Westphal_et_al_2008_n_4

Biol Invasions (2008) 10:391–398
DOI 10.1007/s10530-007-9138-5
ORIGINAL PAPER
The link between international trade and the global
distribution of invasive alien species
Michael I. Westphal Æ Michael Browne Æ
Kathy MacKinnon Æ Ian Noble
Received: 12 June 2007 / Accepted: 3 July 2007 / Published online: 27 July 2007
Ó Springer Science+Business Media B.V. 2007
Abstract Invasive alien species (IAS) exact large
biodiversity and economic costs and are a significant
component of human-induced, global environmental
change. Previous studies looking at the variation in
alien species across regions have been limited
geographically or taxonomically or have not considered
economics. We used a global invasive species
database to regress IAS per-country on a suite of
socioeconomic, ecological, and biogeographical variables.
We varied the countries included in the
regression tree analyses, in order to explore whether
certain outliers were biasing the results, and in most
of the cases, merchandise imports was the most
important explanatory variable. The greater the
Disclaimer: This paper does not represent the views of AAAS,
the EPA, or the World Bank Group.
M. I. Westphal (&)
AAAS Science and Technology Policy Fellow, Office of
International Affairs, United States Environmental
Protection Agency, Washington, DC 20460, USA
e-mail: mwestphal@worldbank.org
M. I. Westphal K. MacKinnon I. Noble
Environment Department, The World Bank, 1818 H St
NW, Washington, DC 20433, USA
M. Browne
Invasive Species Specialist Group, World Conservation
Union (IUCN), University of Auckland, Auckland,
NZ, USA
degree of international trade, the higher the number
of IAS. We also found a positive relationship
between species richness and the number of invasives,
in accord with other investigations at large
spatial scales. Island status (overall), country area,
latitude, continental position (New World versus Old
World) or other measures of human disturbance (e.g.,
GDP per capita, population density) were not found
to be important determinants of a country’s degree of
biological invasion, contrary to previous studies. Our
findings also provide support to the idea that more
resources for combating IAS should be directed at the
introduction stage and that novel trade instruments
need to be explored to account for this environmental
externality.
Keywords Environmental externality
Exotic species Regression tree Species richness
Trade and environment
Introduction
Invasive alien species (IAS) are a significant component
of human-caused global environmental change
(Vitousek et al. 1997), responsible for dramatic
deleterious effects on biodiversity and large economic
costs. In the United States, for example, 49%
of imperiled species are at risk due at least partially to
the impacts of IAS (Wilcove et al. 1998). The total
annual economic costs for IAS species in the United
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392 M. I. Westphal et al.
States alone is thought to be around US$ 120 billion
(Pimentel et al. 2005).
The degree to which an area is invaded by alien
species is a function of: (a) ecosystem-level properties,
including resistance to invasion and the degree
of disturbance; (b) propagule pressure of the invasives;
(c) the properties of the invasive species, such
as invasion potential; and (d) the properties of the
individual native species themselves, such as their
competitive ability (Lonsdale 1999). Various hypotheses
have been offered as to the invasive susceptibility
of a region based on biogeography (e.g., Old
World versus New World, mainland versus island,
biome type), species richness, or degree of human
visitation (Lonsdale 1999).
Economics and trade have been implicated in the
spread of invasive species. There are many examples
of alien species being carried on conveyances
of international trade. In the Great Lakes, for
example, commercial shipping (usually via ballast
water) has been implicated in 60% of the new
introductions of IAS (Horan and Lupi 2005),
including the infamous case of the zebra mussel
(Dresseina polymorpha) (Benson and Boydstun
1995). Cassey et al. (2004a) found a positive
relationship between the probability of establishment
of exotic parrot species and whether the
species was part of the international pet trade.
Models have been fitted relating international trade
to the establishment of alien plants, insects and
mollusks in the United States (Levine and D’Antonio
2003). Moreover, Vila and Pujadas (2001)
found that imports and the Human Development
Index best explained the variation in alien plan
species in Europe and North Africa countries.
Dalmazzone (2002) found that socioeconomic measures
of disturbance (i.e., human population density,
GDP per capita and land tenure) explain a great
deal of the variance in alien plant species for 26
countries. Other economic measures, such as real
estate gross state product, have been shown to be
good correlates of the number of alien plants in
Canadian provinces and other regions (Taylor and
Irwin 2004). Measures of economic activity, such as
real estate gross state product and GDP per capita,
and human population density may be thought of as
surrogates for both propagule pressure and ecological
disturbance, which facilitates the establishment
of alien species.
