Global economic costs of aquatic invasive alien species

Abstract

Much research effort has been invested in understanding ecological impacts of invasive alien species (IAS) across ecosystems and taxonomic groups, but empirical studies about economic effects lack synthesis. Using a comprehensive global database, we determine patterns and trends in economic costs of aquatic IAS by examining: (i) the distribution of these costs across taxa, geographic regions and cost types; (ii) the temporal dynamics of global costs; and (iii) knowledge gaps, especially compared to terrestrial IAS. Based on the costs recorded from the existing literature, the global cost of aquatic IAS conservatively summed to US345billion,withthemajorityattributedtoinvertebrates(62345 billion, with the majority attributed to invertebrates (62%), followed by vertebrates (28%), then plants (6%). The largest costs were reported in North America (48%) and Asia (13%), and were principally a result of resource damages (74%); only 6% of recorded costs were from management. The magnitude and number of reported costs were highest in the United States of America and for semi-aquatic taxa. Many countries and known aquatic alien species had no reported costs, especially in Africa and Asia. Accordingly, a network analysis revealed limited connectivity among countries, indicating disparate cost reporting. Aquatic IAS costs have increased in recent decades by several orders of magnitude, reaching at least US23 billion in 2020. Costs are likely considerably underrepresented compared
to terrestrial IAS; only 5% of reported costs were from aquatic species, despite 26% of known invaders being aquatic. Additionally, only 1% of aquatic invasion costs were from marine species. Costs of aquatic IAS are thus substantial, but likely underreported. Costs have increased over time and are expected to continue rising with future invasions. We urge increased and improved cost reporting by managers, practitioners and researchers to reduce knowledge gaps. Few costs are proactive investments; increased management spending is urgently needed to prevent and limit current and future aquatic IAS damages

Full text

Global economic costs of aquatic invasive alien species
Ross N. Cuthbert
a,b,
⁎, Zarah Pattison
c
, Nigel G. Taylor
d
, Laura Verbrugge
e,f
, Christophe Diagne
g
,
Danish A. Ahmed
h
, Boris Leroy
i
,ElenaAngulo
g
,ElizabetaBriski
a
, César Capinha
j
,JaneA.Catford
k,l
,
Tatenda Dalu
m,b
, Franz Essl
n
, Rodolphe E. Gozlan
o
, Phillip J. Haubrock
p,q
, Melina Kourantidou
r,s,t
,
Andrew M. Kramer
u
, David Renault
v,w
, Ryan J. Wasserman
x,b
, Franck Courchamp
g
a
GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel, 24105 Kiel, Germany
b
South African Institute for Aquatic Biodiversity, Makhanda 6140, South Africa
c
Modelling, Evidence and Policy Research Group, School of Natural and Environmental Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
d
Tour du Valat, Research Institute for the Conservation of Mediterranean Wetlands, 13200 Arles, France
e
University of Helsinki, Faculty of Agriculture and Forestry, Department of Forest Sciences, P.O. Box 27, 00014 Helsinki, Finland
f
Aalto University, Department of Built Environment, Water & Development Research Group, Tietotie 1E, FI-00076 Aalto, Finland
g
Université Paris-Saclay, CNRS, AgroParisTech, Ecologie Systématique Evolution, 91405 Orsay, France
h
Centerfor Applied Mathematics and Bioinformatics(CAMB), Department of Mathematics andNatural Sciences,Gulf Universityfor Science and Technology, P.O.Box 7207, Hawally32093, Kuwait
i
Biologie des Organismes et Ecosystèmes Aquatiques (BOREA), Muséum national d'Histoire naturelle, CNRS, IRD, Sorbonne Université, Université Caen-Normandie, Université desAntilles, 43 rue
Cuvier, CP 26, 75005 Paris, France
j
Centro de Estudos Geográficos, Instituto de Geografia e Ordenamento do Território –IGOT, Universidade de Lisboa, Lisboa, Portugal
k
Department of Geography, King's College London, Strand WC2B 4BG, UK
l
School of BioSciences, University of Melbourne, Vic 3010, Australia
m
School of Biology and Environmental Sciences, University of Mpumalanga, Nelspruit 1200, South Africa
n
BioInvasions, Global Change, Macroecology-Group, Department of Botany and Biodiversity Research, University Vienna, Rennweg 14, 1030 Vienna, Austria
o
ISEM UMR226, Université de Montpellier, CNRS, IRD, EPHE, 34090 Montpellier, France
p
Senckenberg Research Institute and Natural History Museum, Frankfurt, Department of River Ecology and Conservation, Gelnhausen, Germany
q
University of South Bohemia in České Budějovice, Faculty of Fisheries and Protection of Waters, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Zátiší 728/II,
389 25 Vodňany, Czech Republic
r
Woods Hole Oceanographic Institution, Marine Policy Center, Woods Hole, MA 02543, United States
s
Institute of Marine Biological Resources and Inland Waters, Hellenic Center for Marine Research, Athens 164 52, Greece
t
University of Southern Denmark, Department of Sociology, Environmental and Business Economics, Esbjerg 6705, Denmark
u
Department of Integrative Biology, University of South Florida, Tampa, FL 33620, United States
v
Univ Rennes,CNRS, ECOBIO [(Ecosystèmes, biodiversité, évolution)], - UMR 6553, F 35000 Rennes, France
w
Institut Universitaire de France, 1 Rue Descartes, 75231 Paris cedex 05, France
x
Department of Zoology and Entomology, Rhodes University, Makhanda 6140, South Africa
HIGHLIGHTS
•Aquatic invasions have cost the global
economy US$345 billion.
•Most costs are caused by invertebrates,
in North America and damages to re-
sources.
•Costs have increased exponentially over
time, to at least US$23 billion in 2020.
•Aquatic invasion costs are underrepre-
sented compared to terrestrial invasion
costs.
•Taxonomic, geographic and tempo-
ral gaps make these costs severely
underestimated.
GRAPHICAL ABSTRACT
Science of the Total Environment 775 (2021) 145238
⁎Corresponding author at: GEOMAR, Helmholtz-Zentrum für Ozeanforschung Kiel, 24105 Kiel, Germany.
