Validating Species Distribution Models With Standardized Surveys for Ixodid Ticks in Mainland Florida

Refining Ixodes scapularis (Acari: Ixodidae) distribution models: a comparison of current methods to an established protocol

Blacklegged ticks (Ixodes scapularis Say) pose an enormous public health risk in eastern North America as the vector responsible for transmitting 7 human pathogens, including those causing the most common vector-borne disease in the United States, Lyme disease. Species distribution modeling is an increasingly popular method for predicting the potential distribution and subsequent risk of blacklegged ticks, however, the development of such models thus far is highly variable and would benefit from the use of standardized protocols. To identify where standardized protocols would most benefit current distribution models, we completed the "Overview, Data, Model, Assessment, and Prediction" (ODMAP) distribution modeling protocol for 21 publications reporting 22 blacklegged tick distribution models. We calculated an average adherence of 73.4% (SD ± 29%). Most prominently, we found that authors could better justify and connect their selection of variables and associated spatial scales to blacklegged tick ecology. In addition, the authors could provide clearer descriptions of model development, including checks for multicollinearity, spatial autocorrelation, and plausibility. Finally, authors could improve their reporting of variable effects to avoid undermining the models' utility in informing species-environment relationships. To enhance future model rigor and reproducibility, we recommend utilizing several resources including the ODMAP protocol, and suggest that journals make protocol compliance a publication prerequisite.

Changes in the geographic distribution of the blacklegged tick, Ixodes scapularis, in the United States

Ixodes scapularis (the blacklegged tick) was considered a species of no medical concern until the mid-1970s. By that time, the tick's geographic distribution was thought to be mainly in the southeastern United States (US), with additional localized populations along the Eastern Seaboard north to southern Massachusetts and in the Upper Midwest. Since 1975, I. scapularis has been implicated as a vector of seven human pathogens and is now widely distributed across the eastern US up to the border with Canada. Geographic expansion of tick-borne diseases associated with I. scapularis (e.g., Lyme disease, anaplasmosis, and babesiosis) is attributed to an expanding range of the tick. However, due to changes in tick surveillance efforts over time, it is difficult to differentiate between range expansion and increased recognition of already established tick populations. We provide a history of the documented occurrence of I. scapularis in the US from its description in 1821 to present, emphasizing studies that provide evidence of expansion of the geographic distribution of the tick. Deforestation and decimation of the white-tailed deer (Odocoileus virginianus), the primary reproductive host for I. scapularis adults, during the 1800s presumably led to the tick disappearing from large areas of the eastern US where it previously had been established. Subsequent reforestation and deer population recovery, together with recent climate warming, contributed to I. scapularis proliferating in and spreading from refugia where it had persisted into the early 1900s. From documented tick collection records, it appears I. scapularis was present in numerous locations in the southern part of the eastern US in the early 1900s, whereas in the north it likely was limited to a small number of refugia sites during that time period. There is clear evidence for established populations of I. scapularis in coastal New York and Massachusetts by 1950, and in northwestern Wisconsin by the late 1960s. While recognizing that surveillance for I. scapularis increased dramatically from the 1980s onward, we describe multiple instances of clearly documented expansion of the tick's geographic distribution in the Northeast, Upper Midwest, and Ohio Valley regions from the 1980s to present. Spread and local population increase of I. scapularis, together with documentation of Borrelia burgdorferi sensu stricto in host-seeking ticks, was universally followed by increases in Lyme disease cases in these areas. Southward expansion of northern populations of I. scapularis, for which the host questing behavior of the nymphal stage leads to substantially higher risk of human bites compared with southern populations, into Virginia and North Carolina also was followed by rising numbers of Lyme disease cases. Ongoing surveillance of ticks and tick-borne pathogens is essential to provide the data needed for studies that seek to evaluate the relative roles of land cover, tick hosts, and climate in explaining and predicting geographic expansion of ticks and tick-borne diseases.

Predicting the distribution of Ixodes ricinus and Dermacentor reticulatus in Europe: a comparison of climate niche modelling approaches

BACKGROUND

The ticks Ixodes ricinus and Dermacentor reticulatus are two of the most important vectors in Europe. Climate niche modelling has been used in many studies to attempt to explain their distribution and to predict changes under a range of climate change scenarios. The aim of this study was to assess the ability of different climate niche modelling approaches to explain the known distribution of I. ricinus and D. reticulatus in Europe.

