Combining public participatory surveillance and occupancy modelling to predict the distributional response of Ixodes scapularis to climate change

Volume 9, Issue 3, March 2018, Pages 695-706

David J Lieske, Vett K. Lloyd

https://doi.org/10.1016/j.ttbdis.2018.01.018

Abstract

Ixodes scapularis, a known vector of Borrelia burgdorferi sensu stricto (Bbss), is undergoing range expansion in many parts of Canada. The province of New Brunswick, which borders jurisdictions with established populations of I. scapularis, constitutes a range expansion zone for this species. To better understand the current and potential future distribution of this tick under climate change projections, this study applied occupancy modelling to distributional records of adult ticks that successfully overwintered, obtained through passive surveillance. This study indicates that I. scapularis occurs throughout the southern-most portion of the province, in close proximity to coastlines and major waterways. Milder winter conditions, as indicated by the number of degree days <0 °C, was determined to be a strong predictor of tick occurrence, as was, to a lesser degree, rising levels of annual precipitation, leading to a final model with a predictive accuracy of 0.845 (range: 0.828–0.893). Both RCP 4.5 and RCP 8.5 climate projections predict that a significant proportion of the province (roughly a quarter to a third) will be highly suitable for I. scapularis by the 2080s. Comparison with cases of canine infection show good spatial agreement with baseline model predictions, but the presence of canine Borrelia infections beyond the climate envelope, defined by the highest probabilities of tick occurrence, suggest the presence of Bbss-carrying ticks distributed by long-range dispersal events. This research demonstrates that predictive statistical modelling of multi-year surveillance information is an efficient way to identify areas where I. scapularis is most likely to occur, and can be used to guide subsequent active sampling efforts in order to better understand fine scale species distributional patterns.

Read full text

Leave a Reply

Your email address will not be published. Required fields are marked *