Predator-prey interactions influence prey traits through both consumptive and non-consumptive effects, and variation in these traits can shape vector-borne disease dynamics. Meta-analysis methods were employed to generate predation effect sizes by different categories of predators and mosquito prey. This analysis showed that multiple families of aquatic predators are effective in consumptively reducing mosquito survival, and that the survival of Aedes, Anopheles, and Culex mosquitoes is negatively impacted by consumptive effects of predators. Mosquito larval size was found to play a more important role in explaining the heterogeneity of consumptive effects from predators than mosquito genus. Mosquito survival and body size were reduced by non-consumptive effects of predators, but development time was not significantly impacted. In addition, Culex vectors demonstrated predator avoidance behavior during oviposition. The results of this meta-analysis suggest that predators limit disease transmission by reducing both vector survival and vector size, and that associations between drought and human West Nile virus cases could be driven by the vector behavior of predator avoidance during oviposition. These findings are likely to be useful to infectious disease modelers who rely on vector traits as predictors of transmission.

A systematic literature search was conducted for studies on predation of mosquitoes that were published between 1970 and July 1, 2019 using both PubMed® and Web of ScienceTM search engines, according to the PRISMA protocol (Moher et al., 2009). The search returned 1,136 studies, and after abstract screening using the “metagear” package in R (Lajeunesse, 2016, R Core Team, 2020), 306 studies remained. These studies were fully reviewed, and data from sixty studies were included in the final database of predator effects on mosquitoes.

Meta-analysis methods were used to assess the consumptive and non-consumptive effects of predators on different mosquito traits. All analyses were conducted in R version 4.0.2 (R Core Team, 2020). Data exclusion steps rely on functions from the “tidyverse” package (Wickham et al., 2019), and meta-analysis steps, including calculating effect sizes, evaluating heterogeneity, assessing publication bias, and testing moderators, rely on functions from the “metafor” package (Viechtbauer, 2010).

References:

LAJEUNESSE, M. J. 2016. Facilitating systematic reviews, data extraction and meta-analysis with the metagear package for r. Methods in Ecology and Evolution, 7, 323-330.

MOHER, D., LIBERATI, A., TETZLAFF, J. & ALTMAN, D. G. 2009. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. BMJ, 339, b2535.

R CORE TEAM 2020. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.

VIECHTBAUER, W. 2010. Conducting Meta-Analyses in R with the metafor Package. Journal of Statistical Software, 36, 1-48.

WICKHAM, H., AVERICK, M., BRYAN, J., CHANG, W., MCGOWAN, L., FRANÇOIS, R., GROLEMUND, G., HAYES, A., HENRY, L., HESTER, J., KUHN, M., PEDERSEN, T., MILLER, E., BACHE, S., MÜLLER, K., OOMS, J., ROBINSON, D., SEIDEL, D., SPINU, V. & YUTANI, H. 2019. Welcome to the Tidyverse. Journal of Open Source Software, 4, 1686.

Please refer to information provided in "ReadMe.pdf".