METHODS

A series of climate niche models, using different combinations of input data, were constructed and assessed. Species occurrence records obtained from systematic literature searches and Global Biodiversity Information Facility data were thinned to different degrees to remove sampling spatial bias. Four sources of climate data were used: bioclimatic variables, WorldClim, TerraClimate and MODIS satellite-derived data. Eight different model training extents were examined and three modelling frameworks were used: maximum entropy, generalised additive models and random forest models. The results were validated through internal cross-validation, comparison with an external independent dataset and expert opinion.

RESULTS

The performance metrics and predictive ability of the different modelling approaches varied significantly within and between each species. Different combinations were better able to define the distribution of each of the two species. However, no single approach was considered fully able to capture the known distribution of the species. When considering the mean of the performance metrics of internal and external validation, 24 models for I. ricinus and 11 models for D. reticulatus of the 96 constructed were considered adequate according to the following criteria: area under the receiver-operating characteristic curve > 0.7; true skill statistic > 0.4; Miller's calibration slope 0.25 above or below 1; Boyce index > 0.9; omission rate < 0.15.

CONCLUSIONS

This comprehensive analysis suggests that there is no single 'best practice' climate modelling approach to account for the distribution of these tick species. This has important implications for attempts to predict climate-mediated impacts on future tick distribution. It is suggested here that climate variables alone are not sufficient; habitat type, host availability and anthropogenic impacts, not included in current modelling approaches, could contribute to determining tick presence or absence at the local or regional scale.

Molecular detection and characterisation of Theileria in hard ticks of small ruminants in Zarrin Dasht County, Southern Iran

BACKGROUND

Ticks are important ectoparasites of small ruminants in tropics and subtropics including Iran. They transmit serious zoonotic pathogens such as Babesia and Theileria. These parasites cause major burden on small ruminants jeopardising livelihoods of rural people in Zarrin Dasht County.

OBJECTIVES

This study was carried out to investigate the diversity and distribution of hard ticks of small ruminants and their piroplasm infection in a bid to contribute to Theileria and/or Babesia detection and control in Zarrin Dasht County of Fars province, Iran.

METHODS

We examined 751 sheep and goats from 10 sites of the County during four seasons for hard tick infestation. The collected hard ticks (994) were taxonomically identified before being separately confined in microtubes coded to indicate their species and host animals as well as site and date of collection. In total 50 pooled samples were analysed by PCR technique for Theileria and Babesia infection.

RESULTS

The identified ticks included Hyalomma marginatum 994/362); 36.4%), Rhipicephalus turanicus 994/352); 35.51%), Hyalomma anatolicum 994/264); 26.6%), Hyalomma dromedarii 994/14); 1.41%) and Hyalomma asiaticum 994/2) 0.2%). Molecular analyses showed that 7 out of 50 pooled sample were infected with piroplasm genome in ticks shared by Theileria ovis (6:50) and Theileria lestoquardi (1:50). Babesia was absent in collected hard ticks.

CONCLUSIONS

This is the first report on the presence of piroplasm infection in hard ticks of small ruminants in Zarrin Dasht County. Theileria ovis was more prevalent than Theileria lestoquardi but Babesia was absent. Piroplasm infection was detected in Hyalomma marginatum, Hyalomma anatolicum and Rhipicephalus turanicus. Hyalomma marginatum appears to be more competent to vector Theileria spp. This study may contribute to risk assessment and prevention of epizootic theileriosis in the County.

Prediction of Vitamin D Deficiency in Older Adults: The Role of Machine Learning Models

CONTEXT

Conventional prediction models for vitamin D deficiency have limited accuracy.

BACKGROUND

Using cross-sectional data, we developed models based on machine learning (ML) and compared their performance with those based on a conventional approach.

METHODS

Participants were 5106 community-resident adults (50-84 years; 58% male). In the randomly sampled training set (65%), we constructed 5 ML models: lasso regression, elastic net regression, random forest, gradient boosted decision tree, and dense neural network. The reference model was a logistic regression model. Outcomes were deseasonalized serum 25-hydroxyvitamin D (25(OH)D) <50 nmol/L (yes/no) and <25 nmol/L (yes/no). In the test set (the remaining 35%), we evaluated predictive performance of each model, including area under the receiver operating characteristic curve (AUC) and net benefit (decision curves).

