Ixodes scapularis density and Borrelia burgdorferi prevalence along a residential-woodland gradient in a region of emerging Lyme disease risk

The environmental risk of Lyme disease, defined by the density of Ixodes scapularis ticks and their prevalence of Borrelia burgdorferi infection, is increasing across the Ottawa, Ontario region, making this a unique location to explore the factors associated with environmental risk along a residential-woodland gradient. In this study, we collected I. scapularis ticks and trapped Peromyscus spp. mice, tested both for tick-borne pathogens, and monitored the intensity of foraging activity by deer in residential, woodland, and residential-woodland interface zones of four neighbourhoods. We constructed mixed-effect models to test for site-specific characteristics associated with densities of questing nymphal and adult ticks and the infection prevalence of nymphal and adult ticks. Compared to residential zones, we found a strong increasing gradient in tick density from interface to woodland zones, with 4 and 15 times as many nymphal ticks, respectively. Infection prevalence of nymphs and adults together was 15 to 24 times greater in non-residential zone habitats. Ecological site characteristics, including soil moisture, leaf litter depth, and understory density, were associated with variations in nymphal density and their infection prevalence. Our results suggest that high environmental risk bordering residential areas poses a concern for human-tick encounters, highlighting the need for targeted disease prevention.

Mapping the distribution of Lyme disease at a mid-Atlantic site in the United States using electronic health data

Lyme disease is a spatially heterogeneous tick-borne infection, with approximately 85% of US cases concentrated in the mid-Atlantic and northeastern states. Surveillance for Lyme disease and its causative agent, including public health case reporting and entomologic surveillance, is necessary to understand its endemic range, but currently used case detection methods have limitations. To evaluate an alternative approach to Lyme disease surveillance, we have performed a geospatial analysis of Lyme disease cases from the Johns Hopkins Health System in Maryland. We used two sources of cases: a) individuals with both a positive test for Lyme disease and a contemporaneous diagnostic code consistent with a Lyme disease-related syndrome; and b) individuals referred for a Lyme disease evaluation who were adjudicated to have Lyme disease. Controls were individuals from the referral cohort judged not to have Lyme disease. Residential address data were available for all cases and controls. We used a hierarchical Bayesian model with a smoothing function for a coordinate location to evaluate the probability of Lyme disease within 100 km of Johns Hopkins Hospital. We found that the probability of Lyme disease was greatest in the north and west of Baltimore, and the local probability that a subject would have Lyme disease varied by as much as 30-fold. Adjustment for demographic and ecological variables partially attenuated the spatial gradient. Our study supports the suitability of electronic medical record data for the retrospective surveillance of Lyme disease.

The risk of contact between visitors and Borrelia burgdorferi-infected ticks is associated with fine-scale landscape features in a southeastern Canadian nature park

BACKGROUND

Infectious diseases are emerging across temperate regions of the world, and, for some, links have been made between landscapes and emergence dynamics. For tick-borne diseases, public parks may be important exposure sites for people living in urbanized areas of North America and Europe. In most cases, we know more about the ecological processes that determine the hazard posed by ticks as disease vectors than we do about how human population exposure varies in urban natural parks.

METHODS

In this study, infrared counters were used to monitor visitor use of a public natural park in southern Quebec, Canada. A risk index representing the probability of encounters between humans and infected vectors was constructed. This was done by combining the intensity of visitor trail use and the density of infected nymphs obtained from field surveillance. Patterns of risk were examined using spatial cluster analysis. Digital forest data and park infrastructure data were then integrated using spatially explicit models to test whether encounter risk levels and its components vary with forest fragmentation indicators and proximity to park infrastructure.

RESULTS

Results suggest that, even at a very fine scales, certain landscape features and infrastructure can be predictors of risk levels. Both visitors and Borrelia burgdorferi-infected ticks concentrated in areas where forest cover was dominant, so there was a positive association between forest cover and the risk index. However, there were no associations between indicators of forest fragmentation and risk levels. Some high-risk clusters contributed disproportionately to the risk distribution in the park relative to their size. There were also two high-risk periods, one in early summer coinciding with peak nymphal activity, and one in early fall when park visitation was highest.

