Inferring habitat suitability and spread patterns from large-scale distributions of an exotic invasive pasture grass in north Australia.
The understanding of large-scale patterns in expanding populations of alien invasive plants can be used to infer the environmental limiting factors, habitat heterogeneity and, ultimately, the range expansion potential of invasive plants. We used multivariate analysis and a novel quantile regression technique accounting for spatial autocorrelation to compare and contrast factors influencing the abundance and distribution of the African grass Andropogon gayanus (gamba grass) at two large-scale invasion sites in the tropical savanna region of Australia. We collected data using aerial and ground surveys and tested for limiting factors using three landscape-scale indices related to soil quality, soil moisture and invasion history. In one site, gamba grass was principally colonising drainage lines and riparian areas. Occupation of these areas was limited in proportion to the distance from the original gamba grass source. In the second site, gamba grass abundance was independent of distance from the original source and was well established in all vegetation communities, although abundance was also limited in higher elevation sites away from drainage lines. Comparisons between these sites with different patterns of invasion enabled the estimation of both the invasion pathways and range expansion potential of gamba grass. Our results indicated that gamba grass spreads from riparian communities to invade upland sites and has the potential to invade 70% of north Australia's upland savanna communities. Aerial surveys comprehensively assessed patterns over a larger area than ground surveys and were much more economical. Synthesis and applications. Large-scale surveys across multiple sites are critical to understanding the dynamics of recent alien species invasions where little is known about the pattern and potential range of spread. The application of quantile regression and aerial surveys shows promise as aerial surveys are efficient at capturing a large amount of data. The novel quantile regression technique we demonstrate here can account for both spatial autocorrelation and noisy ecological data from aerial surveys while returning robust results. We were thus able to demonstrate widespread colonisation of creek lines by gamba grass and recommend that management focuses on detection and eradication along drainage lines in addition to the present focus on transport corridors.