RESULTS

Overall, 1270 (25%) and 91 (2%) had 25(OH)D <50 and <25 nmol/L, respectively. Compared with the reference model, the ML models predicted 25(OH)D <50 nmol/L with similar accuracy. However, for prediction of 25(OH)D <25 nmol/L, all ML models had higher AUC point estimates than the reference model by up to 0.14. AUC was highest for elastic net regression (0.93; 95% CI 0.90-0.96), compared with 0.81 (95% CI 0.71-0.91) for the reference model. In the decision curve analysis, ML models mostly achieved a greater net benefit across a range of thresholds.

CONCLUSION

Compared with conventional models, ML models predicted 25(OH)D <50 nmol/L with similar accuracy but they predicted 25(OH)D <25 nmol/L with greater accuracy. The latter finding suggests a role for ML models in participant selection for vitamin D supplement trials.

A Scoping Review of Species Distribution Modeling Methods for Tick Vectors

Background

Globally, tick-borne disease is a pervasive and worsening problem that impacts human and domestic animal health, livelihoods, and numerous economies. Species distribution models are useful tools to help address these issues, but many different modeling approaches and environmental data sources exist.

Objective

We conducted a scoping review that examined all available research employing species distribution models to predict occurrence and map tick species to understand the diversity of model strategies, environmental predictors, tick data sources, frequency of climate projects of tick ranges, and types of model validation methods.

Design

Following the PRISMA-ScR checklist, we searched scientific databases for eligible articles, their references, and explored related publications through a graphical tool (www.connectedpapers.com). Two independent reviewers performed article selection and characterization using a priori criteria.

Results

We describe data collected from 107 peer-reviewed articles that met our inclusion criteria. The literature reflects that tick species distributions have been modeled predominantly in North America and Europe and have mostly modeled the habitat suitability for Ixodes ricinus (n = 23; 21.5%). A wide range of bioclimatic databases and other environmental correlates were utilized among models, but the WorldClim database and its bioclimatic variables 1–19 appeared in 60 (56%) papers. The most frequently chosen modeling approach was MaxEnt, which also appeared in 60 (56%) of papers. Despite the importance of ensemble modeling to reduce bias, only 23 papers (21.5%) employed more than one algorithm, and just six (5.6%) used an ensemble approach that incorporated at least five different modeling methods for comparison. Area under the curve/receiver operating characteristic was the most frequently reported model validation method, utilized in nearly all (98.9%) included studies. Only 21% of papers used future climate scenarios to predict tick range expansion or contraction. Regardless of the representative concentration pathway, six of seven genera were expected to both expand and retract depending on location, while Ornithodoros was predicted to only expand beyond its current range.

Conclusion

Species distribution modeling techniques are useful and widely employed tools for predicting tick habitat suitability and range movement. However, the vast array of methods, data sources, and validation strategies within the SDM literature support the need for standardized protocols for species distribution and ecological niche modeling for tick vectors.

Ensemble Models for Tick Vectors: Standard Surveys Compared with Convenience Samples

Ensembles of Species Distribution Models (SDMs) represent the geographic ranges of pathogen vectors by combining alternative analytical approaches and merging information on vector occurrences with more extensive environmental data. Biased collection data impact SDMs, regardless of the target species, but no studies have compared the differences in the distributions predicted by the ensemble models when different sampling frameworks are used for the same species. We compared Ensemble SDMs for two important Ixodid tick vectors, Amblyomma americanum and Ixodes scapularis in mainland Florida, USA, when inputs were either convenience samples of ticks, or collections obtained using the standard protocols promulgated by the U.S. Centers for Disease Control and Prevention. The Ensemble SDMs for the convenience samples and standard surveys showed only a slight agreement (Kappa = 0.060, A. americanum; 0.053, I. scapularis). Convenience sample SDMs indicated A. americanum and I. scapularis should be absent from nearly one third (34.5% and 30.9%, respectively) of the state where standard surveys predicted the highest likelihood of occurrence. Ensemble models from standard surveys predicted 81.4% and 72.5% (A. americanum and I. scapularis) of convenience sample sites. Omission errors by standard survey SDMs of the convenience collections were associated almost exclusively with either adjacency to at least one SDM, or errors in geocoding algorithms that failed to correctly locate geographic locations of convenience samples. These errors emphasize commonly overlooked needs to explicitly evaluate and improve data quality for arthropod survey data that are applied to spatial models.