CONCLUSIONS

Here, we demonstrate the importance of integrating indicators of human behaviour visitation with tick distribution data to characterize risk patterns for tick-borne diseases in public natural areas. Indeed, understanding the environmental determinants of human-tick interactions will allow organisations to deploy more effective risk reduction interventions targeted at key locations and times, and improve the management of public health risks associated with tick-borne diseases in public spaces.

The association between the incidence of Lyme disease in the USA and indicators of greenness and land cover

Lyme disease (LD) is the most common vector-borne illness in the USA. Incidence is related to specific environmental conditions such as temperature, metrics of land cover, and vertebrate species diversity. To determine whether greenness, as measured by the Normalized Difference Vegetation Index (NDVI), and other selected indices of land cover were associated with the incidence of LD in the northeastern USA for the years 2000-2018, we conducted an ecological analysis of incidence rates of LD in counties of 15 "high" incidence states and the District of Columbia for 2000-2018. Annual counts of LD by county were obtained from the US Centers for Disease Control and values of NDVI were acquired from the Moderate Resolution Imaging Spectroradiometer instrument aboard Terra and Aqua Satellites. County-specific values of human population density, area of land and water were obtained from the US Census. Using quasi-Poisson regression, multivariable associations were estimated between the incidence of LD, NDVI, land cover variables, human population density, and calendar year. We found that LD incidence increased by 7.1% per year (95% confidence interval: 6.8-8.2%). Land cover variables showed complex non-linear associations with incidence: average county-specific NDVI showed a "u-shaped" association, the standard deviation of NDVI showed a monotonic upward relationship, population density showed a decreasing trend, areas of land and water showed "n-shaped" relationships. We found an interaction between average and standard deviation of NDVI, with the highest average NDVI category; increased standard deviation of NDVI showed the greatest increase in rates. These associations cannot be interpreted as causal but indicate that certain patterns of land cover may have the potential to increase exposure to infected ticks and thereby may contribute indirectly to increased rates of LD. Public health interventions could make use of these results in informing people where risks may be high.

Host contributions to the force of Borrelia burgdorferi and Babesia microti transmission differ at edges of and within a small habitat patch

In the northeastern United States, the emergence of Lyme disease has been associated, in part, with the increase of small forest patches. Such disturbed habitat is exploited by generalist species, such as white-footed mice, which are considered the host with the greatest reservoir capacity for the agents of Lyme disease (Borrelia burgdorferi sensu stricto) and human babesiosis (Babesia microti). Spatial risk analyses have identified edge habitat as particularly risky. Using a retrotransposon-based quantitative PCR assay for host bloodmeal remnant identification, we directly measured whether the hosts upon which vector ticks fed differed at the edge or within the contiguous small habitat patch. Questing nymphal deer ticks, Ixodes dammini, the northern clade of Ixodes scapularis, were collected from either the edge or within a thicket on Nantucket Island over 3 transmission seasons and tested for evidence of infection as well as bloodmeal hosts. Tick bloodmeal hosts significantly differed by site as well as by year. Mice and deer were identified most often (49.9%), but shrews, rabbits and birds were also common. Ticks from the edge fed on a greater diversity of hosts than those from the thicket. Surprisingly, mice were not strongly associated with either infection at either sampling site (OR<2 for all). Although shrews were not the most common host utilized by ticks, they were highly associated with both infections at both sites (OR= 4.5 and 7.9 B. burgdorferi and 7.9 and 19.0 B. microti, edge and thicket). We conclude that reservoir hosts may differ in their contributions to infecting ticks between edge and contiguous vegetated patches. Importance Habitat fragmentation is thought to be a main factor in the emergence of Lyme disease and other of the deer tick-transmitted infections. The patchwork of forest and edges promotes altered biodiversity, favoring the abundance of generalist rodents such as white footed mice, heretofore considered a key tick and reservoir host in the northeastern U.S. We used tick bloodmeal analyses to directly identify the hosts from which nymphal deer ticks became infected. We demonstrate that there is considerable microfocality in host contributions to the cohort of infected ticks and that shrews, although they fed fewer ticks than mice, disproportionately influenced the force of pathogen transmission in our site. The venue of transmission of certain deer tick-transmitted agents may comprise a habitat scale of 10 meters or fewer and depend on alternative small mammal hosts such as shrews.

