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30 April 2024 Refining Ixodes scapularis (Acari: Ixodidae) distribution models: a comparison of current methods to an established protocol
Allison K. Williams, William E. Peterman, Risa Pesapane
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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.

Allison K. Williams, William E. Peterman, and Risa Pesapane "Refining Ixodes scapularis (Acari: Ixodidae) distribution models: a comparison of current methods to an established protocol," Journal of Medical Entomology 61(4), 827-844, (30 April 2024). https://doi.org/10.1093/jme/tjae052
Received: 18 October 2023; Accepted: 15 April 2024; Published: 30 April 2024
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KEYWORDS
ENM
phenomenological
SDM
ticks
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