Fostering effective and sustainable scientific collaboration and knowledge exchange: a workshop-based approach to establish a national ecological observatory network (NEON) domain-specific user group.

The decision to establish a network of researchers centers on identifying shared research goals. Ecologically specific regions, such as the USA's National Ecological Observatory Network's (NEON's) eco-climatic domains, are ideal locations by which to assemble researchers with a diverse range of expertise but focused on the same set of ecological challenges. The recently established Great Lakes User Group (GLUG) is NEON's first domain specific ensemble of researchers, whose goal is to address scientific and technical issues specific to the Great Lakes Domain 5 (D05) by using NEON data to enable advancement of ecosystem science. Here, we report on GLUG's kick off workshop, which comprised lightning talks, keynote presentations, breakout brainstorming sessions and field site visits. Together, these activities created an environment to foster and strengthen GLUG and NEON user engagement. The tangible outcomes of the workshop exceeded initial expectations and include plans for (i) two journal articles (in addition to this one), (ii) two potential funding proposals, (iii) an assignable assets request and (iv) development of classroom activities using NEON datasets. The success of this 2.5-day event was due to a combination of factors, including establishment of clear objectives, adopting engaging activities and providing opportunities for active participation and inclusive collaboration with diverse participants. Given the success of this approach we encourage others, wanting to organize similar groups of researchers, to adopt the workshop framework presented here which will strengthen existing collaborations and foster new ones, together with raising greater awareness and promotion of use of NEON datasets. Establishing domain specific user groups will help bridge the scale gap between site level data collection and addressing regional and larger ecological challenges.

Detection of Rickettsia montanensis in Dermacentor variabilis in Northern Wisconsin.

Background: Spotted fever rickettsiosis is caused by a group of closely related bacteria that includes Rickettsia rickettsii, the etiological agent of Rocky Mountain spotted fever. Recently, Rickettsia montanensis has been reported to cause clinical and subclinical symptoms in both humans and animal models. Materials and Methods: In this study, we collected ticks in Ashland County, located in northern Wisconsin, and tested 16 ticks identified as Dermacentor variabilis for the presence of rickettsial bacteria using PCR techniques. Results: Four positive results identified using gel electrophoresis were then sequenced to determine the rickettsiae species. Of the samples sequenced, three matched for R. montanensis (∼19% of the 16 ticks tested). Conclusion: In this study, we report the presence and prevalence of R. montanensis in northern Wisconsin.

Epidemic West Nile Virus Infection Rates and Endemic Population Dynamics Among South Dakota Mosquitoes: A 15-yr Study from the United States Northern Great Plains.

Mosquito surveillance has been conducted across South Dakota (SD) to record and track potential West Nile virus (WNV) vectors since 2004. During this time, communities from 29 counties collected nearly 5.5 million mosquitoes, providing data from over 60,000 unique trapping nights. The nuisance mosquito, Aedes vexans (Meigen) was the most abundant species in the state (39.9%), and most abundant in most regions. The WNV vector, Culex tarsalis Coquillett (Diptera: Culicidae), was the second most abundant species (20.5%), and 26 times more abundant than the other Culex species that also transmit WNV. However, geographic variation did exist between WNV vector species, as well as relative abundance of vector and nuisance mosquitoes. The abundance of Ae. vexans decreased from east to west in South Dakota, resulting in an increase in the relative abundance of Cx. tarsalis. Other species are reported in this study, with various relative abundances throughout the different regions of South Dakota. WNV infection rates of mosquitoes showed that Cx. tarsalis had the most positive sampling pools and the highest vector index of all the species tested. This study addressed the need for an updated summary of the predominant mosquito species present in the United States Northern Great Plain and provides infection rate data for WNV among these predominant species.

Permethrin Susceptibility for the Vector Culex tarsalis and a Nuisance Mosquito Aedes vexans in an Area Endemic for West Nile Virus.

