Predictable invasion dynamics in North American populations of the Eurasian collared dove Streptopelia decaocto.

Species invasions represent a significant dimension of global change yet the dynamics of invasions remain poorly understood and are considered rather unpredictable. We explored interannual dynamics of the invasion process in the Eurasian collared dove (Streptopelia decaocto) and tested whether the advance of the invasion front of the species in North America relates to centrality (versus peripherality) within its estimated fundamental ecological niche. We used ecological niche modelling approaches to estimate the dimensions of the fundamental ecological niche on the Old World distribution of the species, and then transferred that model to the New World as measures of centrality versus peripherality within the niche for the species. Although our hypothesis was that the invasion front would advance faster over more favourable (i.e. more central) conditions, the reverse was the case: the invasion expanded faster in areas presenting less favourable (i.e. more peripheral) conditions for the species as it advanced across North America. This result offers a first view of a predictive approach to the dynamics of species' invasions, and thereby has relevant implications for the management of invasive species, as such a predictive understanding would allow better anticipation of coming steps and advances in the progress of invasions, important to designing and guiding effective remediation and mitigation efforts.

Broadscale spatial synchrony in a West Nile virus mosquito vector across multiple timescales.

Insects often exhibit irruptive population dynamics determined by environmental conditions. We examine if populations of the Culex tarsalis mosquito, a West Nile virus (WNV) vector, fluctuate synchronously over broad spatial extents and multiple timescales and whether climate drives synchrony in Cx. tarsalis, especially at annual timescales, due to the synchronous influence of temperature, precipitation, and/or humidity. We leveraged mosquito collections across 9 National Ecological Observatory Network (NEON) sites distributed in the interior West and Great Plains region USA over a 45-month period, and associated gridMET climate data. We utilized wavelet phasor mean fields and wavelet linear models to quantify spatial synchrony for mosquitoes and climate and to calculate the importance of climate in explaining Cx. tarsalis synchrony. We also tested whether the strength of spatial synchrony may vary directionally across years. We found significant annual synchrony in Cx. tarsalis, and short-term synchrony during a single period in 2018. Mean minimum temperature was a significant predictor of annual Cx. tarsalis spatial synchrony, and we found a marginally significant decrease in annual Cx. tarsalis synchrony. Significant Cx. tarsalis synchrony during 2018 coincided with an anomalous increase in precipitation. This work provides a valuable step toward understanding broadscale synchrony in a WNV vector.

Climate, landscape, and life history jointly predict multidecadal community mosquito phenology.

Phenology of adult host-seeking female mosquitoes is a critical component for understanding potential for vector-borne pathogen maintenance and amplification in the natural environment. Despite this importance, long-term multi-species investigations of mosquito phenologies across environments and differing species' life history traits are rare. Here we leverage long-term mosquito control district monitoring data to characterize annual phenologies of 7 host-seeking female mosquito species over a 20-year time period in suburban Illinois, USA. We also assembled data on landscape context, categorized into low and medium development, climate variables including precipitation, temperature and humidity, and key life history traits, i.e. overwintering stage and Spring-Summer versus Summer-mid-Fallseason fliers. We then fit linear mixed models separately for adult onset, peak abundances, and flight termination with landscape, climate and trait variables as predictors with species as a random effect. Model results supported some expectations, including warmer spring temperatures leading to earlier onset, warmer temperatures and lower humidity leading to earlier peak abundances, and warmer and wetter fall conditions leading to later termination. However, we also found sometimes complex interactions and responses contrary to our predictions. For example, temperature had generally weak support on its own, impacting onset and peak abundance timing; rather temperature has interacting effects with humidity or precipitation. We also found higher spring precipitation, especially in low development contexts, generally delayed adult onset, counter to expectations. These results emphasize the need to consider how traits, landscape and climatic factors all interact to determine mosquito phenology, when planning management strategies for vector control and public health protection.

Ticks and Tick-Borne Pathogens from Wild Pigs in Northern and Central Florida.

