# A global-scale screening of non-native aquatic organisms to identify potentially invasive species under current and future climate conditions

Lorenzo Vilizzi, Gordon Copp, Jeffrey Hill, Boris Adamovich, Luke Aislabie, Daniel Akin, Abbas Al-Faisal, David Almeida, M.N. Amal Azmai, Rigers Bakiu, et al.

► To cite this version:

Lorenzo Vilizzi, Gordon Copp, Jeffrey Hill, Boris Adamovich, Luke Aislabie, et al. A global-scale screening of non-native aquatic organisms to identify potentially invasive species under current and future climate conditions. Science of the Total Environment, 2021, 788, pp.147868. 10.1016/j.scitotenv.2021.147868. hal-03544887

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https://hal.univ-lorraine.fr/hal-03544887

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A global-scale screening of non-native aquatic organisms to identify potentially invasive species under current and future climate conditions

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Lorenzo Vilizzi<sup>a</sup>, Gordon H. Copp<sup>a,b,c,d</sup>, Jeffrey E. Hill<sup>e</sup>, Boris Adamovich<sup>f</sup>, Luke Aislabie<sup>b</sup>, Daniel Akin<sup>g</sup>, Abbas J. Al-Faisal<sup>h</sup>, David Almeida<sup>i</sup>, M.N. Amal Azmai<sup>j</sup>, Rigers Bakiu<sup>k,l</sup>, Adriana Bellati<sup>m</sup>, Renée Bernier<sup>n</sup>, Jason M. Bies<sup>o</sup>, Gökçen Bilge<sup>p</sup>, Paulo Branco<sup>q</sup>, Thuyet D. Bui<sup>r</sup>, João Canning-Clode<sup>s,t</sup>, Henrique Anatole Cardoso Ramos<sup>u</sup>, Gustavo A. Castellanos-Galindo<sup>v,w</sup>, Nuno Castro<sup>s</sup>, Ratcha Chaichana<sup>x</sup>, Paula Chainho<sup>y,z,aa</sup>, Joleen Chan<sup>ab</sup>, Almir M. Cunico<sup>ac</sup>, Amelia Curd<sup>ad</sup>, Punyanuch Dangchana<sup>ae</sup>, Dimitriy Dashinov<sup>af</sup>, Phil I. Davison<sup>b</sup>, Mariele P. de Camargo<sup>ac</sup>, Jennifer A. Dodd<sup>ag</sup>, Allison L. Durland Donahou<sup>e,ah</sup>, Lennart Edsman<sup>ai</sup>, F. Güler Ekmekçi<sup>aj</sup>, Jessica Elphinstone-Davis<sup>ak</sup>, Tibor Erős<sup>al</sup>, Charlotte Evangelista<sup>am</sup>, Gemma Fenwick<sup>an</sup>, Árpád Ferincz<sup>ao</sup>, Teresa Ferreira<sup>ap</sup>, Eric Feunteun<sup>aq</sup>, Halit Filiz<sup>p</sup>, Sandra C. Forneck<sup>ac</sup>, Helen S. Gajduchenko<sup>at</sup>, João Gama Monteiro<sup>s</sup>, Ignacio Gestoso<sup>s,t</sup>, Daniela Giannetto<sup>as</sup>, Allan S. Gilles Jr<sup>at</sup>, Francesca Gizzi<sup>s</sup>, Branko Glamuzina<sup>au</sup>, Luka Glamuzina<sup>au</sup>, Jessica Goldsmit<sup>av,aw</sup>, Stephan Gollasch<sup>ax</sup>, Philippe Gouletquer<sup>ay</sup>, Joanna Grabowska<sup>a</sup>, Rogan Harmer<sup>b</sup>, Phillip J. Haubrock<sup>az,ba,bb</sup>, Dekui He<sup>bc</sup>, Jeffrey W. Hean<sup>bd,be</sup>, Gábor Herczeg<sup>bf</sup>, Kimberly L. Howland<sup>aw</sup>, Ali İlhan<sup>bg</sup>, Elena Interesova<sup>bh,bi,bj</sup>, Katarína Jakubčinová<sup>bk</sup>, Anders Jelmert<sup>bl</sup>, Stein I. Johnsen<sup>bm</sup>, Tomasz Kakareko<sup>bn</sup>, Kamalaporn Kanongdate<sup>bo</sup>, Nurçin Killi<sup>p</sup>, Jeong-Eun Kim<sup>bp</sup>, Şerife Gülsün Kırankaya<sup>bq</sup>, Dominika Kňazovická<sup>br</sup>, Oldřich Kopecký<sup>br</sup>, Vasil