In 2016, we compared susceptibility to the insecticide, permethrin, between the West Nile virus vector, Culex tarsalis Coquillett, and a major nuisance mosquito, Aedes vexans (Meigen), using baseline diagnostic dose and time values determined using the CDC bottle bioassay protocol. Mosquitoes were collected in the wild in Brookings County, South Dakota, situated in the Northern Great Plains of the USA. The determined diagnostic dose and time were then used in 2017 to validate these measurements for the same 2 mosquito species, collected at a second location within Brookings County. The diagnostic dose was determined for multiple time periods and ranged from 27.0 µg/ml at 60 min to 38.4 µg/ml at 30 min. There was no significant difference detected in mortality rates between Cx. tarsalis and Ae. vexans for any diagnostic time and dose. For practical purposes, mosquitoes in 2017 were tested at 38 µg/ml for 30 min; expected mortality rates were 93.38% for Cx. tarsalis and 94.93% for Ae. vexans. Actual 2017 mortality rates were 92.68% for Cx. tarsalis and 96.12% for Ae. vexans, validating the usefulness of this baseline at an additional location and year.

Improving the prediction of arbovirus outbreaks: A comparison of climate-driven models for West Nile virus in an endemic region of the United States.

Models that forecast the timing and location of human arboviral disease have the potential to make mosquito control and disease prevention more effective. A common approach is to use statistical time-series models that predict disease cases as lagged functions of environmental variables. However, the simplifying assumptions required for standard modeling approaches may not capture important aspects of complex, non-linear transmission cycles. Here, we compared a set of alternative models of human West Nile virus (WNV) in 2004-2017 in South Dakota, USA. We used county-level logistic regressions to model historical human case data as functions of distributed lag summaries of air temperature and several moisture indices. We tested two variations of the standard model in which 1) the distributed lag functions were allowed to change over the transmission season, so that dependence on past meteorological conditions was time varying rather than static, and 2) an additional predictor was included that quantified the mosquito infection growth rate estimated from mosquito surveillance data. The best-fitting model included temperature and vapor pressure deficit as meteorological predictors, and also incorporated time-varying lags and the mosquito infection growth rate. The time-varying lags helped to predict the seasonal pattern of WNV cases, whereas the mosquito infection growth rate improved the prediction of year-to-year variability in WNV risk. These relatively simple and practical enhancements may be particularly helpful for developing data-driven time series models for use in arbovirus forecasting applications.

Integrating Environmental Monitoring and Mosquito Surveillance to Predict Vector-borne Disease: Prospective Forecasts of a West Nile Virus Outbreak.

INTRODUCTION: Predicting the timing and locations of future mosquito-borne disease outbreaks has the potential to improve the targeting of mosquito control and disease prevention efforts. Here, we present and evaluate prospective forecasts made prior to and during the 2016 West Nile virus (WNV) season in South Dakota, a hotspot for human WNV transmission in the United States. METHODS: We used a county-level logistic regression model to predict the weekly probability of human WNV case occurrence as a function of temperature, precipitation, and an index of mosquito infection status. The model was specified and fitted using historical data from 2004-2015 and was applied in 2016 to make short-term forecasts of human WNV cases in the upcoming week as well as whole-year forecasts of WNV cases throughout the entire transmission season. These predictions were evaluated at the end of the 2016 WNV season by comparing them with spatial and temporal patterns of the human cases that occurred. RESULTS: There was an outbreak of WNV in 2016, with a total of 167 human cases compared to only 40 in 2015. Model results were generally accurate, with an AUC of 0.856 for short-term predictions. Early-season temperature data were sufficient to predict an earlier-than-normal start to the WNV season and an above-average number of cases, but underestimated the overall case burden. Model predictions improved throughout the season as more mosquito infection data were obtained, and by the end of July the model provided a close estimate of the overall magnitude of the outbreak. CONCLUSIONS: An integrated model that included meteorological variables as well as a mosquito infection index as predictor variables accurately predicted the resurgence of WNV in South Dakota in 2016. Key areas for future research include refining the model to improve predictive skill and developing strategies to link forecasts with specific mosquito control and disease prevention activities.