E-mail address: rossnoelcuthbert@gmail.com (R.N. Cuthbert).
https://doi.org/10.1016/j.scitotenv.2021.145238
0048-9697/© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Contents lists available at ScienceDirect
Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv

abstractarticle info
Article history:
Received 12 November 2020
Received in revised form 6 January 2021
Accepted 13 January 2021
Available online 20 January 2021
Editor: Damia Barcelo
Keywords:
Brackish
Freshwater
Habitat biases
InvaCost
Marine
Monetary impact
Much research effort has been invested in understanding ecological impacts of invasive alien species (IAS)across
ecosystems and taxonomic groups, but empirical studies about economic effects lack synthesis. Using a compre-
hensive global database,we determine patternsand trends in economic costs of aquatic IASby examining: (i)the
distribution of these costs across taxa, geographic regions and cost types; (ii) the temporal dynamics of global
costs; and (iii) knowledge gaps, especially compared to terrestrial IAS. Based on the costs recorded from the
existing literature,the global cost of aquatic IAS conservatively summed to US$345 billion, with the majority at-
tributed to invertebrates (62%), followed by vertebrates (28%), then plants (6%). The largest costs were reported
in North America (48%) and Asia (13%), and were principally a result of resource damages (74%); only 6% of re-
corded costs were from management. The magnitude and number of reported costs were highest in the United
States of America and for semi-aquatic taxa. Many countries and known aquatic alien species had no reported
costs, especially in Africa and Asia. Accordingly, a network analysis revealed limited connectivity among coun-
tries, indicating disparate cost reporting. Aquatic IAS costs have increased in recent decades by several orders
of magnitude, reaching at least US$23 billion in 2020. Costs are likely considerably underrepresented compared
to terrestrial IAS; only 5% of reported costs were from aquatic species, despite 26% of known invaders being
aquatic. Additionally, only 1% of aquatic invasion costs were from marine species. Costs of aquatic IAS are thus
substantial, but likely underreported. Costs have increased over timeand are expected to continue rising with fu-
ture invasions. We urgeincreased and improved cost reporting by managers, practitioners and researchers to re-
duce knowledge gaps. Few costs are proactive investments; increased management spending is urgently needed
to prevent and limit current and future aquatic IAS damages.
© 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
1. Introduction
The impacts of invasive alien species (IAS) on biodiversity (Mollot
et al., 2017;Spatz et al., 2017;Shabani et al., 2020), ecosystem services
(Vanbergen, 2013;Blackburn et al., 2019) and human wellbeing
(Pejchar and Mooney, 2009) are well recognized (Pyšek et al., 2020). Ac-
cordingly, there are numerous national and international policies, regula-
tions and mandates in place to prevent new introductions and limit the
geographic spread of IAS [e.g. Convention on Biological Diversity (UNEP,
2011); European Union Regulation 1143/2014 on IAS]. However, records
of IAS are continuously increasing, owing to factors such as habitat distur-
bance, climate change, and an increasing diversity, frequency and inten-
sity of anthropogenic vectors associated with globalising trade and
transport networks (Capinha et al., 2015;Seebens et al., 2017, 2018;
Turbelin et al., 2017;McGeoch and Jetz, 2019). Alien species numbers
are burgeoning across geographical regions and habitat types, with the
number of established alien species expected to increase by 36% in the
next three decades (Seebens et al., 2020).
Aquatic ecosystems can be severely threatened by IAS, which
contribute to extinctions of individual species, substantially
change the structure of native communities, and alter ecosystem
functioning (Vitousek et al., 1997;Ricciardi and MacIsaac, 2011;
Jackson et al., 2017). Aquatic ecosystems provide numerous ser-
vices to people, from food provision to flood protection and recrea-
tion; these services can also be critically altered by the presence of
IAS (e.g. Katsanevakis et al., 2014). The vulnerability of aquatic eco-
systems to invasions is increased by high interconnectedness
among habitats, specifically man-made waterways and shipping,
as well as other anthropogenic pressures (Strayer and Findlay,
2010;Poulin et al., 2011;Darwall et al., 2018) and climate shifts
(Woodward et al., 2010).
In recent years there have been significant advances across habitat
types in understanding ecological impacts of IAS (Kumschick et al.,
2015;Dick et al., 2017; but see Crystal-Ornelas and Lockwood, 2020)
and the drivers of invasion success (Cuthbert et al., 2019, 2020;
Fournier et al., 2019;van Kleunen et al., 2020), as well as methodologi-
cal advances in assessing the economic dimensions of IAS and their
management (Lovell et al., 2006;Hanley and Roberts, 2019). However,
studies of economic aspects of IAS have been limited to certain
taxonomic groups (Bradshaw et al., 2016), communities, or regions
(Pimentel et al., 2000;2005;Kettunen et al., 2009;Cuthbert et al.,
2021;Haubrock et al., 2021). In particular, costs of aquatic IAS are
generally less well understood than costs of terrestrial IAS, despite
some estimates indicating high costs (Lovell et al., 2006;Aldridge and
Oreska, 2011). Comprehensive and systematically-assembled data on
the costs of aquatic IAS would greatly help planning and prioritisation
for their management, in the context of limited resources (McGeoch
et al., 2015). Such data would also provide a useful resource for commu-
nications with policymakers and the general public: impacts expressed
in economic terms are more tangible and comprehensible than complex
ecological impacts (Diagne et al., 2020a).
This paper is the first systematic effort to describe global pat-
terns and trends in reported costs of aquatic IAS. Our analysis,
based on the recently developed InvaCost database (Diagne et al.,
2020b), allows us to synthesise standardised costs, identify knowl-
edge gaps and provide recommendations for management and fur-
ther research. We describe the global monetary costs associated
with aquatic IAS based on taxonomic, geographic and temporal de-
scriptors, as well as between fully aquatic and semi-aquatic taxa. In
doing so, we examine (i)howcostsarestructuredbyimplementa-
tion method (i.e. observed vs. potential/expected), (ii) reliabilities
of cost estimates and (iii) their typology, i.e., whether costs result
from damages and losses or management expenditure. Further,
we model the yearly and cumulative dynamics of costs and investi-
gate whether they are likely to saturate in the near future. Finally,
we assess potential biases between aquatic and terrestrial cost
reporting. These biases are then used to identify gaps in manage-
ment spending between habitats.