Invasive wild pigs are distributed across much of the U.S. and are hosts to tick vectors of human disease. Herein, adult ticks were collected from 157 wild pigs in 21 northern and central Florida counties from 2019-2020 during removal efforts by USDA-APHIS Wildlife Services personnel and evaluated for their potential to be used as a method of tick-borne disease surveillance. Collected ticks were identified, screened for pathogens, and the effects of landscape metrics on tick community composition and abundance were investigated. A total of 1415 adult ticks of four species were collected. The diversity of tick species collected from wild pigs was comparable to collections made throughout the state with conventional surveillance methods. All species collected have implications for pathogen transmission to humans and other animals. Ehrlichia, Anaplasma-like, and Rickettsia spp. were detected in ticks collected from wild pigs. These results suggest that tick collection from wild pigs is a suitable means of surveillance for pathogens and vectors. The strongest drivers of variation in tick community composition were the developed open space and mixed forest landcover classes. Fragmented shrub/scrub habitat was associated with increased tick abundance. Similar models may be useful in predicting tick abundance and distribution patterns.

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.

Survey of Invasive Mosquito Surveillance and Control Capacity in Southeastern USA Reveals Training and Resource Needs.

Several invasive mosquito species that are nuisances or of medical and veterinary importance have been introduced into the Southeastern region of the USA, posing a threat to other species and the local ecosystems and/or increasing the risk of pathogen transmission to people, livestock, and domestic pets. Prompt and effective monitoring and control of invasive species is essential to prevent them from spreading and causing harmful effects. However, the capacity for invasive mosquito species surveillance is highly variable among mosquito control programs in the Southeast, depending on a combination of factors such as regional geography and climate, access to resources, and the ability to interact with other programs. To facilitate the development of invasive mosquito surveillance in the region, we, the Mosquito BEACONS (Biodiversity Enhancement and Control of Non-native Species) working group, conducted a survey on the capacities of various public health agencies and pest control agencies engaged in mosquito surveillance and control in seven Southeastern states (Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, and South Carolina). Ninety control programs completed the survey, representing an overall response rate of 25.8%. We report key findings from our survey, emphasizing the training and resource needs, and discuss their implications for future invasive mosquito surveillance and control capacity building. By increasing communication and collaboration opportunities (e.g., real-time sharing of collection records, coordinated multistate programs), the establishment of Mosquito BEACONS and the implementation of this survey can accelerate knowledge transfer and improve decision support capacity in response to or in preparation for invasive mosquito surveillance and can establish infrastructure that can be used to inform programs around the world.

Predicting the potential distribution of Culex (Melanoconion) cedecei in Florida and the Caribbean using ecological niche models.

Everglades virus (EVEV), an enzootic subtype of Venezuelan equine encephalitis virus, along with its endemic mosquito vector, Culex cedecei, is known only from South Florida. The taxonomy of Cx. cedecei is complex and was once synonymous with Culex opisthopus and Culex taeniopus. We modeled potential distribution of Cx. cedecei in Florida and the Caribbean using an ecological niche model and compared this distribution to the recorded distribution of EVEV in Florida as well as historical records of Cx. opisthopus/Cx. taeniopus. We used recent collections and occurrence data from scientific publications and temperature/precipitation variables and vegetation greenness values to calibrate models. We found mean annual temperature contributed the greatest to model performance. Everglades virus in humans and wildlife corresponded with areas predicted suitable for Cx. cedecei in Florida but not with incidence of antibodies reported in dogs. Most records of Cx. opisthopus/Cx. taeniopus in the Caribbean did not correspond to areas predicted suitable for Cx. cedecei, which may be due to mean annual temperature values in the Caribbean exceeding values within the calibration region, imposing model constraints. Results indicated that this model may adequately predict the distributions of Cx. cedecei within Florida but cannot predict areas suitable in the Caribbean.

Predicting potential transmission risk of Everglades virus in Florida using mosquito blood meal identifications.

