Risk of a vector-borne endemic zoonosis for wildlife: Hosts, large-scale geography, and diversity of vector-host interactions for Trypanosoma cruzi.

Drivers for wildlife infection are multiple and complex, particularly for vector-borne diseases. Here, we studied the role of host competence, geographic area provenance, and diversity of vector-host interactions as drivers of wild mammal infection risk to Trypanosoma cruzi, the aetiological agent of Chagas disease. We performed a systematic sampling of wild mammals in 11 states of Mexico, from 2017 to 2018. We tested the positivity of T. cruzi with the Tc24 marker in tissues samples for 61 wild mammal species (524 specimens sampled). 26 mammal species were positive for T. cruzi, of which 11 are new hosts recorded in Mexico 75 specimens were positive and 449 were negative for T. cruzi infection, yielding an overall prevalence of 14.3%. The standardized infection risk of T. cruzi of our examined specimens was similar, no matter the host species or their geographic origins. Additionally, we used published data of mammal positives for T. cruzi to complement records of T. cruzi infection in wild mammals and inferred a trophic network of Triatoma spp. (vectors) and wild mammal species in Mexico, using spatial data-mining modelling. Infection with T. cruzi was not homogeneously distributed in the inferred trophic network. This information allowed us to develop a predictive model for T. cruzi infection risk for wild mammals in Mexico, considering risk as a function of the diversity of vector-host spatial associations in a large-scale geographic context, finding that the addition of competent vectors to a multi-host parasite system amplifies host infection risk. The diversity of vector-host interactions per se constitutes a relevant driver of infection risk because hosts and vectors are not isolated from each other.

The Low Variability of Tc24 in Trypanosoma cruzi TcI as an Advantage for Chagas Disease Prophylaxis and Diagnosis in Mexico.

(1) Background: Chagas disease is the main neglected tropical disease in America. It is estimated that around 6 million people are currently infected with the parasite in Latin America, and 25 million live in endemic areas with active transmission. The disease causes an estimated economic loss of USD 24 billion dollars annually, with a loss of 75,200 working years per year of life; it is responsible for around ~12,000 deaths annually. Although Mexico is an endemic country that recorded 10,186 new cases of Chagas disease during the period of 1990-2017, few studies have evaluated the genetic diversity of genes that could be involved in the prophylaxis and/or diagnosis of the parasite. One of the possible candidates proposed as a vaccine target is the 24 kDa trypomastigote excretory-secretory protein, Tc24, whose protection is linked to the stimulation of T. cruzi-specific CD8+ immune responses. (2) Methods: The aim of the present study was to evaluate the fine-scale genetic diversity and structure of Tc24 in T. cruzi isolates from Mexico, and to compare them with other populations reported in the Americas with the aim to reconsider the potential role of Tc24 as a key candidate for the prophylaxis and improvement of the diagnosis of Chagas disease in Mexico. (3) Results: Of the 25 Mexican isolates analysed, 48% (12) were recovered from humans and 24% (6) recovered from Triatoma barberi and Triatoma dimidiata. Phylogenetic inferences revealed a polytomy in the T. cruzi clade with two defined subgroups, one formed by all sequences of the DTU I and the other formed by DTU II-VI; both subgroups had high branch support. Genetic population analysis detected a single (monomorphic) haplotype of TcI throughout the entire distribution across both Mexico and South America. This information was supported by Nei's pairwise distances, where the sequences of TcI showed no genetic differences. (4) Conclusions: Given that both previous studies and the findings of the present work confirmed that TcI is the only genotype detected from human isolates obtained from various states of Mexico, and that there is no significant genetic variability in any of them, it is possible to propose the development of in silico strategies for the production of antigens that optimise the diagnosis of Chagas disease, such as quantitative ELISA methods that use this region of Tc24.

"Does a Respiratory Virus Have an Ecological Niche, and If So, Can It Be Mapped?" Yes and Yes.