2. Materials and methods
2.1. Original data
For the purpose of quantifying global costs of aquatic IAS, we used the
most comprehensive and up-to-date dataset of costs caused by alien spe-
cies globally, assembled by the InvaCost project (Diagne et al., 2020a,
2020b). At time of writing, this includes 9823 entries in various lan-
guages from systematic and opportunistic literature searches (Diagne
et al., 2020b;Angulo et al., 2021; full database version 3 at https://doi.
org/10.6084/m9.figshare.12668570). This database captures any re-
ported economic costs associated with IAS in their novel range, including
those for species that have already become invasive (e.g. management,
damages and losses) and species that may become invasive in the future
(e.g. prevention and rapid eradication).
R.N. Cuthbert, Z. Pattison, N.G. Taylor et al. Science of the Total Environment 775 (2021) 145238
2

The InvaCost version 3 database contains a column ("Environment_
IAS") which classifies species as either aquatic (species with a close associ-
ationwithaquaticsystemsatanylifestage, including for reproduction,
development and/or foraging; n= 2317 cost entries after our below cor-
rections) or terrestrial (n= 6433 cost entries after our below correc-
tions), independently of where costs occurred. For some analyses, we
split out costs for semi-aquatic species: the subset of aquatic species
with a looser association with aquatic systems (see Supplementary
Material 1). Remaining entries, linked to species from diverse habitats
(i.e. a mixture of aquatic and terrestrial) or unspecified habitats, were
excluded from analyses. Wealso carefully screened the published data-
base, removing clear duplicates and correcting clear mistakes. All mod-
ifications made in our dataset were sent to updates@invacost.fr as
recommended by the database managers.
Briefly, costs in InvaCost are standardised against a single currency
for comparability (2017 US$); costs in the database may be ‘expanded’
so that entries can be considered on an annual basis. That means that
costs spanning multiple years (e.g. $10 million between 2001 and
2010) are divided according to their duration (e.g. $1 million for each
year between 2001 and 2010); we considered this expanded database
version in all analyses (Supplementary Materials 1; n= 5682 aquatic en-
tries). Expansion was done using the expandYearlyCosts function of the
‘invacost’Rpackage(R Core Team, 2020;Leroy et al., 2020). The final,
unexpanded dataset used in our analyses is provided as Supplementary
Material 2. We note that 1 billion = 1 × 10
9
.
2.2. Cost descriptors
To obtain a general overview of the costs associated with IAS, we
first illustrated them across a number of key database descriptors
(see Supplementary Material 1 and https://doi.org/10.6084/m9.
figshare.12668570 for complete details). These included (1) broad
taxonomic grouping of species presenting costs (invertebrates, ver-
tebrates, plants, other), (2) perceived reliability of each cost entry
(“High”vs. “Low”), (3) cost implementation type (“Observed”vs.
“Potential”), (4) geographic region in which the cost occurred
(within continent- and country- scales) and (5) cost type (“Damage”
vs. “Management”). We summed the expanded entries (see above)
to quantify cost totals among these descriptors.
2.3. Spatial and taxonomic connectivity
We investigated spatial and taxonomic patterns in costs of aquatic
IAS with a network analysis (see Supplementary Material 1). Here, we
created a bipartite network composed of two types of nodes: countries
and IAS. When a species had a reportedeconomic impact in a country, a
link was drawn between the two nodes. The weight of the link was
equal to the cumulative cost, since 1960. We defined the size of nodes
on the network as proportional to their total costs with a log spline,
such that country or species nodes with higher costs are easier to distin-
guish from those with lower economic impacts.
2.4. Prediction of annual costs for aquatic IAS
To examine themost appropriate temporal relationship forthe accu-
mulation of costs over time, we used the modelCosts function of the
‘invacost’package (Leroy et al., 2020). We fitted multiple models to
the data and identified the best model(s) by quantitative and qualitative
criteria (see Supplementary Material 1). As we were dealing with
econometric data, we selected models that were robust to issues of
heteroskedasticity, temporal autocorrelation and outliers. We exam-
ined the long-term trend of annual costs worldwide between 1960
and 2020, i.e., we predicted costs as a function of years. First, owing to
time lags in cost reporting, we corrected the data by removing the
most recent, thus incomplete years; not making this correction would
result in an inherent underestimation of costs (Supplementary Material
1). Second, we employed and compared a range ofstatistical techniques
on the resulting data:ordinary least squares regression (linearand qua-
dratic), robust regression (linear and quadratic), multivariate adaptive
regression splines, generalised additive models(GAMs) and quantile re-
gression [0.1 (lower boundary of cost), 0.5 (median cost value), 0.9
(upper boundary of cost)].
2.5. Trend in cumulated costs for aquatic IAS
In addition to modelling annual costs, we mathematically described
temporal changes in cumulated costs of aquatic IAS. We chose to rely on
a variation of the functional form proposed by Yokomizo et al. (2009)
for density-impact curves, where we considered the cumulative cost C
in terms of population density u. By assuming logistic growth in the
population, Ccan then be expressed as a function of time and therefore
serves as a model for the cumulative temporal cost of impacts (Supple-
mentary Material 1). We used a non-linear regression curve-fitting tool
to estimate the best fit parameters, such as cost saturation C
max
, carrying
capacity Kand intrinsic growth rate α.Wequantified the fit by comput-
ing the squared correlation coefficient (r
2
) and root mean square error
(RMSE).
2.6. Reporting of invasion costs from aquatic IAS compared to terrestrial IAS
We obtained known numbers of established alien species (n=
13,867) in aquatic and terrestrial habitats globally, using databases
such as the inventory of IAS in Europe (DAISIE; see Supplementary
Material 1 for full list of sources). Categorising entries originating from
either aquatic or terrestrial species, we then counted for the two habi-
tats in InvaCost: the numbers of species havingcosts (excluding unspe-
cific entries), the number of documents reporting these costs, total
costs, and costs only reporting management actions (not reporting
damage). Then, we compared these numbers to the proportions of
known established IAS between habitats. Finally, we predicted the ex-
pected costs of management actions for aquatic IAS, under the hypoth-
esis of an unbiased expenditure between aquatic and terrestrial habitats
(based on the known proportion of global aliens that are aquatic).