A descriptive epidemiological study of the incidence of newly diagnosed Lyme disease cases in a UK primary care cohort, 1998-2016

BACKGROUND

Primary care is likely to see the highest number of Lyme disease patients. Despite this, there is limited published data regarding Lyme disease patients accessing primary care in the UK. We aim to describe trends in the incidence of a new diagnosis, and demographics of patients identified in a primary care electronic health database.

METHODS

A descriptive epidemiological study of Lyme disease coded patients in UK primary care. 3725 patients coded for Lyme disease during 1998-2016 were identified within The Health Improvement Network (THIN). Incidence rates and the demographics of cases identified were described. Poisson regression was used to analyse socio-demographic characteristics of the cases.

RESULTS

There was an increase in annual crude incidence rates, peaking in 2015 at 5.47 (95% CI 4.85-6.14) cases per 100,000 population per year. Multivariable analysis showed there were significant differences in the ages of those affected, incidence of a new diagnosis rose as deprivation levels improved, and that there was a higher incidence of cases living in rural areas compared to urban areas. There was no significant difference between sexes for the UK. Cases were significantly more likely to identify with being white compared to the national population.

CONCLUSIONS

An increasing incidence of patients newly coded with Lyme disease related Read codes was identified using data from a UK national primary care database. By comparing these incidence figures with national laboratory-confirmed surveillance data, a multiplication factor of 2.35 (95%CI 1.81-2.88) can be calculated in order to estimate the annual number of cases seen in primary care. The significant socio-demographic variables associated with a Lyme disease diagnosis likely reflect a complex interplay of socio-economic issues, which needs to be further explored. Future work is needed to examine the treatment and management of patients within this database.

Peridomestic and community-wide landscape risk factors for Lyme disease across a range of community contexts in Pennsylvania

Land use and forest fragmentation are thought to be major drivers of Lyme disease incidence and its geographic distribution. We examined the association between landscape composition and configuration and Lyme disease in a population-based case control study in the Geisinger health system in Pennsylvania. Lyme disease cases (n = 9657) were identified using a combination of diagnosis codes, laboratory codes, and antibiotic orders from electronic health records (EHRs). Controls (5:1) were randomly selected and frequency matched on year, age, and sex. We measured six landscape variables based on prior literature, derived from the National Land Cover Database and MODIS satellite imagery: greenness (normalized difference vegetation index), percent forest, percent herbaceous, forest edge density, percent forest-herbaceous edge, and mean forest patch size. We assigned landscape variables within two spatial contexts (community and ½-mile [805 m] Euclidian residential buffer). In models stratified by community type, landscape variables were modeled as tertiles and flexible splines and associations were adjusted for demographic and clinical covariates. In general, we observed positive associations between landscape metrics and Lyme disease, except for percent herbaceous, where associations differed by community type. For example, compared to the lowest tertile, individuals with highest tertile of greenness in residential buffers had higher odds of Lyme disease (odds ratio: 95% confidence interval [CI]) in townships (1.73: 1.55, 1.93), boroughs (1.70: 1.40, 2.07), and cities (3.71: 1.74, 7.92). Similarly, corresponding odds ratios (95% CI) for forest edge density were 1.34 (1.22, 1.47), 1.56 (1.33, 1.82), and 1.90 (1.13, 3.18). Associations were generally higher in residential buffers, compared to community, and in cities, compared to boroughs or townships. Our results reinforce the importance of peridomestic landscape in Lyme disease risk, particularly measures that reflect human interaction with tick habitat. Linkage of EHR data to public data on residential and community context may lead to new health system-based approaches for improving Lyme disease diagnosis, treatment, and prevention.

