Predicting adult mosquito ( <i>Culex pipiens</i> ) emergence in response to temperature, nutrients, and salinity

Abstract While human activities lead to unprecedented declines in biodiversity, mosquitoes tend to thrive in human‐disrupted environments. A high mosquito abundance is a clear risk for the transmission of vector‐borne diseases, and understanding how environmental factors influence mosquito development is crucial for better predicting their abundance. There is a growing awareness that the population dynamics of mosquitoes are driven by an interplay of climatological factors (temperature) and local ecological drivers (nutrients and salinity). However, relatively little is known about the combined effects of these factors on mosquito development. We quantified the combined effects of changes in temperature, nutrient concentrations, and salinity on the emergence of Culex pipiens s.l., using two complementary indicators: the emergence time (the number of days after which 50% of adults have emerged from the larval phase) and the emergence probability (the proportion of larvae that developed into adults). To that end, we used a novel stage‐based matrix population model, parametrized with data from two multifactorial and one unifactorial mesocosm experiments. Our results suggest that warming, eutrophication, and salinization will likely lead to increases in Cx. pipiens abundance. We found that emergence time is lowest (6–8 days) for temperatures of 25–30°C, towards the highest tested total nitrogen (TN) concentration (100 mg/L), and salinity values around 4 g/L Cl − . Similarly, emergence probability is highest (0.70–0.75) for temperatures of 24–28°C and salinity values of 2–6 g/L Cl − , with virtually no relation to TN concentration. Given the co‐occurrence of climate warming, eutrophication, and salinization, there is a clear need to consider these factors together in both the prediction and management of mosquito population dynamics. Our model can help to understand the combined effects of these environmental variables on mosquito emergence and identify areas at risk of mosquito proliferation. Furthermore, our model could assist in predicting future mosquito abundance when combined with future projections of temperature, nutrient concentrations, and salinity. Finally, our model also serves as a framework for quantifying environmental responses of other mosquito species.