Download D 2.2 Traits analysis Final April 2010

PRATIQUE
No. 212459
Deliverable number: 2.2
Date: 09/05/2017
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ENHANCEMENTS OF PEST RISK ANALYSIS TECHNIQUES
Species traits analysis – environmental and economic impacts
PD No. 2-2
Author(s): Therese Pluess, Martin Hejda, Philip E. Hulme, Vojtěch
Jarošík, Marc Kenis, Marie-Laure Desprez-Loustau, David
Makowski, Jan Pergl, Aurore Philibert, Petr Pysek, Cécile Robin,
Urs Schaffner, Corinne Vacher, Johan van Vlaenderen, Sven Bacher
Partner(s): UniFr, CABI, IBOT, INRA, Bioprotection
Final Draft Submission date: 23rd October 2009
EU Framework 7 Research Project
Enhancements of Pest Risk
Analysis Techniques
(Grant Agreement No. 212459)
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PROJECT OVERVIEW: PRATIQUE is an EC-funded 7th Framework research project designed to
address the major challenges for pest risk analysis (PRA) in Europe. It has three principal objectives:
(i) to assemble the datasets required to construct PRAs valid for the whole of the EU, (ii) to conduct
multi-disciplinary research that enhances the techniques used in PRA and (iii) to provide a decision
support scheme for PRA that is efficient and user-friendly. For further information please visit the
project website or e-mail the project office using the details provided below:
Email: [email protected]
Internet: www.pratiqueproject.eu
Authors of this report and contact details
Name: Therese Pluess
Partner: UniFr
E-mail: [email protected]
Name: Marc Kenis
Partner: Cabi
E-mail: [email protected]
Name: Johan van Vlaenderen
Partner: Cabi
E-mail: [email protected]
Name: Marie-Laure Desprez-Loustau
Partner: INRA
E-mail: [email protected]
Name: Cécile Robin
Partner: INRA
E-mail: [email protected]
Name: Corinne Vacher
Partner: INRA
E-mail: [email protected]
Name: David Makowski
Partner: INRA
E-mail: [email protected]
Name: Aurore Philibert
Partner: INRA
E-mail: [email protected]
Name: Petr Pysek
Partner: IBOT
E-mail: [email protected]
Name: Jan Pergl
Partner: IBOT
E-mail: [email protected]
Name: Vojtěch Jarošík
Partner: IBOT
E-mail: [email protected]
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Name: Philip E. Hulme
Partner: Bioprotection
E-mail: [email protected]
Name: Martin Hejda1
Partner: IBOT
E-mail:
Name: Urs Schaffner
Partner: CABI
E-mail: [email protected]
Name: Sven Bacher
Partner:UniFr
E-mail: [email protected]
Disclaimer:
This publication has been funded under the small collaborative project PRATIQUE, an EU 7th
Framework Programme for Research, Technological Development and Demonstration addressing
theme: [kbbe-2007-1-2-03: development of more efficient risk analysis techniques for pests and
pathogens of phytosanitary concern call: fp7- kbbe-2007-1]. It does not necessarily reflect the
European Commission's views and in no way anticipates the Commission’s future policy in
this
area.
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Table of Contents:
Summary ........................................................................................................................ 6
General Introduction ...................................................................................................... 8
Insects .......................................................................................................................... 11
Introduction .............................................................................................................. 11
Material and Methods .............................................................................................. 12
Environmental impact .......................................................................................... 12
Economic impact ................................................................................................. 14
Results ...................................................................................................................... 18
Environmental impact .......................................................................................... 18
Economic impact ................................................................................................. 18
Discussion ................................................................................................................ 20
Recommendations for best practice ......................................................................... 25
Environmental impact .......................................................................................... 25
Economic impact ................................................................................................. 26
Literature .................................................................................................................. 26
Tables ....................................................................................................................... 29
Plants ............................................................................................................................ 41
Introduction .............................................................................................................. 41
Materials and methods ............................................................................................. 41
Data ...................................................................................................................... 41
Statistical analysis ................................................................................................ 43
Results and discussion ............................................................................................. 45
Conclusions .............................................................................................................. 48
Literature .................................................................................................................. 49
Tables and figures .................................................................................................... 52
Pathogens ..................................................................................................................... 62
Introduction .............................................................................................................. 62
Material and Methods .............................................................................................. 62
1. Data set............................................................................................................. 62
2. Statistical analyses ........................................................................................... 69
Results ...................................................................................................................... 70
Discussion - Conclusions ......................................................................................... 77
Literature .................................................................................................................. 79
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Appendices
Appendix 1: References to case studies on impact of invasive plants reviewed in the
present paper
Appendix 2: Agricultural insect pests with scores and significant traits
Appendix 3: Forest insect pests with scores and significant trait
Appendix 4: Orchard insect pests with scores and significant traits
Appendix 5: Ornamental insect pests with scores and significant traits
Appendix 6: Storage insect pests with scores and significant traits
Appendix 7: Species list of insect pests and number of websites for analysis 2.2 Swiss
database
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Summary
Assessing the future impact of alien plant pests is a major challenge in pest risk
analysis. The three studies in this deliverable looked for the first time at species traits
associated with the impact of plant pests from three taxonomic groups: insects, plants
and fungi. Such traits could be integrated in pest risk analysis schemes to help assess
economic and environmental impacts. We collected data describing the environmental
impacts of insects, plants and fungi and, additionally, on the economic impact of
insect plant pests. This report discusses each taxon separately because species traits
and data availability differed markedly between the three taxa considered and because
a generic basis for impact definition was not available.
Despite data scarcity (34 species) and a bias towards forest pests, it was possible to
identify a trait (mode of reproduction) that is significantly related to high
environmental impact in insects. Economic impact in the insects studied was
particularly important in those species that reduced photosynthetic activity
(defoliating and honeydew producing insects). It was also found that different traits
are associated with impact in different economic sectors (agriculture, forestry,
orchards, ornamentals, storage). A further analysis looked at website counts for
insecsts that are alien to Switzerland. This analysis revealed that insects that are
transmitting diseases have high website counts. A comparison of the results for
environmental and economic impacts (including the website counts) was not feasible
because the sets of species analysed were not the same. A possible integration of the
findings for insects into the EPPO PRA scheme1 is proposed. For plants (173 species),
the data basis was better than for insects, but was also biased towards a few well
studied species. Shrubs and short trees were most likely to have a significant impact
on most characteristics of invaded populations, communities and ecosystems,
including native species diversity. An invasive plant species might have a significant
impact on some characteristics of the receptor environment, and a non-significant
impact on others. Therefore, a universal measure of impact was not feasible and a
clear definition of impact categories will be crucial for a successful risk analysis. The
1
http://www.eppo.org/QUARANTINE/Pest_Risk_Analysis/PRA_intro.htm
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plant analysis suggests that traits determining invasiveness may be similar to those
determining impact. For fungi (47 species), we analysed relationships between species
traits and (i) invasiveness and (ii) high ecological impacts. We also included the
possible effects of confounding variables: date of introduction, host surface area and
phylogeny (the latter showing no effect in our data set). Most discriminant traits were
long distance-dispersal and sexual reproduction for invasiveness and optimal growth
temperature and infection of perennial organs for high ecological impact.
Surprisingly, wide host range (i.e. generalist pathogens) was negatively correlated
with invasiveness. Possible adaptations of the EPPO PRA scheme, according to these
findings, are proposed.
Overall, the approaches and findings of the three taxa differed considerably.
Moreover, even though the EPPO scheme uses a five level scoring system, these
levels are not clearly defined and may not be readily applicable to environmental
impact assessment because of data scarcity. However, tasks 2.3 and 2.4 in
PRATIQUE will investigate this topic further and will be using the results from this
deliverable. There was also a bias towards a few well studied species and a knowledge
gap for low-impact or “non-pest” species, but this is a general characteristic in
invasion ecology and is partly dealt with by appropriate statistical analysis. The
patchiness of information on environmental impacts can be seen as a caveat in this
study. But this situation can only be amended over time, when more studies will be
done that quantify rather than only describe the impact of alien organisms.
The three studies each identified relevant traits and some of them (reproductive mode,
host range) are already part of the EPPO scheme, others (photosynthesis reducing
insects, optimal growth temperature for pathogens and plant life form and plant
height) are not directly considered in the scheme. However, the integration of these
traits into the EPPO PRA scheme, together with a cross-taxonomic analysis of species
traits relevant for impacts will take place in task 2.4 (development of modules for
assessing economic, environmental and social impact). The decision as to whether our
findings are best included as toolkits or notes added to the PRA scheme questions will
be taken in task 2.5 (development of a generic integrated model).
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It can also be argued that species traits are not useful predictors for impact because
other factors (host traits, characteristics of the receptor ecosystem, habitat and climate
suitability, pathway characteristics, etc.) are equally or more important. This is
certainly true and it would not be appropriate to only rely on pest traits when doing a
PRA. Our investigations on species traits can be seen as complementary information
for a PRA. The knowledge that a trait correlated with impact in the past could be
especially important if other information needed to do a PRA is scarce. The lack of
detailed information is often stated to be a considerable problem in the PRA process.
Thus in the absence of complete knowledge, species traits could help us to assess the
intrinsic biological potential of an organism to become a pest in a new area.
General Introduction
In a globalizing world, an increasing number of organisms reach areas far outside
their native range (Enserink, 1999). Some of these non-native organisms cause
considerable economic costs, alter ecosystem processes, are responsible for a
multitude of diseases in humans, animals and plants and are a major threat to
biodiversity (Vila et al., 2009).
Countries and trading blocs are increasingly implementing appropriate measures to
prevent or reduce the entry, establishment, spread and impacts of plant pests and
invasive alien species in general. In the EU, a more coordinated and harmonized
strategy against invasive alien species needs to be developed (Hulme et al., 2009) and
the techniques already applied against plant pests need to be enhanced (Baker et al.,
2009). With the increase in the volumes, commodity types and origins of trade in
plant material, the introduction of new crops, and the impact of climate change, the
threats posed by new plant pests in Europe are now greater than ever. However, the
free movement of people and commodities is the basis of the European and world
economy. For this reason countries may implement phytosanitary measures only for
quarantine pests and regulated non-quarantine pests (FAO, 1997, Heather &
Hallman, 2008). A Pest Risk Analysis (PRA) is required to determine whether a pest
should be regulated and what phytosanitary measures should be taken (Baker et al.,
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2009). These measures must be based on scientific principles and cannot be
maintained without sufficient scientific evidence.
Risk assessment has proven to be an effective method for identifying and prioritising
environmental problems with high uncertainty, particularly for predicting the
likelihood and severity of potential environmental impacts (Andersen, 2007). Over the
last few years, risk assessment schemes have been developed for plants (Pheloung et
al., 1999, Daehler et al., 2004, Krivanek & Pysek, 2006), birds (Vall-Ilosera & Sol,
2009), fish (Kolar & Lodge, 2002, Copp et al., 2009) and mammals (Andersen, 2007,
Nentwig et al., 2009). In Europe, the PRA scheme for plant pests developed by the
European Plant Protection Organisation EPPO, is much in use (EPPO, 1997) and has
been successfully adapted in the UK to assess the risks posed by all non-native
species (Baker et al., 2008). In some European countries, techniques for producing
black lists of alien organisms potentially harming the environment have also been
developed and used (Branquart, 2007, Essl et al., 2008). However, there is still
considerable scope for improvement ((Baker et al., 2009), especially for predicting
and comparing environmental and economic impacts (Vila et al., 2009). The current
EPPO PRA scheme (EPPO, 2007) is based on expert judgments justified by the
existing literature. This may lead to expert-biased risk assessment outcomes (Maguire,
2004) but qualitative assessment techniques provide the only realistic approach for
assessing such topics as environmental impacts since they are rarely quantified in the
scientific literature. Defining and measuring impact is important when attempting to
set priorities for managing invasive species (Parker et al., 1999, Smith et al., 1999,
Ricciardi et al., 2000, Duncan et al., 2003). Parker et al. (1999) called for studies that
measure impacts at multiple scales and levels of organization and the development of
a framework to predict future damages, but little progress has been made. And even if
useful and accurate tools have been developed, such as the risk assessment tool for
fish introductions to the Great Lakes (Kolar & Lodge, 2002), the political will for
implementation is small (Keller & Lodge, 2009). A review of schemes presently used
to assess environmental impact of plant pests was produced by PRATIQUE
(Deliverable 2.1). It concluded that, for the moment, no scheme provides satisfactory
guidelines on how to predict the environmental impact of a species that has not yet
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invaded any region, or for which the environmental impact has never been studied.
Hence, while some tools may be available for vertebrates, a scheme to assess the
ecological impacts of plant pests still needs to be developed. A more quantitative
approach including statistical methods is needed, especially to improve the
comparability with economic impacts in future PRA schemes (Vila et al., 2009).
Statistical methods proved to be valuable instruments to predict establishment and
spreading success of invaders and should help to predict impact (Kolar & Lodge,
2001). Only recently Nentwig et al. (2009) published a generic scoring-system for
alien mammals in Europe that is based on thorough statistical methods and ranks alien
mammals based on their biological traits. They found that the ecological flexibility of
a mammal species is a good predictor for its environmental and economic impact in a
new environment.
Predicting both successful entry and establishment and the impact of an alien species
is difficult. However, many studies, focusing on factors associated with entry and
establishment success of alien species, have identified species traits associated with
invasiveness (Sol & Lefebvre, 2000, Prinzing et al., 2002, Jeschke & Strayer, 2005,
Pysek & Richardson, 2007, Sol, 2007, Hayes & Barry, 2008, Sol et al., 2008).
Certain species traits, such as brain size in birds and mammals, have proved to be
useful in predicting the potential for the entry and establishment of alien species in
new areas (Sol & Lefebvre, 2000, Sol et al., 2008). The scoring system developed by
Nentwig et al. (2009) used rigorous statistical methods and showed that the impact of
mammals can be predicted equally well by a species trait (ecological flexibility) and
the information on the damage caused by the species in other parts of the world.
Nentwig et al. (2009) thus identified a trait that could reliably predict the possible
impact of future mammal introductions. Similar studies for plant pests are still lacking
and are being explored by PRATIQUE. Lovett (2006) described short-term and longterm impacts of forest pest outbreaks in Eastern North American Forest Ecosystems
and identified species traits such as the feeding niche, host specificity and virulence as
the key predictors for determining the nature and severity of insect pest outbreaks.
