ArticleExperimental & applied acarology2020
Predicting the potential distribution of Amblyomma americanum (Acari: Ixodidae) infestation in New Zealand, using maximum entropy-based ecological niche modelling.
Article in Experimental & applied acarology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Habitat Suitability Analysis forInsects · 2025Article
- Predicting the distribution ofParasitology · 2024Article
- Predicting the distribution of Ixodes ricinus and Dermacentor reticulatus in Europe: a comparison of climate niche modelling approaches.Parasites & vectors · 2023Review
- Predicting climate-driven distribution shifts inParasitology · 2023Article
- Article
- Predicting the Potential Global Distribution ofBiology · 2021Article
- Climatic suitability of the eastern paralysis tick, Ixodes holocyclus, and its likely geographic distribution in the year 2050.Scientific reports · 2021Article
- Distribution modeling of Amblyomma rotundatum and Amblyomma dissimile in Brazil: estimates of environmental suitability.Parasitology research · 2021Article
- Nested Species Distribution Models ofApplied and environmental microbiology · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Although currently exotic to New Zealand, the potential geographic distribution of Amblyomma americanum (L.), the lone star tick, was modelled using maximum entropy (MaxEnt). The MaxEnt model was calibrated across the native range of A. americanum in North America using present-day climatic conditions and occurrence data from museum collections. The resulting model was then projected onto New Zealand using both present-day and future climates modelled under two greenhouse gas emission scenarios, representative concentration pathways (RCP) 4.5 (low) and RCP 8.5 (high). Three sets of WorldClim bioclimatic variables were chosen using the jackknife method and tested in MaxEnt using different combinations of model feature class functions and regularization multiplier values. The preferred model was selected based on partial receiver operating characteristic tests, the omission rate and the lowest Akaike information criterion. The final model had four bioclimatic variables, Annual Mean Temperature (BIO
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