. Novel methods improve prediction of species' distributions from occurrence data. Á/ Ecography 29: 129 Á/151.Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve. J. Elith

Aim In response to a recent paper suggesting the failure of ecological niche models to predict between native and introduced distributional areas of fire ants ( Solenopsis invicta ), we sought to assess methodological causes of this failure.Location Ecological niche models were developed on the species' native distributional area in South America, and projected globally. MethodsWe developed ecological niche models based on six different environmental data sets, and compared their respective abilities to anticipate the North American invasive distributional area of the species. ResultsWe show that models based on the 'bioclimatic variables' of the WorldClim data set indeed fail to predict the full invasive potential of the species, but that models based on four other data sets could predict this potential correctly. Main conclusionsThe difference in predictive abilities appears to centre on the complexity of the environmental variables involved. These results emphasize important influences of environmental data sets on the generality and ability of ecological niche models to anticipate novel phenomena, and offer a simpler explanation for the lack of predictive ability among native and invaded distributional areas than that of niche shifts.

During 2012, 2013 and 2015, we collected small mammals within 25 km of the town of Boende in Tshuapa Province, the Democratic Republic of the Congo. The prevalence of monkeypox virus (MPXV) in this area is unknown; however, cases of human infection were previously confirmed near these collection sites. Samples were collected from 353 mammals (rodents, shrews, pangolins, elephant shrews, a potamogale, and a hyrax). Some rodents and shrews were captured from houses where human monkeypox cases have recently been identified, but most were trapped in forests and agricultural areas near villages. Real-time PCR and ELISA were used to assess evidence of MPXV infection and other Orthopoxvirus (OPXV) infections in these small mammals. Seven (2.0%) of these animal samples were found to be anti-orthopoxvirus immunoglobulin G (IgG) antibody positive (six rodents: two Funisciurus spp.; one Graphiurus lorraineus; one Cricetomys emini; one Heliosciurus sp.; one Oenomys hypoxanthus, and one elephant shrew Petrodromus tetradactylus); no individuals were found positive in PCR-based assays. These results suggest that a variety of animals can be infected with OPXVs, and that epidemiology studies and educational campaigns should focus on animals that people are regularly contacting, including larger rodents used as protein sources.

Chagas disease is one of the most important yet neglected parasitic diseases in Mexico and is transmitted by Triatominae. Nineteen of the 31 Mexican triatomine species have been consistently found to invade human houses and all have been found to be naturally infected with Trypanosoma cruzi. The present paper aims to produce a state-of-knowledge atlas of Mexican triatomines and analyse their geographic associations with T. cruzi, human demographics and landscape modification. Ecological niche models (ENMs) were constructed for the 19 species with more than 10 records in North America, as well as for T. cruzi. The 2010 Mexican national census and the 2007 National Forestry Inventory were used to analyse overlap patterns with ENMs. Niche breadth was greatest in species from the semiarid Nearctic Region, whereas species richness was associated with topographic heterogeneity in the Neotropical Region, particularly along the Pacific Coast. Three species, Triatoma longipennis, Triatoma mexicana and Triatoma barberi, overlapped with the greatest numbers of human communities, but these communities had the lowest rural/urban population ratios. Triatomine vectors have urbanised in most regions, demonstrating a high tolerance to human-modified habitats and broadened historical ranges, exposing more than 88% of the Mexican population and leaving few areas in Mexico without the potential for T. cruzi transmission.