Various environmental drivers influence life processes of insect vectors that transmit human disease. Life histories observed under experimental conditions can reveal such complex links; however, designing informative experiments for insects is challenging. Furthermore, inferences obtained under controlled conditions often extrapolate poorly to field conditions. Here, we introduce a pseudo-stage-structured population dynamics model to describe insect development as a renewal process with variable rates. The model permits representing realistic life stage durations under constant and variable environmental conditions. Using the model, we demonstrate how random environmental variations result in fluctuating development rates and affect stage duration. We apply the model to infer environmental dependencies from the life history observations of two common disease vectors, the southern (Culex quinquefasciatus) and northern (Culex pipiens) house mosquito. We identify photoperiod, in addition to temperature, as pivotal in regulating larva stage duration, and find that carefully timed life history observations under semi-field conditions accurately predict insect development throughout the year. The approach we describe augments existing methods of life table design and analysis, and contributes to the development of large-scale climate- and environment-driven population dynamics models for important disease vectors.

Toscana virus (TOSV) is a prominent arthropod-borne viral agent of human central nervous system infections occurring in the Mediterranean region. The main transmission route to susceptible individuals involves sandflies as vectors. Despite several reports revealing widespread TOSV activity in Turkey, vectors remained unidentified. A sandfly field survey was carried out in five provinces in Central, Southeast and Mediterranean Anatolia in 2017 to identify TOSV and related sandfly-borne phleboviruses and Leishmania parasites, with evidence for circulation in the region. A total of 7136 sandfly specimens, collected via standard methods, were evaluated in 163 pools. TOSV was detected in 11 pools (6.7%), comprising Phlebotomus major sensu lato, Sergentomyia dentata and Phlebotomus papatasi species. TOSV partial L and S segment sequences were characterized, that phylogenetically clustered with local and global genotype A strains. An amino acid substitution outside the conserved motifs of the viral polymerase, also present in previous TOSV sequences in endemic regions, was observed. Leishmania tropica was detected in a single pool of Ph. sergentii (0.6%). This is the first report of TOSV in sandflies from Turkey, and this study further provides evidence for additional sandfly species with the potential to transmit TOSV.

AbstractLife tables help identify physiological differences in distinct development stages and detect potential vulnerabilities for conservation and control. However, cataloguing mortality, development, and fecundity by following each individual could be challenging in insects due to interweaving generations and development stages.Here, we propose to use age- and stage-structured population dynamics modelling to help derive life table characteristics from the observed dynamics of reared populations. We examine a hypothetical case, a simulated population with known life parameters, and two experimental cases, observations of the population dynamics of the mosquito vectors Culex quinquefasciatus and Culex pipiens, to demonstrate that model-based inference can correctly identify life parameters from longitudinal observations. The analysis reveals not only the differential physiological behaviour of distinct development stages, but also identifies the degree to which each parameter can be inferred from the data.The methods introduced constitute a model-based approach to identifying life table characteristics from incomplete longitudinal data, and help to improve the design of life table experiments. The approach is readily applicable to the development of climate- and environment-driven population dynamics models for important vectors of disease.