The overlap between arbovirus host, arthropod vectors, and pathogen distributions in environmentally suitable habitats represents a nidus where risk for pathogen transmission may occur. Everglades virus (EVEV), subtype II Venezuelan equine encephalitis virus (VEEV), is endemic to southern Florida where it is transmitted by the endemic vector Culex cedecei between muroid rodent hosts. We developed an ecological niche model (ENM) to predict areas in Florida suitable for EVEV transmission based upon georeferenced vector-host interactions from PCR-based blood meal analysis from blood-engorged female Cx. cedecei females. Thirteen environmental variables were used for model calibration, including bioclimatic variables derived from Daymet 1 km daily temperature and precipitation values, and land use and land cover data representing percent land cover derived within a 2.5 km buffer from 2019 National Land Cover Database (NLCD) program. Maximum temperature of the warmest month, minimum temperature of the coldest month, and precipitation of the driest month contributed 31.6%, 28.5% and 19.9% to ENM performance. The land cover types contributing the greatest to the model performance were percent landcover of emergent herbaceous and woody wetlands which contributed 5.2% and 4.3% to model performance, respectively. Results of the model output showed high suitability for Cx. cedecei feeding on rodents throughout the southwestern portion of the state and pockets of high suitability along the northern east coast of Florida, while areas with low suitability included the Miami-Dade metropolitan area and most of northern Florida and the Panhandle. Comparing predicted distributions of Cx. cedecei feeding upon rodent hosts in the present study to historical human cases of EVEV disease, as well as antibodies in wildlife show substantial overlap with areas predicted moderate to highly suitable for these vector/host associations. As such, the findings of this study likely predict the most accurate distribution of the nidus of EVEV to date, indicating that this method allows for better inference of potential transmission areas than models which only consider the vector or vertebrate host species individually. A similar approach using host blood meals of other arboviruses can be used to predict potential areas of virus transmission for other vector-borne diseases.

Application of Deep Learning to Community-Science-Based Mosquito Monitoring and Detection of Novel Species.

Mosquito-borne diseases account for human morbidity and mortality worldwide, caused by the parasites (e.g., malaria) or viruses (e.g., dengue, Zika) transmitted through bites of infected female mosquitoes. Globally, billions of people are at risk of infection, imposing significant economic and public health burdens. As such, efficient methods to monitor mosquito populations and prevent the spread of these diseases are at a premium. One proposed technique is to apply acoustic monitoring to the challenge of identifying wingbeats of individual mosquitoes. Although researchers have successfully used wingbeats to survey mosquito populations, implementation of these techniques in areas most affected by mosquito-borne diseases remains challenging. Here, methods utilizing easily accessible equipment and encouraging community-scientist participation are more likely to provide sufficient monitoring. We present a practical, community-science-based method of monitoring mosquito populations using smartphones. We applied deep-learning algorithms (TensorFlow Inception v3) to spectrogram images generated from smartphone recordings associated with six mosquito species to develop a multiclass mosquito identification system, and flag potential invasive vectors not present in our sound reference library. Though TensorFlow did not flag potential invasive species with high accuracy, it was able to identify species present in the reference library at an 85% correct identification rate, an identification rate markedly higher than similar studies employing expensive recording devices. Given that we used smartphone recordings with limited sample sizes, these results are promising. With further optimization, we propose this novel technique as a way to accurately and efficiently monitor mosquito populations in areas where doing so is most critical.

West Nile Virus Surveillance in Sentinel Chickens and Mosquitoes in Panama City Beach, Florida, from 2014 To 2020.

Over 20 years since its introduction, the West Nile virus (WNV) continues to be the leading cause of arboviral disease in the USA. In Panama City Beach (Bay County, FL), WNV transmission is monitored using sentinel chickens and testing mosquito pools for presence of viral RNA. In the current work, we monitored WNV transmission from 2014 to 2020 through weekly serology sampling of sentinel chickens; mosquito populations through biweekly mosquito collections by suction traps (1 m and 9 m) and weekly gravid trap collections; and mosquito infection rates using a reverse transcriptase-polymerase chain reaction (RT-PCR) assay. Samples were sent to the Bureau of Public Health Laboratories (Tampa, FL) for testing presence/absence of WNV via RT-PCR assay. Our results indicated that canopy surveillance could augment ground collections, providing greater proportions of Culex mosquitoes with less bycatch compared with ground collections. Serology indicated 94 seroconversions to WNV in the study area from 2014 to 2020. The most active year was 2016, which accounted for 32% (n = 30) of all seroconversions reported during the study period. We detected 20 WNV-positive mosquito pools from Culex quinquefasciatus during 2014-17; mosquito infection rates ranged from 2.02 to 23.81 per thousand (95% CI). Climate data indicated anomalously high precipitation in 2014-19 preceding WNV transmission. Data analyzed herein indicate utility in year-round continuous and diversified surveillance methodologies. This information is needed to properly calibrate future models that could assist with predicting transmission events of WNV in Panama City Beach, FL.