Materials and methods
Invasive alien species database
The Invasive Species Specialist Group of the IUCN
(World Conservation Union) has compiled the most
geographically comprehensive database on invasive
species worldwide Global Invasive Species Database
(GISD). It includes 227 countries and profiles on 357
IAS 1 across all taxa that are significant threats to
native biodiversity. Many of these species have large
economic and social impacts. Whilst the GISD is a
compilation of the world’s most pernicious invasives,
it well-representative taxonomically (Table 1).
IUCN’s GISD is a metadatabase, composed of a
variety of sources:
1. About 100 of the world’s worst IAS, as compiled
by experts in 1999–2000. Species were selected
based on two criteria: their serious impact on
biodiversity and/or human activities, and their
illustration of important issues surrounding biological
invasion.
2. About 120 IAS present in the Pacific region,
selected by experts.
3. About 150 IAS threatening North America,
primarily but not exclusively, the United States
and Canada. The National Biological Information
Infrastructure (NBII) selected experts to
choose candidate species.
4. About 40 IAS present in New Zealand.
5. Emerging IAS of concern (e.g., the cycad scale,
Aulacaspis yasumatsui).
6. Other IAS of interest as determined by experts
involved with the Invasive Species Specialist
Group.
1 The IUCN definition of IAS is used: ‘‘Alien invasive species
means an alien species which becomes established in natural or
semi-natural ecosystems or habitat, is an agent of change, and
threatens native biological diversity.’’Ultimately, the degree to
which alien species impact biodiversity is the most important
consideration; thus, we did not take a more liberal approach
and include all alien species in a country.Moreover, country
lists for alien species are notoriously poor, with a constellation
of differing terminology, e.g. ‘‘exotic’’, ‘‘non-native’’, ‘‘spreading’’,
‘‘pest’’, ‘‘non-indigenous’’, ‘‘incursive’’, ‘‘alien’’, etc.,
with inconsistent meanings.
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The link between international trade and the global distribution of invasive alien species 393
Table 1 The taxonomic distribution of the ISSG database (not
inclusive of all phyla/divisions in the database)
Phyla/division
Magnoliophyta 195
Arthropoda 54
Chordata 71
Mollusca 16
There is some overlap between the above sources,
because many of the IAS are highly cosmopolitan.
Once a species is added to the database, the global
distribution of that species is determined as best as
possible. That is, even though a species is first added
to the database because it occurs in one of the above
regions/countries, it is likely it is also present in other
countries worldwide. A species is only included in a
country list if it is an alien invasive in that country,
not whether it is an invasive alien elsewhere.
Globally, the GISD would include *15–20% of
any country’s known IAS.
Statistical analyses
Number of
species
We regressed IAS on a per-country basis against a
suite of ecological, biogeographical, and socioeconomic
dependent variables (Tables 2, 3), whose
correlations have been removed. We used the
RPART routine for S-Plus 4.5 (MathSoft Inc. 1998)
to construct regression trees (Breiman et al. 1984;
Atkinson and Therneau 2000; De’ath and Fabricus
2000). A tree is constructed by repeatedly splitting
the data, defined by a simple rule based on a single
explanatory variable. At each split, the data are
partitioned into two mutually exclusive groups, each
of which is as homogenous as possible (De’ath and
Fabricus 2000). Regression trees have certain advantages
over traditional linear regression, including
being able to deal with non-linear relationships, nonnormality,
higher-order interactions and missing
explanatory variable values (De’ath and Fabricus
2000). They also are easy to interpret and have great
heuristic value. We first removed any large correlations
(Pearson correlation coefficients) (Cohen 1988)
between explanatory variables by fitting general
linear models and using the residuals in the regression
tree analysis. We found this preferable and the
results more easily understandable than using a
multivariate technique, such as principal components
analysis. We employed the 1—Standard Error rule to
select trees with the best number of splits and avoid
overfitting (Breiman et al. 1984; Atkinson and Therneau
2000).
Results
When all countries are included in the analysis, the
most important explanatory variable is country area
(Fig. 1a). However, the presence of the United States
seems to be driving the model, and we suspect that
there is some sampling bias, with surveying and
cataloging of IAS in the United States more extensive
Table 2 The explanatory variables used in the regression tree analyses with their broad categorization
Ecological/biogeographical Propagule pressure Disturbance
Area (km 2 ) Agricultural imports (proportion) Deforestation rate
Continent (North America, South America,
Africa, Asia—including Europe, Oceania)
Gross Domestic Product (GDP)
per capita
GDP change
Cropland/mosaic (proportion of area) Merchandise imports ($) GDP per capita
Endemism (percentage of species) Perimeter: area Population density
Forest (proportion of area) Population density Population growth
Grassland, savanna, shrubland (proportion of area) Road density (km/km 2 ) Road density (km/km 2 )
Island (versus Mainland) Tourism (visitors) Urbanization (% of population)
Latitude Urbanization (% of population) Urbanization change
Species richness (number)
Water ecosystems (proportion of area)
Some variables can placed into multiple categories
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394 M. I. Westphal et al.