Although the utility of Ecological Niche Models (ENM) and Species Distribution Models (SDM) has been demonstrated in many ecological applications, their suitability for modelling epidemics or pandemics, such as SARS-Cov-2, has been questioned. In this paper, contrary to this viewpoint, we show that ENMs and SDMs can be created that can describe the evolution of pandemics, both in space and time. As an illustrative use case, we create models for predicting confirmed cases of COVID-19, viewed as our target "species", in Mexico through 2020 and 2021, showing that the models are predictive in both space and time. In order to achieve this, we extend a recently developed Bayesian framework for niche modelling, to include: (i) dynamic, non-equilibrium "species" distributions; (ii) a wider set of habitat variables, including behavioural, socio-economic and socio-demographic variables, as well as standard climatic variables; (iii) distinct models and associated niches for different species characteristics, showing how the niche, as deduced through presence-absence data, can differ from that deduced from abundance data. We show that the niche associated with those places with the highest abundance of cases has been highly conserved throughout the pandemic, while the inferred niche associated with presence of cases has been changing. Finally, we show how causal chains can be inferred and confounding identified by showing that behavioural and social factors are much more predictive than climate and that, further, the latter is confounded by the former.

Disentangling the contributions of biotic and abiotic predictors in the niche and the species distribution model of Trypanosoma cruzi, etiological agent of Chagas disease.

The potential benefits of incorporating biotic, as well as abiotic, predictors in niche and species distribution models (SDMs), as well as how to achieve this, is still debated, with their interpretability and explanatory potential being particularly questioned. It is therefore important to stress test modelling methodologies that include biotic factors against use cases where there is ample knowledge of the potential biotic component of the niche. Relatively well studied and important vector-borne diseases offer just such an opportunity, where knowledge of the agents involved in the transmission cycle -vectors and hosts- can serve to calibrate and test the niche model and corresponding SDM. Here, we study the contributions of biotic -14 vectors, 459 potential hosts- and abiotic -258 climatic categories- predictors to the explanatory and predictive features of the niche and corresponding SDM for the etiological agent of Chagas disease, Trypanosoma cruzi, in Mexico. Using an established spatial data mining technique, we generate biotic, abiotic and biotic+abiotic niche and SDM models. We test our models by comparing predictions of the most important probable hosts of Chagas disease with a previously published list of confirmed hosts. We quantify, compare, and contrast the individual and total contributions of predictors to the niche and distribution of Chagas disease in Mexico. We assess the relative predictive potential of these variables to model performance, showing that models that include relevant biotic niche variables lead to more predictive, more ecologically realistic SDMs. Our research illustrates a useful general procedure for identifying and ranking potential biotic interactions and for assessing the relative importance of biotic and abiotic predictors. We conclude that the inclusion of both abiotic and biotic predictors in SDMs not only provides more predictive and accurate models but also models that are more understandable and explainable from an ecological niche perspective.

Toward New Epidemiological Landscapes of Trypanosoma cruzi (Kinetoplastida, Trypanosomatidae) Transmission under Future Human-Modified Land Cover and Climatic Change in Mexico.