However, they looked at only two insect pests and one pathogen and did not perform
statistical analyses to identify traits nor did they quantify impact. A review by Kenis
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et al. (2009) was more exhaustive (including 72 alien insects), but comprised many
insects that are not plant pests and also did not quantify the impact. A recent study by
Sefrova and Lastuvka (2009) identified body size, host specificity and feeding mode
as being related to invasive Lepidopteran species in the Czech Republic, thus showing
that it is possible to identify traits typical for certain invasive insect species taxa.
However, studies that quantify environmental and economic impacts of plant pests
and compare these impacts with relevant biological traits of the pest are still lacking.
Here, we have attempted to identify species traits of plant pests that are associated
with impacts to the environment and the economy. Such traits can then be integrated
as predictors in PRA schemes to make estimation of environmental and economic
impacts more objective and reliable, despite the difficulties in relating explanations to
predictions (Williamson, 2006).
Insects
Introduction
For insects, we hypothesize that two sets of traits can be associated with high impacts
to plants in both wild and crop plants. An insect species can cause significant impacts
to a host plant either by (i) the feeding behaviour of the individual insects or (ii) by
high feeding pressure due to high population densities. Thus, the first set of traits
consists of those related to the feeding behaviour of herbivorous insects. We expect
that the host range of a species may have an effect on the impact. The majority of the
most environmentally damaging pests or diseases are monophagous because these are
the ones that can kill or even locally eradicate host plants (Lovett et al., 2006, Kenis et
al., 2009). For economic pests however, it is likely that a polyphagous insect would
cause higher impacts than a monophagous or oligophagous insect because it has
shown that it can overcome the defences of many host plants. Also, if an insect is
polyphagous, it is more likely to include crops of economic importance in its diet. We
thus expect to observe an effect of host specificity but are expecting opposing
outcomes for environmental and economic impact respectively. Furthermore, we
expect those species feeding in dense groups on a single plant (Lawrence 1990,
Hunter 1991, Codella and Raffa 1995, Clark and Faeth 1997) would also be
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associated with higher impacts because, by feeding gregariously, the individuals
might more easily overcome plant defence mechanisms. There might also be a
difference between external and internal, above- and belowground feeders and those
insects preferentially feeding on vegetative or reproductive parts of the host plant. The
second set of traits relates to the population growth capacity of an insect species. We
expect that insects that are small, have a high fecundity, several generations per year
and asexual reproduction reproduce faster and are thus more able to quickly build up
high population densities. High population density in turn will lead to higher feeding
pressure on host plants (both in wild and crop plants) which will thus be associated
with higher impacts. For predicting the economic impact of insect pests, we used a
third set of predictors describing the pest history of a species and hypothesize that
species that are recorded as pests in their native range (without considering the
seriousness of the pest in the native range) will have a higher impact in general than
those not regarded as a problem in their native range. Furthermore, we also included
in our analyses the predictor “being invasive elsewhere”, a characteristic that is
known to be associated with likelihood of entry and establishment (Williamson,
2006). Two further traits for the economic pests were tested: Insects can affect the
photosynthetic activity of their hosts either by defoliation or sooty moulds originating
from excessive honeydew production and covering the leaf area. Both mechanisms
may lead to reduced photosynthetic activity (Boote et al., 1983, Spitters, 1990). This
trait was defined as “affecting photosynthesis”. Another trait expected to be important
for economic impact was the capacity to transmit plant pathogens.
Material and Methods
Environmental impact
1.1 Data collection
Species traits and species list
Based on our hypotheses, information on the following species traits was collated: i)
host specificity, feeding niche, gregariousness, ii) asexual reproduction, fecundity,
generations/year, adult longevity and adult body size. Traits were retrieved from the
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Crop Protection Compendium CPC (www.cabcompendium.org, accessed in 2008),
CAB Abstracts and the literature search engine, Google scholar. The species list
(Table 1) consisted of the insect species reviewed by Kenis et al. (2009). This review
included primary research publications studying the ecological effects of invasive
alien insects and/or mechanisms underlying these effects (Kenis et al., 2009). From
this list we excluded the social insects because of their special life-history traits and
those species for which Kenis et al. (2009) did not describe the impacts on plants. In
addition, species from the list of insect defoliators in North America (Mattson et al.
(1991) were only included in the dataset if impact reports were available. Because this
list also includes native pests, we introduced pest location status (native vs. alien) as
another variable.
Impact reports
The internet was screened for case studies describing the ecological impacts of the
species selected (Google scholar, accessed 2008). The search words “ecological
impact”, “ecological damage”, “environmental impact” and “environmental damage”
plus the scientific species name were used to obtain impact information for each
species. The impacts, described in the retrieved literature and in Kenis et al. (2009),
were scored based on the following principles.
1.2 Impact scoring
We followed Parker et al. (1999) by quantifying the impacts of plant pests from the
individual host plant to the ecosystem/landscape level. To do so, we used an approach
also taken by Nentwig et al. (2009) to quantify the impact of alien mammals in
Europe, where ecological impacts are categorized into: herbivory, competition,
predation, hybridisation and ability to transmit of diseases and subsequently scored.
The categories were adapted for the purpose of scoring the environmental impacts of
plant pests. Possible impacts of plant pests on the native flora and ecosystem services
were thus categorized as: herbivory, transmitting plant diseases, exploitation
competition, apparent competition and negative changes to the ecosystem. Each
category was divided into five scoring levels according to the possible consequences
of a pest outbreak from 0 (no interaction between pest and plant reported or
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mechanism not studied) to 5 (massive consequences for the flora or the ecosystem due
to presence of the pest). A detailed description of the impact categories is given in
Table 2.
1.3 Analysis
The data were analyzed with General Linear Models (GLMs), using the function glm
in R (Version 2.7.1; R Development Core Team 2008). GLMs are standard statistical
models that are equivalent to linear regression if the explanatory variable is
continuous and equivalent to ANOVA if the explanatory variable is categorical. From
each of the five impact categories the highest scores were taken and summed to obtain
the overall impact, which served as the dependent variable. Each species trait was
included as the fixed factor in separate analyses.
Economic impact
The analysis of species traits associated with economic impact was made using two
different databases. The first is a European database of 182 alien plant pests (DAISIE,
2009) and the second is a database of 313 alien species in Switzerland (Kenis, 2006).
The two analyses included similar species traits (explanatory variables) but different
socio-economic impact scoring systems (dependent variables).
European database
Data collection
Species traits and species lists
Based on our hypotheses, we used three sets of species traits; those related to i) the
feeding behaviour, ii) the population growth capacity and iii) the pest history of a
species. An insect was considered a native pest if at least one paper from the last 40
years, describing it as a pest (causing impacts and/or needing control) in its native
range was found in CAB Abstracts. Other traits, such as the capacity to transmit
vectors or to reduce photosynthetic activity (by defoliators and excessive honeydew
producers causing sooty moulds), were also analysed. Table 3 lists and defines the
traits considered. The species traits analysis for economic impact was performed with
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182 plant pests that are alien to Europe but have already been established or
intercepted in Europe (DAISIE, 2009); see appendices for the species list.
Impact scoring
The economic impact described in the Crop Protection Compendium (CPC, accessed
in spring 2009) was used as the dependent variable in the analysis for each species.
Two different scoring methods were developed to assess the impacts.
Overall impact scoring
In order to obtain a large sample size and increase statistical power, we scored
impacts semi-quantitatively in four levels: no impact (no economic impact reported in
CPC), low impact (economic impact is reported but not quantified or no notion is
given on the severity of the damage), medium impact (the economic impact report
describes the medium importance of the plant pest and/or reports yield/quality losses
below 25%) and high impact (the CPC qualifies the species as a serious pest and/or
reports losses over 25%). A detailed description of the four scoring levels is given in
Table 4. This method allowed us to score and analyse the impacts of 176 species. The
same insect can be a minor pest for one crop and a major pest for another crop. Hence,
if the CPC cites different levels of damage, the worst case was always scored,
representing the highest potential damage a species could cause. This method does not
consider the spatial extent of the damage caused by an insect species.
Quantification of monetary losses
For 63 species from our list of 176, the CPC reports the percentage of yield loss or
lost income for producers. For these species, a second method was envisaged with the
aim of calculating a continuous dependent variable (a percentage or an index with
more than four levels). The references in CPC were scored according to the monetary
losses that they caused or that were estimated in the literature. This method promised
a more accurate evaluation of impact with more than four levels and thus a more
accurate analysis. However, it could only be applied to a subset of the insects listed
because exact figures were not available for many species.
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Analysis
The data were analyzed with General Linear Mixed Models (GLMM), using the
function lme of the software R, Version 2.7.1; R Development Core Team (accessed
2008). The highest impact score of a species served as the dependent variable and
each species trait was included as the fixed factor in separate analyses. Because
related species have similar traits, they are likely to achieve similar impact levels
(Manchester & Bullock, 2000). GLMMs deal directly with non-independent data
points and the taxonomic hierarchy at the order level was integrated as a random
effect in our model (Goldstein, 1999, Diggle et al., 2007). First, an analysis including
all 176 species was performed.
Because insects can affect several types of cropping systems, we split the dataset into
five sectors: “agriculture” (mainly annual food or fibre crops), “forest” (without
ornamental trees), “orchard” (including perennials such as apple, citrus, coffee,
pineapple, avocado, cassava, banana, palms, etc), “ornamental” (including ornamental
trees and flowers) and “storage” (including stored plant products such as wood, but
also cereals). Each pest species was included in every sector affected. The same
analysis was then performed separately for each sector.
Swiss database
Data collection
In this analysis, we used the database of 313 alien insects established in Switzerland
(Kenis, 2006). This list not only contains herbivores but also detritivores, parasitoids
and predators. Using the whole list, we first tested four basic traits or characteristics
of
all
insects:
feeding
niche,
habitat
(EUNIS
classification,
http://eunis.eea.europa.eu/habitats-code-browser.jsp), continent of origin and insect
order. For the trait “feeding niche”, we classified the insects into five categories: 1:
omnivorous, saprophagous and mycetophagous; 2: sap feeders; 3: parasites,
parasitoids, predators; 4: external defoliators, leaf miners, gall makers; 5: borers
(fruit/shoot/stem/etc.) and seed feeders. The number of Swiss websites describing
socio-economic impact in Switzerland (covering all Swiss languages), or control
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methods for application in Switzerland was used as the dependent variable. Only one
website was counted per host organisation. The data were analyzed with Generalized
Linear Mixed Models (GLMM). Then, using a subset of 100 species (Appendix 7),
which live at least partly as herbivores (but including stored product pests), we tested
an additional list of 11 traits, similar to those tested in the analysis of the European
database: asexual reproduction; fecundity; mean number of generations per year in
Europe; specificity; longevity of adults; adult size; alien elsewhere and pest in the
native range (see Table 3 for definitions). The feeding niche was defined as above for
the first analysis. Finally, we also included the traits “vector or facilitator (facilitating
infection processus) of plant disease – yes or no” and “congeneric-species native to
Europe – yes or no”. In this latter analysis, as the dependent variable, we again used
the number of Swiss websites describing impact in Switzerland, or control methods
for application in Switzerland. In addition, we also used as the dependent variable the
number of scientific publications in Switzerland and worldwide over a ten-year period
(1999-2008), retrieved using CAB Abstracts. For a few insects that are commonly
used as hosts for rearing parasitoids or predators (e.g. Ephestia kuehniella, used for
rearing Trichogramma spp) or as model species for ecological studies the list of
publications was sorted to select only those where the insect was considered a pest.
For the number of publications in Switzerland, only publications of studies carried out
in Switzerland (or by a Swiss senior author in a neighbouring country) were taken into
account. Studies made by Swiss authors on other continents were discarded.
Analysis
The distribution of these three counts followed a Poisson distribution and we therefore
used the LMER function of the software R (Version 2.7.1; R Development Core Team
2008). We first performed univariate analyses with each of the 11 species traits as
fixed factors and included insect order as the random factor. Because correlations
between the different species traits were weak, we then built multivariate models
including all 11 traits as fixed factors and insect order as the random factor and then
reduced the number of fixed factors in a stepwise approach, selecting the minimum
adequate model by minimising Akaike’s Information Criterion (AIC).
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Results
Environmental impact
Environmental impacts were scored for 34 species from 4 orders, reported in 74
papers (see Table 1 for a species list). Asexual reproduction was the only factor
explaining high impact levels (p = 0.005). However, most high-impact species came
from the same order (Hemiptera) and it is currently impossible to disentangle species
traits from phylogenetic relatedness (p = 0.014 if the insect order was used as the
explanatory variable). Alien insects (n=13) caused significantly higher impacts than
native species (n=21) (p = 0.002).
Economic impact
European database
Overall impact scoring
For all 176 species (see Appendices for the species list), a score between 0 (no
impact) and 3 (major impact) was attributed. For 26 species no impact record was
reported in the CPC, hence we assumed their impact would not be serious enough to
justify an impact record in CPC and therefore, they were scored 0, 23 species were
classified as minor pests (score 1), 32 species as medium (score 2) and 95 species as
major pests (score 3).
An analysis of all species revealed that insects affecting the photosynthetic activity
(see Table 5 for an overview of the results) of their host plant were significantly
related to high economic impacts (p < 0.01). Furthermore, species that are also alien
on a continent other than Europe had a significantly higher economic impact (p <
0.01). The overall analysis also showed that species already considered to be a pest in
their native range were also of higher economic importance (p = 0.06)
In the agricultural sector (61 species), insects that were alien elsewhere were
significantly correlated with high impact (p = 0.04). There was also a tendency for
insects affecting the photosynthetic activity of their host plants to be of higher
economic importance (p = 0.07). This trait was significant for the forest sector (35
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species) (p = 0.02). In the orchard sector (79 species), species with many generations
per year were significantly related to high economic damage (p = 0.01). Also, species
being alien elsewhere were significantly associated with high economic damage (p <
0.01). There was also a tendency for polyphagous insects to have higher economic
impacts (p = 0.10) than mono- or oligophagous species. In the ornamental sector (47
species), the number of generations (p = 0.04), host specificity (p = 0.003),
gregariousness (p = 0.03) and alienness (p < 0.001) and affecting photosynthesis (p =
0.009) were significant factors. Fecundity (p = 0.02) was also significantly correlated
with high impacts, but this correlation was negative. For storage pests, the feeding
behaviour hypothesis could not be tested because all traits were the same for the
relevant species. However, adult longevity correlated positively with high impact (p =
0.08) while fecundity was also negatively correlated with high impact (p < 0.01)
Quantification of monetary losses
A first screening of the impacts reported in CPC revealed that this method had its
drawbacks and could not be applied: the impacts reported were too heterogeneous.