Table 3 Metadata for dependent variables
Variable Source Time period a Explanation (all values averaged over the time frame)
Agricultural imports (percentage of
merchandise imports)
World Resources Institute b 1990–2002 Includes both food imports and raw agricultural products, such as
hides, cork, wood, pulp and waste paper and crude animal and
vegetable products
Area (sq. km) World Resources Institute – –
Continent (North America, South America, Africa, Asia, Oceania—
Australia, New Zealand, South Pacific
Islands, etc.)
– – –
Cropland/mosaic (proportion of area) World Resources Institute 1992–1993 Mosaic areas are those with a mosaic of cropland, forests, shrublands,
and grasslands, with no component comprising more than 60%
Deforestation rate World Resources Institute 1990–2000 Average annual % change in natural forest cover
Endemism (proportion of species) World Resources Institute See above No data for fish. Taxon excluded from calculation for a country if data
not available for the number of endemics
Forest (proportion of area) World Resources Institute 2000 Includes both natural forests and plantations
GDP change World Resources Institute 1990–2003 % Change over the period
GDP per capita World Resources Institute 1990–2003 Current (2006) US dollars per capita
Grassland, savanna, shrubland
World Resources Institute 1992–1993
(proportion of area)
Island (versus Mainland) – – –
Latitude CIA World factbook c 2006
Merchandise imports ($) World Resources Institute 1990–2003 Current (2006) US dollars. Merchandise imports represents the value
of all goods purchased from other countries, including the value of
merchandise, freight, insurance, travel, and other non-factor service
Perimeter: area World Resources Institute, CIA Perimeter—2006 Perimeter includes boundaries with other countries and coastline
world factbook (perimeter)
Population density (people/km 2 ) World Resources Institute 1990—2005
Population growth World Resources Institute 1990–2005 Average of 2000–2005, 1995–2000, 1990–1995
Road density (km/km 2 ) World Resources Institute 1996–2000
Species richness (number) World Resources Institute Amphibians—2004 Freshwater and marine fish
Birds—2004
Fish—1992–2003
Mammals—2004
Reptiles—2004
Vascular plants—2004
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The link between international trade and the global distribution of invasive alien species 395
Table 3 continued
Variable Source Time period a Explanation (all values averaged over the time frame)
Tourism (visitors) World tourism organization 1990–2003
Urbanization (% of population) World Resources Institute 1990–2005 Population is divided into the binary of rural or urban
Urbanization change World Resources Institute 1990–2005 % Change over the period
Water ecosystems (proportion of area) World Resources Institute 1992–1993 Includes all salt and freshwater bodies
Most of the IAS in the database have been recorded in the last 10 years, so when possible, we have tried to obtain explanatory variable data from 1990 to the present
EarthTrends: http://earthtrends.wri.org/
http://www.cia.gov/cia/publications/factbook/
http://www.world-tourism.org/
a
b
c
d
(the United States has by far the largest number of
IAS of any country in the GISD at 293). When we
remove the United States from the analysis, a much
different picture emerges, with degree of endemism
and merchandise imports the most important explanatory
variables (Fig. 1b). Surprisingly, country area is
not included in the tree.
Australia (156) and New Zealand (144) have the
second and third most IAS recorded in the database,
and their flora and fauna have a high degree of
endemism. To explore whether the presence of
endemism in the tree is confounded by the figures
for Australia and New Zealand, we excluded these
two countries (Fig. 1c). Endemism is not present in
this regression tree, nor is it with Canada (fourth most
IAS in the database) and the South Pacific Islands
excluded (both separately and together) (Fig. 1d).
Merchandise imports and species richness are the
only dependent variables in this final tree, with the
former explaining a greater proportion (0.29) of the
null deviance. The regression tree explains almost
half of the variance in per-country IAS figures (R 2 -
value of 0.45). Considering that the database is
aggregated across taxa and the varying species
introduction, control and management histories for
countries, this is remarkably good.
Discussion
We conclude from these models that the best
predictor of the number of IAS in a country is the
degree of international trade. The role of international
trade in the distribution of IAS has been rather
axiomatic, but it has not been explored empirically on
a global scale across taxa (but see Vila and Pujadas
2001; Dalmazzone 2002; Levine and D’Antonio
2003). Sampling bias may explain the presence of
the explanatory variable, Continent, though perhaps it
is indicative of increased susceptibility of South
Pacific Islands to invasions, due to great geographic
isolation when compared to even other islands,
leading to more depauperate communities and more
vacant ‘‘niche space’’ (D’Antonio and Dudley 1995).