The demographics and geographic distribution of laboratory-confirmed Lyme disease cases in England and Wales (2013-2016): an ecological study

OBJECTIVE

Lyme disease is a tick-borne disease of increasing incidence and public concern across the Northern Hemisphere. However, the socio-demographics and geographic distribution of the population affected in England and Wales are poorly understood. Therefore, the proposed study was designed to describe the demographics and distribution of laboratory-confirmed cases of Lyme disease from a national testing laboratory.

DESIGN

An ecological study of routinely collected laboratory surveillance data.

SETTING

Public Health England's national Lyme disease testing laboratory.

PARTICIPANTS

3986 laboratory-confirmed cases of Lyme disease between 2013 and 2016.

RESULTS

In England and Wales, the incidence of laboratory-confirmed Lyme disease rose significantly over the study period from 1.62 cases per 100 000 in 2013 to 1.95 cases per 100 000 in 2016. There was a bimodal age distribution (with peaks at 6-10 and 61-65 years age bands) with a predominance of male patients. A significant clustering of areas with high Lyme disease incidence was located in southern England. An association was found between disease incidence and socioeconomic status, based on the patient's resident postcode, with more cases found in less deprived areas. Cases were disproportionately found in rural areas compared with the national population distribution.

CONCLUSIONS

These results suggest that Lyme disease patients originate from areas with higher socioeconomic status and disproportionately in rural areas. Identification of the Lyme disease hotspots in southern England, alongside the socio-demographics described, will enable a targeted approach to public health interventions and messages.

Lyme borreliosis incidence in Lombardy, Italy (2000-2015): Spatiotemporal analysis and environmental risk factors

Lyme borreliosis cases have been reported from Lombardy in northern Italy, where Ixodes ricinus is the main vector of Borrelia burgdorferi sensu lato. However, spatial and temporal variation in the incidence of Lyme borreliosis is not well understood. In the present study, based on new notified cases of Lyme borreliosis from 2000 to 2015, an average of 1.24 new cases per million residents per year was documented. New cases, georeferenced at the municipal level, were analyzed by retrospective space-time analysis (using SaTScan v. 9.3.1); and land cover, extrapolated from a Corine Land Cover dataset (using QGIS 2.8.1), was used to implement an environmental risk factor analysis. Firstly, a temporal high-risk cluster was detected in Lombardy: the relative risk of Lyme borreliosis was 3.73 times higher during 2008-2015 compared with the entire study period. Moreover, in a spatiotemporal high-risk cluster with a circular base, land cover consisting of wildland-urban interface, meadow, forest and meadow-forest transition were significantly higher compared to low-risk areas. Results of the present study demonstrate that the incidence of Lyme borreliosis is increasing in Lombardy and that environmental conditions are suitable for I. ricinus ticks infected with B. burgdorferi s.l.: citizens and health systems should be aware of Lyme borreliosis to reduce tick bites with personal protective behaviors and to avoid misdiagnosis, particularly within the area including the observed high-risk cluster. Economic resources should be invested to inform about methods to prevent tick bites, how to check people and pets after frequenting risk areas, and ways of removing the biting ticks when they are found.

Critical Evaluation of the Linkage Between Tick-Based Risk Measures and the Occurrence of Lyme Disease Cases