Some reports were based on scientific experiments; others reported lost income of
producers in a certain year, while others were more general and stated costs from a
country over a certain time period. Furthermore, a certain amount of money will have
different consequences in different countries and different years because of inflation
and fluctuating monetary stability in many agrarian countries. It also became
apparent, that the information that the method requires was only available for certain
well studied insects, presumably those responsible for high economic impacts.
Swiss database
The analysis of full Swiss database using the number of Swiss websites as the
dependent variable did not provide significant differences for the traits “feeding
niche”, “habitat” and “taxon”. In contrast, the continent of origin significantly
affected the number of websites (p=0.022) species of European origin being
mentioned as pests in fewer websites than species from other continents.
Results from the univariate analyses using the subset of 100 species and 11
traits are shown in Table 6. Several traits showed a clear trend throughout the three
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dependent variables. Being a vector or facilitator of a disease has a positive effect on
the number of websites or publications. In contrast, having a congeneric native
species in Europe was negatively correlated with the number of websites and
publications. Being a native pest in the region of origin and having a high fecundity
was significantly affecting the number of websites in Switzerland and publications
worldwide. In contrast, species with long-lived adults had fewer publications than
short-lived species. For a couple of traits, we observed contradictory results between
dependent variables. For example, the number of Swiss websites and worldwide
publications were positively affected by the host range size while it was the contrary
for the number of Swiss publications.
Tables 7, 8, and 9 show the best fitting models for the three dependent variables. The
models for the number of Swiss websites, Swiss publications and world publications
include four, seven and eleven factors, respectively. The model for worldwide CAB
publications is statistically over-fitted, with all factors being of significant importance.
It cannot be interpreted meaningfully and hence is not discussed further.
Discussion
Environmental impact
Asexual reproduction was the only insect trait significantly related with
environmental impacts on the native flora. With one exception, all species with
asexual reproduction belonged to the order Hemiptera. Thus we cannot rule out that it
is indeed the reproductive mode that correlates with higher environmental damage or
whether other traits, shared by all Hemiptera, are responsible for this result.
Furthermore, our dataset consists of a majority of forest pests and this makes
generalisations difficult.
While there is a plethora of literature concerning impacts on plants and many studies
have also investigated the traits of high-impact species (Pysek & Richardson, 2007),
there are only few detailed studies on herbivorous insects and their environmental
impact that go beyond the well studied high-impact species such as Adelges piceae, A.
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tsugae or Lymantria dispar (Kenis et al., 2009). However, besides herbivorous forest
pests, many studies describing impact of insects focus on non-herbivorous species and
are thus not relevant to PRATIQUE. This may well be because insects act on many
trophic levels and impacts can be direct (such as herbivory) or indirect (such as
apparent competition or hybridisation). Furthermore, aliens may remain undetected
for a long time until their impact is obvious and research efforts often focus only on
high-impact species. From most world regions there currently exist several lists of
alien insects, including many herbivores, but their impact is seldom described or even
studied in detail (Mattson et al., 1991, Causton et al., 2006, DAISIE, 2009).
Hence, while the categorization and development of the scoring scheme for
environmental impact is relatively straightforward, it will remain a challenge to use
the scheme due to knowledge gaps. We therefore suggest widening our view and
scoring all impacts described in the literature that alien plant pests may have on the
environment. This will enable us to enlarge the species list and gain statistical power.
The present dataset also includes native insect pests, whereas alien insects are more
harmful to the environment. We will thus now focus on alien insects and, because the
sample size is too low, we will need to collect new data. Possible data sources for this
approach are: the 17 herbivore species reviewed by Kenis et al. (2008) that have so far
been excluded, the study on alien insect species for Galapagos published by Causton
(2006) listing 463 alien insects (42% herbivores), a list of insect forest pests in
Canada,
published
by
the
Canadian
Forestry
Service:
(http://www.exoticpests.gc.ca/documents_eng.asp), and the 13 invasive Lepidopteran
species in the Czech Republic studied by Sefrova & Lastuvka (2009).
For the moment, we still lack a thorough scheme to assess the environmental impact
of plant pests (PRATIQUE, Deliverable 2.1) and our recommendations from the
insect traits analysis for additions to the EPPO PRA scheme need to be considered
with caution because of the low sample size. However, Task 2.4 of PRATIQUE will
develop modules for environmental impact assessment and the impact scoring system
and trait analysis will contribute to this task.
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Economic impact
European database
Insects affecting the photosynthetic activity of their host plants include defoliators
(such as Lepidoptera and Coleoptera species) and honeydew producing species
(Hemiptera) that have a record in CPC for causing problems with sooty moulds that
cover the surface of the photosynthetic tissue of their host plant, thus reducing
photosynthesis. Our findings might be of importance because crop growth models
include light utilisation in some form or another (Spitters, 1990). Boote et al. (1983)
distinguish between light stealers and leaf area reducers because the mechanisms
affected differ. Defoliators reduce the leaf area where photosynthesis can occur, while
sooty moulds growing on honeydew cover the leaf area and thus reduce
photosynthetic activity. However, since both mechanisms affect the C fixation process
in plant growth and we are interested in a generic model, we combined them in this
study. This is also in accordance with Spitters (1990) who states that the yield
reduction by pests and diseases can be explained by a reduction in cumulative light
interception. This reduction is attributable either to an accelerated leaf senescence (not
taken into account here) or to the coverage or consumption of green foliage, without
affecting the rate of photosynthesis of the remaining green leaf area (Spitters, 1990).
The focus on photosynthesis reduction from defoliators and sooty moulds due to
honeydew production might be criticised, especially since hemipteran species damage
host plants with other mechanisms (toxic saliva, transmitting diseases) that could be
taken in account in separate analyses of this taxa only.
Another important factor is the history of invasiveness, here represented by the trait of
being alien elsewhere than Europe (although the species studied were all alien to
Europe, they may already have been introduced to other continents). Being alien gives
no information about their level of invasiveness (Ricciardi &
Cohen, 2007).
However, in the present study, being alien is the best surrogate that we have for the
invasiveness of a species and we consider that an alien (potentially invasive) species
also has the potential to be an economic pest. We did, however, find that being alien is
significantly associated with high impacts in the overall analysis as well as in the
agricultural, orchard and ornamental sectors (see Appendices 1 to 5). The history of
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invasive success has previously been found to be a useful predictor for invasion
success (Kolar & Lodge, 2001, Wittenberg & Cock, 2001, Hayes & Barry, 2008).
However, in the context of impact, one could also argue, that a species, that has been
introduced to other countries, is more widely spread and has therefore more chances
to become a pest somewhere because its chances to be pre-adapted to new biotic and
abiotic conditions will increase the wider a species is distributed. For plant pests
however, it might be more likely that repeated introductions increase the chances to
arrive in a place where an economically important crop is grown.
Although it is not very surprising that species defined as pests in their native range
also had a significant economic impact, this point needs clarification: the definition of
native range is not always straightforward because the exact native range of an insect
is not always known or is disputed in the literature. This might be problematic if only
the extent of occurrence is known and is given at the continental scale, as described
by Gaston (1991). At this scale, most insects might be a pest problem somewhere on
the continent and thus be considered a native pest. On the other hand, if the native
range is considered at a smaller scale, which is probably closer to the reality,
comparatively fewer species will be considered a native pest. Hence, while this
predictor is significantly related with high economic impacts, it is inherently
dependent on the spatial scale and care has to be taken when it is used for predictions.
However, from some continents, such as Africa, Asia or Southern America, country
specific pest lists may not exist and continental pest records are the most accurate
information available at the moment.
Furthermore, the growth of plants mainly depends on the availability of light, hence
on the photosynthetic activity in the green tissue of a plant. Thus it makes sense that
insects that particularly affect this physiological activity might be of higher economic
importance, especially for crops where yield matters. We thus suggest assessing risks
for each sector (agriculture, forestry, orchards, ornamentals, storage) separately and to
consider the relevant traits for each of these sectors only (see recommendation for best
practice)
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Overall, it appears that some findings from other taxonomic groups, such as plants
(Pysek & Richardson, 2007)) also seem to hold for insect plant pests. The history of
an organism matters if we want to predict its possible impact in a new receptor
environment, even though it cannot explain why it is damaging (Nentwig et al., 2009).
However, we also found other significant relationships between traits and economic
impact. Since our results indicate that the importance of certain traits varies across the
different economic sectors distinguished in our study, we advocate that predictions of
impacts should be made for each of the sectors separately to increase accuracy.
Swiss database
In general, factors that had a significant effect on the economic impact score using the
European (DAISIE) database also had an effect on the number of websites and/or
publications using the Swiss database. In particular, being a pest in the region of
origin, being alien elsewhere or being polyphagous tended to have the same effect in
both studies. The effect of reducing photosynthesis was not tested with the Swiss
database because the feeding niche was analysed differently. In contrast, some factors
that were not tested in the European analysis were found to be highly significant in the
Swiss analysis. In particular, being a vector or facilitator of a disease had a positive
effect on the number of websites and publications. This is not surprising, knowing that
many of the most serious insect pests worldwide are vectors of plant viruses or other
diseases, causing problems even at low densities. It seems also that insects invading in
Europe or having congeneric species in Europe are less cited in websites and
publications than species from other continents that have no congeneric species in
Europe. Both observations may be due to the fact that European species or species
having a congeneric species in Europe will be more likely to be controlled by natural
enemies than fully exotic species. It would be interesting to test these three factors
using the European database and the economic impact score described in Table 4.
It may be questioned whether using the number of websites or scientific publications
is a good indicator for socio-economic impact. Although we would certainly not
advise their use as impact indicators in pest risk analysis, they provide an objective
and replicable measurement and, we believe, a reasonable approximation for our
analyses. When websites and publications are counted for a particular country (in this
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case Switzerland), it gives a good indication of the socio-economic importance of the
species in the country, especially since only websites and publications mentioning the
insect as a pest were considered. Furthermore, in Switzerland, hardly any alien insect
is known to have an environmental impact, suggesting that any mention of an alien
insect as a pest implies an economic or social impact. The number of Swiss
publications suffers from a high number of zeros, i.e. the majority of insects had no
citation from Switzerland in the last ten years. However, including older citations may
overemphasize the importance of old introductions compared to newly introduced
insects. In contrast, the number of publications worldwide is a good indicator of the
importance of the insect worldwide. However, this variable is not only influenced by
the impact of an insect, but also by its distribution. Insects that have a worldwide
distribution or are pests in North America or Europe are more likely to be cited in a
higher number of publications than insects that are pests in other regions. The
distribution of the pest was not taken into account in our analysis but could be easily
incorporated in future analyses.
However, because a correlation between the number of websites and economic impact
is not certain and would need to be investigated, the outcome of these analyses has to
be treated with caution.
Recommendations for best practice
The trait analysis can not stand alone in a PRA. It can contribute to new modules for
impact assessments and should thus be part of a generic integrated model. Such a
model is being developed in task 2.5 of PRATIQUE and the usefulness and
integration of the species traits analysis should be further evaluated in this task.
Environmental impact
We found a significant influence of the reproductive mode on environmental impact.
We suggest including the following text into a tool kit that would be part of the
generic integrated model (being developed in task 2-5 of PRATIQUE):
-
Are potential host plants present in the natural flora of the PRA area? if yes:
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-
Is the pest a hemipteran species or does it reproduce asexually? if yes, there is
an increased risk for the native flora
However, we recommend treating this suggestion with caution, because it is based on
a small dataset, including both alien and native pests. A new analysis is being done
that focuses on alien insects only.
.
Economic impact
Based on the European dataset, we found no generic traits, but it became apparent,
that different traits are of importance in different sectors. Because the economic
impact of a pest will not only depend on its biological traits but also on the economic
importance of its host plants, this needs to be taken into account and could be
included as follows into a tool kit as part of the generic integrated model (being
developed in task 2-5 of PRATIQUE):
-
Is an economically important host plant for this pest grown in the PRA area? if
yes:
-
Specify which sectors are likely to be affected by this pest and pay attention to the
relevant traits for each sector separately.
-
The weight of the risk for each sector will depend on its economic importance and
should be part of the generic integrated model.
The development of the exact form of the tool kit and the decision whether our
findings are best included as notes into the PRA scheme will be part of task 2.5.
