Relative roles of climatic suitability and anthropogenic influence in determining the pattern of spread in a global invader.

Because invasive species threaten the integrity of natural ecosystems, a major goal in ecology is to develop predictive models to determine which species may become widespread and where they may invade. Indeed, considerable progress has been made in understanding the factors that influence the local pattern of spread for specific invaders and the factors that are correlated with the number of introduced species that have become established in a given region. However, few studies have examined the relative importance of multiple drivers of invasion success for widespread species at global scales. Here, we use a dataset of >5,000 presence/absence records to examine the interplay between climatic suitability, biotic resistance by native taxa, human-aided dispersal, and human modification of habitats, in shaping the distribution of one of the world's most notorious invasive species, the Argentine ant (Linepithema humile). Climatic suitability and the extent of human modification of habitats are primarily responsible for the distribution of this global invader. However, we also found some evidence for biotic resistance by native communities. Somewhat surprisingly, and despite the often cited importance of propagule pressure as a crucial driver of invasions, metrics of the magnitude of international traded commodities among countries were not related to global distribution patterns. Together, our analyses on the global-scale distribution of this invasive species provide strong evidence for the interplay of biotic and abiotic determinants of spread and also highlight the challenges of limiting the spread and subsequent impact of highly invasive species.

Predicting Argentine ant spread over the heterogeneous landscape using a spatially explicit stochastic model.

The characteristics of spread for an invasive species should influence how environmental authorities or government agencies respond to an initial incursion. High-resolution predictions of how, where, and the speed at which a newly established invasive population will spread across the surrounding heterogeneous landscape can greatly assist appropriate and timely risk assessments and control decisions. The Argentine ant (Linepithema humile) is a worldwide invasive species that was inadvertently introduced to New Zealand in 1990. In this study, a spatially explicit stochastic simulation model of species dispersal, integrated with a geographic information system, was used to recreate the historical spread of L. humile in New Zealand. High-resolution probabilistic maps simulating local and human-assisted spread across large geographic regions were used to predict dispersal rates and pinpoint at-risk areas. The spatially explicit simulation model was compared with a uniform radial spread model with respect to predicting the observed spread of the Argentine ant. The uniform spread model was more effective predicting the observed populations early in the invasion process, but the simulation model was more successful later in the simulation. Comparison between the models highlighted that different search strategies may be needed at different stages in an invasion to optimize detection and indicates the influence that landscape suitability can have on the long-term spread of an invasive species. The modeling and predictive mapping methodology used can improve efforts to predict and evaluate species spread, not only in invasion biology, but also in conservation biology, diversity studies, and climate change studies.

Integrated feature and parameter optimization for an evolving spiking neural network: exploring heterogeneous probabilistic models.

This study introduces a quantum-inspired spiking neural network (QiSNN) as an integrated connectionist system, in which the features and parameters of an evolving spiking neural network are optimized together with the use of a quantum-inspired evolutionary algorithm. We propose here a novel optimization method that uses different representations to explore the two search spaces: A binary representation for optimizing feature subsets and a continuous representation for evolving appropriate real-valued configurations of the spiking network. The properties and characteristics of the improved framework are studied on two different synthetic benchmark datasets. Results are compared to traditional methods, namely a multi-layer-perceptron and a naïve Bayesian classifier (NBC). A previously used real world ecological dataset on invasive species establishment prediction is revisited and new results are obtained and analyzed by an ecological expert. The proposed method results in a much faster convergence to an optimal solution (or a close to it), in a better accuracy, and in a more informative set of features selected.

Risk assessment of the gypsy moth, Lymantria dispar (L), in New Zealand based on phenology modelling.

The gypsy moth is a global pest that has not yet established in New Zealand despite individual moths having been discovered near ports. A climate-driven phenology model previously used in North America was applied to New Zealand. Weather and elevation data were used as inputs to predict where sustainable populations could potentially exist and predict the timing of hatch and oviposition in different regions. Results for New Zealand were compared with those in the Canadian Maritimes (New Brunswick, Nova Scotia, and Prince Edward Island) where the gypsy moth has long been established. Model results agree with the current distribution of the gypsy moth in the Canadian Maritimes and predict that the majority of New Zealand's North Island and the northern coastal regions of the South Island have a suitable climate to allow stable seasonality of the gypsy moth. New Zealand's climate appears more forgiving than that of the Canadian Maritimes, as the model predicts a wider range of oviposition dates leading to stable seasonality. Furthermore, we investigated the effect of climate change on the predicted potential distribution for New Zealand. Climate change scenarios show an increase in probability of establishment throughout New Zealand, most noticeably in the South Island.