Chagas disease, caused by the protozoa Trypanosoma cruzi, is an important yet neglected disease that represents a severe public health problem in the Americas. Although the alteration of natural habitats and climate change can favor the establishment of new transmission cycles for T. cruzi, the compound effect of human-modified landscapes and current climate change on the transmission dynamics of T. cruzi has until now received little attention. A better understanding of the relationship between these factors and T. cruzi presence is an important step towards finding ways to mitigate the future impact of this disease on human communities. Here, we assess how wild and domestic cycles of T. cruzi transmission are related to human-modified landscapes and climate conditions (LUCC-CC). Using a Bayesian datamining framework, we measured the correlations among the presence of T. cruzi transmission cycles (sylvatic, rural, and urban) and historical land use, land cover, and climate for the period 1985 to 2012. We then estimated the potential range changes of T. cruzi transmission cycles under future land-use and -cover change and climate change scenarios for 2050 and 2070 time-horizons, with respect to "green" (RCP 2.6), "business-as-usual" (RCP 4.5), and "worst-case" (RCP 8.5) scenarios, and four general circulation models. Our results show how sylvatic and domestic transmission cycles could have historically interacted through the potential exchange of wild triatomines (insect vectors of T. cruzi) and mammals carrying T. cruzi, due to the proximity of human settlements (urban and rural) to natural habitats. However, T. cruzi transmission cycles in recent times (i.e., 2011) have undergone a domiciliation process where several triatomines have colonized and adapted to human dwellings and domestic species (e.g., dogs and cats) that can be the main blood sources for these triatomines. Accordingly, Chagas disease could become an emerging health problem in urban areas. Projecting potential future range shifts of T. cruzi transmission cycles under LUCC-CC scenarios we found for RCP 2.6 no expansion of favourable conditions for the presence of T. cruzi transmission cycles. However, for RCP 4.5 and 8.5, a significant range expansion of T. cruzi could be expected. We conclude that if sustainable goals are reached by appropriate changes in socio-economic and development policies we can expect no increase in suitable habitats for T. cruzi transmission cycles.

Interacciones potenciales parásito-hospedero entre el escarabajo Dendroctonus (Coleoptera: Scolytidae) y Pinus (Pinaceae) en México / Potential parasite-host interactions between Dendroctonus (Coleoptera: Scolytidae) beetle and Pinus (Pinaceae) in Mexico

Resumen Introducción: La relación parásito-hospedero entre Dendroctonus y Pinus es reconocida como una amenaza importante para los bosques de coníferas, debido a que puede causar una alta mortalidad de árboles y consecuentemente una pérdida de cobertura forestal. Objetivo: Identificar potenciales interacciones parásito-huésped entre descortezadores y pinos, así como la contribución de las condiciones ambientales para el establecimiento de estas interacciones ecológicas. Métodos: Se realizaron modelos de nicho ecológico y análisis de redes ecológicas con la plataforma web de minería de datos espaciales SPECIES (http://species.conabio.gob.mx/). Se incluyeron siete especies de descortezadores, 52 de pinos y 19 variables bioclimáticas del portal WorldClim. Resultados: Se identificaron 140 interacciones potenciales, de las cuales el 42 % no han sido registradas previamente. El análisis de redes ecológicas nos permitió caracterizar las relaciones parásito-hospedero en generalistas y especialistas. En cuanto al análisis de nicho ecológico, se determinó la distribución potencial de los descortezadores combinando las distribuciones de Pinus y variables climáticas como predictores. Se observó que las variables climáticas contribuyen de forma negativa para la mayoría de las especies de Dendroctonus, es decir, estas variables son restrictivas en casi toda su distribución, por el contrario, las variables bióticas (Pinus) fueron positivas e informativas sobre la mayoría de las distribuciones. Conclusiones: Los patrones de coexistencia de descortezadores y pinos nos proporcionan información sobre las interacciones que se pueden establecer entre estas especies y que hasta hoy pueden ser desconocidas. Además, la integración de factores bióticos (hospederos) y abióticos (clima), permite obtener modelos de distribución geográfica que caracteriza las regiones con condiciones favorables para la presencia de las especies, así como las especies de hospederos con los que podrían estar interactuando en dichas regiones. Considerando el riesgo fitosanitario por descortezadores, nuestros resultados brindan escenarios geográficos y ecológicos de riesgo donde infestaciones por descortezadores podrían darse, así como la posible emergencia de nuevas interacciones parásito-hospedero desconocidas hasta hoy. Estos modelos son una herramienta que en el futuro pueden dirigir esfuerzos de trabajo de campo para validar y complementar nuestros resultados.

