Identifying hotspots for plant invasions and forecasting focal points of further spread

Abstract

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 umbellata and Rosa 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.

Out of the weeds? Reduced plant invasion risk with climate change in the continental United States

“…Greater availability of abundance data, in addition to occurrence data, will open new avenues to assess invasion risk and refine priority areas for monitoring and active management. Modeling frameworks are already available to create such forecasts (Ibáñez et al, 2009), yet data to parameterize such models across many species and at large spatial scales are lacking (but see Oswalt et al, 2015) for continental U.S. forest invasive plants). Continued collection and more robust sharing of invasive plant distribution data would aid in directing near-term management at landscape and local scales.…”

Plant invasions are context‐dependent: multiscale effects of climate, human activity and habitat

“…For example, identical climates may result in a different level of invasion depending on the habitat type and bioclimatic region (i.e. biogeographical region sensu EEA, 2008) (Ibáñez et al, 2009b). In addition, the effects of human activity, derived from land-cover variables, may also vary across bioclimatic regions.…”

At the intersection of cultural and natural heritage: Distribution and conservation of the type localities of Italian endemic vascular plants

“…Species distribution models can help scientists and conservation planners estimate centres of biodiversity (Barthlott et al, 2005;Brotons et al, 2004) and identify priority areas for conservation (Elith and Leathwick, 2009) as well as patterns of major threats across the landscape, such as habitat loss, fragmentation and other anthropogenic pressures (e.g., Aben et al, 2016;Ibáñez et al, 2009;Newbold et al, 2016). In contrast, one dilemma with mapping concerns which species should be evaluated because it is impossible to map them all (Miller and Allen, 1994;Mittermeier et al, 2004;Trisurata et al, 2012).…”