3. Results
3.1. Global costs and taxonomic groupings
Global costs of aquatic IAS summed to US$345 billion, based on 5682
records from the expanded InvaCost database. These were all published
since 1971. Semi-aquatic species cost US$185 billion (n= 2971 records)
and fully aquatic species US$149 billion (n= 2518 records), with diverse
costs (that spanned semi-aquatic and fully aquatic species) comprising
the remaining US$11 billion (n= 193 records). Only 1% of the cost was
from fully marine species (US$3.6 billion; n=234records).
Costs were unevenly distributed across taxonomic groups, with the
majority (62%, US$214 billion) attributed to invertebrates, 28% (US$97
billion) to vertebrates and 6% (US$20 billion) to plants. All other taxo-
nomic groups accounted collectively for 4% (US$14 billion) of the total
costs (Fig. 1). Highly reliable (i.e. peer-reviewed or traceable) sources
contributed 79% (US$274 billion) of the documented total costs of
aquatic IAS (Fig. 1a). The majority of thetotal costs for animals (inverte-
brates: 76%; vertebrates: 88%) and plants (65%) were based on highly
reliable sources.
Most (65%, US$224 billion) of the costs were derived from empirical
observations, rather than predictions (Fig. 1b). The majority of costs for
aquatic invertebrates were derived from empirical observations (92%).
However, just 17% of the costs for aquatic vertebrates and 42% of plant
costs, were based on empirical observations.
The 10 aquatic genera with the highest documented costs accounted
for US$304 billion (88%) of total costs (Fig. 2). These taxa included four
invertebrates, three vertebrates and three plants. Mosquitoes belonging
R.N. Cuthbert, Z. Pattison, N.G. Taylor et al. Science of the Total Environment 775 (2021) 145238
3

to three species of the Aedes genus caused 50% (US$153 billion) of the
total top 10 cost. These were followed by ruffes Gymnocephalus cernua
(18%, US$53 billion), mussels Dreissena spp. (two species, 16%, US$50
billion), coypus Myocastor coypus (6%, US$19 billion) and primroses
Ludwigia spp. (three species, 3%, US$8 billion). Contributions from
the remaining genera were relatively small. For all genera, excepting
Lithobates, damages outweighed reported management spending
(Fig. 2).
3.2. Geographic regions
Reported economic costs of aquatic IAS were unevenly distributed
across geographic regions (Fig. 3). North America, owing to costs
primarily from the United Statesof America (USA), reported the highest
costs (48%, US$166 billion), followed by costs that were not attributed
to specific regions (26%, US$91 billion) and costs from Asia (13%, US
$45 billion). The costs in Europe and South America accounted
collectively for 12% (US$41 billion) of total reported costs, whilst
Africa, Oceania-Pacific Islands and the Antarctic-Subantarctic, combined
accounted for 0.6% (US$2.1 billion) (Fig. 3). Regarding cost types, 74%
(US$256 billion) of global costs were driven by damages, whereas
only 6% (US$21 billion) consisted of management-related expenditure
(Fig. 3b). Mixed spending (i.e. combined records of damage and
management-related spending) comprised 20% of global costs (US$68
billion). Further information on taxonomic and cost typology break-
downs per region is provided in Supplementary Material 1.
At the country level, the USA had the highest recorded cost for
aquatic IAS, followed by Brazil, India and France (Fig. 4a); other
Fig. 1. Balloon plots illustrating global monetary costs of aquatic invasive alien species across major taxonomic groupings, with respect to (a) method reliability and (b) implementation
type. Figures below each balloon correspond to the numbers of entries from the expanded database.
Fig. 2. Totalmonetary costsof the top 10 costly aquaticinvasive alien genera,alongside species-specificinformationof underlying data pertaining to each genus. Unspecifiedspecies within
each genus were also included. Fills illustrate cost type contributions per genus. Note that “Management”corresponds to expenditure related to activities such as prevention, control,
eradication and research, whilst “Mixed”is a mixture of cost types.
R.N. Cuthbert, Z. Pattison, N.G. Taylor et al. Science of the Total Environment 775 (2021) 145238
4

countries were rela tively similar in costs. The USA also had the largest
number of studies. Other countries that reported costs generally had
similar numbers of studies, with numbers from, for example, Spain,
Brazil and Australia relatively high (Fig. 4b). We found no reported
costs for aquatic IAS from the majority of African and Asian countries.
3.3. Spatial and taxonomic connectivity
We found eight clusters of IAS costs that were composed of at least
five nodes (coloured clusters in Fig. 5), and eleven minor clusters that
were composed of only two nodes (grey nodes in Fig. 5). We found two
types of clusters. First, most clusters were composed of one or a few coun-
tries and a unique combination of IAS. This was the case, for example, for
countries with the highest costs (mainly European and North American
countries), which often had clusters of their own. Among these unique
country clusters, the USA example was pervasive, with highest reported
costs for Dreissena spp., G. cernua and Melaleuca quinquenervia,alongside
many other IAS that were unique to that country. Second, there was one
cluster that was driven by one genus, Aedes, which had pantropical eco-
nomic impacts as well as impacts in temperate countries. For most of
the Southern Hemisphere countries, Aedes was the only genus for which
costs were reported. Despite these specific clusters of costly IAS per
country, several IAS taxa had widespread economic impacts on multiple
countries, such as Lithobates catesbeianus,M. coypus,Neovison vison,
Dreissena spp., Hydrocotyle ranunculoides and Eichhornia crassipes.Inter-
estingly, we found no strong biogeographical structure in the network.
Australia, for example, shared costly IAS with geographically disparate re-
gions such as European countries, South Africa, Argentina and Chile.
3.4. Prediction of annual costs for aquatic IAS
The linear models projected the highest costs of aquatic IAS in the
year 2020 (since 1960; data from years 2013 to 2020 were removed
owing to <75% completeness), however they had a relatively poor
fit, with high RMSE (Fig. 6; Supplementary Material 1). The quadratic
robust regression was removed owing to cost reductions in recent
years, but it also had the highest RMSE (0.63). The GAM approach
thus provided the best fittothedata(ΔRMSE ≥0.08; Fig. 6). This
model indicated a rapid increase in costs by three orders of magni-
tude between 1970 and 2000, followed by a relatively gradual in-
crease within a further magnitude since 2000 (Fig. 6c).Overall,the
best-fitting GAM predicted a cost o f aquatic IAS of US$23 bil lion glob-
ally in the year 2020.