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Tables
Table 1: Species list with significant traits in the analysis and environmental impact
score
Order
Family
Genus
Species
Coleoptera
Buprestidae
Scolytidae
Curculionidae
Agrilus
Dendroctonus
Rhinocyllus
planipennis
frontalis
conicus
Hemiptera
Adelgidae
Adelges
Adelges
Pineus
Aphididae
Elatobium
piceae
tsugae
boerneri
coloradensis
abietinum
Asexual?
no
no
no
Status (a/n=
alien/native)
a
n
a
Impact
Score
4
4
4
yes
yes
yes
yes
yes
a
a
a
n
a
5
9
8
4
4
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Eriococcidae
Margarodidae
Ortheziidae
Cryptococcus
Icerya
Orthezia
Diprion
Neodiprion
Tenthredinidae Pristiphora
Hymenoptera Diprionidae
Lepidoptera
fagisuga
purchasi
insignis
yes
yes
yes
a
a
a
5
10
4
pini
sertifer
erichsonii
no
no
yes
a
n
n
2
2
4
no
no
no
no
a
n
n
n
2
3
2
4
no
n
4
no
no
no
no
no
no
no
no
no
no
no
no
no
no
no
n
n
n
n
a
n
n
n
n
a
n
n
n
n
n
1
4
2
8
10
3
3
2
2
5
2
4
2
3
3
cunea
pomelaria
piniaria
fiscellaria
fiscellaria
Lambdina
fiscellaria
lugubrosa
Operophtera
brumata
disstria
Lasiocampidae Malacosoma
neustria
parallela
Lymantria
dispar
Lymantriidae
Orgyia
pseudotsugata
Notodontidae Heterocampa guttivita
Lochmaeus
manteo
Thaumetopoea pityocampa
Cactoblastis
cactorum
Pyralidae
Acleris
gloverana
Tortricidae
Choristoneura fumiferana
lambertiana
occidentalis
pinus
Arctiidae
Geometridae
Hyphantria
Alsophila
Bupalus
Lambdina
Table 2: Description of possible environmental impact categories
1) herbivory on native plants
0
no impact; herbivore not feeding on native plants
1
plant material is removed
fitness of native host plants (growth, reproduction) not affected
population structure of native host plants unchanged
native plant community structure unchanged
2
occasionally reduced fitness of plants (growth, reproduction)
population structure of native host plants unchanged
native plant community structure unchanged
3
regularly reduced fitness of plants (growth, reproduction)
increased mortality of host plants occasionally observed
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population structure of native host plants may be affected
native plant community structure unchanged
4
fitness of plants (growth, reproduction) is reduced to the extent that population
structure is changed or
regularly increased mortality of host plants
native plant community structure changes, but no species disappeared yet
5
very high mortality of native host plants
massive changes in community structure
native host plant species disappear
native host plant may be replaced by newcomers (invasive plants)
2) plant pest is vector of plant diseases
0
organism is not transferring plant diseases
1
organism transmits disease occasionally, but no consequences for native host plants
are known
2
organism transmits disease occasionally
disease can affect growth/reproduction of native host plants
3
the transmitted disease regularly affects fitness of native host plants
mortality of native host plants occasionally increased
4
mortality of native host plants regularly increased
population and community structure may change
5
massively increased mortality of native host plants
massive changes in community structure
native host plant species disappear
native host plant may be replaced by newcomers (invasive plants)
3) plant pest competes with native relative that is not a pest
0
no competition known or observed
1
exploitation competition with native species occasionally observed
interference with native competitors occasionally observed
2
exploitation competition or interference with native species is regularly observed,
without impact on native species
3
exploitation competition or interference is common and native species population
may be negatively affected (increased mortality)
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4
exploitation competition or interference is widespread and negative effects on native
species population and community is observed (native species displaced)
5
community structure is altered due to exploitation competition or interference
between the new pest and native herbivore,
native herbivores threatened
4) plant pest promotes predator/parasitoid of native herbivores
0
no impact
1
a native predator is observed to occasionally prey on new plant pest
but no effect on predator density
2
predation is common, positive effect on predator density likely
3
predator density increased and higher predator pressure on native herbivore is
observed
4
population size of native herbivore species is reduced due to increased predation and
plant
5
native herbivore species is being extinguished
5) plant pest affects ecosystem (nutrient cycle, water quality, seed dispersal etc.)
0
no impact found
1
occasional signs that ecosystem processes such as pollination, seed dispersal, nutrient
cycles, water purification or decomposition are affected, but no changes in ecosystem
functioning observed
2
frequent signs that ecosystem processes such as pollination, seed dispersal, nutrient
cycles, water purification or decomposition are affected, but no changes in ecosystem
functioning observed
(e.g. decomposition rates are changed in patches, soil chemistry slightly changed, but
isolated, reduced pollination or seed dispersal is observed, but plant fitness not
affected)
3
ecosystem processes are altered to an extent, that occasionally, the ecosystem
functions
are
changed
(e.g. changes in nutrient cycles / decomposition reach outside the infested area, but
changes are too small to have impacts there; reduced pollination or seed dispersal
negatively affects plant fitness)
4
ecosystem processes altered constantly
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ecosystems functions are not reliable anymore
erosion
persistent changes in nutrient cycling
5
disruption of ecosystem processes and services, system collapsing
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Table 3: List of species traits considered for the economic impact analysis. The
information for each species were retrieved from CPC
(www.cabi.org/compendia/cpc/) and Cab Abstracts (www.cabi.org), websites
consulted in spring 2009.
i) feeding behaviour
Host specificity (3 classes)
- monophagous = species feeding on hosts of 1 genus
- oligophagous = species feeding on hosts of 1 family
- polyphagous= species feeding on hosts from more than 1 family
Plant part
- feeding on vegetative parts of host plant
- feeding on reproductive parts of host plant
Feeding niche (2 classes)
- internal
- external
Feeding place (2 classes)
- above ground feeding
- below ground feeding
Feeding mode
- chewing
- sucking
ii) population growth capacity
Asexual reproduction (yes or no)
- Includes all species being capable of asexual reproduction (yes), all other species have
exclusively sexual reproduction (no)
Fecundity
- 1 = less than 200 eggs per female during her lifetime (average)
- 2 = equal to, or more than 200 eggs per female during her lifetime (average)
Mean number of generations per year in Europe (3 classes)
- 1 = one generation per year or less
- 2 = more than 1 and up to 3 generations per year
- 3 = more than 3 generations per year
Longevity of adults (3 classes)
- 1 = one month or less
- 2 = between 1 and 4 months
- 3= more than 4 months
Adult size (3 classes)
- 1 = smaller than 5 mm
- 2 = between 5 an 9,9 mm
- 3 = equal to or larger than 10mm
Affects photosynthesis (yes or no)
- Defoliators (excluding leaf miners and gall makers) and species with a diagnostic record as
honeydew producers in CPC are considered to affect the photosynthetic rate of their host
plant (yes), all other species are classified as not affecting it (no)
iii) pest history
Alien elsewhere
- A species is classified as being alien elsewhere, if it is recorded as an alien species
elsewhere than in Europe (yes), else, it is not considered alien elsewhere (no)
Native pest (yes, no, cryptogenic)
- A species is considered a pest in its native region, if the area of origin is known and if a
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-
-
search in CAB Abstracts identified at least one publication from the last 40 years describing
the species as a pest in its region of origin (yes)
If the origin of the species is known and if a search in CAB Abstracts failed to identify a
publication from the last 40 years describing the species as a pest in its region of origin, the
species is considered to be not a pest in its native region (no)
if the origin of a species is not known, the species is classified as cryptogenic
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Table 4: Definitions of scoring levels for economic impact of insect plant pests,
following method 1. The impact was scored based on the description in the Cab
Compendium (www.cabi.org/compendia/cpc/), consulted in spring 2009. The
following descriptions were decisive for the categorization:
unknown
-
no study found on economic impact
species has no record in CPC
0)
-
no economic impact observed in a study
species has a record in CPC, but no impact record at all
1) minor
-
is a minor pest in a crop or a region
is a minor pest in its native range
causes 1% to 5% reduction of yield, weight, etc.
did not usually cause severe damage
seldom of economic importance
little is known on the economic impact of this pest in its indigenous
area
is not of particular importance
may damage
has an impact record in CPC, but without description of
seriousness or even references with quantifications
is a secondary pest
2) medium
-
can be serious pest
> 5% and up to 25% reduction of yield, weight, etc.
tree vigour decreased
attacks on branches and leaves lead to leaf fall, and possibly to
complete dieback
only locally damaging
substantial losses may occur
is an important and common pest, although losses have not been
quantified
is intermittently injurious
is an important secondary pest
-
> 25% reduction of yield, weight, etc.
is a major pest
is a serious pest
causes significant yield/quality loss
is of primary importance
among most common and serious pests in the region xx industry
cause the most damage
loss of large amounts of raw materials
is one of the most destructive cerambycid pests
causes dieback
trees die
very toxic to the leaves, twigs, branches and fruit
can infest and kill whole trees and plantations
serious damage was recorded
was recorded devastating orchards, killing even large trees
-
3) major
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-
is an important economic pest
is a common and highly injurious pest
is a key pest
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Table 5: Relationships between economic impact and traits of plant pests using the
European database. Given are the error probabilities (p values) for the fixed variable
of the models. Values below 0.1 are shown in bold and those with a negative
relationship are shown in Italics. Order was included as random variable in the models
to account for phylogenetic relationship.
Agriculture
Forest
Orchard
Ornamental
Storage
Overall
(n=61)
(n=35)
(n=79)
(n=47)
(n=21)
(n=176)
Host specificity
0.99
0.39
0.35
0.00
0.27
Gregariousness
0.13
0.41
0.66
0.03
0.50
0.87
0.74
0.30
0.39
0.82
0.88
0.96
0.40
0.17
0.84
0.91
0.75
0.64
0.68
0.50
0.57
0.67
0.08
0.53
0.88
Reproductive mode
0.19
0.66
0.83
0.65
0.75
Fecundity
0.14
0.44
0.81
0.04
0.02
0.90
Generation per year
0.80
0.46
0.01
0.03
0.61
0.14
Adult longevity
0.31
0.48
0.99
0.80
0.08
0.40
Adult size
0.18
0.56
0.12
0.50
0.75
0.72
iii) Alien elsewhere
0.05
0.48
0.00
0.00
0.43
0.00
Native Pest
0.77
0.98
0.54
0.59
0.88
0.08
Vector
0.52
0.54
0.67
0.57
0.24
0.41
Photosynthesis affecting
0.09
0.02
0.28
0.01
Trait
i)
Plant part (vegetative or
reproductive)
Feeding niche (internal
or external)
Feeding place (above
or below)
Feeding mode (sucking
or chewing)
ii)
0.00
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Table 6. Error probabilities (p values) of species traits obtained in univariate analysis
using the database on 100 alien insects in Switzerland. Values below 0.05 are shown
in bold. Those with a negative relationship are shown in Italics. Traits with an asterix
(*) are significant for all 3 dependant variables.
Trait
Swiss
Websites
Dependant variables
No. of Swiss
No. of world
publications
publications
Asexual
0.450
0.157
0.012
Fecundity
0.006
0.052
<0.001
Generation
0.500
0.042
<0.001
Specificity (*)
0.021
0.041
<0.001
Longevity
0.071
0.030
0.039
FeedingNiche
0.236
<0.001
0.129
Size
0.583
0.874
<0.001
GenusPresent (*)
0.002
0.003
<0.001
InvasiveElsewhere
0.352
0.308
<0.001
<0.001
0.206
<0.001
0.007
<0.001
<0.001
NativePest
Vector (*)
Table 7: Best fitting model for Swiss Websites, using the database of 100 insects
alien in Switerland. Eleven traits were included as fixed factors and then the number
of fixed factors was reduced in a stepwise approach, selecting the minimum adequate
model by minimising Akaike’s Information Criterion (AIC).
Factor
Estimate Std. Error
z value
Pr(>|z|)
Intercept
0.051
0.510
0.099
0.921
NativePest
0.425
0.102
4.157
0.000
GenusPresent
-0.455
0.150
-3.040
0.002
Vector
0.338
0.156
2.169
0.030
Fecundity
0.278
0.137
2.033
0.042
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Table 8: Best fitting model for Swiss CAB publications, using the database of 100
insects alien in Switzerland. Eleven traits were included as fixed factors and then the
number of fixed factors was reduced in a stepwise approach, selecting the minimum
adequate model by minimising Akaike’s Information Criterion (AIC)
Factor
Estimate
Intercept
-0.202
Vector
1.707
FeedingNiche
0.236
Fecundity
-0.436
Longevity
-0.285
NativePest
-0.271
Size
-0.262
Generation
-0.191
Std. Error
0.673
0.193
0.068
0.190
0.132
0.137
0.137
0.124
z value
-0.301
8.823
3.475
-2.294
-2.166
-1.978
-1.913
-1.536
Pr(>|z|)
0.763
0.000
0.001
0.022
0.030
0.048
0.056
0.125
Table 9: Best fitting model for worldwide CAB publications, using the database of
100 insects alien in Switerland. Eleven 11 traits were included as fixed factors and
then the number of fixed factors was reduced in a stepwise approach, selecting the
minimum adequate model by minimising Akaike’s Information Criterion (AIC)
Factor
Estimate Std. Error
z value
Pr(>|z|)
Intercept
-4.695
0.379
-12.4
<0.001
<0.001
Asexual
-0.215
0.021
-10
<0.001
Fecundity
0.354
0.022
15.76
<0.001
FeedingNiche
0.162
0.009
17.2
<0.001
Generation
0.713
0.016
45.83
<0.001
GenusPresent
-0.572
0.022
-25.74
<0.001
InvasiveElsewhere
1.061
0.042
25.44
<0.001
Longevity
-0.186
0.014
-13.47
<0.001
NativePest
0.774
0.016
47.14
<0.001
Size
0.725
0.018
39.45
<0.001
Specificity
0.532
0.017
31.4
<0.001
Vector
1.705
0.017
98.32
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Plants
Introduction
Invasive species can threaten biological diversity in various ways; environmental
impacts include reducing the species richness of native biota, changing the genetic
variation of native populations and altering habitat and ecosystem functioning (Hulme
2007, Vilà et al. 2009). However, despite recently increasing awareness of the
necessity to collect better data on impacts and a number of published studies (e.g.
Levine et al. 2003, Liao et al. 2007, Hejda et al. 2009, DAISIE 2009), the information
on impacts of biological invasions is still rather scarce and so is the understanding of
the determining mechanisms. For example Europe has the most up-to-date
information on numbers of aliens and their impacts (Vilà et al. 2009). Yet, of the
5,789 plant species alien to this continent registered in the DAISIE database
(Lambdon et al. 2008, DAISIE 2008), ecological impacts are only documented for
326 (5.3%) of of these species (Vilà et al. 2009).
The present study is based on a review of available data on invasive plants globally
and focuses on their ecological and environmental impacts. It aims at quantifying the
frequency of significant and non-significant impacts on a number of population,
community and ecosystem characteristics at various trophic levels. Further, invasion
biology is still at the stage of describing the patterns of impact and mechanisms acting
in individual invasions, and little has been known about the effects of underlying
factors, including species traits (but see Shirley & Kark 2009). Here we evaluate the
effect of species traits of invasive plants in a given environmental and geographical
context (e.g., invaded habitat, biome and geographical region).
Materials and methods
Data
Classification of impacts
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We screened 296 studies (Appendix 1) addressing impact of invasive plant species. A
hierarchical classification of impact was adopted. On the first level, the impact was
classified according to the characteristics of the invaded plant and animal populations
and communities and their environment into the following groups: (A) impact on
plant population and communities in invaded sites; (B) impact on populations and
communities of animals associated with the invaded environment; (C) impact on soil
characteristics; and (D) impact on disturbance regime (Table 1). On the second level,
impact was classified within each of these categories, using a more detailed scheme
with 10 characteristics in group (A), seven in (B), nine in (C) and one in (D). These
characteristics were further classified as to whether the impact was on populations,
communities and ecosystems (Table 1).