Potential Distribution of Aedes (Ochlerotatus) scapularis (Diptera: Culicidae): A Vector Mosquito New to the Florida Peninsula.

Aedes scapularis is a neotropical mosquito known to transmit pathogens of medical and veterinary importance. Its recent establishment in southeastern Florida has potential public health implications. We used an ecological niche modeling approach to predict the abiotic environmental suitability for Ae. scapularis across much of the Americas and Caribbean Islands. Georeferenced occurrence data obtained from the Global Biodiversity Inventory Facility and recent collection records of Ae. scapularis from southern Florida served as input for model calibration. Environmental layers included bioclimatic variables provided in 2000 to 2010 average Modern Era Retrospective-analysis for Research and Applications climatic (MERRAclim) data. Models were run in the software program Maxent. Isothermality values often found in costal environments, had the greatest contribution to model performance. Model projections suggested that there are areas predicted to be suitable for Ae. Scapularis across portions of the Amazon Basin, the Yucatán Peninsula, the Florida Peninsula, and multiple Caribbean Islands. Additionally, model predictions suggested connectivity of highly suitable or relatively suitable environments spanning the United States Gulf Coast, which may facilitate the geographic expansion of this species. At least sixteen Florida counties were predicted to be highly suitable for Ae. scapularis, suggesting that vigilance is needed by vector control and public health agencies to recognize the further spread of this vector.

Invasive Burmese pythons alter host use and virus infection in the vector of a zoonotic virus.

The composition of wildlife communities can have strong effects on transmission of zoonotic vector-borne pathogens, with more diverse communities often supporting lower infection prevalence in vectors (dilution effect). The introduced Burmese python, Python bivittatus, is eliminating large and medium-sized mammals throughout southern Florida, USA, impacting local communities and the ecology of zoonotic pathogens. We investigated invasive predator-mediated impacts on ecology of Everglades virus (EVEV), a zoonotic pathogen endemic to Florida that circulates in mosquito-rodent cycle. Using binomial generalized linear mixed effects models of field data at areas of high and low python densities, we show that increasing diversity of dilution host (non-rodent mammals) is associated with decreasing blood meals on amplifying hosts (cotton rats), and that increasing cotton rat host use is associated with increasing EVEV infection in vector mosquitoes. The Burmese python has caused a dramatic decrease in mammal diversity in southern Florida, which has shifted vector host use towards EVEV amplifying hosts (rodents), resulting in an indirect increase in EVEV infection prevalence in vector mosquitoes, putatively elevating human transmission risk. Our results indicate that an invasive predator can impact wildlife communities in ways that indirectly affect human health, highlighting the need for conserving biological diversity and natural communities.

Mosquito Control Priorities in Florida-Survey Results from Florida Mosquito Control Districts.

Florida lies within a subtropical region where the climate allows diverse mosquito species including invasive species to thrive year-round. As of 2021, there are currently 66 state-approved Florida Mosquito Control Districts, which are major stakeholders for Florida public universities engaged in mosquito research. Florida is one of the few states with extensive organized mosquito control programs. The Florida State Government and Florida Mosquito Control Districts have long histories of collaboration with research institutions. During fall 2020, we carried out a survey to collect baseline data on the current control priorities from Florida Mosquito Control Districts relating to (1) priority control species, (2) common adult and larval control methods, and (3) major research questions to address that will improve their control and surveillance programs. The survey data showed that a total of 17 distinct mosquito species were considered to be priority control targets, with many of these species being understudied. The most common control approaches included truck-mounted ultra-low-volume adulticiding and biopesticide-based larviciding. The districts held interest in diverse research questions, with many prioritizing studies on basic science questions to help develop evidence-based control strategies. Our data highlight the fact that mosquito control approaches and priorities differ greatly between districts and provide an important point of comparison for other regions investing in mosquito control, particularly those with similar ecological settings, and great diversity of potential mosquito vectors, such as in Florida. Our findings highlight a need for greater alignment of research priorities between mosquito control and mosquito research. In particular, we note a need to prioritize filling knowledge gaps relating to understudied mosquito species that have been implicated in arbovirus transmission.

Mosquito Community Composition, Seasonal Distributions, and Trap Bias in Northeastern Florida.

