Evidence of niche shift and global invasion potential of the Tawny Crazy ant, Nylanderia fulva

Abstract

Analysis of an invasive species' niche shift between native and introduced ranges, along with potential distribution maps, can provide valuable information about its invasive potential. The tawny crazy ant, Nylanderia fulva, is a rapidly emerging and economically important invasive species in the southern United States. It is originally from east‐central South America and has also invaded Colombia and the Caribbean Islands. Our objectives were to generate a global potential distribution map for N. fulva, identify important climatic drivers associated with its current distribution, and test whether N. fulva's realized climatic niche has shifted across its invasive range. We used MaxEnt niche model to map the potential distribution of N. fulva using its native and invaded range occurrences and climatic variables. We used principal component analysis methods for investigating potential shifts in the realized climatic niche of N. fulva during invasion. We found strong evidence for a shift in the realized climatic niche of N. fulva across its invasive range. Our models predicted potentially suitable habitat for N. fulva in the United States and other parts of the world. Our analyses suggest that the majority of observed occurrences of N. fulva in the United States represent stabilizing populations. Mean diurnal range in temperature, degree days at ≥10°C, and precipitation of driest quarter were the most important variables associated with N. fulva distribution. The climatic niche expansion demonstrated in our study may suggest significant plasticity in the ability of N. fulva to survive in areas with diverse temperature ranges shown by its tolerance for environmental conditions in the southern United States, Caribbean Islands, and Colombia. The risk maps produced in this study can be useful in preventing N. fulva's future spread, and in managing and monitoring currently infested areas.

Invasion stages and potential distributions of seven exotic terrestrial isopods in Japan

“…These variables in-cluded temperature and precipitation data, which have been recommended for and widely applied to species distribution modelling (e.g. Broennimann et al 2007, Kumar et al 2015, Zhu et al 2017. Highly collinear variables (Pearson correlation coefficient, |r| ≥ 0.75) were removed and only one variable from a set of highly correlated variables was included in each model (Suppl.…”

“…The invasion stages of the exotic species in Japan were evaluated using an approach that was theoretically developed by Gallien et al (2012) and applied analytically by Kumar et al (2015). According to this framework, a species would be at quasi-equilibrium if the regional and global models predict higher probabilities (> 0.5) for the species' presence (i.e.…”

“…However, few studies have applied these methods to the analysis of soil animals, excluding ants (Fitzpatrick et al 2007, Kumar et al 2015. The present study was a first attempt to apply ENMs to evaluate the important factors determining the distribution areas and invasion stages of exotic terrestrial isopods.…”

“…The results also indicated that combined data from native and introduced regions were useful for estimating the potential distributions of these exotic species in the invaded region (Le Maitre et al 2008, Elith 2015, Ray et al 2016). The present study evaluated the invasive stages of these species in Japan by applying the theoretical framework of Gallien et al (2012), which has been successfully used to evaluate the invasive stages of some exotic species (Kumar et al 2015, Zhu et al 2017. The evaluation revealed that many populations of five species (A. vulgare, H. danicus, P. laevis, P. scaber and P. pruinosus) in Japan were stabilising and colonising.…”

“…Thus, it is important to evaluate the invasive stages of species of interest to predict future distributions and risks of those species. Gallien et al (2012) developed a methodological framework for evaluating the invasive stage by plotting probabilities of two prediction models to identify the leading edge of an invasion and to determine whether the invasive population had reached the equilibrium (Kumar et al 2015, Zhu et al 2017.…”