Only studies in which the effect of invasion on the characteristics addressed was
statistically tested were considered, and it was recorded whether the impact of
invasion on a given characteristic was non-significant or significant. If multiple
characteristics of the invaded community or environment were addressed in a single
study (e.g., effect on species richness and diversity), they were all considered as
individual cases and included into the analysis. In total, the data set included 1551
individual cases of a statistically tested impact of a plant invasion of which 412 were
attributed to (A), 203 to (B), 876 to (C) and 60 to (D).
Invasive plant species
For each case of invasion, the information on the following traits of invading species
was obtained using regional floras, checklists of invasive species, global compendia
(e.g., Weber 2003) and internet sources:




Taxonomic affiliation (genus, family, subclass, class)
Region of origin: Europe (n = 541 cases), Asia (692), North America (207),
Central America (67), South America (91), Africa (265), Australasia (95),
Pacific region (30), hybrid origin (12)
Life form: annual grass (170), perennial grass (285; incl. 4 Cyperaceae),
annual herb (256), perennial herb (808, incl. 14 biennial), shrub (347), tree
(341), vine (44)
Height, taken as an average value of minimum and maximum height (in m;
data available for n = 1551 cases)
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








Reproduction: only generative (727), only vegetative (141), both (682)
Seed size (in m; n = 907)
Defense: presence of thorns, spikes and other mechanical defensive structures
(yes=152/no)
Toxicity: (yes = 266/no)
Nitrogen fixation: (yes = 264/no)
Pollination: self (355), wind (640), insect (1125), water (94)
Dispersal: wind (695), water (478), endozoochory (422), exozoochory (361),
self (80),
Breeding: mating partner needed yes (exclusively allogamous, n = 379)/no
(apomictic and/or self-compatible, n = 608)
Flowering period (in months; n = 1373)
For each case of invasion, the following site characteristics were recorded
 Region invaded (see Fig. 1): Old World (n = 487 cases, including Europe 327,
Asia 53, Africa 107) vs. New World (n = 1064, including North America 721,
South America 77, Australasia 126, Pacific 140).
 Biome invaded: temperate (925), mediterranean (302), subtropical (141),
tropical (183)
 Insularity: (yes = 173/no)
 Habitat invaded: riparian (209, including coastal), arid (23), grassland (657),
shrubland (226), woodland (441), rocky (62), anthropogenic (90)
The data set included 173 invasive plant species from 51 families (Table 2). In terms
of geographical distribution of invaded regions, 47% of cases studied come from
North America, 21% from Europe, 9% from islands in Indian and Pacific Ocean, 8%
from Australasia, 7% from Africa, 5% from South and Central America and 3% from
Asia (Fig. 1). This reflects previously reported geographical biases in invasion
ecology (Pyšek et al. 2008) and focus on studies on impact especially in North
America (Levine et al. 2003, Vilà et al. 2009). In terms of life forms, there were 30
annual (11.2%), 4 biennial (1.6%) and 146 perennial (54.7%) herbaceous plants, 42
shrubs (15.7%), 39 trees (15.0%) and 5 vines (1.9%) among the species studied.
Statistical analysis
Impact score (significant or non-significant) was the response variable. To make the
effect of individual species in analyses comparable, the impact score for each species
was weighted by the number of records of the species in the analysis and by the
number of species in a study (if a single study explored the impact of several invasive
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species on e.g., native species diversity). Impact type (Level 2: impact on individual
characteristics, see Table 1), taxonomic affiliation (genus, family, order, subclass),
and species and site characteristics were the explanatory variables. Classification trees
were used for the analyses, due to their flexibility and robustness, invariance to
monotonic transformations of predictor variables, their ability to use combinations of
explanatory variables that are either categorical and/or numeric, ability to deal with
nonlinear relationships and high-order interactions, and ability to treat missing data
which appeared for some of the explanatory variables (De’ath & Fabricius, 2000).
The classification trees were constructed in CART v. 6.0 (Breiman et al. 1984,
Steinberg & Colla 1995) by binary recursive partitioning, using the default “Gini”
impurity measure as the splitting index. To determine the optimal tree, a sequence of
nested trees of decreasing size, each of them being the best of all trees of its size, were
constructed, and their resubstitution relative errors were estimated. Ten-fold crossvalidation was used to obtain estimates of cross-validated relative errors of these trees.
These estimates were then plotted against tree size, and the optimal tree chosen based
on the 1–SE rule, which minimizes cross-validated error within one standard error of
the minimum (Breiman et al. 1984). Following De’ath & Fabricius (2000), a series of
50 cross-validations were run, and the modal (most likely) single optimal tree chosen
for description. The quality of the chosen tree was evaluated as the overall
misclassification rate by comparing the misclassification rate of the optimal tree with
misclassification rate of the null model (De’ath & Fabricius 2000), and using crossvalidated samples (Steinberg & Colla 1995) as specificity (i.e. the ability of the model
to predict that the impact is not significant when it is not) and sensitivity (the ability
of the model to predict that the impact is significant when it is) (Bourg et al. 2005).
Because high categorical explanatory variables have higher splitting power than
continuous variables, to prevent these variables to have inherent advantage over
continuous variables, penalization rules for high category variables (Steinberg &
Colla 1997, p. 88) were applied. Similarly, explanatory variables with missing values
have advantage as splitters. Consequently, these variables were first penalized in
proportion to the degree to which their values were missing, and then treated by backup rules using surrogates that closely mimicked the action of the missing primary
splitters (Steinberg & Colla 1997).
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The optimal tree was represented graphically, with the root standing for undivided
data at the top, and the terminal nodes, describing the most homogeneous groups of
data, at the bottom of the hierarchy. The quality of each split was expressed by its
improvement value, corresponding to the overall misclassification rate at the node,
with high scores of improvement values corresponding to splits of high quality. In
graphical representation, vertical depth of each node was expressed as proportional to
its improvement value. Vertical depth of each node thus represented a value similar to
explained variance in a linear model. Surrogates of each split, describing splitting
rules that closely mimicked the action of the primary split, were assessed and ranked
according to their association values, with the highest possible value 1.0
corresponding to the surrogate producing exactly the same split as the primary split.
Using weighted values, which were expressed for each species as fractions decreasing
with increasing number of replicates of the species in the analysis, the minimum size
of each terminal node was limited to one.
Results and discussion
Frequency of significant and non-significant impacts
In the majority of cases studied, invasion caused a significant change in the
characteristics studied. For pooled data across the 1551 cases, the impact was
significant in 982 cases (63.3%) and non-significant in 569 cases (36.7%). In more
than one third of cases, invasive plants do not exert significant impact on invaded
populations, communities and ecosystems. However, it needs to be borne in mind that
a non-significant result does not necessarily mean that there is no impact and can be
due to insufficient number of replicates hence a low test power, inappropriate
experimental design, etc. The proportion of significant impacts was highest on plants
(76.2%), followed by soil (57.8%) and animals (50.2%). The impact of invasive plants
on fire regime, which was the only disturbance type for which enough data were
available to be analysed, was always significant (Fig. 2).
The impact of invasive species is often labeled as “negative” or “positive”. However,
this assessment brings about interpretation difficulties. For the effects on native plants
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and animals, this is relatively straightforward; reduced values in population and
community characteristics (Table 1) imply decreased vigour and population status of
affected native biota. However, for soil characteristics, an increase in, e.g. soil
nutrients, may not necessarily mean an improved state of the affected ecosystem. For
example, in oligotrophic ecosystems increased nutrient status may lead to further
invasion (Vitousek et al. 1987, Vitousek & Walker 1989). In the same vein, an
increase in the frequency of fires, i.e. the change in the natural fire regime, which
often supports the invasive species (D’Antonio & Vitousek 1992), may have a
negative effect on ecosystem functioning. Much depends also on the way impacts are
measured. Invasive alien species could be considered as increasing biodiversity (since
the new species adds to species lists) and invasions rarely lead to extinctions. Impacts
can also appear differently depending on perspective. Thus, Impatiens glandulifera
has an attractive flower, grows in areas that are already invaded or where native
vegetation primarily consists of weeds (Hulme & Bremner, 2005) and provides a
copious nectar source for bees. However, it also invades nature reserves, can attract
bees away from native flowers (Chittka & Schurkens, 2001) and may enhance erosion
of river banks2. Therefore, we refrain from an attempt to classify impacts as “positive”
or “negative” and only focus on whether the invasion by alien plants causes a
significant impact or not. Nevertheless, all the species studied are considered to be
invasive aliens and therefore threaten biological diversity in some way.
Ranking of species according to significant impact
Using a simple measure of the total number of significant impacts caused by an
invading species relative to the total number of cases in which its impact was studied
provides a ranking of invasive plant species according to their effect on invaded biota
and environment. There were 38 species that were studied frequently enough (at least
10 cases) and their impact was more often significant than not (Fig. 3). All life forms
are represented among this group: annual grasses (Aegilops triuncialis, Bromus
tectorum), annual herb (Alliaria petiolata), perennial grasses (Typha ×glauca,
Cortaderia selloana, Phragmites australis, Hyparrhenia rufa, Elymus junceus,
2
http://www.europe-aliens.org/speciesFactsheet.do?speciesId=17367#
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Melinis minutiflora, Andropogon bladhii, Agropyron cristatum, Ehrharta calycina),
perennial herbs (Lepidium latifolium, Hieracium pilosella, Solidago gigantea,
Fallopia sachalinensis, Euphorbia esula, Lythrum salicaria), shrubs (Elaeagnus
umbellata, Lonicera maackii, Myrica faya, Lantana camara, Acacia cyclops, Acacia
saligna, Tamarix ramosissima) and trees (Acer platanoides, Acacia longifolia,
Falcataria mollucana, Sapium sebiferum, Melaleuca quinquenervia, Pseudotsuga
menziesii, Ailanthus altissima, Pinus radiata, Robinia pseudacacia). Nevertheless,
among the 15 species in which the proportion of negative impacts exceeds 75% of
cases studied, woody species prevail, represented by 10 species (Fig. 3).
Factors determining the significance of impacts
For pooled data (Fig. 4), the invasion was more likely to exert a significant impact on
litter decomposition, biomass production of native plants, animals and soil biota,
establishment of native plant species, plant community cover, fire frequency and
activity of animals; these characteristics were significantly affected in 92.4% of cases
(Terminal node 1). The remaining characteristics were significantly impacted in
62.8% of cases and the proportion of significant impacts in this group depended on
taxonomy. Plants from the subclasses Commelinidae, Dilleniidae, Hamamelidae,
Liliidae, Magnoliidae and Pinopsida had more often a significant effect in the
mediteranean biome (Terminal node 2) than in temperate, subtropical and tropical
biomes. In the latter biomes, shrubs caused more often a significant impact than other
life forms (Terminal nodes 3 and 4). The impact of plants from the subclasses
Asteridae, Caryophyllidae, Rosidae and Zingiberidae depended on their height; a
significant impact was least likely to occur when invading species was shorter than
4.8 m (45.8% of cases, Terminal node 5) and most likely (94.4%) for those with
height between 4.8 and 13.3 m (Terminal nodes 6 and 7).
The taxonomic pattern suggests that species from subclasses, some of them
evolutionarily advanced (e.g. Asteridae, Rosidae), that were previously reported to
include a high proportion of invasive plants (Pyšek 1998) tend to cause significant
impacts regardless of environmental settings (biome invaded) and life form, and the
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only trait that affects whether or not their impact will be significant is height. Height
is a trait closely associated with invasiveness (Pyšek & Richardson 2007) and this
study indicates that it is also one of the few affecting the impact. Therefore, the
factors driving ability to invade and that to exert impact may not differ. That there is a
range of heights, within which the impact of invading plant species is likely to be
more often significant, implies that the impact of tree invaders (height of which
generally exceeds the threshold of 4.8 m) on some characteristics of invaded systems,
namely those related to diversity, is less severe than impact of shrub invaders that
supress native species by means of a rapidly built high cover. On the other hand the
shrub life form, with majority of representatives within the range of heights with a
high likelihood of significant impacts, also determines the significance of impact
among the other subclasses in all biomes except the Mediterranean. The nonsignificant effect of life form in the Mediterranean biome can be attributed to a high
diversity of native shrubs in this biome which probably results in less effective impact
of this life form due to the competition from native vegetation.
Conclusions
1. So far impact has been rigorously studied for only a fraction of invasive plant
species globally. A previous analysis, based on screening of the Web of Science,
identified that there are at least 395 alien plant species, most of them invasive, that
were subjects of ecological case studies (Pyšek et al. 2008). Our analysis identified
173 species, i.e. less than a half of that number, for which the impact of invasive plant
species was tested. However, the frequency distribution of species with respect to the
intensity of research on impact is strongly skewed, with the 21 most intensively
studied species (Table 2) accounting for 50% of all cases testing impact on individual
characteristics (Table 1). Therefore, our knowledge of how the most invasive plant
species affect recipient biota and the invaded environment in target regions results
from studies of a limited number of species and may be biased in a similar way as that
of the ecology of invasive species in general (Pyšek et al. 2008). Moreover,
considering all alien species with a potential to exert impact in invaded regions, the
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information is only available for about 5% of such plant species in Europe (Vilà et al.
2009).
2. By considering impacts on various characteristics of invaded communities and
ecosystems and different trophic levels, our study shows that an invasive plant species
might have a significant impact on some characteristics and non-significant on others.
This points to the fact that there is no universal measure of impact and it is crucial
how the impact is defined in a concrete study.
3. Invasive plant species differ greatly in the significance of their impacts which is
negative in the majority of cases. Nevertheless, among the species exerting negative
impacts in more 75% of cases studied, two thirds are woody species. This life form
should be therefore paid serious attention in risk-assessment procedures. Among
woody species, shrubs and short trees, from evolutionary advanced taxonomic groups,
up to ca 13 m tall are most likely to have a significant impact on most characteristics
of invaded populations, communities and ecosystems, including native species
diversity.
4. Whether the effect of invasive plants is significant or not depends on both species
traits (phylogenetic affiliation, life form and plant height) and the invaded
environment (biome in which the invasion occurs). Height and life form are traits that
are also known to affect invasiveness; although further research is needed to confirm
this. Our analysis suggests that traits determining invasiveness may be similar to those
determining impact. This has potentially important practical consequences since the
determinants of invasiveness have been studied much more intensively so far than
those of impact; this is due to the availability of data (which is still rather scarce on
impact) and research focus (impacts have only started to be intensively studied
recently). If both invasiveness and impact are associated with a similar suite of traits,
the body of information available from screening systems addressing invasiveness
would be also applicable to impact.