3.5. Trend in cumulated costs for aquatic IAS
We found that the linear curve and high threshold curve models
performed well, with the former providing a slightly better fit
(Table S1; Supplementary Material 1). In the long term, the cumula-
tive cost saturates to a fixed value C
max
(i.e. maximum cumulative
cost of impact), where the invasion is completely controlled and no
further impact costs are incurred (Fig. 7). A clear saturation in costs
(i.e. carrying capacity) was not reached for either the full or adjusted
dataset (with outliers removed), indicating that costs will continue
to increase in the near future. The reduction in rate of cost increases
over recent years was likely an artefact of time lags in cost reporting
versus occurrence.
3.6. Reporting of invasion costs of aquatic IAS compared to terrestrial IAS
Of 13,867 known established alien species worldwide (see Supple-
mentaryMaterial 1), 26% are associated with aquatichabitats, compared
Fig. 3. Totalaquatic invasioncosts across geographic regions with respect to (a) taxonomic
groupingsand (b) cost types. Note in (b), that “Management”corresponds to expenditure
relatedto activities suchas prevention,control, eradication and research, whilst “Mixed”is
a mixture of cost types.
Fig. 4. Maps illustrating global distribution of (a) total economic costs and (b) number of
studies (i.e. unique documents) for aquatic invasive alien species. Costs unattributable to
individual countries were excluded (US$110 billion, out of a total US$345 billion; n =
37 study per country data points, out of a total 526). Costs with a known location in the
territorial waters of each country are also included in the displayed data. Total costs are
presented on a log
10
scale.
R.N. Cuthbert, Z. Pattison, N.G. Taylor et al. Science of the Total Environment 775 (2021) 145238
5

Dreissena spp.
Gymnocephalus cernua
Melaleuca quinquenervia
Corbicula fluminea
Petromyzon marinus
Hydrilla sp.
Lythrum salicaria
Hydrilla verticillata
Teredo navalis
Sporobolus cynosuroides
Myriophyllum spicatum
Sporobolus alterniflorus
Spartina spp.
Carcinus maenas
Styela clava
Eichhornia sp.
Spartina sp.
Lepidium latifolium
Codium fragile Bythotrephes longimanus
Phragmites australis
Myriophyllum sp.
Morone chrysops
Panicum repens Orconectes rusticus
Salmo trutta
Ciona intestinalis
Python bivittatus
Channa argus
Myriophyllum heterophyllum
Hymenachne amplexicaulis
Esox lucius
Ascophyllum nodosum
Solanum tampicense
Brachiaria mutica
Aedes spp.
Anopheles darlingi
Ludwigia peploides
Limnoperna fortunei
Ludwigia sp.
Mytilopsis trautwineana
Egeria densa
Potamogeton sp.
Threskiornis aethiopicus
Xenopus laevis
Ficopomatus enigmaticus
Pterois volitans
Coptodon zillii
Pa nic um maxi mum
Pterygoplichthys sp.
Ameiurus nebulosus
Saururus cernuus
Cygnus atratus
Lithobate s c ates bei anus
Ondatra zibethicus
Neovison vison
Eriocheir sinensis
Alopochen aegyptiaca
Eichhornia crassipes
Lissorhoptrus brevirostris
Lissorhoptrus oryzophilus
Altern anthera phil oxeroi des
Baccharis halimifolia
Cyprinus carpio
Nymphaea mexicana
Lepomis gibbosus
Sander lucioperca
Callinectes sapidus
Orconectes limosus
Fundulus heteroc litus
Sinanodonta woodiana
Oncorhynchus mykiss
Perca fluviatilis
Batrachochytrium dendrobatidis
Gambusia holbrooki
Cyperus alterniflorus
Zantedeschia aethiopica
Sporobolus pumilus
Silurus glanis
Carassius auratus
Nymphaea sp.
Typha domingensis
Typha angustifolia
Salvinia natans
Ludwigiarepens
Graptemys pseudogeographica
Gyrodactylus salaris
Didemnum vexillum
Ludwigia spp.
Hydrocotyle ranunculoi des
Elodea c anadensis
Anguillicoloides crassus
Oxyura jamaicensis
Elodea nuttallii
Lagarosiphon major
Pacifas tacus lenius culus
Crassula helmsii
Branta canadensis
Myriophyllum aquaticum
Crepidula porcellana
Pseudoc hattonella verruculos a
Aphanomyces astac i
Pseudorasbora parva
Phoxinus phoxinus
Paralithodes camtschaticus
Cygnus olor
Sargassum muticum
Pomacea spp.
Azolla filiculoides
Castor canadensis
Caulerpa taxifolia
Rhinella marina
Cabomba caroliniana
Sporobolus anglicus
Salvinia spp.
Hymenachne spp.
Bubalus bubalis
Myocastor coypus
Micropterus salmoides
Trachemys scripta
Ludwigia grandiflora
Pistia stratiotes
Spartina spp.
