Vibrio spp. have an important role in biogeochemical cycles; some species are disease agents for aquatic animals and/or humans. Predicting population dynamics of Vibrio spp. in natural environments is crucial to predicting how the future conditions will affect the dynamics of these bacteria. The majority of existing Vibrio spp. population growth models were developed in controlled environments, and their applicability to natural environments is unknown. We collected all available functional models from the literature, and distilled them into 28 variants using unified nomenclature. Next, we assessed their ability to predict Vibrio spp. abundance using two new and five already published longitudinal datasets on Vibrio abundance in four different habitat types. Results demonstrate that, while the models were able to predict Vibrio spp. abundance to an extent, the predictions were not reliable. Models often underperformed, especially in environments under significant anthropogenic influence such as aquaculture and urban coastal habitats. We discuss implications and limitations of our analysis, and suggest research priorities; in particular, we advocate for measuring and modeling organic matter.
Aquaculture provides more than 50% of all seafood for human consumption. This important industrial sector is already under pressure from climate-change-induced shifts in water column temperature, nutrient loads, precipitation patterns, microbial community composition, and ocean acidification, all affecting fish welfare. Disease-related risks are also shifting with important implications for risk from vibriosis, a disease that can lead to massive economic losses. Adaptation to these pressures pose numerous challenges for aquaculture producers, policy makers, and researchers. The dataset AqADAPT aims to help the development of management and adaptation tools by providing (i) measurements of physicochemical (temperature, salinity, total dissolved solids, pH, dissolved oxygen, conductivity, transparency, total nitrogen, ammonia, nitrate, nitrite, total phosphorus, total particulate matter, particulate organic matter, and particulate inorganic matter) and microbiological (heterotrophic (total) bacteria, fecal indicators, and Vibrio abundance) parameters of seawater and (ii) biochemical determination of culturable bacteria in two locations near floating cage fish farms in the Adriatic Sea. Water sampling was conducted seasonally in two fish farms (Cres and Vrgada) and corresponding reference (control) sites between 2019 and 2021 of four vertical layers for a total of 108 observations: the surface, 6 m, 12 m, and the bottom.
Environmental contamination due to pest control in general, and mosquito control in particular, is an important issue expected to increase with climate change. We use a validated model for population dynamics of mosquitoes and historical environmental data to explore performance of larvicidal, adulticidal, and combined treatments. Results show that depending on treatment timing, larvicidal treatments can induce very good results, or have negative outcomes that increase overall mosquito population. Combined larvicidal and adulticidal treatments, however, exhibit much lesser dependence on timing, and therefore give the greatest chance of positive outcomes if environmental conditions are not known. Based on the results, we argue for adaptive mosquito management, in which weather data and forecasts are used to drive a model that identifies best intervals for insecticide use. Such an approach can have considerably better results than static, calendar-driven management and, therefore, considerably reduce environmental contamination. Adaptive management could consider larvicidal treatment because it gives good results if the timing is correct. Static management should, however, combine larvicidal and adulticidal treatments for the greatest chance of success.
Environmental contamination due to pest control in general, and mosquito control in particular, is an important issue expected to increase with climate change. We use a validated model for population dynamics of mosquitoes and historical environmental data to explore performance of larvicidal, adulticidal, and combined treatments. Results show that depending on treatment timing, larvicidal treatments can induce very good results, or have negative outcomes that increase overall mosquito population. Combined larvicidal and adulticidal treatments, however, exhibit much lesser dependence on timing, and therefore give the greatest chance of positive outcomes if environmental conditions are not known. Based on the results, we argue for adaptive mosquito management, in which weather data and forecasts are used to drive a model that identifies best intervals for insecticide use. Such an approach can have considerably better results than static, calendar-driven management and, therefore, considerably reduce environmental contamination. Adaptive management could consider larvicidal treatment because it gives good results if the timing is correct. Static management should, however, combine larvicidal and adulticidal treatments for the greatest chance of success.
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