Identifying hotspots for plant invasions and forecasting focal points of further spread
dc.contributor.author | Ibáñez, Inés | en_US |
dc.contributor.author | Silander Jr, JohN. A. | en_US |
dc.contributor.author | Allen, Jenica M. | en_US |
dc.contributor.author | Treanor, Sarah A. | en_US |
dc.contributor.author | Wilson, Adam | en_US |
dc.date.accessioned | 2011-01-13T19:53:12Z | |
dc.date.available | 2011-01-13T19:53:12Z | |
dc.date.issued | 2009-12 | en_US |
dc.identifier.citation | Ibáñez, Inés; Silander Jr, JohN. A.; Allen, Jenica M.; Treanor, Sarah A.; Wilson, Adam; (2009). "Identifying hotspots for plant invasions and forecasting focal points of further spread." Journal of Applied Ecology 46(6): 1219-1228. <http://hdl.handle.net/2027.42/78698> | en_US |
dc.identifier.issn | 0021-8901 | en_US |
dc.identifier.issn | 1365-2664 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/78698 | |
dc.description.abstract | 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. | en_US |
dc.format.extent | 551962 bytes | |
dc.format.extent | 3106 bytes | |
dc.format.mimetype | application/pdf | |
dc.format.mimetype | text/plain | |
dc.publisher | Blackwell Publishing Ltd | en_US |
dc.subject.other | Berberis | en_US |
dc.subject.other | Celastrus | en_US |
dc.subject.other | Elaeagnus | en_US |
dc.subject.other | Euonymus | en_US |
dc.subject.other | Hierarchical Bayes | en_US |
dc.subject.other | Invasive Species | en_US |
dc.subject.other | IPANE | en_US |
dc.subject.other | Rosa | en_US |
dc.title | Identifying hotspots for plant invasions and forecasting focal points of further spread | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Ecology and Evolutionary Biology | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | School of Natural Resources and Environment, University of Michigan, 440 Church St, Ann Arbor, MI 48109 1041, USA | en_US |
dc.contributor.affiliationother | Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT 06269, USA | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/78698/1/j.1365-2664.2009.01736.x.pdf | |
dc.identifier.doi | 10.1111/j.1365-2664.2009.01736.x | en_US |
dc.identifier.source | Journal of Applied Ecology | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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