Biology Reference
In-Depth Information
Hampshire Administrative Code, Chapter Agr 3802.1., http://www.gencourt.state.
nh.us/Rules/agr3800.html. Accessed 9 June 2008) and Massachusetts (as of
January 2009; Massachusetts Prohibited Plant List, http://www.mass.gov/agr/
farmproducts/proposed_prohibited_plant_list_v12-12-05.htm. Accessed 9 June
2008) ban the propagation, sale, purchase, or distribution of three common inva-
sive nonindigenous landscape species (
Acer platanoides
,
Berberis thunbergii
, and
Euonymus alatus
).
9.2.4 Predicting Invasive Potential
Predicting which species will be invasive in a particular area is a very difficult task
due to the complexity of nature (Drake 2005). There has been an abundance of work
to determine which plant characteristics and what ecological factors lead to plant
invasion (Dekker 2005; Kolar and Lodge 2001; Rejmanek and Richardson 2005;
Myers and Bazely 2003). The interest in this subfield of invasion biology is substan-
tiated by the fact that the number of scientific papers addressing invasion prediction
increased fivefold from 1986 to 1999 (Kolar and Lodge 2001). At present, the most
reliable and powerful predictor of a species' invasiveness is its record of invasiveness
in other nonnative sites (Wittenberg and Cock 2001). Many prediction schemes have
been developed to assess the potential of plant taxa to be invasive. These approaches
to understanding the invasive potential significantly increase our ability to predict
which taxa will be invasive. Prediction models have correctly identified (postpriori)
80-90% of invasive NIS (Reichard and Hamilton 1997; Widrlechner et al. 2004;
Pheloung et al. 1999; Daehler and Carino 2000). The shortcoming of these models
is that they have a relatively high rate (
10%) of false positives (identifying a nonin-
vasive species as invasive). Perhaps this high rate of false positives is the price we
should pay to exclude invaders from our natural areas. Another shortcoming of inva-
sive potential prediction is the knowledge needed for most of these schemes (plant
and region specific), and scheme methodology has not been integrated so it can eas-
ily be used by those who are not well versed in ecology (Rejmanek et al. 2005).
Mack (2005) emphasizes the need for prediction schemes to include, among other
variables, the role humans play in overcoming the effect of environmental stochas-
ticity on immigrant plant populations.
Prediction based on biological characteristics can reliably foretell if a plant will
be invasive (i.e., establishment and spread); however, prediction is less reliable in
forecasting the impact a taxon will have on an environment (Rejmanek et al. 2005).
Rejmanek et al. (2005) note that “it is important to realize that invasiveness and
impact are not necessarily positively correlated.” These authors are in favor of cat-
egorizing invasive NIS that have had a profound effect on biodiversity, about 10%
of invasive plants, with the term “transformer species,” a term first proposed by
Wells et al. (1986). Transformer species, because of their impact, would receive the
majority of resources for containment, eradication, and control.
≥