Mosquito control agencies monitor mosquito diversity and abundance through a variety of trap types. Although various long-term ecological data sets exist, little work has been done to address the sampling effort required to capture mosquito community diversity by trap type and few spatiotemporal distributions of vector species have been described. Here, we describe the seasonal distributions of vector species of importance, assess trapping effort needed to capture the diversity of the mosquito community, and use a partial redundancy analysis to identify trap bias from four commonly deployed adult mosquito traps in Volusia County, Florida. Collections were made with American Biophysics Corporation (ABC) light traps, Biogents Sentinel (BGS) traps, chicken coop exit traps, and gravid traps. We collected a total of 238,301 adult female mosquitoes belonging to 11 genera and 36 species, 12 of which we deemed to be vector species of epidemiological importance. We found that ABC traps not only yielded the greatest abundance and diversity but also captured several nonvector species. BGS and gravid traps yielded the highest proportions of vector species; exit traps recorded the lowest abundances and species richness. Wintertime abundances of several species demonstrated a need for year-round surveillance in the study area; partial redundancy analysis revealed that trap type explained a significant proportion of the variance in our data set, with certain vector species associated with specific trap types. Increased awareness regarding the amount of trapping effort needed to detect vector species diversity will help to optimize efforts in the field, leading to more effective resource allocation.

Potential distributions of Bacillus anthracis and Bacillus cereus biovar anthracis causing anthrax in Africa.

BACKGROUND: Bacillus cereus biovar anthracis (Bcbva) is an emergent bacterium closely related to Bacillus anthracis, the etiological agent of anthrax. The latter has a worldwide distribution and usually causes infectious disease in mammals associated with savanna ecosystems. Bcbva was identified in humid tropical forests of Côte d'Ivoire in 2001. Here, we characterize the potential geographic distributions of Bcbva in West Africa and B. anthracis in sub-Saharan Africa using an ecological niche modeling approach. METHODOLOGY/PRINCIPAL FINDINGS: Georeferenced occurrence data for B. anthracis and Bcbva were obtained from public data repositories and the scientific literature. Combinations of temperature, humidity, vegetation greenness, and soils values served as environmental variables in model calibrations. To predict the potential distribution of suitable environments for each pathogen across the study region, parameter values derived from the median of 10 replicates of the best-performing model for each pathogen were used. We found suitable environments predicted for B. anthracis across areas of confirmed and suspected anthrax activity in sub-Saharan Africa, including an east-west corridor from Ethiopia to Sierra Leone in the Sahel region and multiple areas in eastern, central, and southern Africa. The study area for Bcbva was restricted to West and Central Africa to reflect areas that have likely been accessible to Bcbva by dispersal. Model predicted values indicated potential suitable environments within humid forested environments. Background similarity tests in geographic space indicated statistical support to reject the null hypothesis of similarity when comparing environments associated with B. anthracis to those of Bcbva and when comparing humidity values and soils values individually. We failed to reject the null hypothesis of similarity when comparing environments associated with Bcbva to those of B. anthracis, suggesting that additional investigation is needed to provide a more robust characterization of the Bcbva niche. CONCLUSIONS/SIGNIFICANCE: This study represents the first time that the environmental and geographic distribution of Bcbva has been mapped. We document likely differences in ecological niche-and consequently in geographic distribution-between Bcbva and typical B. anthracis, and areas of possible co-occurrence between the two. We provide information crucial to guiding and improving monitoring efforts focused on these pathogens.

Predicting Abundances of Aedes mcintoshi, a primary Rift Valley fever virus mosquito vector.

Rift Valley fever virus (RVFV) is a mosquito-borne zoonotic arbovirus with important livestock and human health, and economic consequences across Africa and the Arabian Peninsula. Climate and vegetation monitoring guide RVFV forecasting models and early warning systems; however, these approaches make monthly predictions and a need exists to predict primary vector abundances at finer temporal scales. In Kenya, an important primary RVFV vector is the mosquito Aedes mcintoshi. We used a zero-inflated negative binomial regression and multimodel averaging approach with georeferenced Ae. mcintoshi mosquito counts and remotely sensed climate and topographic variables to predict where and when abundances would be high in Kenya and western Somalia. The data supported a positive effect on abundance of minimum wetness index values within 500 m of a sampling site, cumulative precipitation values 0 to 14 days prior to sampling, and elevated land surface temperature values ~3 weeks prior to sampling. The probability of structural zero counts of mosquitoes increased as percentage clay in the soil decreased. Weekly retrospective predictions for unsampled locations across the study area between 1 September and 25 January from 2002 to 2016 predicted high abundances prior to RVFV outbreaks in multiple foci during the 2006-2007 epizootic, except for two districts in Kenya. Additionally, model predictions supported the possibility of high Ae. mcintoshi abundances in Somalia, independent of Kenya. Model-predicted abundances were low during the 2015-2016 period when documented outbreaks did not occur, although several surveillance systems issued warnings. Model predictions prior to the 2018 RVFV outbreak indicated elevated abundances in Wajir County, Kenya, along the border with Somalia, but RVFV activity occurred west of the focus of predicted high Ae. mcintoshi abundances.

