Frequent Prescribed Fires Can Reduce Risk of Tick-borne Diseases

Recently, a two-year study found that long-term prescribed fire significantly reduced tick abundance at sites with varying burn regimes (burned surrounded by burned areas [BB], burned surrounded by unburned areas [BUB], and unburned surrounded by burned areas [UBB]). In the current study, these ticks were tested for pathogens to more directly investigate the impacts of long-term prescribed burning on human disease risk. A total of 5,103 ticks (4,607 Amblyomma americanum, 76 Amblyomma maculatum, 383 Ixodes scapularis, two Ixodes brunneus, and 35 Dermacentor variabilis) were tested for Borrelia spp., Rickettsia spp., Ehrlichia spp., and Anaplasma phagocytophilum. Long-term prescribed fire did not significantly impact pathogen prevalence except that A. americanum from burned habitats had significantly lower prevalence of Rickettsia (8.7% and 4.6% for BUB and UBB sites, respectively) compared to ticks from control sites (unburned, surrounded by unburned [UBUB])(14.6%). However, during the warm season (spring/summer), encounter rates with ticks infected with pathogenic bacteria was significantly lower (98%) at burned sites than at UBUB sites. Thus, despite there being no differences in pathogen prevalence between burned and UBUB sites, risk of pathogen transmission is lower at sites subjected to long-term burning due to lower encounter rates with infected ticks

Use of LYMESIM 2.0 to assess the potential for single and integrated management methods to control blacklegged ticks (Ixodes scapularis; Acari: Ixodidae) and transmission of Lyme disease spirochetes

Annual Lyme disease cases continue to rise in the U.S. making it the most reported vector-borne illness in the country. The pathogen (Borrelia burgdorferi) and primary vector (Ixodes scapularis; blacklegged tick) dynamics of Lyme disease are complicated by the multitude of vertebrate hosts and varying environmental factors, making models an ideal tool for exploring disease dynamics in a time- and cost-effective way. In the current study, LYMESIM 2.0, a mechanistic model, was used to explore the effectiveness of three commonly used tick control methods: habitat-targeted acaricide (spraying), rodent-targeted acaricide (bait boxes), and white-tailed deer targeted acaricide (4-poster devices). Work was done to evaluate their effectiveness when used alone and in combination with one another. Optimized application strategies were also identified. Additionally, pilot work was done to incorporate prescribed fire into the model and compare its efficacy to the acaricide-based approaches. It was determined that any singular use or combination of methods that included spraying were most effective amongst acaricide-based treatments, suppressing the density of I. scapularisnymphs (DON) by \u3e80%. Furthermore, the best time to apply treatments was between January and mid-April, and mid-September to early December. Optimized treatment strategies identified by the model include application of treatment twice annually, every other year at a minimum effectiveness of 25%, which achieves 80% DON suppression and no increases in I. scapularis nymphs once treatments are complete. Interestingly, preliminary work to integrate prescribed fire in the model indicated that it achieved 93-100% efficacy in burn years and one-year post burn, making prescribed fire more effective than all acaricide-based treatments. Overall, this study illustrates the value in using models to identify the best method of blacklegged tick population control that is both time- and cost-effective. Future field research should be done to validate the findings of this model

The Phenology of Ticks and the Effects of Long-Term Prescribed Burning on Tick Population Dynamics in Southwestern Georgia and Northwestern Florida

