AimDue to the socioeconomic and environmental damages caused by invasive species, predicting the distribution of invasive plants is fundamental for effectively targeting management efforts. A habitat suitability model (HSM) is a powerful tool to predict potential habitat of invasive species to help guide the early detection of invasive plants. Despite numerous studies of the predictors used in HSMs, there is little consensus about the most appropriate predictors to use in creating ecologically realistic predictions from HSMs.LocationThe contiguous United States.MethodsWe explore 220 invasive terrestrial plant species' existing HSMs constructed with consistent modelling algorithms, background generation methods, predictor resolution, and geographic extent, and calculate the relative importance of predictors for each species. We sort predictors into eight groups (topography, temperature, disturbance, atmospheric water, landscape water, substrate, biotic interaction, and radiation) and compare the importance of predictor groups by plant lifeforms and phylogenetic relatedness.ResultsHuman modification and minimum winter temperature were generally the two highest performing individual predictors across the species studied. The highest-performing predictor groups were disturbance, temperature, and atmospheric water. Across lifeforms, there were minimal differences in the influences of predictor groups, although woody plant models exhibited the largest differences in predictor importance when compared with non-woody plant models. Additionally, we found no significant relationship between the importance of predictor groups and phylogenetic relatedness.Main ConclusionsThis study has implications for informing predictor selection in invasive plant HSMs, leading to more reliable and accurate models of invasive terrestrial plants. Our results emphasize the need to critically select predictors included in HSMs, with special consideration to temperature and disturbance predictors, to accurately predict habitat of invasive plant for detection and response of invasive plant species. With more accurate predictions, managers will be better prepared to address invasive species and reduce their threats to landscapes.

Context. The South American grass Nassella trichotoma (serrated tussock) is widely distributed in central Argentina and one of the most damaging invasive species in Australia, New Zealand, and South Africa. In Australia, it is a weed of national significance. Aims. Our aim was to characterise the fungi able to colonise N. trichotoma seeds buried at a site in south-western Buenos Aires province, Argentina, and measure their impact on seed survival and germination. Methods. We tested the germination of healthy seeds at the beginning of the experiment (control). We buried 10 mesh bags containing 100 disinfected N. trichotoma seeds each, during 3 months in spring and autumn. At the end of each experiment, the contents of five of the bags were counted and classified as germinated, healthy, damaged, or disintegrated. Germination tests were then conducted with seeds of the latter three categories. The remaining seeds were used for recording fungal signs/symptoms, and the isolation of fungi. Key results. We identified and described a diverse group of fungi associated with the seeds and a seasonal variation in the specific composition. Seeds showing fungal signs and/or symptoms germinated less than the control. Conclusions. This is the first study on fungi associated with seeds of this species in the native range, which can affect their survival and longevity. Implications It is expected that studies on the seed microbiome may help us understand the differences in behaviour of the plant between ranges, and test the enemy-release hypothesis.