Save
Email
Print
ShareMar. 19, 2009 — Predicting the future spread of invasive species presents a challenge to ecologists that has until recently only been addressed by comparing species climate tolerance with established climate maps to predict susceptible areas. A new study finds, however, that when land-use and habitat data are incorporated, models predict that species may also be likely to colonize areas with unfavorable climates.
Inés Ibáñez of the University of Michigan and her colleagues examined not only historical and current climatic data, but also historical environmental information from both the native and invaded ranges of three New England invasive plants: Japanese barberry, bittersweet and winged euonymus (or burning bush). The models took into account human development, disturbances and agricultural land use; habitat measures of local ground cover, such as forest type and wetlands type, were also included.
The researchers found that although climate plays a large role in predicting invasive species distribution, the inclusion of land use and habitat data improve the explanatory power of their models. In some instances, the combination of an unfavorable climate with a suitable landscape cover increased the probability of species establishment. On the other hand, some areas with favorable climates became less so when their unfavorable habitat data were included.
Most importantly, the researchers write, their models can be modified and used in other systems to predict biological invasions anywhere in the world.
Other social bookmarking and sharing tools:
Story Source:
The above story is reprinted from materials provided by Ecological Society of America.
Note: Materials may be edited for content and length. For further information, please contact the source cited above.
Journal Reference:
- Ibáñez et al. Multivariate forecasts of potential distributions of invasive plant species. Ecological Applications, 2009; 19 (2): 359 DOI: 10.1890/07-2095.1
Note: If no author is given, the source is cited instead.
more 
