Co-occurrence probabilities between mosquito vectors of West Nile and Eastern equine encephalitis viruses using Markov Random Fields (MRFcov).

Mosquito vectors of eastern equine encephalitis virus (EEEV) and West Nile virus (WNV) in the USA reside within broad multi-species assemblages that vary in spatial and temporal composition, relative abundances and vector competence. These variations impact the risk of pathogen transmission and the operational management of these species by local public health vector control districts. However, most models of mosquito vector dynamics focus on single species and do not account for co-occurrence probabilities between mosquito species pairs across environmental gradients. In this investigation, we use for the first time conditional Markov Random Fields (CRF) to evaluate spatial co-occurrence patterns between host-seeking mosquito vectors of EEEV and WNV around sampling sites in Manatee County, Florida. Specifically, we aimed to: (i) quantify correlations between mosquito vector species and other mosquito species; (ii) quantify correlations between mosquito vectors and landscape and climate variables; and (iii) investigate whether the strength of correlations between species pairs are conditional on landscape or climate variables. We hypothesized that either mosquito species pairs co-occur in patterns driven by the landscape and/or climate variables, or these vector species pairs are unconditionally dependent on each other regardless of the environmental variables. Our results indicated that landscape and bioclimatic covariates did not substantially improve the overall model performance and that the log abundances of the majority of WNV and EEEV vector species were positively dependent on other vector and non-vector mosquito species, unconditionally. Only five individual mosquito vectors were weakly dependent on environmental variables with one exception, Culiseta melanura, the primary vector for EEEV, which showed a strong correlation with woody wetland, precipitation seasonality and average temperature of driest quarter. Our analyses showed that majority of the studied mosquito species' abundance and distribution are insignificantly better predicted by the biotic correlations than by environmental variables. Additionally, these mosquito vector species may be habitat generalists, as indicated by the unconditional correlation matrices between species pairs, which could have confounded our analysis, but also indicated that the approach could be operationalized to leverage species co-occurrences as indicators of vector abundances in unsampled areas, or under scenarios where environmental variables are not informative.