The nymphal stage of the blacklegged tick, Ixodes scapularis Say, is considered the primary vector to humans in the eastern United States of the Lyme disease spirochete Borrelia burgdorferi sensu stricto. The abundance of infected host-seeking nymphs is commonly used to estimate the fundamental risk of human exposure to B. burgdorferi, for the purpose of environmental risk assessment and as an outcome measure when evaluating environmentally based tick or pathogen control methods. However, as this tick-based risk measure does not consider the likelihoods of either human encounters with infected ticks or tick bites resulting in pathogen transmission, its linkage to the occurrence of Lyme disease cases is worth evaluating. In this Forum article, we describe different tick-based risk measures, discuss their strengths and weaknesses, and review the evidence for their capacity to predict the occurrence of Lyme disease cases. We conclude that: 1) the linkage between abundance of host-seeking B. burgdorferi-infected nymphs and Lyme disease occurrence is strong at community or county scales but weak at the fine spatial scale of residential properties where most human exposures to infected nymphs occur in Northeast, 2) the combined use of risk measures based on infected nymphs collected from the environment and ticks collected from humans is preferable to either one of these risk measures used singly when assessing the efficacy of environmentally based tick or pathogen control methods aiming to reduce the risk of human exposure to B. burgdorferi, 3) there is a need for improved risk assessment methodology for residential properties that accounts for both the abundance of infected nymphs and the likelihood of human-tick contact, and 4) we need to better understand how specific human activities conducted in defined residential microhabitats relate to risk for nymphal exposures and bites.

An Examination of the Demographic and Environmental Variables Correlated with Lyme Disease Emergence in Virginia

Lyme disease is the United States' most significant vector-borne illness. Virginia, on the southern edge of the disease's currently expanding range, has experienced an increase in Lyme disease both spatially and temporally, with steadily increasing rates over the past decade and disease spread from the northern to the southwestern part of the state. This study used a Geographic Information System and a spatial Poisson regression model to examine correlations between demographic and land cover variables, and human Lyme disease from 2006 to 2010 in Virginia. Analysis indicated that herbaceous land cover is positively correlated with Lyme disease incidence rates. Areas with greater interspersion between herbaceous and forested land were also positively correlated with incidence rates. In addition, income and age were positively correlated with incidence rates. Levels of development, interspersion of herbaceous and developed land, and population density were negatively correlated with incidence rates. Abundance of forest fragments less than 2 hectares in area was not significantly correlated. Our results support some findings of previous studies on ecological variables and Lyme disease in endemic areas, but other results have not been found in previous studies, highlighting the potential contribution of new variables as Lyme disease continues to emerge southward.

Spatial and temporal emergence pattern of Lyme disease in Virginia

The emergence of infectious diseases over the past several decades has highlighted the need to better understand epidemics and prepare for the spread of diseases into new areas. As these diseases expand their geographic range, cases are recorded at different geographic locations over time, making the analysis and prediction of this expansion complicated. In this study, we analyze spatial patterns of the disease using a statistical smoothing analysis based on areal (census tract level) count data of Lyme disease cases in Virginia from 1998 to 2011. We also use space and space-time scan statistics to reveal the presence of clusters in the spatial and spatiotemporal distribution of Lyme disease. Our results confirm and quantify the continued emergence of Lyme disease to the south and west in states along the eastern coast of the United States. The results also highlight areas where education and surveillance needs are highest.

Landscape risk factors for Lyme disease in the eastern broadleaf forest province of the Hudson River valley and the effect of explanatory data classification resolution

This study assessed how landcover classification affects associations between landscape characteristics and Lyme disease rate. Landscape variables were derived from the National Land Cover Database (NLCD), including native classes (e.g., deciduous forest, developed low intensity) and aggregate classes (e.g., forest, developed). Percent of each landcover type, median income, and centroid coordinates were calculated by census tract. Regression results from individual and aggregate variable models were compared with the dispersion parameter-based R(2) (Rα(2)) and AIC. The maximum Rα(2) was 0.82 and 0.83 for the best aggregate and individual model, respectively. The AICs for the best models differed by less than 0.5%. The aggregate model variables included forest, developed, agriculture, agriculture-squared, y-coordinate, y-coordinate-squared, income and income-squared. The individual model variables included deciduous forest, deciduous forest-squared, developed low intensity, pasture, y-coordinate, y-coordinate-squared, income, and income-squared. Results indicate that regional landscape models for Lyme disease rate are robust to NLCD landcover classification resolution.