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Bourg N.A., McShea W.J. & Gill D.E. (2005) Putting a CART before the search:
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Tables and figures
Table 1. Overview of characteristics on which the impact of invasive plants was addressed in the 296
studies considered. Four groups of impact: (A) on native plants, (B) animals associated with invaded
vegetation, (C) soil characteristics and (D) disturbance regime, were considered. The activity of native
animals includes e.g., nesting activities, pollinator visits etc.
Impacted characteristic
Abundance of native biota
Species richness of native biota
Species diversity of native biota
Productivity of native biota
Community cover
Fecundity of native biota
Seedling establishment
Mortality/survival of native biota
Contents of minerals in plant tissues
Nutrient contents in plant tissues
Mineral contents of soil
Activity of native animals
Water contents in soil
Nutrient content in soil
pH
Rate of litter decomposition
Frequency of fires
A. Plants
15
95
65
105
27
20
16
9
36
24
B. animals
85
37
25
10
C. Soil
31
9
4
45
D. Disturbance
8
20
242
18
22
436
62
25
60
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Table 2. List of invasive plant species that were studied with respect to impact. The numbers (N)
indicate total number of cases in which the impact on various characteristics was studied, separated into
significant and non-significant results; multiple characteristics in a study were considered as cases.
Species are ranked according to the decreasing number of cases studied.
Species
Bromus tectorum
Fallopia japonica
Acacia saligna
Solidago gigantea
Chrysanthemoides monilifera
Heracleum mantegazzianum
Phragmites australis
Lythrum salicaria
Agropyron cristatum
Robinia pseudoacacia
Falcataria molluccana
Euphorbia esula
Hieracium pilosella
Melinis minutiflora
Tamarix ramosissima
Impatiens glandulifera
Pelargonium capitatum
Prunus serotina
Mesembryanthemum crystallinum
Lepidium latifolium
Eleagnus umbellata
Hyparrhenia rufa
Lonicera maackii
Rosa rugosa
Aegilops triuncialis
Melaleuca quinquenervia
Pinus radiata
Acer platanoides
Centaurea maculosa
Elymus junceus
Acacia cyclops
Acacia longifolia
Ailanthus altissima
Andropogon bladhii
Lantana camara
Myrica faya
Spartina anglica
Tradescantia fluminensis
Cytisus scoparius
Ehrharta calycina
Mimosa pigra
Pseudotsuga menziesii
Typha x glauca
Alliaria petiolata
Fallopia sachalinensis
Poa pratensis
Family
Poaceae
Polygonaceae
Mimosaceae
Asteraceae
Asteraceae
Apiaceae
Poaceae
Lythraceae
Poaceae
Fabaceae
Fabaceae
Euphorbiaceae
Asteraceae
Poaceae
Tamaricaceae
Balsaminaceae
Geraniaceae
Rosaceae
Aizoaceae
Brassicaceae
Elaeagnaceae
Poaceae
Caprifoliaceae
Rosaceae
Poaceae
Myrtaceae
Pinaceae
Sapindaceae
Asteraceae
Poaceae
Mimosaceae
Mimosaceae
Simaroubaceae
Poaceae
Verbenaceae
Myricaceae
Poaceae
Commelinaceae
Fabaceae
Poaceae
Mimosaceae
Pinaceae
Typhaceae
Brassicaceae
Polygonaceae
Poaceae
Life form
an
per
shr tree
per
shr
per
per
per
per
tree
tree
per
per
per
shr
an
shr
shr tree
an per
per
shr tree
per
shr vine
shr
an
tree
tree
tree
per
per
shr tree
tree
tree
per
shr
shr tree
per
per
shr
per
shr vine
tree
per
an per
per
per
N Significant Non-significant
118
76
42
94
38
56
62
35
27
56
35
21
37
15
22
36
6
30
36
26
10
34
17
17
32
17
15
32
17
15
30
25
5
25
13
12
25
23
2
25
14
11
25
14
11
21
10
11
20
8
12
20
8
12
19
7
12
18
18
0
17
17
0
17
12
5
17
15
2
16
1
15
15
14
1
15
11
4
15
9
6
14
13
1
14
9
5
14
9
5
13
10
3
13
12
1
13
8
5
13
8
5
13
11
2
13
11
2
13
6
7
13
7
6
12
11
1
12
6
6
12
5
7
12
8
4
12
12
0
11
7
4
11
6
5
11
4
7
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Sapium sebiferum
Ageratina adenophora
Cortaderia selloana
Delairea odorata
Chromolaena odorata
Lupinus luteus
Microstegium vimineum
Pennisetum setaceum
Berberis thunbergii
Mikania micrantha
Mimulus guttatus
Oxalis pes-caprae
Senecio jacobaea
Spartina alternifolia
Bromus inermis
Elymus athericus
Hedychium gardnerianum
Rhamnus cathartica
Solidago canadensis
Agropyron repens
Arundo donax
Carpobrotus edulis
Fraxinus uhdei
Hymenachne amplexicaulis
Melilotus alba
Melilotus officinalis
Orbea variegata
Pennisetum polystachion
Phragmites communis
Pinus sylvestris
Schizachyrium condensahtm
Agrostis stolonifera
Bromus japonicus
Cenchrus ciliaris
Fallopia ×bohemica
Lupinus polyphyllus
Populus tremuloides
Brassica nigra
Carex kobomugi
Centaurea diffusa
Cinchona pubescens
Lespedeza cuneata
Lolium multiflorum
Lonicera japonica
Lupinus arboreus
Morella cerifera
Phalaris arundinacea
Pinus caribaea
Prosopis glandulosa
Taraxacum officinale
Agave americana
Euphorbiaceae
Asteraceae
Poaceae
Asteraceae
Asteraceae
Fabaceae
Poaceae
Poaceae
Berberidaceae
Asteraceae
Scrophulariaceae s. l.
Oxalidaceae
Asteraceae
Poaceae
Poaceae
Poaceae
Zingiberaceae
Rhamnaceae
Asteraceae
Poaceae
Poaceae
Aizoaceae
Oleaceae
Poaceae
Fabaceae
Fabaceae
Asclepiadaceae
Poaceae
Poaceae
Pinaceae
Poaceae
Poaceae
Poaceae
Poaceae
Polygonaceae
Fabaceae
Salicaceae
Brassicaceae
Cyperaceae
Asteraceae
Rubiaceae
Fabaceae
Poaceae
Caprifoliaceae
Fabaceae
Myricaceae
Poaceae
Pinaceae
Fabaceae
Asteraceae
Agavaceae
tree
shr
per
per vine
shr
an
an
per
shr
per
per
per
per
per
per
per
per
shr
per
per
per
per
tree
per
an bi
an bi
per
an per
per
tree
per
per
an
per
per
per
tree
an
per
an
tree
per
an per
shr vine
shr
shr tree
per
tree
shr
per
shr
11
10
10
9
9
9
9
9
8
8
8
8
8
8
7
7
7
7
7
6
6
6
6
6
6
6
6
6
6
6
6
5
5
5
5
5
5
4
4
4
4
4
4
4
4
4
4
4
4
4
3
9
7
8
9
6
2
8
8
6
6
3
5
1
7
7
6
2
4
3
4
6
6
6
3
4
4
4
5
5
3
5
5
4
5
5
2
3
4
3
0
4
4
3
4
1
4
2
4
4
2
2
2
3
2
0
3
7
1
1
2
2
5
3
7
1
0
1
5
3
4
2
0
0
0
3
2
2
2
1
1
3
1
0
1
0
0
3
2
0
1
4
0
0
1
0
3
0
2
0
0
2
1
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Andropogon virginicus
Juniperus pinchotii
Koelreuteria elegans
Lavatera arborea
Lonicera tatarica
Panicum maximum
Pinus contorta
Plantago lanceolata
Rhamnus frangula
Ulex europaeus
Ammophila arenaria
Andropogon guayanus
Aster novi-belgii
Bromus diandrus
Bromus madritensis
Bromus rubens
Carpinus betulus
Carpobrotus affine
Castanea crenata
Centaurea solstitialis
Cinnamomum verum
Cortaderia jubata
Eragrostis lehmanniana
Festuca arundinacea
Hedera helix
Helianthus tuberosus
Hordeum marinum gussonearum
Imperata cylindrica
Imperatoria ostruthium
Leucaena leucocephala
Ligustrum lucidum
Ligustrum sinense
Opuntia stricta
Pittosporum undulatum
Prosopis velutina
Pyracantha angustifolia
Quercus acutissima
Rosa mutliflora
Rubus discolor
Rudbeckia laciniata
Rumex alpinus
Salix x rubens
Schinus molle
Schismus barbatus
Urochloa mutica
Anthoxanthum odoratum
Anthriscus caucalis
Asparagus asparagoides
Avena barbata
Azorella monatha
Brachiaria mutica
Poaceae
Juniperaceae
Sapindaceae
Malvaceae
Caprifoliaceae
Poaceae
Pinaceae
Plantaginaceae
Rhamnaceae
Fabaceae
Poaceae
Poaceae
Asteraceae
Poaceae
Poaceae
Poaceae
Fagaceae
Aizoaceae
Fagaceae
Asteraceae
Lauraceae
Poaceae
Poaceae
Poaceae
Araliaceae
Asteraceae
Poaceae
Poaceae
Apiaceae
Mimosaceae
Oleaceae
Oleaceae
Cactaceae
Pittosporaceae
Fabaceae
Rosaceae
Fagaceae
Rosaceae
Rosaceae
Asteraceae
Polygonaceae
Salicaceae
Anacardiaceae
Poaceae
Poaceae
Poaceae
Apiaceae
Liliaceae
Poaceae
Apiaceae
Poaceae
per
shr tree
tree
shr
shr
per
tree
per
shr
shr
per
per
per
an
an
an
tree
per
tree
an
shr tree
per
per
per
shr vine
per
an
per
per
tree
shr tree
tree
shr
shr tree
tree
shr tree
tree
shr
shr
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per
shr tree
tree
an
per
shr
an
shr
an
shr
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3
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
3
3
2
3
2
3
1
3
1
3
2
2
1
2
2
2
2
1
1
1
1
2
2
2
0
1
2
2
1
2
2
0
1
2
2
0
1
1
2
1
2
2
2
2
0
1
1
1
1
0
1
0
0
1
0
1
0
2
0
2
0
0
0
1
0
0
0
0
1
1
1
1
0
0
0
2
1
0
0
1
0
0
2
1
0
0
2
1
1
0
1
0
0
0
0
2
0
0
0
0
1
0
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Bromus hordeaceus
Carduus nutans
Centaurea melitensis
Cryptomeria japonica
Cynara cardunculus
Dipsacus sylvestris
Erodium cicutarium
Hakea sericea
Holcus lanatus
Hypochoeris glabra
Lolium perenne
Medicago polymorpha
Myriophyllum spicatum
Pennisetum clandestinum
Phacelia tanacetifolia
Pinus halepensis
Poa annua
Psidium catleianum
Salsola kali
Schinus terebinthifolius
Stachytartheta jamaicensis
Tainiatherum asperum
Teline monspessulana
Trifolium pratense
Vincetoxicum rossicum
Poaceae
Asteraceae
Asteraceae
Taxodiaceae
Asteraceae
Dipsacaceae
Geraniaceae
Proteaceae
Poaceae
Asteraceae
Poaceae
Fabaceae
Haloragaceae
Poaceae
Hydrophyllaceae
Pinaceae
Poaceae
Myrtaceae
Chenopodiaceae
Anacardiaceae
Verbenaceae
Poaceae
Fabaceae
Fabaceae
Asclepiadaceae
an
bi
an
tree
per
bi
an
shr
per
per
per
an
per
per
an
tree
an per
shr tree
an
shr tree
shr
an
shr
per
per
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
1
1
1
1
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
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North America (721)
5%
3%
Europe (327)
Pacific islands (140)
7%
Australasia (126)
Africa (107)
8%
South and Central America (77)
Asia (53)
9%
47%
21%
Fig. 1. Geographical distribution of primary studies addressing the impact of invasive plants. Number
of cases (referring to test of impact on individual characteristics, with multiple characteristics all
considered; total n = 1551) is given in parentheses.
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120
Significant
100
Percentage of cases
Non-significant
80
60
40
20
0
A. Plants
B. Animals
C. Soil
D. Disturbance
Fig. 2. Frequency of significant and non-significant impacts as recorded in studies focused on the
impact of invasive plants on (A) native plants and vegetation, (B) native animals associated with
invaded vegetation, (C) soil characteristics and (D) disturbance regime.
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Typha x glauca
Eleagnus umbellata
Lepidium latifolium
Aegilops triuncialis
Acer platanoides
Acacia longifolia
Hieracium pilosella
Cytisus scoparius
Lonicera maackii
Myrica faya
Lantana camara
Falcataria molluccana
Sapium sebiferum
Cortaderia selloana
Acacia cyclops
Melaleuca quinquenervia
Phragmites australis
Hyparrhenia rufa
Ageratina adenophora
Pseudotsuga menziesii
Bromus tectorum
Elymus junceus
Centaurea maculosa
Alliaria petiolata
Solidago gigantea
Andropogon bladhii
Ailanthus altissima
Pinus radiata
Acacia saligna
Tamarix ramosissima
Melinis minutiflora
Fallopia sachalinensis
Tradescantia fluminensis
Robinia pseudoacacia
Agropyron cristatum
Euphorbia esula
Ehrharta calycina
Lythrum salicaria
0,0
10,0
20,0
30,0
40,0
50,0
60,0
70,0
80,0
90,0
Percentage of cases with significant impact
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Fig. 3. Species with the highest proportions of significant impacts recorded among all cases in which
the impact of a given species was studied. Only species with at least 10 cases were considered (see
Table 2 for complete data).
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significant 73.1
n.s.
26.9
N = 1551
40
1.0
0.9
35
0.8
30
0.7
25
0.6
20
0.5
0.4
15
0.3
10
0.2
5
0.1
0
Commelinidae
Dilleniidae
Hamamelidae
Liliidae
Magnoliidae
Pinopsida
Terminal node 1
Class
%
significant 92.4
n.s.
7.6
N = 306
subtrop
temperate
tropical
BIOME INVADED
Class
%
mediteranean
Terminal node 2
Class
%
significant 73.9
n.s.
