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Williams AK, Peterman WE, Pesapane R. Refining Ixodes scapularis (Acari: Ixodidae) distribution models: a comparison of current methods to an established protocol. JOURNAL OF MEDICAL ENTOMOLOGY 2024; 61:827-844. [PMID: 38686854 DOI: 10.1093/jme/tjae052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 03/11/2024] [Accepted: 04/15/2024] [Indexed: 05/02/2024]
Abstract
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.
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Affiliation(s)
- Allison K Williams
- School of Environment and Natural Resources, College of Food, Agriculture, and Environmental Science, The Ohio State University, 210 Kottman Hall, 2021 Coffey Road, Columbus, OH 43210, USA
| | - William E Peterman
- School of Environment and Natural Resources, College of Food, Agriculture, and Environmental Science, The Ohio State University, 210 Kottman Hall, 2021 Coffey Road, Columbus, OH 43210, USA
| | - Risa Pesapane
- School of Environment and Natural Resources, College of Food, Agriculture, and Environmental Science, The Ohio State University, 210 Kottman Hall, 2021 Coffey Road, Columbus, OH 43210, USA
- Department of Veterinary Preventive Medicine, College of Veterinary Medicine, The Ohio State University, 1920 Coffey Road, Columbus, OH 43210, USA
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Eisen L, Eisen RJ. Changes in the geographic distribution of the blacklegged tick, Ixodes scapularis, in the United States. Ticks Tick Borne Dis 2023; 14:102233. [PMID: 37494882 PMCID: PMC10862374 DOI: 10.1016/j.ttbdis.2023.102233] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 06/29/2023] [Accepted: 07/17/2023] [Indexed: 07/28/2023]
Abstract
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.
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Affiliation(s)
- Lars Eisen
- Division of Vector-Borne Diseases, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, 3156 Rampart Road, Fort Collins, CO 80521, United States.
| | - Rebecca J Eisen
- Division of Vector-Borne Diseases, National Center for Emerging and Zoonotic Infectious Diseases, Centers for Disease Control and Prevention, 3156 Rampart Road, Fort Collins, CO 80521, United States
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Noll M, Wall R, Makepeace BL, Newbury H, Adaszek L, Bødker R, Estrada-Peña A, Guillot J, da Fonseca IP, Probst J, Overgaauw P, Strube C, Zakham F, Zanet S, Rose Vineer H. Predicting the distribution of Ixodes ricinus and Dermacentor reticulatus in Europe: a comparison of climate niche modelling approaches. Parasit Vectors 2023; 16:384. [PMID: 37880680 PMCID: PMC10601327 DOI: 10.1186/s13071-023-05959-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 09/01/2023] [Indexed: 10/27/2023] Open
Abstract
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.
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Affiliation(s)
- Madeleine Noll
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK.
| | - Richard Wall
- School of Biological Sciences, University of Bristol, Bristol, UK
| | - Benjamin L Makepeace
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
| | | | - Lukasz Adaszek
- Department of Epizootiology and Clinic of Infectious Diseases, Faculty of Veterinary Medicine, University of Life Sciences, Lublin, Poland
| | - René Bødker
- Section of Animal Welfare and Disease Control, Department of Veterinary and Animal Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Agustín Estrada-Peña
- Department of Animal Health, Faculty of Veterinary Medicine, University of Zaragoza, Saragossa, Spain
- Instituto Agroalimentario de Aragón (IA2), Saragossa, Spain
| | - Jacques Guillot
- Department of Dermatology-Parasitology-Mycology, École Nationale Vétérinaire, Oniris, Nantes, France
| | - Isabel Pereira da Fonseca
- CIISA-Centre for Interdisciplinary Research in Animal Health, Faculty of Veterinary Medicine, University of Lisbon, Lisbon, Portugal
- Associate Laboratory for Animal and Veterinary Sciences (AL4AnimalS), Vila Real, Portugal
| | - Julia Probst
- Institute for Parasitology, Centre for Infection Medicine, University of Veterinary Medicine Hannover, Hannover, Germany
| | - Paul Overgaauw
- Department Population Health Sciences, Division of Veterinary Public Health, Faculty of Veterinary Medicine, Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands
| | - Christina Strube
- Institute for Parasitology, Centre for Infection Medicine, University of Veterinary Medicine Hannover, Hannover, Germany
| | - Fathiah Zakham
- Department of Virology, Faculty of Medicine, University of Helsinki, Helsinki, Finland
- Department of Veterinary Biosciences, Faculty of Veterinary Medicine, University of Helsinki, Helsinki, Finland
| | - Stefania Zanet
- Department of Veterinary Sciences, University of Turin, Grugliasco, Italy
| | - Hannah Rose Vineer
- Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Liverpool, UK
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Chenery ES, Harms NJ, Fenton H, Mandrak NE, Molnár PK. Revealing large‐scale parasite ranges: An integrated spatiotemporal database and multisource analysis of the winter tick. Ecosphere 2023. [DOI: 10.1002/ecs2.4376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Affiliation(s)
- Emily S. Chenery
- Department of Physical and Environmental Sciences University of Toronto Scarborough Scarborough Ontario Canada
| | - N. Jane Harms
- Animal Health Unit Department of Environment Whitehorse Yukon Canada
| | - Heather Fenton
- Department of Environment and Natural Resources Government of Northwest Territories Yellowknife Northwest Territories Canada
| | - Nicholas E. Mandrak
- Department of Physical and Environmental Sciences University of Toronto Scarborough Scarborough Ontario Canada
- Department of Biological Sciences University of Toronto Scarborough Scarborough Ontario Canada
- Department of Ecology and Evolutionary Biology University of Toronto Toronto Ontario Canada
| | - Péter K. Molnár
- Department of Physical and Environmental Sciences University of Toronto Scarborough Scarborough Ontario Canada
- Department of Biological Sciences University of Toronto Scarborough Scarborough Ontario Canada
- Department of Ecology and Evolutionary Biology University of Toronto Toronto Ontario Canada
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Norouzi M, Dayer MS, Ghaffarifar F. Molecular detection and characterisation of Theileria in hard ticks of small ruminants in Zarrin Dasht County, Southern Iran. Vet Med Sci 2022; 9:372-379. [PMID: 36495157 PMCID: PMC9857125 DOI: 10.1002/vms3.1027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
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.
