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.
Arthropod vectors are responsible for the transmission of pathogens in humans and other species. The transmission rate depends on the size and activity of the vector population, factors which are in turn strongly affected by environmental conditions. Therefore, in order to develop realistic representations of vector population dynamics, it is necessary to properly account for the impact of a changing environment on the duration of life processes. Here, we use a pseudo-stage-structured population to model the accumulative process of development as a renewal process with a variable rate that depends on the environment. We incorporate this into sPop, formerly an age-structured population dynamics model. This framework allows the modeller to represent realistic life stage durations by choosing from three alternative probability schemes: an Erlang distribution, a Pascal distribution, or a fixed duration, while enabling the model to respond appropriately to variations in stage duration characteristics. Using this approach, we demonstrate that introducing random variation into the environmental conditions, which results in fluctuating development rates, on average decreases the time required for stage completion. An exception to this is an already optimum development rate being perturbed by noise towards a less efficient course. The proposed framework is suitable for performing inverse modelling with data collected from highly variable environmental conditions, the results of which can be used to develop realistic climate-driven population dynamics models.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.