Some tick populations have increased dramatically in the past several decades leading to an increase in the incidence and emergence of tick-borne diseases. Management strategies that can effectively reduce tick populations while better understanding regional tick phenology is needed. One promising management strategy is prescribed burning. However, the efficacy of prescribed burning as a mechanism for tick control is unclear because past studies have provided conflicting data, likely due to a failure of some studies to simulate operational management scenarios and/or account for other predictors of tick abundance. Therefore, our study was conducted to increase knowledge of tick population dynamics relative to long-term prescribed fire management. Furthermore, we targeted a region, southwestern Georgia and northwestern Florida (USA), in which little is known regarding tick dynamics so that basic phenology could be determined. Twenty-one plots with varying burn regimes (burned surrounded by burned [BB], burned surrounded by unburned [BUB], unburned surrounded by burned [UBB], and unburned surrounded by unburned [UBUB]) were sampled monthly for two years while simultaneously collecting data on variables that can affect tick abundance (e.g., host abundance, vegetation structure, and micro- and macro-climatic conditions). In total, 47,185 ticks were collected, of which, 99% were Amblyomma americanum, 0.7% were Ixodes scapularis, and fewer numbers of Amblyomma maculatum, Ixodes brunneus, and Dermacentor variabilis. Monthly seasonality trends were similar between 2010 and 2011. Long-term prescribed burning consistently and significantly reduced tick counts (overall and specifically for A. americanum and I. scapularis) regardless of the burn regimes and variables evaluated. Tick species composition varied according to burn regime with A. americanum dominating at UBUB, A. maculatum at BB, I. scapularis at UBB, and a more even composition at BUB. These data indicate that regular prescribed burning is an effective tool for reducing tick populations and ultimately may reduce risk of tick-borne disease

Prevalence of Cryptosporidium spp. and Giardia intestinalis in Swimming Pools, Atlanta, Georgia

Cryptosporidium spp. and Giardia intestinalis have been found in swimming pool filter backwash during outbreaks. To determine baseline prevalence, we sampled pools not associated with outbreaks and found that of 160 sampled pools, 13 (8.1%) were positive for 1 or both parasites; 10 (6.2%) for Giardia sp., 2 (1.2%) for Cryptosporidium spp., and 1 (0.6%) for both

The Battle Against Lyme Disease & Beyond: The Fascinating Intersection of Ecology & Public Health

Often referred to as, the tick lady by those who know her research (a title only a vector ecologist can truly relish), Liz has spent her career studying the complex roles that humans, animals, and the environment play in disease dynamics, especially tick-borne diseases. Join Liz as she shares the findings of some of her latest research that have identified promising measures for controlling and preventing tick-borne diseases while shedding light on the rapidly changing Lyme dynamics in southwestern Virginia. Liz will also talk more broadly about the role of disease ecologists and the One Health movement in better understanding, controlling, and preventing the spread and emergence of zoonotic diseases---a topic that has garnered particular interest amidst the COVID pandemic

Rickettsiales in Ticks Removed from Outdoor Workers, Southwest Georgia and Northwest Florida, USA

We determined the prevalence of selected Rickettsiales in 362 ticks removed from outdoor workers in southwest Georgia and northwest Florida, USA. Persons submitted an average of 1.1 ticks/month. We found Ehrlichia chaffeensis in an Amblyomma maculatum tick, and Panola Mountain Ehrlichia sp. in 2 A. maculatum ticks and 1 Dermacentor variabilis tick

Locations and burn data for the 21 plots sampled during this study.

<p>Prescribed burns took place every 2–4 years during the dormant season on pre-determined schedules.</p><p>JERC =  Joseph W. Jones Ecological Research Center; BU =  burn unit; WMA =  wildlife management area; n/a =  not applicable; BB =  burned, surrounded by burned area; BUB =  burned, surrounded by unburned area; UBB =  unburned, surrounded by burned area; UBUB =  unburned, surrounded by unburned area.</p><p>*JERC plots were distributed throughout this 12,000 ha area.</p>†<p>Unless noted, all counties are in Georgia.</p><p>Locations and burn data for the 21 plots sampled during this study.</p

Generalized estimating equation negative binomial regression model for the prediction of <i>A. americanum</i> counts at all study 21 plots.

<p>SE =  Standard error. RR =  Relative rate. ND =  Not determined; RR is not given because it depends on the interacting variable. NA =  Not applicable.</p><p>*Indicates the reference category.</p><p>Generalized estimating equation negative binomial regression model for the prediction of <i>A. americanum</i> counts at all study 21 plots.</p