Integrating systematic surveys with historical data to model the distribution of Ornithodoros turicata americanus, a vector of epidemiological concern in North America

TL;DR

This study demonstrates how integrated species distribution models (ISDM) combining systematic surveys and historical data can effectively predict the distribution of the tick Ornithodoros turicata americanus, a vector of epidemiological concern in North America.

Contribution

It introduces the application of ISDM using the RISDM R package to disease vector distribution modeling, outperforming traditional Maxent models in accuracy.

Findings

Climatic variables and soil sand content influence tick distribution.

ISDM predicted 62% of Florida as suitable habitat.

ISDM showed higher TSS and AUC than Maxent.

Abstract

Globally, vector-borne diseases are increasing in distribution and frequency, affecting humans, domestic animals and livestock, and wildlife. Science-based management and prevention of these diseases requires a sound understanding of the distribution and environmental requirements of the vectors and hosts involved in disease transmission. Integrated Species Distribution Models (ISDM) account for diverse data types through hierarchical modeling and represent a significant advancement in species distribution modeling that have not yet been leveraged in disease ecology. We used this approach, as implemented in the recently developed R package RISDM, to assess the distribution of the soft tick subspecies Ornithodoros turicata americanus. We created an ISDM for O. t. americanus, using systematically collected field data and historical records of this tick species in the southeastern US, to predict its distribution and assess potential correlations with environmental variables. Given the novelty of this method, we compared the results to a conventional Maxent SDM and validated the results through data partitioning using true skills statistics (TSS), sensitivity, and area under the ROC curve (AUC) metrics. We found that a combination of climatic variables describing seasonality and temperature extremes, along with the amount of sand in the soil, determined the predicted intensity of occurrence of this tick species. When projected in geographic space, this distribution model predicted 62% of Florida as suitable habitat for this tick species. The ISDM presented a higher TSS and AUC than the Maxent conventional model, while sensitivity was similar between both models. Our case example shows the utility of ISDMs in disease ecology studies and highlights the broad range of geographic suitability for this important disease vector.