Landscape Genetics of Aedes mcintoshi (Diptera: Culicidae), an Important Vector of Rift Valley Fever Virus in Northeastern Kenya.

Rift Valley fever virus (RVFV) is a vector-borne, zoonotic disease that affects humans, wild ungulates, and domesticated livestock in Africa and the Arabian Peninsula. Rift Valley fever virus exhibits interepizootic and epizootic phases, the latter defined by widespread virus occurrence in domesticated livestock. Kenya appears to be particularly vulnerable to epizootics, with 11 outbreaks occurring between 1951 and 2007. The mosquito species Aedes mcintoshi (subgenus Neomelaniconion) is an important primary vector for RVFV in Kenya. Here, we investigate associations between genetic diversity and differentiation of one regional subclade of Ae. mcintoshi in Northeastern Kenya with environmental variables, using a multivariate statistical approach. Using CO1 (cytochrome oxidase subunit 1) sequence data deposited in GenBank, we found no evidence of isolation by distance contributing to genetic differentiation across the study area. However, we did find significant CO1 subpopulation structure and associations with recent mean precipitation values. In addition, variation in genetic diversity across our seven sample sites was associated with both precipitation and percentage clay in the soil. The large number of haplotypes found in this data set indicates that a great deal of diversity remains unsampled in this region. Additional sampling across a larger geographic area, combined with next-generation sequencing approaches that better characterize the genome, would provide a more robust assessment of genetic diversity and differentiation. Further understanding of the genetic structure of Ae. mcintoshi could provide useful information regarding the potential for RVFV to spread across East African landscapes.

Automated identification of insect vectors of Chagas disease in Brazil and Mexico: the Virtual Vector Lab.

Identification of arthropods important in disease transmission is a crucial, yet difficult, task that can demand considerable training and experience. An important case in point is that of the 150+ species of Triatominae, vectors of Trypanosoma cruzi, causative agent of Chagas disease across the Americas. We present a fully automated system that is able to identify triatomine bugs from Mexico and Brazil with an accuracy consistently above 80%, and with considerable potential for further improvement. The system processes digital photographs from a photo apparatus into landmarks, and uses ratios of measurements among those landmarks, as well as (in a preliminary exploration) two measurements that approximate aspects of coloration, as the basis for classification. This project has thus produced a working prototype that achieves reasonably robust correct identification rates, although many more developments can and will be added, and-more broadly-the project illustrates the value of multidisciplinary collaborations in resolving difficult and complex challenges.

Influences of climate change on the potential distribution of Lutzomyia longipalpis sensu lato (Psychodidae: Phlebotominae).

This study explores the present day distribution of Lutzomyia longipalpis in relation to climate, and transfers the knowledge gained to likely future climatic conditions to predict changes in the species' potential distribution. We used ecological niche models calibrated based on occurrences of the species complex from across its known geographic range. Anticipated distributional changes varied by region, from stability to expansion or decline. Overall, models indicated no significant north-south expansion beyond present boundaries. However, some areas suitable both at present and in the future (e.g., Pacific coast of Ecuador and Peru) may offer opportunities for distributional expansion. Our models anticipated potential range expansion in southern Brazil and Argentina, but were variably successful in anticipating specific cases. The most significant climate-related change anticipated in the species' range was with regard to range continuity in the Amazon Basin, which is likely to increase in coming decades. Rather than making detailed forecasts of actual locations where Lu. longipalpis will appear in coming years, our models make interesting and potentially important predictions of broader-scale distributional tendencies that can inform heath policy and mitigation efforts.