Microhabitat modeling of the invasive Asian longhorned tick (Haemaphysalis longicornis) in New Jersey, USA

Abstract

The Asian longhorned tick (Haemaphysalis longicornis) is a vector of multiple arboviral and bacterial pathogens in its native East Asia and expanded distribution in Australasia. This species has both bisexual and parthenogenetic populations that can reach high population densities under favorable conditions. Established populations of parthenogenetic H. longicornis were detected in the eastern United States in 2017 and the possible range of this species at the continental level (North America) based on climatic conditions has been modeled. However, little is known about factors influencing the distribution of H. longicornis at geographic scales relevant to local surveillance and control. To examine the importance of local physiogeographic conditions such as geology, soil characteristics, and land cover on the distribution of H. longicornis we employed ecological niche modeling using three machine learning algorithms - Maxent, Random Forest (RF), and Generalized Boosting Method (GBM) to estimate probability of finding H. longicornis in a particular location in New Jersey (USA), based on environmental predictors. The presence of H. longicornis in New Jersey was positively associated with Piedmont physiogeographic province and two soil types - Alfisols and Inceptisols. Soil hydraulic conductivity was the most important predictor explaining H. longicornis habitat suitability, with more permeable sandy soils with higher hydraulic conductivity being less suitable than clay or loam soils. The models were projected over the state of New Jersey creating a probabilistic map of H. longicornis habitat suitability at a high spatial resolution of 90×90 meters. The model's sensitivity was 87% for locations sampled in 2017-2019 adding to the growing evidence of the importance of soil characteristics to the survival of ticks. For the 2020-2022 dataset the model fit was 57%, suggestive of spillover to less optimal habitats or, alternatively, heterogeneity in soil characteristics at the edges of broad physiographic zones. Further modeling should incorporate abundance and life-stage information as well as detailed characterization of the soil at collection sites. Once critical parameters that drive the survival and abundance of H. longicornis are identified they can be used to guide surveillance and control strategies for this invasive species.