Abstract Introduction: The parasite-host relationship between bark beetles of the genus Dendroctonus and species of the genus Pinus has been recognized as important threat to coniferous forests because it can cause high tree mortality and therefore a loss of forests. Objectives: We aimed to identify the potential unknown parasite-host relationships among bark beetles and pine trees species, as well as the contribution of environmental conditions as driver of these ecological interactions. Methods: We carried out ecological niche models and ecological network analysis using the spatial datamining platform SPECIES (http://species.conabio.gob.mx/). Our study included seven species of bark beetles, 52 species of the genus Pinus, and 19 bioclimatic variables from Worldclim dataset. Results: We identified 140 potential interactions between Dendroctonus and Pinus species, of which 42 % have not been previously registered. Complex inference network analysis allowed us to characterize ecological parasite-host interactions in generalists and specialists. Regarding the ecological niche analysis, we determined potential bark beetle distributions by combining Pinus species ranges and climatic variables as predictors. We noted that climatic variables contributed negatively to Dendroctonus distribution for the most species, i.e. these abiotic variables are restrictive in almost the entire distribution of the beetles; conversely, biotic variables (i.e. Pinus species) were positive and informative on the most of species ranges. Conclusions: Coexistence patterns of bark beetles and Pinus provide us information on the interactions that are able to establish among these species and that until now can be unknown. In addition, integrating biotic (hosts) and abiotic (climate) factors, allows us to obtain geographic distribution models that characterize the regions with favorable conditions for the presence of bark beetles, as well as the host species with which they could be interacting in those regions. Considering the phytosanitary risk due to bark beetles, our results provide geographic and ecological scenarios where bark beetles infestations may occur, as well as the possible emergence of new parasite-host interactions. These models can be a tool to address future fieldwork efforts to validate and complement our results.

Using Data Mining and Network Analysis to Infer Arboviral Dynamics: The Case of Mosquito-Borne Flaviviruses Reported in Mexico.

Given the significant impact of mosquito-borne flaviviruses (MBFVs) on both human and animal health, predicting their dynamics and understanding their transmission cycle is of the utmost importance. Usually, predictions about the distribution of priority pathogens, such as Dengue, Yellow fever, West Nile Virus and St. Louis encephalitis, relate abiotic elements to simple biotic components, such as a single causal agent. Furthermore, focusing on single pathogens neglects the possibility of interactions and the existence of common elements in the transmission cycles of multiple pathogens. A necessary, but not sufficient, condition that a mosquito be a vector of a MBFV is that it co-occurs with hosts of the pathogen. We therefore use a recently developed modeling framework, based on co-occurrence data, to infer potential biotic interactions between those mosquito and mammal species which have previously been identified as vectors or confirmed positives of at least one of the considered MBFVs. We thus create models for predicting the relative importance of mosquito species as potential vectors for each pathogen, and also for all pathogens together, using the known vectors to validate the models. We infer that various mosquito species are likely to be significant vectors, even though they have not currently been identified as such, and are likely to harbor multiple pathogens, again validating the predictions with known results. Besides the above "niche-based" viewpoint we also consider an assemblage-based analysis, wherein we use a community-identification algorithm to identify those mosquito and/or mammal species that form assemblages by dint of their significant degree of co-occurrence. The most cohesive assemblage includes important primary vectors, such as A. aegypti, A. albopictus, C. quinquefasciatus, C. pipiens and mammals with abundant populations that are well-adapted to human environments, such as the white-tailed deer (Odocoileus virginianus), peccary (Tayassu pecari), opossum (Didelphis marsupialis) and bats (Artibeus lituratus and Sturnira lilium). Our results suggest that this assemblage has an important role in the transmission dynamics of this viral group viewed as a complex multi-pathogen-vector-host system. By including biotic risk factors our approach also modifies the geographical risk profiles of the spatial distribution of MBFVs in Mexico relative to a consideration of only abiotic niche variables.

Assessment of the potential establishment of Lyme endemic cycles in Mexico.