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Affiliation(s)
- Maliheh Norouzi
- Faculty of Medical SciencesDepartment of Parasitology and Medical Entomology, Tarbiat Modares UniversityTehranIran
| | - Mohammad Saaid Dayer
- Faculty of Medical SciencesDepartment of Parasitology and Medical Entomology, Tarbiat Modares UniversityTehranIran
| | - Fatemeh Ghaffarifar
- Faculty of Medical SciencesDepartment of Parasitology and Medical Entomology, Tarbiat Modares UniversityTehranIran
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De Vivo M, Huang J. Modeling the geographical distributions of
Chordodes formosanus
and its mantis hosts in Taiwan, with considerations for their niche overlaps. Ecol Evol 2022; 12:e9546. [PMCID: PMC9702995 DOI: 10.1002/ece3.9546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 10/28/2022] [Accepted: 11/08/2022] [Indexed: 11/29/2022] Open
Affiliation(s)
- Mattia De Vivo
- Biodiversity Research Center Academia Sinica Taipei Taiwan
- Department of Life Science National Taiwan Normal University Taipei Taiwan
- Biodiversity Program, Taiwan International Graduate Program Academia Sinica and National Taiwan Normal University Taipei Taiwan
| | - Jen‐Pan Huang
- Biodiversity Research Center Academia Sinica Taipei Taiwan
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Sluyter JD, Raita Y, Hasegawa K, Reid IR, Scragg R, Camargo CA. Prediction of Vitamin D Deficiency in Older Adults: The Role of Machine Learning Models. J Clin Endocrinol Metab 2022; 107:2737-2747. [PMID: 35876536 DOI: 10.1210/clinem/dgac432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Indexed: 11/19/2022]
Abstract
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.
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Affiliation(s)
- John D Sluyter
- School of Population Health, University of Auckland, Auckland 1023, New Zealand
| | - Yoshihiko Raita
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Kohei Hasegawa
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Ian R Reid
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand
| | - Robert Scragg
- School of Population Health, University of Auckland, Auckland 1023, New Zealand
| | - Carlos A Camargo
- Department of Emergency Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115, USA
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Kopsco HL, Smith RL, Halsey SJ. A Scoping Review of Species Distribution Modeling Methods for Tick Vectors. Front Ecol Evol 2022. [DOI: 10.3389/fevo.2022.893016] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
BackgroundGlobally, 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.ObjectiveWe 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.DesignFollowing 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.ResultsWe 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.ConclusionSpecies 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.
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Kessler WH, De Jesus C, Wisely SM, Glass GE. Ensemble Models for Tick Vectors: Standard Surveys Compared with Convenience Samples. Diseases 2022; 10:32. [PMID: 35735632 PMCID: PMC9222110 DOI: 10.3390/diseases10020032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/14/2022] [Accepted: 06/03/2022] [Indexed: 11/29/2022] Open
Abstract
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.
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Affiliation(s)
- William H. Kessler
- Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611, USA; (W.H.K.); (S.M.W.)
- Geography Department, University of Florida, Gainesville, FL 32611, USA
| | - Carrie De Jesus
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL 32611, USA;
| | - Samantha M. Wisely
- Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611, USA; (W.H.K.); (S.M.W.)
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, FL 32611, USA;
| | - Gregory E. Glass
- Emerging Pathogens Institute, University of Florida, Gainesville, FL 32611, USA; (W.H.K.); (S.M.W.)
- Geography Department, University of Florida, Gainesville, FL 32611, USA
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