Measuring artificial light at night in the field, and its impact on mosquito vector abundances

Abstract

The regular variations of light in natural light:dark cycle serve as crucial cues for the timing of biological events. However, the increasing prevalence of artificial light at night (ALAN) disrupts these natural light cycles, having harmful effects on both human and environmental health. ALAN impacts various insects, including key disease vectors like Anopheles mosquitoes, several of which are responsible for transmitting malaria. While there are several methods to measure ALAN, most lack the ability to capture spectral information, making it challenging to quantify ALAN for ecological applications. In this study, I utilized the globally novel Environmental Light Field (ELF) method to characterize the artificial light environments in and around exemplar rural houses in South Africa, Uganda, and Mozambique. Given that the lighting in these areas may significantly influence the risk of vector-borne diseases, especially malaria, it is essential to understand these light conditions. The ELF method makes use of repeat photography to characterise the light detected by an animal’s eye in an entire environment, taking environmental influences such as reflection, refraction and transmission of light into account. My findings indicate that light-emitting diode (LED) lighting dominates across the study sites, with large variability in both intensity and spectral composition among houses with different structures. Interestingly, light conditions inside houses showed more variability than those outside, with light intensities outside ranging from 11.2 to 14 lit (i.e., spanning 2.8 orders of magnitude) and that inside from 11.9 to 15 lit (i.e., spanning 3.1 orders of magnitude). The ELF method effectively measures light as perceived by the animal eye, providing a robust tool for comparing artificial lighting conditions. In this study I also investigated the relationship between the characteristics of the light environment and the abundance of Anopheline mosquitoes in Mamfene, South Africa. While overall white light intensity was not a significant predictor of Anopheline abundance, the percentage of blue light had a significant negative impact on their abundance. By including light environment characteristics, such as blue light content, as predictor variables, we can gain a more comprehensive understanding of factors affecting Anopheline species distribution, abundance, and consequently, potential disease transmission risks.