Network Prediction of Red Imported Fire Ant Spread for Better Invasive Alien Species Management
Helen K. Liu, Huan-Kai Tseng, Rong-Nan HuangInvasive alien species are a major threat to social, health, economics, agriculture, and biological diversity (Sala et al. 2000; Pimentel et al. 2005). In recent years, a new wave of invasive alien species (IAS), red imported fire ant (RIFA), has been detected in new regions, such as Kobe, Japan in 2017 (Murakami 2018), and Daegu, South Korea in 2018. Meanwhile, an increasing number of cases were reported in the northern part of Taiwan and new locations in Kaohsiung in 2018. Different from the first invasion wave that occurred in Latin America and North America (1930) and eventually moved to Australia, Taiwan, and China in early 2000s, through increasing international trades (Ascunce et al. 2011), a second invasion wave is reported to originate from China, given the majority of new cases have been found in shipment cargo from China.
Previous studies reveal three categories of cases for IAS spreads, biotechnological, ecological, and sociopolitical (Ricciardi et al 2017). Biologists suspect the spread of RIFA or other IAS ants are associated with biotechnological phenomenon, such as evolution of the invasive patterns (Bertelsmeier et al. 2018; Schmidt et al. 2010; Yang et al. 2012). Others focus on ecological changes, such as climate change (Bertelsmeier et al. 2014; Wang et al, 2018), However, scientists investigate from the biotechnological and ecological perspectives focus on RIFA interception data and the patterns of spreads without emphasizing how the spreads took places. While these approaches provide certain degrees of predictions to the potential outbreaks, they are less informative for policy makers to how to prevent without knowing the actual paths. Meanwhile, studies that focus on sociopolitical perspective exam trade and human activities (Ascunce et al. 2011; Brenton-rule et al. 2016). Brenton-rule et al. (2016) show that international trades, mediated through increasing trading agreement, is the leading pathway for the introduction of RIFA and IAS.
The purpose of this study is to model the spread of RIFA through applying a network approach. First, through the National Red Imported Fire Ant Control Center in Taiwan, we constructed a geo-coded RIFA distribution data set since 2003 as well as the trade and shipment data. We then applied the epidemic models (the class of SIR models) to illustrate and predict the spread of RIFA. While most statistical studies of RIFA or IAS propagation make the assumption that populations co-located within the same region are “fully mixed,” with equally possibility to spread to other regions. However, the spread of RIFA and IAS is constrained and limited by the potential carriers, trading or other human economic activity networks. The crucial element that all models lack is a network topology and an alternative method that allows the inclusion of trading dynamics. The implications of our models and results will help the policy makers to allocate RIFA prevention resources as well as formulate effective prevention policy.