-based 6-step systematic methodology was used to explore the primary literature using the case study of Côte d’Ivoire and two native species of snail predator to make evidence-based conclusions on the viability of this method formore »controlling schistosomiasis. This detailed thematic examination of the literature concluded that using specific approaches and species, aquaculture could be effective in reducing economic poverty and chronic malnourishment along with high levels of schistosomiasis infection. More current species-specific aquaculture data and consumer survey data are, however, needed to determine the economic and logistical effectiveness of farming native snail predators in-country. These and other opportunities for future research are highlighted.« less

Abstract Background Water resources development promotes agricultural expansion and food security. But are these benefits offset by increased infectious disease risk? Dam construction on the Senegal River in 1986 was followed by agricultural expansion and increased transmission of human schistosomes. Yet the mechanisms linking these two processes at the individual and household levels remain unclear. We investigated the association between household land use and schistosome infection in children. Methods We analyzed cross-sectional household survey data ( n  = 655) collected in 16 rural villages in August 2016  across demographic, socio-economic and land use dimensions, which were matched to Schistosoma haematobium ( n  = 1232) and S. mansoni ( n  = 1222) infection data collected from school-aged children. Mixed effects regression determined the relationship between irrigated area and schistosome infection presence and intensity. Results Controlling for socio-economic and demographic risk factors, irrigated area cultivated by a household was associated with an increase in the presence of S. haematobium infection (odds ratio [ OR ] = 1.14; 95% confidence interval [95% CI ]: 1.03–1.28) but not S. mansoni infection ( OR  = 1.02; 95% CI : 0.93–1.11). Associations between infection intensity and irrigated area were positive but imprecise ( S. haematobium: rate ratio [ RR ] = 1.05; 95% CI :more »0.98–1.13, S. mansoni : RR  = 1.09; 95% CI : 0.89–1.32). Conclusions Household engagement in irrigated agriculture increases individual risk of S. haematobium but not S. mansoni infection. Increased contact with irrigated landscapes likely drives exposure, with greater impacts on households relying on agricultural livelihoods.« less

Schistosomiasis is a debilitating parasitic disease of poverty that affects more than 200 million people worldwide, mostly in sub-Saharan Africa, and is clearly associated with the construction of dams and water resource management infrastructure in tropical and subtropical areas. Changes to hydrology and salinity linked to water infrastructure development may create conditions favorable to the aquatic vegetation that is suitable habitat for the intermediate snail hosts of schistosome parasites. With thousands of small and large water reservoirs, irrigation canals, and dams developed or under construction in Africa, it is crucial to accurately assess the spatial distribution of high-risk environments that are habitat for freshwater snail intermediate hosts of schistosomiasis in rapidly changing ecosystems. Yet, standard techniques for monitoring snails are labor-intensive, time-consuming, and provide information limited to the small areas that can be manually sampled. Consequently, in low-income countries where schistosomiasis control is most needed, there are formidable challenges to identifying potential transmission hotspots for targeted medical and environmental interventions. In this study, we developed a new framework to map the spatial distribution of suitable snail habitat across large spatial scales in the Senegal River Basin by integrating satellite data, high-definition, low-cost drone imagery, and an artificial intelligence (AI)-powered computermore »vision technique called semantic segmentation. A deep learning model (U-Net) was built to automatically analyze high-resolution satellite imagery to produce segmentation maps of aquatic vegetation, with a fast and robust generalized prediction that proved more accurate than a more commonly used random forest approach. Accurate and up-to-date knowledge of areas at highest risk for disease transmission can increase the effectiveness of control interventions by targeting habitat of disease-carrying snails. With the deployment of this new framework, local governments or health actors might better target environmental interventions to where and when they are most needed in an integrated effort to reach the goal of schistosomiasis elimination.« less

Dams enable the production of food and renewable energy, making them a crucial tool for both economic development and climate change adaptation in low- and middle-income countries. However, dams may also disrupt traditional livelihood systems and increase the transmission of vector- and water-borne pathogens. These livelihood and health impacts diminish the benefits of dams to rural populations dependent on rivers, as hydrological and ecological alterations change flood regimes, reduce nutrient transport and lead to the loss of biodiversity. We propose four agricultural innovations for promoting equity, health, sustainable development, and climate resilience in dammed watersheds: (1) restoring migratory aquatic species, (2) removing submerged vegetation and transforming it into an agricultural resource, (3) restoring environmental flows and (4) integrating agriculture and aquaculture. As investment in dams accelerates in low- and middle-income countries, appropriately addressing their livelihood and health impacts can improve the sustainability of modern agriculture and economic development in a changing climate.