Although Lyme disease is currently classified as exotic in Mexico, recent studies have suggested that it might be endemic there. We assessed the potential risk for the establishment of Borrelia burgdorferi transmission in Mexico. To identify the potential routes of B. burgdorferi spread, Complex Inference Networks were used initially to identify potential vector-host interactions between hard ticks (Ixodes) and migratory birds in the U.S., and a model for predicting the most important potential bird hosts of hard ticks was then obtained. By using network metrics, keystone-vectors were identified as those species with highest connectivity within and between network communities and had the potential to keep the pathogen circulating with many birds and to be dispersed to several regions. The climatic profile where these interactions occur in the U.S. was characterized and a geographic model for each keystone-vector was built. The accuracy of these models to predict areas where hard ticks have been reported positive for B. burgdorferi allows one to identify areas of greater risk of Lyme disease emergence. These hard tick-bird interactions and their climatic profile were mapped into the winter ranges of birds in Mexico. Thus, those regions in Mexico with the highest potential for becoming endemic areas of Lyme disease through the arrival of hard ticks and birds infected by B. burgdorferi were identified. These areas are candidates for future surveillance programs.

Solitary ecology as a phenomenon extending beyond insular systems: exaptive evolution in Anolis lizards.

The mechanisms driving phenotypic evolution have been of interest to biologists since Darwin. Ecological release-wherein adaptive evolution occurs following relaxation of constraining selective pressures-and environmental filtering-wherein exaptive traits allow colonization of a new area-have been studied in several insular cases. Anolis lizards, which may exist in solitude or sympatry with multiple congeners, are an excellent system for evaluating whether ecological release and environmental filtering are associated with phenotypic shifts across phylogenetic and geographical scales. Insular solitary Anolis exhibit phenotypic differentiation in body size and sexual size dimorphism-SSD-through exaptive and adaptive evolution, respectively. But, the generality of these effects has not yet been addressed. Here, we analyse the evolution of body size and SSD relative to sympatry in mainland Anolis. We found that mainland species co-occurring with few congeners exhibit uniform body size and greater SSD relative to other random mainland assemblages, consistent with the insular solitary pattern. The locations of evolutionary shifts for both traits do not coincide with evolutionary transitions to decreased levels of sympatry. These results are consistent with exaptive environmental filtering but not adaptive ecological release. Future studies should be conducted at local scales to evaluate the role of these factors in the evolution of solitary existence in mainland and island species.

SPECIES: A platform for the exploration of ecological data.

The modeling of ecological data that include both abiotic and biotic factors is fundamental to our understanding of ecosystems. Repositories of biodiversity data, such as GBIF, iDigBio, Atlas of Living Australia, and SNIB (Mexico's National System of Biodiversity Information), contain a great deal of information that can lead to knowledge discovery about ecosystems. However, there is a lack of tools with which to efficiently extract such knowledge. In this paper, we present SPECIES, an open, web-based platform designed to extract implicit information contained in large scale sets of ecological data. SPECIES is based on a tested methodology, wherein the correlations of variables of arbitrary type and spatial resolution, both biotic and abiotic, discrete and continuous, may be explored from both niche and network perspectives. In distinction to other modeling systems, SPECIES is a full stack exploratory tool that integrates the three basic components: data (which is incrementally growing), a statistical modeling and analysis engine, and an interactive visualization front end. Combined, these components provide a powerful tool that may guide ecologists toward new insights. SPECIES is optimized to support fast hypothesis prototyping and testing, analyzing thousands of biotic and abiotic variables, and presenting descriptive results to the user at different levels of detail. SPECIES is an open-access platform available online (http://species.conabio.gob.mx), that is, powerful, flexible, and easy to use. It allows for the exploration and incorporation of ecological data and its subsequent integration into predictive models for both potential ecological niche and geographic distribution. It also provides an ecosystemic, network-based analysis that may guide the researcher in identifying relations between different biota, such as the relation between disease vectors and potential disease hosts.

Coat colour adaptation of post-glacial horses to increasing forest vegetation.