Lyme disease risk influences human settlement in the wildland-urban interface: evidence from a longitudinal analysis of counties in the northeastern United States

The expansion of human settlement into wildland areas, including forests in the eastern United States, has resulted in fragmented forest habitat that has been shown to drive higher entomological risk for Lyme disease. We investigated an alternative pathway between fragmentation and Lyme disease, namely whether increased risk of Lyme disease results in a reduced propensity to settle in high-risk areas at the interface of developed and undeveloped lands. We used longitudinal data analyses at the county level to determine whether Lyme disease incidence (LDI) influences the proportion of the population residing in the wildland-urban interface in 12 high LDI states in the eastern United States. We found robust evidence that a higher LDI reduces the proportion of a county's population residing in the wildland-urban interface in high-LDI states. This study provides some of the first evidence of human behavioral responses to Lyme disease risk via settlement decisions.

Integrated assessment of behavioral and environmental risk factors for Lyme disease infection on Block Island, Rhode Island

Peridomestic exposure to Borrelia burgdorferi-infected Ixodes scapularis nymphs is considered the dominant means of infection with black-legged tick-borne pathogens in the eastern United States. Population level studies have detected a positive association between the density of infected nymphs and Lyme disease incidence. At a finer spatial scale within endemic communities, studies have focused on individual level risk behaviors, without accounting for differences in peridomestic nymphal density. This study simultaneously assessed the influence of peridomestic tick exposure risk and human behavior risk factors for Lyme disease infection on Block Island, Rhode Island. Tick exposure risk on Block Island properties was estimated using remotely sensed landscape metrics that strongly correlated with tick density at the individual property level. Behavioral risk factors and Lyme disease serology were assessed using a longitudinal serosurvey study. Significant factors associated with Lyme disease positive serology included one or more self-reported previous Lyme disease episodes, wearing protective clothing during outdoor activities, the average number of hours spent daily in tick habitat, the subject’s age and the density of shrub edges on the subject’s property. The best fit multivariate model included previous Lyme diagnoses and age. The strength of this association with previous Lyme disease suggests that the same sector of the population tends to be repeatedly infected. The second best multivariate model included a combination of environmental and behavioral factors, namely hours spent in vegetation, subject’s age, shrub edge density (increase risk) and wearing protective clothing (decrease risk). Our findings highlight the importance of concurrent evaluation of both environmental and behavioral factors to design interventions to reduce the risk of tick-borne infections.

What do we need to know about disease ecology to prevent Lyme disease in the northeastern United States?

Lyme disease is the most commonly reported vector-borne disease in the United States, with the majority of cases occurring in the Northeast. It has now been three decades since the etiological agent of the disease in North America, the spirochete Borrelia burgdorferi, and its primary North American vectors, the ticks Ixodes scapularis Say and I. pacificus Cooley & Kohls, were identified. Great strides have been made in our understanding of the ecology of the vectors and disease agent, and this knowledge has been used to design a wide range of prevention and control strategies. However, despite these advances, the number of Lyme disease cases have steadily increased. In this article, we assess potential reasons for the continued lack of success in prevention and control of Lyme disease in the northeastern United States, and identify conceptual areas where additional knowledge could be used to improve Lyme disease prevention and control strategies. Some of these areas include: 1) identifying critical host infestation rates required to maintain enzootic transmission of B. burgdorferi, 2) understanding how habitat diversity and forest fragmentation impacts acarological risk of exposure to B. burgdorferi and the ability of interventions to reduce risk, 3) quantifying the epidemiological outcomes of interventions focusing on ticks or vertebrate reservoirs, and 4) refining knowledge of how human behavior influences Lyme disease risk and identifying barriers to the adoption of personal protective measures and environmental tick management.

Repeated holdout cross-validation of model to estimate risk of Lyme disease by landscape characteristics

We previously modeled Lyme disease (LD) risk at the landscape scale; here we evaluate the model's overall goodness-of-fit using holdout validation. Landscapes were characterized within road-bounded analysis units (AU). Observed LD cases (obsLD) were ascertained per AU. Data were randomly subset 2,000 times. Of 514 AU, 411 (80%) were selected as a training dataset to develop parameter estimates used to predict observations in the remaining 103 (20%) AU, the validation subset. Predicted values were subtracted from obsLD to quantify accuracy across iterations. We calculated the percentage difference of over- and under-estimation to assess bias. Predictive ability was strong and similar across iterations and datasets; the exact number of obsLD cases per AU were predicted almost 60% of the time. However, the three highest obsLD AU were under-predicted. Our model appears to be accurate and relatively unbiased, however is conservative at high disease incidence.