26.1
N = 512
LIFE FORM
Class
%
shrub
significant 88.5
n.s.
11.5
N = 93
Cross-validation relative error
IMPACT TYPE 2
Class
%
abundance
diversity
fecundity
mineral
moisture
mortality/survival
nutrients
pH
richness
Frequency of tree
activity of animals
plant cover
establishment
frequency of fires
litter decomposition
biomass production
0.0
1
2
5
7
10
Size of tree
SUBCLASS
Class
%
significant 62.8
n.s.
37.2
N = 1245
Asteridae
Caryophyllidae
Rosidae
Zingiberidae
PLANT HEIGHT
Class
%
annual grass
perennial grass
annual herb
perrenial herb
vine
tree
significant 69.1
n.s.
30.9
N = 419
<= 4.8 m
Terminal node 5
Class
%
significant 45.8
n.s.
54.2
N = 564
Terminal node 3
Class
%
Terminal node 4
Class
%
significant 88.2
n.s.
11.8
N = 41
significant 63.8
n.s.
36.2
N = 378
> 4.8 m
significant 54.7
n.s.
45.3
N = 733
PLANT HEIGHT
Class
%
<= 13.3 m
significant 75.4
n.s.
24.6
N = 179
> 13.3 m
Terminal node 6
Class
%
Terminal node 7
Class
%
significant 94.4
n.s.
5.6
N= 64
significant 60.4
n.s.
39.6
N = 115
Fig. 4. Classification tree analysis of the common probability of significant ■ or nonsignificant □ impacts on plants, animals, soil and disturbance. Each node (polygonal
table with splitting variable name) and terminal node with node number shows table
with columns for the type of impact (significant or non-significant), the number of
cases weighted by the number of records of each species in the analysis and % of
these cases for each class. Below the table is the total number of cases (N,
unweighted) and graphical representation of the percentage of significant and nonsignificant cases in each class (horizontal bar based on weighted numbers). Except the
root node standing for undivided data at the top, there is splitting variable name and
split criterion above each node. Vertical depth of each node is proportional to its
improvement value that corresponds to explained variance at the node. Overall
misclassification rate of the optimal tree is 30.4%, compared to 50% for the null
model; specificity (ability to predict that the impact is not significant when it is not) =
0.772; sensitivity (ability to predict that the impact is significant when it is) = 0.61.
Inset: Cross-validation processes for the selection of the optimal regression tree. The
line shows a single representative 10-fold cross-validation of the most frequent
(modal) optimal tree with standard error (SE) estimate of each tree size. Bar charts are
the numbers of the optimal trees of each size (Frequency of tree) selected from a
series of 50 cross-validations based on the 1–SE rule which minimizes the crossvalidated error within one standard error of the minimum. The most frequent (modal)
tree has 7 terminal nodes.
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Pathogens
Introduction
Relating traits to invasion success has been an important and much debated issue in
invasion ecology, with many studies dealing with plants and animals (Hayes & Barry
2008). The underlying hypothesis is that each invasion is not idiosyncratic, resulting
from the complex interactions of the invading species, the recipient community, the
environment and historic factors, but that some general patterns can be found. This
scientific approach could find useful applications in risk analyses and management by
focusing efforts on the most threatening species and the most vulnerable communities.
However, this question has not yet been explored for plant pathogens in general and
fungi in particular. As emphasised by Hayes & Barry (2008), very few studies have
quantitatively correlated invasion success through accidental introductions because
unbiased reports of successful and unsuccessful alien species, necessary for statistical
analysis, are generally unavailable.
Our study focused on pathogenic fungi (and pseudo-fungi) of forest trees in Europe.
Our aim was (i) to identify the traits that most accurately explained the invasiveness,
from entry to spread and impact; (ii) to identify the traits of the invasive species with
high impact and (iii) to study our capabilities to predict the success of invasion and
high impact from these traits. Only direct ecological impacts (i.e. effects of pathogen
species on the growth and mortality host plants) were considered in detail in this study
because the economic impact of invasive pathogen species also depends on the
economic and social significance of host plants and thus cannot be so easily related to
species traits.
Material and Methods
1. Data set
a. Species
Comparing the traits of native versus invasive species is of limited interest since
differences are likely to be attributable to biogeography and not only to invasion
ecology. The use of native species as a control group is even less relevant for plant
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pathogens than for other groups because, in this case, the invasion process, and
especially impact, is affected by intertrophic interactions (host-parasite) rather than
competitive interactions with species at the same trophic level. For this reason, only
alien species to Europe showing varying degrees of invasion success were included in
our analyses. This inevitably led to a rather small data set, including missing data.
The data set previously built in DAISIE provided a first list of exotic forest
pathogenic fungi and pseudo-fungi (Desprez-Loustau 2009, Desprez-Loustau et al
2009). However, most of the species in this list had a relatively high invasion success.
The difficulty was to complete this list and select a control group of non invasive
species with a sufficient sample size.
We explored several different sources (EPPO, especially interception data sets, Plant
Protection services, literature data bases) to try to obtain the most complete set of
species that are alien to Europe and have been reported at least once. This made a total
of 47 species, 40 true fungi (i.e. belonging to the Eumycota kingdom) and 7
Phytophthora species (belonging to Oomycota, in the Stramenopila kingdom). The
latter were included although Oomycota are not closely related to Eumycota in the
phylogenetic tree (cf Figure 1), since Phytophthora were long considered as fungi
because of strong morphological, biological and ecological convergence (filamentous
mycelium, production of spores, pathogenicity to plants, etc…).
1.2 Variables
1.2.1 Response variables
We first defined two groups: invasive vs. non invasive. Since this depends on the
interpretation of the term “invasive”, we used the following approach. Invasion
success can be viewed as the successful transition between successive stages, from
introduction, to establishment, spread and impact. The extent of spread of the different
species was assessed from data collected in Daisie (number of countries in Europe,
number of regions in France) and completed by new data. The level of ecological and
economic impacts were estimated by two independent experts, with five and four
levels of classification, respectively (cf table 1). Seven different classes related to the
invasion stages were then defined and we ranked the 47 species with this
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classification (Table 2). Finally, “non invasive species” were defined as those in
classes 1 and 2, i.e. not established in natural habitats (20 species). “Invasive species”
were considered to be those in classes 3 to 7.
Table 1: Assessment of impacts
0
1
2
3
4
0
1
2
3
Ecological impact
no impact: species only intercepted
very low impact: species not in natural habitat and not found on
indigenous tree species
low impact: pathogenic on indigenous tree species but mainly
affecting growth
high impact: infection leading to mortality of indigenous species
very high impact: high mortality of indigenous tree species,
affecting population structure
nb species
5
8
24
9
1
Economic impact
No impact
negligible impact
low or medium impact: low estimated production losses
high impact: high estimated losses due to host mortality or
growth loss, justifying costly control measures (e.g. chemical or
biological control)
5
22
12
8
Table 2: Classification of fungal pathogenic species into stages of invasion
Class
Invasion stage
1
2
Introduced, non established (interceptions)
Introduced, no established populations in
natural habitats (only anthropogenic
habitats such as nurseries)
Introduced, established locally, significant
local ecological impact
introduced, widely spread, low ecological
impact
Introduced, widely spread, low ecological
but significant economic impact
Introduced, widely spread, high ecological
and medium to high economic impact
Cryptogenic (alien or emerging) species,
spread and with high ecological impact
3
4
5
6
7
Impact
(ecol. – econ.)
0;0
1-2 ; 1-2
nb
Status
species
5
Non invasive
15
2; 1-3
3
2;1
8
2;2-3
6
3-4;2-3
8
3;2-3
2
Invasive
with
high impact
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A second division was made according to impact. “Species with high ecological
impact” were those in classes 6 and 7 in table 2, i.e. in classes 3-4 of ecological
impact in table 2. They were compared to species with no or low impact, i.e. in
classes 1 to 5 in table 2.
Economic impact was very difficult to assess in the absence of available data on crop
losses, cost analyses, etc. In addition, economic impact is strongly related to the
economic importance of the host species, i.e. high economic impacts were estimated
for pathogen species having medium to high ecological impacts and affecting host
species of high economic value, such as pines, oaks, chestnut and poplars.
Conversely, some pathogen species with high ecological impact were rated with only
medium economic impact because their host species were considered less
economically important (e.g. ash, cypress). For these reasons, the analysis of fungal
traits related to economic impact per se was not considered as relevant. But economic
impact is partly taken into account by considering ecological impact since half of
species rated with high ecological impact were also rated with high economic impact,
and the second half with medium economic impact. More generally, there is a
significant association between ecological and economic impact (Chi2= 16.53, df=2,
proba= 0.0003; Table 3)
Table 3: Co-distribution of ecological and economic impact for the 47 alien pathogen
species
Ecological impact
0
1
2
3
4
Economic impact
0
1
5
7
15
2
3
1
6
4
3
5
1
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1.2.2 Species traits and other explanatory variables
Species traits that might be involved in the successful completion of each stage in the
invasion process were considered as potential explanatory variables and documented
as far as possible according to available data (CABI databases, especially the Crop
Protection Compendium (CABI, 2009), Viennot-Bourgin 1949, Lanier et al 1976,
Sinclair and Lyon 2005). These traits relate to spore characteristics (affecting
dispersal and survival), mode of dispersal, reproduction, life-cycle, parasitic strategy
and abiotic constraints (19 variables, table 4). Only the life-cycle and abiotic
constraints, which affect the capacity for population growth, and parasitic strategy,
which affects the severity of disease, were considered as factors contributing to the
likelihood of high impacts being caused (10 variables).
Previous analyses of species correlates for invasive success have shown that several
factors can potentially be important sources of bias, especially phylogeny, residence
time and propagule pressure (Hayes and Barry 2008). Phylogeny did not bias our
sample, since invasive species (belonging to classes 3-7) and non invasive species (in
classes 1-2) were equally represented in most taxonomical groups (Figure 1).
Residence time was accounted for by using the date of first observation (as a proxy
for the date of introduction) of each species in Europe as an adjustment variable. No
data were available for propagule pressure, but in the case of unintentional
introductions, such as pathogens, propagule pressure could generally be explained by
historic and economic factors (the amount and type of commodities introduced,
duration of travel, etc) which are partly accounted for by the date of introduction.
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Table 4: Explanatory variables for the probability of being invasive
Traits
Variables
Type
mitospore
characteristics
medium size = volume
between 250 and 2500
L3
class
(yes/no)
Small size = volume
<250 L3
shape: L/l mitospore
ratio <3
mitospore coloured
class
(yes/no)
class
(yes/no)
class
(yes/no)
class
(yes/no)
pluri cellular
Nb
missing
data
0
0
0
0
Références
Transport and deposition of spores, like any other particle, is
Niklas KJ
controlled by size, weight, shape and surface morphology"; small
spores could be an advantage for dispersal (e.g. number of spores
carried by rain drops) but with a reduced survival : possible trade-off
for spore size
Cf previous statement ; smaller spores could be favoured if dispersal
is the predominant process
for active ejection of ascopospores, an optimal value < 3 was
Roper et al 2008
demonstrated
coloured spores would have better survival due to resistance to UV radiation
diploid
class
0
(yes/no)
pluri-cellular spores would have a better fitness, with one cell
possibly surviving while the other has died (e.g. survival to
radiations)
general assumption of a positive correlation between fitness and
ploidy level
dispersal
existence of a mode of
long-distance dispersal
class
0
(yes/no)
spores can be dispersed either over a short distance (rain-splashing) or long-distance
(air, insects, rivers); the latter is more favourable to spread
sexual
reproduction
existing
class
0
(yes/no)
class
10
(yes/no)
quantita 5
tive
favouring evolutive potential
selfing possible
life-cycle
parasitic
strategy
latency duration
0
Biological hypothesis
polycyclic
class
3
(yes/no)
Non biotroph
class
0
(yes/no)
class
0
(yes/no)
class
0
(yes/no)
Non heteroxenic
generalist (host range
including several
families)
infection of perennial
class
0
tissues (bark-wood) vs. (yes/no)
only expendable parts
(leaves and twigs)
infection of seeds
class
0
(yes/no)
endophytism
class
4
(yes/no)
Abiotic
constraints
Confounding
variables
Fairand 2001,
Sommer et al
1964
Nuismer & Otto
2004
favours sexual reproduction
the latency corresponds to the generation duration = time from spore Pringle & Taylor
infection to the production of secondary spores; the shortest, the
2002
highest the multiplication rate and population growth
species completing several cycles within a year ; cf closely related previous variable
biotroph pathogens need a living host throughout their cycle; this may be a constraint for
establishment and survival
heteroxenic pathogens need two different host species to complete their life-cycle (only
in Pucciniales = rust fungi); this may be a constraint for establishment and survival
generalists are generally considered as better candidates for
Marvier et al 2004
invasion than specialists, because of more opportunities of a
favourable habitat; this applies mainly for establishment; however
generalist species might be less competitive than specialist ones
this will affect many processes: transport (plants, wood), dispersal,
Sinclair et al 2005
survival, etc…
could favour transport (non symptomatic infected material)
as seeds (endophytic pathogen develop asymtomatically in host tissues)
optimal temperature
growth comprised
between 20 and 25°C
class
12
(yes/no)
species with optimal growth temperature in this range would find favourable condiitons in
the major part of Europe; while cooler or warmer optima would correspond to sub-boreal
and Mediterranean species with a narrower potential geographic range
temperate climate in
the area of origin
class
14
(yes/no)
climate-matching between europe and the area of origin could favour establishment
date of introduction
quantita 0
tive
the earlier the introduction, the longer the time for
establishment and spreading
surface occupied by
host species (> 200
000 Ha in France)
class
0
(yes/no)
higher host occurrence would favour establishment and transmission
Caley et al 2008
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Figure 1 : Location of the species studied in the phylogenetic tree of fungi and
pseudo-fungi (Stramenopiles) (from James et al 2006). Invasive species with high
ecological impact are indicated in red. The category “non-invasive” includes those
species that have yet to become clearly invasive.