Moreover, with greater historical isolation comes
evolutionary divergence, and there is evidence to
suggest that the less phylogenetically related invasive
species are to native species, the greater the degree of
invasiveness (Ricciardi and Atkinson 2004; Strauss
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396 M. I. Westphal et al.
Fig. 1 The best-pruned regression trees for different scenarios.
Each split (non-terminal node) is labeled with the
explanatory variable, the value that determines the split, and
the proportion of the total null deviance that the variable
explains (in parentheses). For each leaf (terminal node), the
mean number of IAS and the number of observations (n) in the
group are shown. The R 2 -value is the amount of variance that
the model explains. (A) Global; (B) excluding the United
States; (C) excluding the United States, Australia, and New
Zealand; (D) excluding the United States, Australia, New
Zealand, Canada, and South Pacific Islands. 1 —The value is
the residual of a regression of Endemism on the variables:
Island, Species Richness, and Area.
2 —The value is the
residual of a regression of Mercantile Imports on the variables:
GDP per capita and Area. 3 —The value is the residual of a
regression of Species Richness on the variable Area
et al. 2006). However, we found no overall island
effect.
Our analyses suggest that the greater the species
richness, the more susceptible a country is to
biological invasions. This has been the subject of a
vigorous theoretic debate for several decades (Elton
1958; May 1973). At small spatial scales, the
relationship between specie richness and invasibilty
is equivocal (Prieur-Richard et al. 2000; Kennedy
et al. 2002; Levine et al. 2004; Eriksson et al. 2006).
However, there is strong evidence that at large spatial
scales, the most diverse natural communities contain
greater numbers of exotic species (Lonsdale 1999;
Stohlgren et al. 1999; Stark et al. 2006). It is
hypothesized that this is due to species-rich areas
having greater resource heterogeneity (Eriksson et al.
2006).
The results are also notable for what explanatory
variables did not appear in the best trees. The absence
of endemism in models, excluding Australia and New
Zealand (but including 224 other countries), is
somewhat surprising, as one could posit that the
greater specialization and/or reduced competitive
ability of endemic species (Lavergne et al. 2004;
Wijesinsinghe and Brooke 2004) would make regions
with high endemism more vulnerable to invasions.
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The link between international trade and the global distribution of invasive alien species 397
Various disturbance measures do not seem to explain
the distribution of IAS (Dalmazzone 2002), nor the
area of certain biome/ecosystem types. The latter is in
accord with the general observation that the IAS in
the database are well distributed across taxa. There
were no (overall) island, latitudinal or New World
effects, contrary to a previous study on exotic plants
(Lonsdale 1999). Looking at several taxa, including
birds, mammals, herptiles and plants, McKinney
(2006) found a latitudinal effect and also a positive
relationship between non-native species richness and
area and human population size, none of which are
confirmed here. The discrepancy may be due to our
more circumscribed definition of IAS and the greater
taxonomic breadth of our data. If GDP was included
in the best-pruned regression trees, then this could
also indicate a ‘‘wealth effect’’ biasing the data, that
is, wealthier countries having greater resources to
survey and catalog the presence of IAS.
It is not the type of trade per se (e.g., the amount of
agricultural products), but the overall degree of trade
that seems to be important. Propagule supply has
received less attention in field studies of biological
invasion (Thomsen et al. 2006), but a historical
survey of bird introductions indicates that introduction
effort is the strongest correlate of introduction
success (Cassey et al. 2004b). Our analyses suggest
that propagule pressure, as measured by the proxy of
international trade, may be more important than
intrinsic properties of the native biota, at least as
measured at the coarse national scale.
Our findings also provide support to the idea that
more resources for combating IAS should be directed
at the introduction stage. This is particularly the case
when one considers that the classic ‘‘tens rule’’
(*10% of introduced species establish themselves in
the non-native environment, and, in turn, *10%
become pests) may be too liberal by a factor or 5, at
least for vertebrates (Jeschke and Strayer 2005).
Although economic analyses of prevention vis-à-vis
control are rarely done (Born et al. 2005; Leung et al.
2005), studies show that allocating resources for the
prevention of introductions can be more cost effective
than control (Leung et al. 2002). Only in New
Zealand are prevention activities of border control
and quarantine assessed economically ex post and
incorporated in a national prevention program (Born
et al. 2005). Our results show that the serious
environmental conservation problem of invasive
species is a consequence, at least partially, of
economic globalization (Perrings et al. 2005). These
results provide cogency to the argument that trade
policy instruments that incorporate IAS, such as
tariffs (Margolis et al. 2005; Perrings et al. 2005) or
tradable risk permits (Horan and Lupi 2005), should
be explored to address the current market failure.
Acknowledgments Many of the data used for this paper have
come from the World Resources Institute’s EarthTrends
database. We thank them for this invaluable resource. We
would also like to thank Peter Baxter for helpful comments on
the manuscript.
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