Invasive plant alters ability to predict disease vector distribution

Risk models for vector-borne diseases rely on accurate quantification of the relationship between vector abundance and habitat, but this relationship can be obscured if habitats are modified by invasive species in ways that alter vector behavior but are undetectable in remotely sensed data. At a forest in eastern Missouri we assessed whether the presence of an invasive shrub, Amur honeysuckle, Lonicera maackii, affects oviposition by treehole mosquitoes, Aedes triseriatus, a primary vector of La Crosse virus in the United States. Oviposition significantly decreased with increasing density of L. maackii. Moreover, our results indicate that L. maackii may hinder the efficacy of models that use remotely sensed data to predict vector abundance: there was a strong relationship between landscape composition around plots and oviposition, but only in plots not invaded by L. maackii. Overlooking potentially important but cryptic effects of invasive plants on habitat selection by vectors may undermine accurate forecasting of disease risk.

Exotic grass invasion reduces survival of Amblyomma americanum and Dermacentor variabilis ticks (Acari: Ixodidae)

Exotic plants often invade areas of high human activity, such as along trails, roads, and forest edges, and in disturbed riparian areas. These same habitat types are also favored by ticks. This convergence suggests that habitat modifications caused by exotic plant invasions may mediate disease vector habitat quality, indirectly affecting human disease risk at the local spatial scale. We tested the hypothesis that experimental invasions of Japanese stiltgrass, Microstegium vimineum (Trin.) A. Camus, alter soil surface microclimate conditions, thereby reducing habitat quality for ticks. Microstegium is an exotic annual grass that is highly invasive throughout the eastern United States where the vector ticks Amblyomma americanum (Linnaeus) and Dermancentor variabilis (Say) occur. Ticks (n=100 per species) were introduced into experimentally invaded and native vegetation control plots (n=5 per treatment). D. variabilis mortality rate increased 173% and A. americanum mortality rate increased 70% in the invaded plots relative to those in control plots. Microstegium invasion also resulted in a 13.8% increase in temperature and an 18.8% decrease in humidity, which are known to increase tick mortality. We predict that areas invaded by Microstegium will have lower densities of host-seeking ticks and therefore reduced human disease risk. Our results emphasize the role of invasive species in mediating disease vector populations, the unpredictable consequences of biological invasions, and the need for integrative management strategies that can simultaneous address exotic plant invasions and vector-borne disease.

Spatial dynamics of lyme disease: a review

Lyme disease (LD), the most frequently reported vector-borne disease in the United States, requires that humans, infected vector ticks, and infected hosts all occur in close spatial proximity. Understanding the spatial dynamics of LD requires an understanding of the spatial determinants of each of these organisms. We review the literature on spatial patterns and environmental correlates of human cases of LD and the vector ticks, Ixodes scapularis in the northeastern and midwestern United States and Ixodes pacificus in the western United States. The results of this review highlight a need for a more standardized and comprehensive approach to studying the spatial dynamics of the LD system. Specifically, we found that the only environmental variable consistently associated with increased LD risk and incidence was the presence of forests. However, the reasons why some forests are associated with higher risk and incidence than others are still poorly understood. We suspect that the discordance among studies is due, in part, to the rapid developments in both conceptual and technological aspects of spatial ecology hastening the obsolescence of earlier approaches. Significant progress in identifying the determinants of spatial variation in LD risk and incidence requires that: (1) existing knowledge of the biology of the individual components of each LD system is utilized in the development of spatial models; (2) spatial data are collected over longer periods of time; (3) data collection and analysis among regions are more standardized; and (4) the effect of the same environmental variables is tested at multiple spatial scales.