Dikerogammarus villosus
Polypedates leucomystax
Chelydra serpentina
Lepomis macrochirus
Rudbeckia laciniata
Gymnocoronis spilanthoides
Balanus improvisus
Nymphoides peltata
Aeromonas salmonicida
Undaria pinnatfida
Didymosphenia geminata
Mnemiopsis leidyi
Salvinia molesta
Procambarus clarkii
Lagocephalus sceleratus
Cercopagis pengoi
Rhopilema nomadica
Portunus pelagicus
USA
Canada/USA
Canada
Germany/Unspecified
Denmark
Brazil
India
Colombia
Thailand
Mexico
Indonesia
Malaysia
Singapore
Peru
Venezuela
Brazil/Guatemala/Panama/Salvador/Venezuela
Ecuador
Cambodia/Malaysia/Thailand
France
Bolivia
Nicaragua
Cuba
Vietnam
Guatemala
Cambodia
Honduras
Myanmar
Salvador
Costa Rica
Panama
Chile
Bangladesh/India/Pakistan/Sri Lanka
Laos
Greece
Paraguay
Uruguay
Suriname
Brunei
Belize Maldives
Timor-Leste
Bhutan
Dominican Republic
Switzerland
Germany
Netherlands
Estonia
China
Benin
Spain
Uganda
Ghana
Nigeria
Burkina Faso Mali
Central African Republic
Madagascar
United Kingdom
Norway
Ireland
Belgium
Philippines
Philippines/Thailand/Vietnam
South Africa
Argentina
Argentina/Chile
Australia
Australia/Brazil/Venezuela
Italy
Japan
Sweden
New Zealand
Bulgaria/Georgia/Roumania/Russia/Turkey/Ukraine
Zimbabwe
Sri Lanka
Senegal
Senegal/Mauritania
Portugal
Turkey
Cyprus
Finland
Israel
Tunisia
Fig. 5. Global network of aquatic invasive alien species costs per country. This bipartite network is composed of both species and country nodes. Links indicate the cumulative costs of
species in countries. The thicker the link, the higher the cost. Likewise, node size is proportional to the total cumulative cost, with a log spline. For species nodes, node size represents
the total cost they had over all countries. For country nodes, the node size represents the total cost of all species in that country.
Fig. 6. Fivemodelling techniques considering globalaquatic invasioncosts over time[ordinary leastsquares (OLS) regressions (a),robust regression(b), generalised additivemodel (GAM)
(c), multivariateadaptive regression splines(MARS) (d) and quantile regressions (e)].Points are annualtotal costs. Notethe scales differamong subplots. Shaded areasare 95% confidence
intervals, and prediction intervals in the case of MARS. Root mean square error (RMSE) is shown for all appropriate models as well as 2020 cost predictions.
R.N. Cuthbert, Z. Pattison, N.G. Taylor et al. Science of the Total Environment 775 (2021) 145238
6

to 74% associated with terrestrial habitats (Fig. 8). Although in InvaCost
the number of aquatic species and the number of documents reporting
their costs constituted relatively similar percentages (20% and 28%,
respectively), the value of their reported cost comprised just 5% of the
global total. This increased to just 9% when considering only costs
reported from management strategies. If management expenditure
was unbiased between habitat types according to numbers of known
established aliens, we estimated that a further US$39 billion should
have been spent on aquatic species to date.
4. Discussion
Our study reveals that aquatic IAS have likely cost the global economy
at least US$345 billion. This estimate is probably highly conservative as it
only includes costs that have been documented and captured in the
InvaCost database. Moreover, the taxonomic, geographic, temporal and
habitat trends among these costs suggest that cost reporting is very un-
even, with many IAS and countries entirely lacking reported costs. Most
costs were attributed to aquatic invertebrates (US$214 billion), with
lower costs for vertebrates (US$97 billion) and plants (US$20 billion).
Our estimate of the costs of aquatic IAS globally in the year 2020 –US
$23 billion, much higher in magnitude than the cost, for example, of man-
aging global marine protected areas (US$5–19 billion; Balmford et al.,
2004)–calls for increased investments in management of IAS.
4.1. Cost distributions across taxa
Globally, mosquitoes are major contributors to the burden of
diseases, with vector-borne pathogens and parasites causing over one
billion infections and one million deaths annually (Kilpatrick and
Randolph, 2012;Campbell-Lendrum et al., 2015). The massive costs at-
tributed to vector-borne diseases from invasive mosquitoes are thus not
surprising, given the costs to healthcare systems worldwide. In Brazil,
for example, the government invested approximately US$48 million
per year from 2015 to 2017 for limiting population outbreaks of
A. aegypti (Bueno et al., 2017). In Columbia, total medical costs for the
treatment of dengue-infected patients reached US$3 billion between
2010 and 2012 (Rodríguez et al., 2015), and the recent chikungunya
outbreak cost about US$76 million to the healthcare system (Cardano
et al., 2015). Mosquitoes can also lead to economic losses associated
with recreation and tourism, as they discourage people from carrying
out certain activities or visiting certain sites (Claeys-Mekdade and
Morales, 2002). In the present study, damages to sectors such as health
comprised 73% of mosquito costs, with just 4% spent on management.
Future range expansions of invasive mosquitoes are expected to in-
crease their economic impact (Iwamura et al., 2020). Although mosqui-
toes vector diseases in their terrestrial-based adult life stage, where most
costs are incurred, larval and pupal life stages are invariably spent in
water where management is often targeted, with the characteristics
Fig. 7. Plotof the linear curve model givenby Eq. (3) (Supplementary Material1) against the cumulativecost data. Circularmarkers representall the data. Wecomputedbestfitparameter
valuesC
max
=335.1,K=26274,α= 0.22 and metric valuesr
2
= 0.996, RMSE=6.73. Square markers represent the adjusteddata set, which excludes four upperend extreme values(any
cost valuegreater than Q
3
+ 1.5 × IQR = US$14.66 billion,where Q
3
is the upperquartile and theIQR is the interquartile rangeof the dataset), i.e.(2003, US$25.06billion), (2005,US$21.07
billion),(2009, US$18.34billion) and (2011,US$52.61 billion),corresponding to timest= 43, 45, 49 and 51, respectively.We found that C
max
=205.6,K=2882,α=0.18,r
2
= 0.999 and
RMSE = 2.26. The shaded areas represent 95% confidence regions indicating the range of predicted cumulative costs.
Fig. 8. Proportions of known established alien species, and with respect to InvaCost
estimates: numbers of species, documents, total costs and management costs between
terrestrial and aquatic habitats. Raw values are presented per habitat type; abbreviations:
b. = billion.
R.N. Cuthbert, Z. Pattison, N.G. Taylor et al. Science of the Total Environment 775 (2021) 145238
7

and distribution of aquatic habitat patches determining mosquito distri-
butions at various scales via key trait- and density-mediated processes
(Pintar et al., 2018;Cuthbert et al., 2019).