Kostov<sup>bs</sup>, Nicholas Koutsikos<sup>bt</sup>, Sebastian Kozic<sup>a</sup>, Tatia Kuljanishvili<sup>br</sup>, Biju Kumar<sup>bu</sup>, Lohith Kumar<sup>bv</sup>, Yoshihisa Kurita<sup>bw</sup>, Irmak Kurtul<sup>bg</sup>, Lorenzo Lazzaro<sup>bb</sup>, Laura Lee<sup>bx</sup>, Maiju Lehtiniemi<sup>by</sup>, Giovanni Leonardi<sup>bz</sup>, Rob S.E.W. Leuven<sup>ca</sup>, Shan Li<sup>cb</sup>, Tatsiana Lipinskaya<sup>cc</sup>, Fei Liu<sup>cd</sup>, Lance Lloyd<sup>ce,cf</sup>, Massimo Lorenzoni<sup>cg</sup>, Sergio Alberto Luna<sup>ch</sup>, Timothy J. Lyons<sup>e,ci</sup>, Kit Magellan<sup>cj,ck</sup>, Martin Malmström<sup>cl</sup>, Agnese Marchini<sup>cm</sup>, Sean M. Marr<sup>bd</sup>, Gérard Masson<sup>cn</sup>, Laurence Masson<sup>co</sup>, Cynthia H. McKenzie<sup>cp</sup>, Daniyar Memedemin<sup>cq</sup>, Roberto Mendoza<sup>ch</sup>, Dan Minchin<sup>cr,cs</sup>, Laurence Miossec<sup>ay</sup>, Seyed Daryoush Moghaddas<sup>ct</sup>, Moleseng C. Moshobane<sup>cu,cv,cw</sup>, Levan Mumladze<sup>cx</sup>, Rahmat Naddafi<sup>cy</sup>, Elnaz Najafi-Majd<sup>cz</sup>, Aurel Năstase<sup>da</sup>, Ion Năvodaru<sup>da</sup>, J. Wesley Neal<sup>o</sup>, Sarah Nienhuis<sup>db</sup>, Matura Nimtim<sup>x</sup>, Emma T. Nolan<sup>dc</sup>, Anna Occhipinti-Ambrogi<sup>m</sup>, Henn Ojaveer<sup>dd,de</sup>, Sergej Olenin<sup>cs</sup>, Karin Olsson<sup>b,df,dg</sup>, Norio Onikura<sup>bw</sup>, Kathryn O'Shaughnessy<sup>dh</sup>, Daniele Paganelli<sup>di</sup>, Paola Parretti<sup>s,dj</sup>, Jiří Patoka<sup>br</sup>, Richard Thomas B. Pavia Jr<sup>at</sup>, Daniele Pellitteri-Rosa<sup>cm</sup>, Michèle Pelletier-Rousseau<sup>av</sup>, Elfritzson M. Peralta<sup>at</sup>, Costas Perdikaris<sup>dk</sup>, Dariusz Pietraszewski<sup>a</sup>, Marina Piria<sup>a,dl,*</sup>, Sophie Pitois<sup>b</sup>, Laura Pompei<sup>cg</sup>, Nicolas Poulet<sup>dm</sup>, Cristina Preda<sup>cq</sup>, Riikka Puntila-Dodd<sup>by</sup>, Ali T. Qashqaei<sup>dn</sup>, Tena Radočaj<sup>dl</sup>, Hossein Rahmani<sup>do</sup>, Smrithy Raj<sup>bu,dp</sup>, David Reeves<sup>dq</sup>, Milica Ristovska<sup>dr</sup>, Viktor Rizevsky<sup>ar</sup>, D. Ross Robertson<sup>w</sup>, Peter Robertson<sup>ds</sup>, Laura Ruykys<sup>dt,du</sup>, Abdulwakil O. Saba<sup>j,dv</sup>, José M. Santos<sup>q</sup>, Hasan M. Sarı<sup>bg</sup>, Pedro Segurado<sup>q</sup>, Vitaliy Semchenko<sup>cc</sup>, Wansuk Senanan<sup>dw</sup>, Nathalie Simard<sup>av</sup>, Predrag Simonović<sup>dx</sup>, Michał E. Skóra<sup>dy</sup>, Kristína Slovák Švolíková<sup>bk</sup>, Evangelia Smeti<sup>bt</sup>, Tereza Šmídová<sup>br</sup>, Ivan Špelić<sup>dl</sup>, Greta Srèbalienè<sup>cs</sup>, Gianluca Stasolla<sup>dz</sup>, Paul Stebbing<sup>ak,ea</sup>, Barbora Števove<sup>bk</sup>, Vettath R. Suresh<sup>eb</sup>, Bettina Szajbert<sup>bf</sup>, Kieu Anh T. Ta<sup>dt</sup>, Ali Serhan Tarkan<sup>a,p</sup>, Jonathan Tempesti<sup>ec</sup>, Thomas W. Therriault<sup>ed</sup>, Hannah J. Tidbury<sup>ak</sup>, Nildeniz Top-Karakuş<sup>p</sup>, Elena Tricarico<sup>bb</sup>, Débora F.A. Troca<sup>ee</sup>, Konstantinos Tsiamis<sup>ef</sup>, Quenton M. Tuckett<sup>e</sup>, Pero Tutman<sup>eg</sup>, Umut Uyan<sup>eh</sup>, Eliza Uzunova<sup>af</sup>