Background Infectious disease risk is driven by three interrelated components: exposure, hazard, and vulnerability. For schistosomiasis, exposure occurs through contact with water, which is often tied to daily activities. Water contact, however, does not imply risk unless the environmental hazard of snails and parasites is also present in the water. By increasing reliance on hazardous activities and environments, socio-economic vulnerability can hinder reductions in exposure to a hazard. We aimed to quantify the contributions of exposure, hazard, and vulnerability to the presence and intensity of Schistosoma haematobium re-infection. Methodology/Principal findings In 13 villages along the Senegal River, we collected parasitological data from 821 school-aged children, survey data from 411 households where those children resided, and ecological data from all 24 village water access sites. We fit mixed-effects logistic and negative binomial regressions with indices of exposure, hazard, and vulnerability as explanatory variables of Schistosoma haematobium presence and intensity, respectively, controlling for demographic variables. Using multi-model inference to calculate the relative importance of each component of risk, we found that hazard (Ʃw i = 0.95) was the most important component of S . haematobium presence, followed by vulnerability (Ʃw i = 0.91). Exposure (Ʃw i = 1.00) was the most importantmore »component of S . haematobium intensity, followed by hazard (Ʃw i = 0.77). Model averaging quantified associations between each infection outcome and indices of exposure, hazard, and vulnerability, revealing a positive association between hazard and infection presence (OR = 1.49, 95% CI 1.12, 1.97), and a positive association between exposure and infection intensity (RR 2.59–3.86, depending on the category; all 95% CIs above 1) Conclusions/Significance Our findings underscore the linkages between social (exposure and vulnerability) and environmental (hazard) processes in the acquisition and accumulation of S . haematobium infection. This approach highlights the importance of implementing both social and environmental interventions to complement mass drug administration.« less

In recent decades, computer vision has proven remarkably effective in addressing diverse issues in public health, from determining the diagnosis, prognosis, and treatment of diseases in humans to predicting infectious disease outbreaks. Here, we investigate whether convolutional neural networks (CNNs) can also demonstrate effectiveness in classifying the environmental stages of parasites of public health importance and their invertebrate hosts. We used schistosomiasis as a reference model. Schistosomiasis is a debilitating parasitic disease transmitted to humans via snail intermediate hosts. The parasite affects more than 200 million people in tropical and subtropical regions. We trained our CNN, a feed-forward neural network, on a limited dataset of 5,500 images of snails and 5,100 images of cercariae obtained from schistosomiasis transmission sites in the Senegal River Basin, a region in western Africa that is hyper-endemic for the disease. The image set included both images of two snail genera that are relevant to schistosomiasis transmission – that is, Bulinus spp. and Biomphalaria pfeifferi – as well as snail images that are non-component hosts for human schistosomiasis. Cercariae shed from Bi. pfeifferi and Bulinus spp. snails were classified into 11 categories, of which only two, S. haematobium and S. mansoni , are major etiological agentsmore »of human schistosomiasis. The algorithms, trained on 80% of the snail and parasite dataset, achieved 99% and 91% accuracy for snail and parasite classification, respectively, when used on the hold-out validation dataset – a performance comparable to that of experienced parasitologists. The promising results of this proof-of-concept study suggests that this CNN model, and potentially similar replicable models, have the potential to support the classification of snails and parasite of medical importance. In remote field settings where machine learning algorithms can be deployed on cost-effective and widely used mobile devices, such as smartphones, these models can be a valuable complement to laboratory identification by trained technicians. Future efforts must be dedicated to increasing dataset sizes for model training and validation, as well as testing these algorithms in diverse transmission settings and geographies.« less