Fig 1.
Female sandfly vectors pick up Leishmania parasites during their blood meal from reservoir hosts. Spillover events occur when a sandfly with Leishmania parasites in its salivary glands takes a blood meal from a human and infects the human with the parasite. Identity of all reservoir hosts and sandfly vectors are still unknown, which makes it hard to model and prevent transmission. Created with BioRender.com.
Fig 2.
The model accurately classifies known vectors and identifies relatively few species of unknown status as likely vectors.
A distribution of predicted probabilities of sandfly species separated by vector status, and scaled by percentage. Red bars indicate the proportion of confirmed vectors that were predicted at that probability, while beige bars indicate the proportion of sandfly species not previously identified as vectors that were predicted at that probability.
Fig 3.
Confirmed (A) and newly-predicted (B) vectors occur throughout the Americas. (A) Observed occurrences of confirmed vectors of Leishmania spp. that cause ACL, taken from GBIF and plotted in ArcGIS (Esri, USGS | Esri, Garmin, FAO, NOAA, USGS) [78,79]. (B) Observed occurrences of sandflies of unknown vector status that our models assigned a predicted probability above 0.5. Most predicted vectors are in Brazil due to more extensive survey efforts and availability of public data [78,79]. Maps showing species richness and vector distribution for each species of Leishmania spp. can be found in S4 and S5 Figs.
Table 1.
The median predicted probability, standard deviation, and percentile for sandfly species of unknown vector status with greater than 0.5 predicted probability of being a vector.
The infection status column indicates whether the sandfly is a potential vector (has been observed carrying the parasite, but not confirmed as transmitting it to humans; ‘potential’), or that the sandfly has not yet been found infected with Leishmania in the wild (‘unknown’).
Fig 4.
Human biting, study effort, and canopy height were the most important features for predicting vector status.
(A) Partial dependence plots of the top three variables from the BRT analysis showing the marginal dependence of each trait (shown in order of importance) on the probability of being a vector of ACL. The variable along with its average importance (on a scale of 0–1) are above each plot, the trait value is shown on the x axis, and the effect on probability is shown on the y-axis. The colored lines represent the marginal dependence of the trait from the 100 BRT models, while the solid black line represents the average dependence. The definition of each variable can be found in S1 Table. (B) Variable importance, scaled from 0–1, for the top 10 most important variables with 95% confidence intervals. Points represent mean gain value across 100 iterations. The importances for binary variables were summed up to obtain a single value for the entire categorical variable.