"Invasive"
"Non invasive"
Discula destructiva, Phomopsis
juniperivora, Stegophora ulmea
Hypocreales
Apiognomonia veneta,
Cryphonectria parasitica ,
Gnomonia leptostyla
Seiridium cardinale , Entoleuca
mammata
Gibberella circinata
Microascales
Ceratocystis platani
Ophiostomatales
Ophiostoma novo-ulmi ,
Ophiostoma ulmi
Erysiphe alphitoides , Erysiphe
flexuosa, Erysiphe platani,
Erysiphe vanbruntiana var.
sambuci-racemosae
Ceratocystis populicola,
Ceratocystis virescens, Chalara
populi
Diaporthales
Sordariomycetes
Leotiomycetes
Xylariales
Erysiphales
Eutypella parasitica
Erysiphe arcuata
Blumeriella jaapii, Chalara
fraxinea , Drepanopeziza
punctiformis, Rhabdocline
pseudotsugae
Didymascella thujina, Neofabraea
populi, Septotis podophyllina
Botryosphaeriales
Diplodia pinea
Dothideales
Phaeocryptopus gaeumannii
Diplodia scrobiculata,
Lasiodiplodia theobromae
Kabatina thujae
Capnodiales
Dothistroma pini, Mycosphaerella
pini, Mycosphaerella dearnessii
Helotiales
Lecanomycetes
Eurotiomycetes
Lichinomycetes
ASCOMYCOTA
Dothideomycetes
Phloeospora robiniae
Sphaceloma murrayae
Myriangiales
Arthoniomycetes
Pezizomycetes
Orbiliomycetes
Saccharomycotina
Taphrinomycotina
BASIDIOMYCOTA
Agaromycotina
Ustilagomycotina
Pucciniomycotina
FUNGI
Pucciniales
Cronartium ribicola,
Melampsoridium hiratsukanum
Gymnosporangium asiaticum,
Melampsora medusae
Peronosporales
Phytophthora alni ,
Phytophthora cambivora ,
Phytophthora cinnamomi,
Phytophthora citricola
Phytophthora kernoviae,
Phytophthora lateralis,
Phytophthora ramorum
Glomeromycota
Zygomycota
Chytridiomycota
Metazoa
Stramenopiles
Rhodophyta
Viridiplantae
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2. Statistical analyses
The general principle employed was to estimate how the probability of invasion
success or high ecological impact responds in relation to the explanatory variables.
Several different statistical techniques are available to treat this question, but their
appropriateness depends on the dataset characteristics, the most important, in our
case, being the number of observations and the missing data.
General linear models (GLM) have been widely used to identify relevant
species traits, especially logistic regression. They allow all variables (including
confounding variables) to be modelled jointly and to provide results that can be
clearly interpreted. However, they cannot cope with missing data, are not well adapted
to analyze a large number of categorical explanatory variables. In addition, the
complexity of models has to be controlled to avoid over fitting in the case of small
data sets, precluding in our case the inclusion of interactions between variables.
In this study, two types of logistic regression models were implemented:
models using procedures of variable selection and models using mixing techniques. In
the first case, explanatory variables were selected using two criteria, namely AIC or
BIC. In the second case, all possible models were fitted to the data, weighted, and
then mixed. Two model mixing methods were used, Bayesian Model Averaging
(Raftery and al., 1997) and AIC-based mixing (Burnham and Anderson, 2002).
Regression tree techniques do not have the same limitations as GLM and can
map the response surface more flexibly with respect to the explanatory variables,
especially taking interactions into account (Hayes & Barry 2008). CART (Breiman et
al., 1984) has been the most widely used regression tree technique in ecology. A
CART tree is built by the following process: first the single variable is found which
best splits the data into two groups, possibly using different criteria (here, the Gini
criterion). The data are then separated, and this process is applied separately to each
sub-group, and so on recursively until no improvement can be made. The second stage
of the procedure consists of using cross-validation to trim back the full tree (Therneau
and Atkinson, 1997). CART trees provide an easy-to-interpret diagram showing the
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splitting rules associated with the selected predictors. However, the robustness of
CART trees can be put into question, especially with small samples. In this study, a
more recent technique named Random Forest (RF) (Breiman, 1996, 2001) was
implemented to deal with this issue. RF includes re-sampling steps of both variables
and observations, therefore allowing more robust results. With RF, the best split is
determined using a subset of randomly chosen predictors and a bootstrap sample of
data (Liaw and Wiener, 2002).
In this study, CART and RF were used to identify and rank the traits
associated with invasiveness and high impact from the full dataset including missing
data. The three methods (GLM, CART and RF) were then compared for their
predictive performance, using three criteria: overall error of prediction, specificity
(i.e. the ability of a model to predict that a species is not invasive when it is not), and
sensitivity (i.e. the ability of a model to predict that a species is invasive when it is).
All analyses were performed on the complete data set (Eumycota and
Oomycota) and on the Eumycota alone.
Results
1. Correlates of invasive success and ecological impact from RF and CART
analyses
1.1 Invasive success
Results are given in table 5 and figure 2. For the complete data set (Eumycota +
Phytophthora spp), the importance of the two confounding variables was confirmed,
especially for the date of introduction which was ranked first. However, the results in
the analyses with or without confounding variables were highly consistent, with a
correlation coefficient of 0.80 for the ranks of variables, with no major discrepancy.
In the two analyses, the most important species trait involved in the classification of
invasive vs. non invasive species was related to long distance dispersal. Other traits
such as generalist vs. specialist, sexual reproduction, the mono or pluricellular content
of mitospores and the duration of the latency period were also selected.
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Table 4: Rank of importance of explanatory variables for the classification of invasive
vs. non invasive species in the Random Forest analysis
Eumycota +
Oomycota
Eumycota +
Oomycota
Eumycota
Eumycota
without
confounding
variables
without
confounding
variables
4
18
10
9
3
with
confounding
variables
1
2
5
6
8
3
4
11
9
13
10
7
13
12
3
4
with
confounding
variables
1
2
3
4
5
6
7
8
9
10
11
7
13
7
14
8
10
12
13
12
16
14
16
15
17
9
17
14
14
15
16
8
19
15
11
17
12
19
20
18
21
11
18
16
15
19
17
18
19
20
21
Introduction date
Long distance Dispersal
Sexual reproduction
Pluricellular mitospore
Mitospore latency
Host area
Large host range
Endophytism
Small mitospores
Optimal temperature
Mitospore shape
Medium size
mitospores
Coloured mitospores
Infection of perennial
organs
Non biotroph
Polycyclism
Temperate climate in
area of origin
Seed infection
No alternate host
diploid
Self crossing
1
6
2
5
5
1
2
6
Results when Phytophthora species were excluded were not very different (r=0.85
and 0.92 without and with confounding variables, respectively). Two main differences
were found (a) in the analyses without confounding variables where long distance
dispersal and sexual reproduction replaced each other at the first and fifth ranks in the
two analyses, and (b) a much better ranking of optimal temperature (3rd) in the
analysis without Phytophthora spp.
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Figure 2: Rank of importance of explanatory variables (including confounding
variables) for the classification of invasive vs. non invasive species in the Random
Forest analysis: Eumycota and Oomycota (left) or Eumycota only (right)
In the CART analyses that included confounding variables, the date of introduction
was the only variable kept in the trimmed trees. The splitting rule was as expected, i.e.
the earliest introductions (before 1959) were associated with invasion success (Figure
3).
When no confounding variables were included, the CART tree for the whole dataset
only selected the long distance dispersal variable, again the expected splitting rule. In
the case of Eumycota only, the CART tree was more complex, with three variables
including those identified by RF. The splitting rules were consistent with biological
hypotheses except for the host range: invasion success was associated with specialist
pathogens.
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Figure 3: CART trees showing the splitting rules associated with invasion success
1.2 High impact
The date of introduction was also the most important variable explaining high impact.
The main species traits involved were the infection of perennial organs (vs.
expendable parts), optimum temperature and latency period (table 5 and figure 4).
These three variables were also the first ones in the analyses without confounding
variables and without Phytophthora spp.
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Figure 4: Rank of importance of explanatory variables (including confounding
variables) for the classification of invasive species with high vs. low ecological
impact in the Random Forest analysis: Eumycota and Oomycota (left) or Eumycota
only (right)
Table 5: Rank of importance of explanatory variables for the classification of invasive
species with high vs. low ecological impact in the Random Forest analysis
Introduction date
Optimal temperature
Infection of perennial
organs
Mitospore latency
Host area
Endophytism
Non biotroph
Generalist
Seed infection
Polycyclism
Eumycota +
Oomycota
Eumycota +
Oomycota
Eumycota
Eumycota
without
confounding
variables
with
confounding
variables
without
confounding
variables
1
3
with
confounding
variables
1
2
2
4
5
6
8
7
10
9
3
4
5
6
7
8
9
10
1
1
3
2
3
2
5
6
7
8
4
4
7
5
8
6
CART trees could only be produced with the whole data set (i.e. Eumycota and
Phytophthora). When no confounding variable was introduced, the duration of the
latency period was the only splitting variable, with a threshold value of 4 days, in the
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expected trend. When confounding variables were included, the single splitting
variable was the date of introduction, again with the expected splitting rule, with a
threshold value of 1852 (results not shown).
2 Predictive performances of the models
Only models including traits as explanatory variables without confounding variables
are considered here since the date of introduction could be used as a prediction.
2.1 Invasion success
The overall error was quite high (30-40%) for all models. GLM techniques tended
to perform better for Eumycota+Phytophtora whereas CART and RF were more
accurate for Eumycota only (Table 6). However, when considering sensitivity, i.e.
the ability of predicting that a species is invasive when it is, the main objective of
PRA, CART and RF performed better than GLM and with high values: CART had
a sensitivity of 89% when all species were considered and RF 87% for Eumycota
only.
Table 6: Comparison of the model predictive performances for the invasive status.
Total error rate, sensitivity and specifity were computed for Eumycota alone and
for Eumycota+Phytophthora.
Eumycota +
Phytophthora
Eumycota
Error (%)
RF
CART
step aic
step bic
mix
bma
40,43
29,79
27,66
27,66
27,66
29,79
35,00
27,66
35,00
35,00
42,50
42,50
Specificity (%)
RF
CART
step aic
step bic
mix
bma
45,00
45,00
65,00
65,00
65,00
65,00
35,29
90,00
52,94
52,94
82,35
82,35
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Sensitivity (%)
RF
CART
step aic
step bic
mix
bma
70,37
88,89
77,78
77,78
77,78
74,07
86,96
59,26
73,91
73,91
39,13
39,13
2.2 High impact
RF performed better than CART and GLM whatever the criterion. Low overall
errors (10%) were obtained with this classification technique (table 7). However,
this high level of accuracy was mainly due to a very high specificity (100%) while
sensitivity was moderate (40-50%).
Table 7: Comparison of the model predictive performances for the ecological
impact level. Total error rate, sensitivity and specifity were computed for
Eumycota alone and for Eumycota+Phytophthora.
Eumycota +
Phytophthora
Eumycota
Error (%)
RF
CART
step aic
step bic
mix
bma
10,64
21,28
23,40
21,28
23,40
23,40
10,00
20.00
20.00
17.50
20.00
20.0
Specificity (%)
RF
CART
step aic
step bic
mix
bma
100,00
100,00
97,30
100,00
97,30
97,30
100,00
93.94
96.97
100.00
96.97
96.97
Sensitivity (%)
RF
CART
step aic
step bic
mix
bma
50,00
0,00
0,00
0,00
0,00
0,00
43.00
14.29
0.00
0.00
0.00
0.00
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Discussion - Conclusions
1. Our study provides good evidence that invasion success in forest pathogenic
fungi, including the severity of impacts, can be partly explained by specific
traits, as already demonstrated in other taxonomic groups (Hayes and Barry
2008). This provides a scientifically based support for the inclusion of such
traits in a PRA.
2. Based on the results of our analyses, the most important traits are:
-for invasiveness:
the existence of a (natural) mode of long distance dispersal,
sexual reproduction,
particular spore characteristics (pluri-cellularity, shape),
the duration of latency
host range
- for high impact:
the optimal growth temperature,
infection of perennial organs
latency.
The relative ranking of these traits was slightly affected when confounding
variables, especially the date of introduction, were included or when the
analyses did or did not include Phytophthora species.
3. Most of these traits are, at least implicitly, already included in the EPPO PRA
scheme. However, our results could lead to the modification of some of the
EPPO scheme questions. For example, sexual reproduction was selected in
addition to long distance dispersal which suggests that the existence of sexual
reproduction not only favours dispersal (ascospores are assumed to be
dispersed at longer distances than conidia in many ascomycetes) but also
directly increases the risk of invasion. Questions 1.25 and 1.27 in the EPPO
PRA scheme, related to reproductive strategy and adaptability, should thus
explicitly mention sexual reproduction for fungi. Conversely, “self-crossing”
(included in the note of question 1.25) was not influential in our analyses.
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4. Some traits were selected at a much higher rank than expected, or with an
unanticipated splitting rule. The high ranking given to the optimal growth
temperature for predicting high impact was not expected and this result shows
that this trait should also be considered as important. The effect of pluricellular
mitospores on the risk of invasiveness was also unpredicted. In addition, the
classification rule defined by CART which gives a higher risk of invasiveness
to specialist pathogens is contradictory to the general assumption that
generalists should be more invasive because they can establish in a wider
range of habitats and on a wider range of hosts (Marvier et al 2004). Question
1.15 in the EPPO PRA scheme (and the ranking of answers) implicitly
assumes that host range width and the probability of establishment are
positively correlated. Based on our results, this assumption might be revised in
the case of pathogens. The existence of at least one host plant species in the
PRA area (question 14 in pest categorization) as a risk factor is itself
questionable, at least for pathogens of non cultivated plants. In the case of
forest pathogenic fungi, most invasive species had no known host species in
the invaded area and were able to perform a “host jump” (see Slippers et al
2005). For “specialist” pathogens, it seems that this host jump mostly occurred
within the same genus, e.g. Cryphonectria parasitica from Asian chestnuts
(Castanea mollissima and C. crenata) to European (C. sativa) or American (C.
dentata) species.
5. The results of the model assessment showed that methods based on a
“machine learning approach” (CART and Random Forest) were well adapted
to the type of available data. Although the dataset included missing values,
CART and RF showed high sensitivity in predicting invasiveness (85-90%).
The sensitivity was lower in predicting high impact (40-50%), but this may be
due to the structure of the dataset (which included a small number of species
with high impact) since the overall error rate was low (10%).
6. Although the EPPO PRA scheme has been adapted to the revised ISPM11 to
improve the assessment of environmental impacts, our study showed that some
additions to the explanatory notes for certain question could improved its
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ability to assess potentially invasive species pathogenic on non crop plants,
such as forest trees.
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