The Eurasian ruffe (G. cernua) was second most costly and has
caused declines of native fish by predation and competition, with con-
siderable economic impacts through reductions in commercially- and
recreationally-valuable fish species (Leigh, 1998). In turn, the zebra
and quagga mussels (Dreissena polymorpha and Dreissena bugensis)
are hyper-successful macrofouling freshwater bivalves, which are
highly costly to infrastructure through impeding navigation structures,
obstruction of water flow in pipes and occlusion of water filters
(Sousa et al., 2014). The coypu (M. coypus) has caused substantial eco-
nomic losses through agricultural impacts, as well as infrastructural
damage (Panzacchi et al., 2007). The primroses (Ludwigia spp.) are
known to reduce water quality that can affect economically important
taxa such as fish, and can be extremely costly to control (Williams
et al., 2010).
Costs attributed to invasive aquatic invertebrates such as the zebra
and quagga mussels were deemed highly reliable and mostly based on
empirical observations rather than extrapolations. In contrast, a large
share of vertebrate costs was potential costs, as in the case of the
three most costly vertebrate taxa, Eurasian ruffe G. cernua, coypu
M. coypus and American bullfrog L.catesbeianus. Therefore, realised ver-
tebrate costs require improved validation and reporting to the extent
possible from their actual invaded habitat. Similarly, reported costs of
plants, including the highly damaging Ludwigia species and broad-
leaved paper bark M. quinquenervia, were primarily potential costs,
not incurred at the time of estimation. Although ecological impacts of
aquatic plants have been well-studied by invasion scientists (Pyšek
et al., 2008;Gallardo et al., 2016), there is scope for more thorough re-
cording of realised economic impacts.
4.2. Cost distributions across geographic regions and types
The costs of aquatic IAS were also unevenly distributed across
geographic regions, with particularly high reported costs in North
America (US$166 billion) and Asia (US$45 billion). In turn, a substantial
proportion (26%) of the costs were unattributed to specific geographic
regions. Moreover, most costs were driven by damages (74%), whilst
management (principally control-related) costs were just 6%. It may
be expected that management costs are lower than damage or loss
costs: if the inverse were true, management would not be economically
justifiable. However, the InvaCost search strategy may have exacerbated
this difference. Reports of management costs may have been dispropor-
tionately missed by the systematic literature searches because manage-
ment studies often do not mention costs, economics or other InvaCost
search terms (Diagne et al., 2020b) in their title, abstract or keywords
(e.g. Sandodden and Johnsen, 2010).
At the country scale, the USA exhibited both the highest magnitude
of costs and the greatest number of studies compared to all other coun-
tries. The high degree of cost reporting in the USA is unsurprising given
that early estimates of costs focused on this country (Pimentel et al.,
2000,Pimentel et al., 2005), which sparked research efforts to better
understand costs and provide a more refined spatial and temporal de-
scription for those costs. The USA also scores highly on several socio-
economic variables that have been found to correlate positively with re-
ported costs of IAS (Haubrock et al., 2021;Kourantidou et al., 2021),
such as GDP (1st in world), human population (3rd), international tour-
ism arrivals (3rd) and research expenditure (9th).
In contrast, the InvaCost database contains no aquatic IAS costs at all
for many countries, particularly in Asia and Africa. However, even in
countries such as South Africa, where research on biological invasion is
leading (van Wilgen et al., 2020), large gaps in our knowledge of eco-
nomic costs are evident. For example, South Africa is a global invasion
hotspot for freshwater fish and has been invaded by numerous inverte-
brate taxa in freshwater, estuarine and marine environments, with
well-known impacts on human wellbeing (Appleton et al., 2009;
Ellender and Weyl, 2014;Weyl et al., 2020;Robinson et al., 2020).
However, we captured no monetary costs for such taxa. Similarly,
in other African countries, IAS without formally documented or
quantified costs are known to affect human societies via impacts
to biological communities, local fisheries and water storage infra-
structures (e.g. Nile perch in East Africa; Harris et al., 1995;Kwena
et al., 2012;Aloo et al., 2017,andcrayfish in Lake Naivasha, Kenya;
Kafue River, Zambia; Madzivanzira et al., 2020). Limited cost
reporting in Africa and Asia is likely reflective of a low priority
given to IAS research, or capabilities (Pyšek et al., 2008), despite
high levels of introduction via, for example, aquaculture (Lin
et al., 2015). However, there may have been some bias introduced
by the original InvaCost search string, as no currencies from these
continents were explicitly included as search terms (even if
searches were performed in 15 non-English languages, Angulo
et al., 2021).
Nonetheless, limited cost reporting in Africa and Asia is alarming
given that invasions in these countries may disproportionately impact
livelihoods, given high levels of poverty, limited resources for research
and management, and an overall limited preparedness to meet chal-
lenges brought by IAS (Early et al., 2016). Limited cost reporting also
hinders management actions, as the extent of IAS cost is not fully
realised by managers.
Network analyses additionally revealed a distinct lack of global
structuring of costs, whereby clustering appeared disparate across
taxa and countries, insinuating a largely random distribution of costs
and vast gaps in cost reporting of well-known aquatic IAS. That is, for
many countries, there was generally only one cluster, indicating unique
combinations of economic impacts associated with particular species,
despite some of these species being highly widespread. One example
of an exception to this is Aedes spp., which had a consistent and pan-
tropical impact, resulting in a distinct cost cluster. Nonetheless, other
context-dependencies, such as differences in climate and pathways,
likely also influence IAS compositions.
4.3. Temporal trends in costs
The majority of fitted models indicated exponentially increasing
annual costs of aquatic IAS since 1960 over several magnitudes, to a
best-fit extrapolated annual global cost of US$23 billion in 2020.
Model differences in recent years likely reflect differential sensitivities
to time lags in cost reporting. Model predictions of cost increases over
time align with increasing rates of biological invasions worldwide
(Seebens et al., 2017), as globalisation and intensification of trade and
transport networks result in high propagule and colonisation pressures
from novel source pools (Seebens et al., 2018). Given that invasion rates
will increase further in future (Seebens et al., 2020), we can expect fur-
ther increases in economic costs –although investments in manage-
ment, especially prevention and rapid eradication, could limit realised
costs (Leung et al., 2002). Moreover, these results align with the find-
ings of Bradshaw et al. (2016) who have suggested, specifically for
invasive insects such as mosquitoes, that costs are generally largely
underestimated and are expected to increase through time. Our
mathematically-modelled density-impact curves also suggest that
costs of IAS to the global economy will continue to increase, as
they were far from an asymptotic plateau, even where extreme
values were removed and time lags not incorporated. Moreover,
this population-level approach does not account for unreported
costs or those arising from future IAS spread, and this likely results
in further underestimation.