Wild horses unexpectedly survived terminal Pleistocene megafaunal extinctions until eventual European extirpation in the twentieth century. This survival is tied to either their occurrence in cryptic open habitats or their adaptation to forests. Our niche modelling inferred an increasing presence of horses in post-glacial forests, and our analysis of ancient DNA suggested significant selection for black phenotypes as indicating adaptation to forests.

Predicting the Potential Role of Non-human Hosts in Zika Virus Maintenance.

Arboviruses are often maintained in complex cycles involving vertebrates such as mammals or birds and blood-feeding mosquitoes. However, the role of wildlife hosts in their emergence or re-emergence in human populations has received little attention. The recent emergence of Zika virus in America, and previous occurrences of chikungunya and dengue, forces us to confront a potential new disease-emergence phenomenon. Using a spatial data mining framework to identify potential biotic interactions, based on the degree of co-occurrence between different species, we identified those mammal species with the highest potential for establishing mammal-vector interactions, considering as principal vector Aedes aegypti. Seven of the top ten identified mammal species with highest potential were bats, with two of them having previously been confirmed as positive hosts for dengue in Mexico. We hope that this will raise interest of Mexican public health authorities and academic institutions to assess the role of wild hosts in the maintenance and spread of arboviruses.

Understanding transmissibility patterns of Chagas disease through complex vector-host networks.

Chagas disease is one of the most important vector-borne zoonotic diseases in Latin America. Control strategies could be improved if transmissibility patterns of its aetiologic agent, Trypanosoma cruzi, were better understood. To understand transmissibility patterns of Chagas disease in Mexico, we inferred potential vectors and hosts of T. cruzi from geographic distributions of nine species of Triatominae and 396 wild mammal species, respectively. The most probable vectors and hosts of T. cruzi were represented in a Complex Inference Network, from which we formulated a predictive model and several associated hypotheses about the ecological epidemiology of Chagas disease. We compiled a list of confirmed mammal hosts to test our hypotheses. Our tests allowed us to predict the most important potential hosts of T. cruzi and to validate the model showing that the confirmed hosts were those predicted to be the most important hosts. We were also able to predict differences in the transmissibility of T. cruzi among triatomine species from spatial data. We hope our findings help drive efforts for future experimental studies.

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

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 Leishmania (L.) mexicana 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 Leishmania (L.) mexicana 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.

Leishmania (L.) mexicana infected bats in Mexico: novel potential reservoirs.

Leishmania (Leishmania) mexicana causes cutaneous leishmaniasis, an endemic zoonosis affecting a growing number of patients in the southeastern states of Mexico. Some foci are found in shade-grown cocoa and coffee plantations, or near perennial forests that provide rich breeding grounds for the sand fly vectors, but also harbor a variety of bat species that live off the abundant fruits provided by these shade-giving trees. The close proximity between sand flies and bats makes their interaction feasible, yet bats infected with Leishmania (L.) mexicana have not been reported. Here we analyzed 420 bats from six states of Mexico that had reported patients with leishmaniasis. Tissues of bats, including skin, heart, liver and/or spleen were screened by PCR for Leishmania (L.) mexicana DNA. We found that 41 bats (9.77%), belonging to 13 species, showed positive PCR results in various tissues. The infected tissues showed no evidence of macroscopic lesions. Of the infected bats, 12 species were frugivorous, insectivorous or nectarivorous, and only one species was sanguivorous (Desmodus rotundus), and most of them belonged to the family Phyllostomidae. The eco-region where most of the infected bats were caught is the Gulf Coastal Plain of Chiapas and Tabasco. Through experimental infections of two Tadarida brasiliensis bats in captivity, we show that this species can harbor viable, infective Leishmania (L.) mexicana parasites that are capable of infecting BALB/c mice. We conclude that various species of bats belonging to the family Phyllostomidae are possible reservoir hosts for Leishmania (L.) mexicana, if it can be shown that such bats are infective for the sand fly vector. Further studies are needed to determine how these bats become infected, how long the parasite remains viable inside these potential hosts and whether they are infective to sand flies to fully evaluate their impact on disease epidemiology.