<footnote>* Corresponding author.
E-mail address: mpiria@agr.hr (M. Piria).
¹ Prof. Weyl passed away suddenly during the final stages of this manuscript's preparation.</footnote>
Leonidas Vardakas<sup>bt</sup>, Gaute Velle<sup>e1,ej</sup>, Hugo Verreycken<sup>ek</sup>, Lizaveta Vintsek<sup>el</sup>, Hui Wei<sup>em,en</sup>, András Weiperth<sup>ao</sup>, Olaf L.F. Weyl<sup>eo,bd,1</sup>, Emily R. Winter<sup>dc</sup>, Radosław Włodarczyk<sup>ep</sup>, Louisa E. Wood<sup>ak</sup>, Ruibin Yang<sup>eq</sup>, Sercan Yapıcı<sup>p</sup>, Shayne S.B. Yeo<sup>ab</sup>, Baran Yoğurtçuoğlu<sup>aj</sup>, Anna L.E. Yunnie<sup>er</sup>, Yunjie Zhu<sup>es</sup>, Grzegorz Zieba<sup>a</sup>, Kristína Žitňanová<sup>bk</sup>, Stacey Clarke<sup>b</sup>

<sup>a</sup> Department of Ecology and Vertebrate Zoology, Faculty of Biology and Environmental Protection, University of Lodz, 90-237 Lodz, Poland

<sup>b</sup> Centre for Environment, Fisheries and Aquaculture Science, Lowestoft, Suffolk NR33 0HT, UK

<sup>c</sup> Centre for Ecology, Environment and Sustainability, Bournemouth University, Poole, Dorset BH12 5BB, UK

<sup>d</sup> School of the Environment, Trent University, Peterborough, Ontario K9L 0G2, Canada

<sup>e</sup> Tropical Aquaculture Laboratory, Program in Fisheries and Aquatic Sciences, School of Forest Resources and Conservation, University of Florida, Ruskin, FL 33570, USA

<sup>f</sup> Faculty of Biology, Belarusian State University, 220030 Minsk, Belarus

<sup>g</sup> College of Science and Mathematics, Auburn University, Auburn, AL 36849, USA

<sup>h</sup> Marine Science Centre, University of Basrah, PO Box 49, Basrah, Iraq

<sup>i</sup> Departamento de Ciencias Médicas Básicas, Facultad de Medicina, Universidad San Pablo CEU, 28003 Madrid, Spain

<sup>j</sup> Department of Biology, Faculty of Science, Universiti Putra Malaysia, 43400 Seri Kembangan, Selangor, Malaysia

<sup>k</sup> Department of Aquaculture and Fisheries, Faculty of Agriculture and Environment, Agricultural University of Tirana, Tirana 1000, Albania