4.4. Reporting of invasion costs of aquatic IAS compared to terrestrial IAS
Despite over one quarter of known alien species using aquatic envi-
ronments, only 5% of the total cost in the InvaCost database was
R.N. Cuthbert, Z. Pattison, N.G. Taylor et al. Science of the Total Environment 775 (2021) 145238
8

attributed to aquatic species. Further, the majority (54%) of these costs
were from semi-aquatic rather than fully aquatic species. On one
hand, this finding potentially reflects a bias in cost reporting towards
terrestrial systems, in line with ecological research in general (Menge
et al., 2009;Richardson and Poloczanska, 2008). With respect to man-
agement costs of IAS, if investments of equivalent magnitude to terres-
trial were made for aquatic systems, one would anticipate a further US
$39 billion to have been spent to date. Note that this extrapolation
does not consider potentially lower costs in aquatic ecosystems (i.e.
less infrastructure to damage) or differences in management efficien-
cies between terrestrial and aquatic environments. On the other hand,
the disparity between aquatic and terrestrial costs may thus reflect gen-
uinely lower costs of aquatic –particularly marine –IAS relative to ter-
restrial IAS. There are limited human assets and infrastructures in aquatic
systems, limiting the scope for easily-quantifiable damages and resulting
in minimal investments in prevention and management. For example,
terrestrial agricultural practices are heavily impacted by crop pests
(Paini et al., 2016;Ahmed and Petrovskii, 2019), whereas agricultural ac-
tivities in aquatic systems (e.g. rice fields) are relatively scarce. However,
aquatic systems do offer highly valuable ecosystem services that could be
affected by IAS, such as aquaculture, and often through cascading effects
that are difficult to predict (Walsh et al., 2016). Thus, we encourage in-
vestment in management of IAS in aquatic systems to limit future costs
that stem from damage and loss (Leung et al., 2002).
5. Conclusions
Urgent and coordinated management actions are required globally to
reduce economic and ecological impacts from aquatic IAS. Whilst costs of
aquatic IAS are escalating, knowledge of impacts across major taxonomic
groupings, geographic regions and habitat types remains diffuse. These
knowledge gaps suggest costs of aquatic IAS are underestimated, partic-
ularly relative to their ecological impacts and to the more intensively-
studied terrestrial species. Equally, geographical biases in reported
costs highlight the need for increased and improved cost reporting,
given that allocation of finite resources to manage IAS is underpinned
by adequate understandings of costs. We urge our results to be applied
as an incentive for managers, stakeholders and scientists to increase
and improve cost reporting and invest in a more adequate protection
of aquatic ecosystems.
CRediT authorship contribution statement
Ross N. Cuthbert: Conceptualization, Data curation, Formal analysis,
Visualization, Writing - original draft, Writing - review & editing. Zarah
Pattison: Conceptualization, Data curation, Writing - review & editing.
Nigel G. Taylor: Conceptualization, Data curation, Writing - review &
editing. Laura Verbrugge: Conceptualization, Data curation, Writing -
review & editing. Christophe Diagne: Conceptualization, Data curation,
Writing - review & editing. Danish A. Ahmed: Conceptualization,
Formal analysis, Visualization, Writing - review & editing. Boris
Leroy: Conceptualization, Data curation, Formal analysis, Visualiza-
tion, Writing - review & editing. Elena Angulo: Conceptualization,
Data curation, Writing –review & editing. Elizabeta Briski: Conceptu-
alization, Writing - review & editing. César Capinha: Conceptualization,
Writing - review & editing. Jane A. Catford: Conceptualization, Writing -
review & editing. Tatenda Dalu: Conceptualization, Writing - review &
editing. Franz Essl: Conceptualization, Writing - review & editing.
Rodolphe E. Gozlan: Conceptualization, Writing - review & editing.
Phillip J. Haubrock: Conceptualization, Writing - review & editing. Melina
Kourantidou: Conceptualization, Writing - review & editing. Andrew M.
Kramer: Conceptualization, Formal analysis, Visualization, Writing -
review & editing. David Renault: Conceptualization, Data curation,
Writing - review & editing. Ryan J. Wasserman: Conceptualization,
Writing - review & editing. Franck Courchamp: Conceptualization, Data
curation, Writing - review & editing.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influ-
ence the work reported in this paper.
Acknowledgements
The authors acknowledge the French National Research Agency
(ANR-14-CE02-0021) and the BNP-Paribas Foundation Climate Initia-
tive for funding the InvaCost project that allowed the construction of
the InvaCost database. The present work was conducted following a
workshop funded by the AXA Research Fund Chair of Invasion Biology
and is part of the AlienScenarios project funded by BiodivERsA and
Belmont-Forum call 2018 on biodiversity scenarios. RNC is funded
through a Humboldt Research Fellowship from the Alexander von
Humboldt Foundation. DAA is funded by the Kuwait Foundation for
the Advancement of Sciences (KFAS) (PR1914SM-01) and the Gulf
University for Science and Technology (GUST) internal seed fund
(187092). CD was funded by the BiodivERsA-Belmont Forum Project
AlienScenarios (BMBF/PT DLR 01LC1807C). EA was funded by the AXA
Research Fund Chair of Invasion Biology of University Paris Saclay. CC
was supported by Portuguese National Funds through Fundação para a
Ciência e a Tecnologia (CEECIND/02037/2017; UIDB/00295/2020 and
UIDP/00295/2020). TD acknowledges funding from National Research
Foundation (NRF_ZA) (Grant Number: 117700). FE appreciates funding
by the Austrian Science Foundation (FWF project no I 4011-B32). AMK
was supported by the NSF Macrosystems Biology program under grant
1834548. DR thanks InEE-CNRS who supports the French national
network Biological Invasions (Groupement de Recherche InvaBio,
2014–2022).
Appendix A. Supplementary material
Underlying data are publicly available in an online repository
(https://doi.org/10.6084/m9.figshare.12668570). The dataset used for
analysis is provided in the Supplementary Material. Supplementary
data to this article can be found online at doi:https://doi.org/10.1016/j.
scitotenv.2021.145238.
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