As a consequence of warming temperatures around the world, spring and autumn phenologies have been shifting, with corresponding changes in the length of the growing season. Our understanding of the spatial and interspecific variation of these changes, however, is limited. Not all species are responding similarly, and there is significant spatial variation in responses even within species. This spatial and interspecific variation complicates efforts to predict phenological responses to ongoing climate change, but must be incorporated in order to build reliable forecasts. Here, we use a long-term dataset (1953–2005) of plant phenological events in spring (flowering and leaf out) and autumn (leaf colouring and leaf fall) throughout Japan and South Korea to build forecasts that account for these sources of variability. Specifically, we used hierarchical models to incorporate the spatial variability in phenological responses to temperature to then forecast species' overall and site-specific responses to global warming. We found that for most species, spring phenology is advancing and autumn phenology is getting later, with the timing of events changing more quickly in autumn compared with the spring. Temporal trends and phenological responses to temperature in East Asia contrasted with results from comparable studies in Europe, where spring events are changing more rapidly than are autumn events. Our results emphasize the need to study multiple species at many sites to understand and forecast regional changes in phenology.

Summary: 1.To ensure the successful detection, control and eradication of invasive plant species, we need information that can identify areas prone to invasions and criteria that can point out which particular populations may become foci of further spread. Specifically, our work aimed to develop statistical models that identify hotspots of invasive plant species and evaluate the conditions that give rise to successful populations of invasive species.2.We combined extensive data sets on invasive species richness and on species per cent ground cover, together with climate, local habitat and land cover data. We then estimated invasive species richness as a function of those environmental variables by developing a spatially explicit generalized linear model within a hierarchical Bayesian framework. In a second analysis, we used an ordinal logistic regression model to quantify invasive species abundance as a function of the same set of predictor variables.3.Our results show which locations in the studied region, north‐eastern USA, are prone to plant species invasions given the combination of climatic and land cover conditions particular to the sites. Predictions were also generated under a range of climate scenarios forecasted for the region, which pointed out at an increase in invasive species incidence under the most moderate forecast. Predicted abundance for some of the most common invasive plant species,Berberis thumbergii,Celastrus orbiculatus,Euonymus alata,Elaeagnus umbellataandRosa multiflora, allowed us to identify the specific conditions that promote successful population growth of these species, populations that could become foci of further spread.4.Synthesis and applications.Reliable predictions of plants’ invasive potential are crucial for the successful implementation of control and eradication management plans. By following a multivariate approach the parameters estimated in this study can now be used on targeted locations to evaluate the risk of invasions given the local climate and landscape structure; they can also be applied under different climate scenarios and changing landscapes providing an array of possible outcomes. In addition, this modelling approach can be easily used in other regions and for other species.

The fact that plant invasions are an ongoing process makes generalizations of invasive spread extraordinarily challenging. This is particularly true given the idiosyncratic nature of invasions, in which both historical and local conditions affect establishment success and hinder our ability to generate guidelines for early detection and eradication of invasive species. To overcome these limitations we have implemented a comprehensive approach that examines plant invasions at three spatial scales: regional, landscape, and local levels. At each scale, in combination with the others, we have evaluated the role of key environmental variables such as climate, landscape structure, habitat type, and canopy closure in the spread of three commonly found invasive woody plant species in New England, Berberis thunbergii, Celastrus orbiculatus, and Euonymus alatus. We developed a spatially explicit hierarchical Bayesian model that allowed us to take into account the ongoing nature of the spread of invasive species and to incorporate presence/absence data from the species' native ranges as well as from the invaded regions. Comparisons between predictions from climate‐only models with those from the multiscale forecasts emphasize the importance of including landscape structure in our models of invasive species' potential distributions. In addition, predictions generated using only native range data performed substantially worse than those that incorporated data from the target range. This points out important limitations in extrapolating distributional ranges from one region to another.