Climate change and risk of leishmaniasis in north america: predictions from ecological niche models of vector and reservoir species.

BACKGROUND: Climate change is increasingly being implicated in species' range shifts throughout the world, including those of important vector and reservoir species for infectious diseases. In North America (México, United States, and Canada), leishmaniasis is a vector-borne disease that is autochthonous in México and Texas and has begun to expand its range northward. Further expansion to the north may be facilitated by climate change as more habitat becomes suitable for vector and reservoir species for leishmaniasis. METHODS AND FINDINGS: The analysis began with the construction of ecological niche models using a maximum entropy algorithm for the distribution of two sand fly vector species (Lutzomyia anthophora and L. diabolica), three confirmed rodent reservoir species (Neotoma albigula, N. floridana, and N. micropus), and one potential rodent reservoir species (N. mexicana) for leishmaniasis in northern México and the United States. As input, these models used species' occurrence records with topographic and climatic parameters as explanatory variables. Models were tested for their ability to predict correctly both a specified fraction of occurrence points set aside for this purpose and occurrence points from an independently derived data set. These models were refined to obtain predicted species' geographical distributions under increasingly strict assumptions about the ability of a species to disperse to suitable habitat and to persist in it, as modulated by its ecological suitability. Models successful at predictions were fitted to the extreme A2 and relatively conservative B2 projected climate scenarios for 2020, 2050, and 2080 using publicly available interpolated climate data from the Third Intergovernmental Panel on Climate Change Assessment Report. Further analyses included estimation of the projected human population that could potentially be exposed to leishmaniasis in 2020, 2050, and 2080 under the A2 and B2 scenarios. All confirmed vector and reservoir species will see an expansion of their potential range towards the north. Thus, leishmaniasis has the potential to expand northwards from México and the southern United States. In the eastern United States its spread is predicted to be limited by the range of L. diabolica; further west, L. anthophora may play the same role. In the east it may even reach the southern boundary of Canada. The risk of spread is greater for the A2 scenario than for the B2 scenario. Even in the latter case, with restrictive (contiguous) models for dispersal of vector and reservoir species, and limiting vector and reservoir species occupancy to only the top 10% of their potential suitable habitat, the expected number of human individuals exposed to leishmaniasis by 2080 will at least double its present value. CONCLUSIONS: These models predict that climate change will exacerbate the ecological risk of human exposure to leishmaniasis in areas outside its present range in the United States and, possibly, in parts of southern Canada. This prediction suggests the adoption of measures such as surveillance for leishmaniasis north of Texas as disease cases spread northwards. Potential vector and reservoir control strategies-besides direct intervention in disease cases-should also be further investigated.

Using biotic interaction networks for prediction in biodiversity and emerging diseases.

Networks offer a powerful tool for understanding and visualizing inter-species ecological and evolutionary interactions. Previously considered examples, such as trophic networks, are just representations of experimentally observed direct interactions. However, species interactions are so rich and complex it is not feasible to directly observe more than a small fraction. In this paper, using data mining techniques, we show how potential interactions can be inferred from geographic data, rather than by direct observation. An important application area for this methodology is that of emerging diseases, where, often, little is known about inter-species interactions, such as between vectors and reservoirs. Here, we show how using geographic data, biotic interaction networks that model statistical dependencies between species distributions can be used to infer and understand inter-species interactions. Furthermore, we show how such networks can be used to build prediction models. For example, for predicting the most important reservoirs of a disease, or the degree of disease risk associated with a geographical area. We illustrate the general methodology by considering an important emerging disease--Leishmaniasis. This data mining methodology allows for the use of geographic data to construct inferential biotic interaction networks which can then be used to build prediction models with a wide range of applications in ecology, biodiversity and emerging diseases.