Ecological niche models for sister species pairs.

<p>A: <i>Triatoma p. protracta</i> (red) and <i>T. barberi</i>; B: <i>T. rubida</i> (red) and <i>T. nitida</i> (blue); C: <i>T. gerstaeckeri</i> (red) and <i>T. mexicana</i> (blue); D: <i>T. recurva</i> (red) and <i>T. longipennis</i> (blue); E: <i>T. phyllosoma</i> (red) and <i>T. mazzottii</i> (blue); F: <i>T. dimidiata</i> group 2 (red) and <i>T. dimidiata</i> group1a (blue). Grey dots and squares represent the collection sites for each species. Diagonal lines in E and F indicate the overlapping niche range between the sister pairs.</p

Divergence time estimates for Triatominae clades.

<p>Asterisks and black circles above branches indicate clades supported by PP of 0.9–0.94 and ≥0.95, respectively.</p

Bayesian phylogram derived from a multilocus analysis of the Triatominae subfamily, includes <i>Zelurus petax</i> and <i>Reduvius personatus</i> from the Reduviinae as outgroup (in blue).

<p>The geographic range for all species modeled in the genus <i>Triatoma</i> is also shown (NCA species are shown in red, South American species in green and from the Antilles in brown). <i>Panstrongylus</i> species are shown in yellow, <i>Mepraia</i> in pink and <i>Rhodnius</i> species are in purple. Branch color indicates PP<0.8 in gray and ≥0.8 in black. Black circles indicate PP≥0.95<; black stars PP≥0.9<0.95.</p

Ecological niche similarity tests between sister species pairs of NCA triatomines.

<p>Observed similarity between niches is indicated with the arrows, while bars indicate the null distribution of ecological niche distance generated randomly. Schoeners' D ranges from 0 (complete different ENM), to 1 (identical ENM). In all cases, the observed similarities were higher that their respective null distribution for random niche models.</p

Advantages and disadvantages of the Disease Vector Database.

<p>Advantages and disadvantages of the Disease Vector Database.</p

Summary of the current contents of the Disease Vector Database.

<p>Summary of the current contents of the Disease Vector Database.</p

The distribution of vector occurrence data currently contained in the Disease Vector Database.

<p>Yellow, malaria vectors; green, dengue vectors; blue, Chagas reservoirs and vectors; red, leishmaniasis reservoirs and vectors.</p

Black triangles show geographic localization of the sites in which infected bat species were collected.

<p>Black triangles show geographic localization of the sites in which infected bat species were collected.</p

Dermal lesions in bats suggestive of <i>Leishmania</i> infection.

<p><i>A</i>. <i>lituratus</i> found dead in Tabasco, Mexico, showing dermal lesions on the edge of the wing membrane, suggestive of a <i>Leishmania</i> infection.</p

Can You Judge a Disease Host by the Company It Keeps? Predicting Disease Hosts and Their Relative Importance: A Case Study for Leishmaniasis

<div><p>Zoonoses are an important class of infectious diseases. An important element determining the impact of a zoonosis on domestic animal and human health is host range. Although for particular zoonoses some host species have been identified, until recently there have been no methods to predict those species most likely to be hosts or their relative importance. Complex inference networks infer potential biotic interactions between species using their degree of geographic co-occurrence, and have been posited as a potential tool for predicting disease hosts. Here we present the results of an interdisciplinary, empirical study to validate a model based on such networks for predicting hosts of <i>Leishmania (L</i>.<i>) mexicana</i> in Mexico. Using systematic sampling to validate the model predictions we identified 22 new species of host (34% of all species collected) with the probability to be a host strongly dependent on the probability of co-occurrence of vector and host. The results confirm that <i>Leishmania</i> (<i>L</i>.) <i>mexicana</i> is a generalist parasite but with a much wider host range than was previously thought. These results substantially change the geographic risk profile for Leishmaniasis and provide insights for the design of more efficient surveillance measures and a better understanding of potential dispersal scenarios.</p></div