Temperature dependence of mosquitoes: Comparing mechanistic and machine learning approaches.

Mosquito vectors of pathogens (e.g., Aedes, Anopheles, and Culex spp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climate and other anthropogenic changes. As small-bodied ectotherms, mosquitoes are strongly affected by temperature, which causes unimodal responses in mosquito life history traits (e.g., biting rate, adult mortality rate, mosquito development rate, and probability of egg-to-adult survival) that exhibit upper and lower thermal limits and intermediate thermal optima in laboratory studies. However, it remains unknown how mosquito thermal responses measured in laboratory experiments relate to the realized thermal responses of mosquitoes in the field. To address this gap, we leverage thousands of global mosquito occurrences and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically plausible spatial background sampling centered around ecoregions for comparison to mosquito occurrence records. We found that thermal minima estimated from laboratory studies were highly correlated with those from the species distributions (r = 0.87). The thermal optima were less strongly correlated (r = 0.69). For most species, we did not detect thermal maxima from their observed distributions so were unable to compare to laboratory-based estimates. The results suggest that laboratory studies have the potential to be highly transportable to predicting lower thermal limits and thermal optima of mosquitoes in the field. At the same time, lab-based models likely capture physiological limits on mosquito persistence at high temperatures that are not apparent from field-based observational studies but may critically determine mosquito responses to climate warming. Our results indicate that lab-based and field-based studies are highly complementary; performing the analyses in concert can help to more comprehensively understand vector response to climate change. Author summary: Mosquito vectors are strongly affected by temperature, and their distributions are likely to shift under climate change. Lab studies show that mosquito abundance has a unimodal response to temperature with thermal optima, upper and lower thermal limits. However, it remains unknown how mosquito laboratory-derived thermal responses relate to the thermal responses of mosquitoes in nature. We used a global database of field-collected mosquito occurrences, geospatial environmental covariates, and species distribution models to estimate the relationship between temperature and probability of mosquito occurrence. We found that thermal minima (r = 0.87) and, to a lesser degree, thermal optima (r = 0.69) estimated from laboratory studies were correlated with those from the species distribution models. For most species, we did not detect thermal maxima. These results suggest that laboratory studies and field-based machine learning studies are complementary. Together, they can help to better understand vector response to climate change. [ABSTRACT FROM AUTHOR]