<sup>l</sup> Albanian Center for Environmental Protection and Sustainable Development, Tirana 1000, Albania

<sup>m</sup> Department of Ecological and Biological Sciences, University of Tuscia, 01100 Viterbo, Italy

<sup>n</sup> Fisheries and Oceans Canada, Gulf Fisheries Centre, Moncton, New Brunswick E1C 5K4, Canada

<sup>o</sup> Department of Wildlife, Fisheries & Aquaculture, Mississippi State University, Mississippi State, MS 39762, USA

<sup>p</sup> Department of Basic Sciences, Faculty of Fisheries, Muğla Sıtkı Koçman University, 48000 Menteşe, Muğla, Turkey

<sup>q</sup> Forest Research Centre, School of Agriculture, University of Lisbon, Tapada da Ajuda 1349-017, Lisbon, Portugal

<sup>r</sup> Faculty of Marine Science, Hanoi University of Natural Resources and Environment, 41A Phu Dien, Bac Tu Liem, Hanoi, Viet Nam

<sup>s</sup> MARE - Marine and Environmental Sciences Centre, Agência Regional para o Desenvolvimento da Investigação Tecnologia e Inovação (ARDITI), 9020-105 Funchal, Madeira, Portugal

<sup>t</sup> Smithsonian Environmental Research Center, Edgewater, MD 21037, USA

<sup>u</sup> Coordination of Sustainable Use of Fisheries Resources, Department of Species Conservation, Ministry of Environment, 70068-900 Brasilia, Brazil

<sup>v</sup> Leibniz Centre for Tropical Marine Research (ZMT), 28359 Bremen, Germany

<sup>w</sup> Smithsonian Tropical Research Institute, Apartado 2072, Balboa, Panamá

<sup>x</sup> Department of Environmental Technology and Management, Faculty of Environment, Kasetsart University, Bangkok 10900, Thailand

<sup>y</sup> MARE - Marine and Environmental Sciences Centre, Faculty of Sciences, University of Lisbon, 1749-016 Lisboa, Portugal

<sup>z</sup> Department of Animal Biology, Faculty of Sciences, University of Lisbon, 1749-016 Lisboa, Portugal

<sup>aa</sup> Polytechnic Institute of Setúbal, 2910-761 Setúbal, Portugal

<sup>ab</sup> Department of Biological Sciences, National University of Singapore, 117558, Singapore

<sup>ac</sup> Laboratory of Ecology, Fisheries and Ichthyology, Biodiversity Department - Palotina Sector, Federal University of Paraná (UFPR), Curitiba 80060-000, Brazil

<sup>ad</sup> Laboratory of Coastal Benthic Ecology, French Research Institute for Exploitation of the Sea (IFREMER), 29280 Plouzané, France

<sup>ae</sup> Division of Research Policy and Plan, National Research Council of Thailand, Bangkok 10900, Thailand

<sup>af</sup> Department of General and Applied Hydrobiology, Faculty of Biology, Sofia University, 1164 g.k. Lozenets, Sofia, Bulgaria

<sup>ag</sup> Animal and Plant Sciences Group, Edinburgh Napier University, Sighthill, Edinburgh EH11 4BN, UK

<sup>ah</sup> Florida Southern College, Lakeland, FL 33801, USA

<sup>ai</sup> Department of Aquatic Resources, Institute of Freshwater Research, Swedish University of Agricultural Sciences, SE-750 07 Drottningholm, Sweden

<sup>aj</sup> Hydrobiology section, Department of Biology, Faculty of Science, Hacettepe University, Çankaya-Ankara 06800, Turkey

<sup>ak</sup> Centre for Environment, Fisheries and Aquaculture Science, Weymouth, Dorset DT4 8UB, UK

<sup>al</sup> Centre for Ecological Research, Balaton Limnological Institute, Tihany 8237, Hungary

<sup>am</sup> Centre for Ecological and Evolutionary Synthesis, University of Oslo, NO-0316 Oslo, Norway

<sup>an</sup> Lancaster Environment Centre, Lancaster University, Lancaster, Lancashire LA1 4YW, UK

<sup>ao</sup> Institute for Natural Resources Conservation, Department of Aquaculture, Faculty of Agriculture and Environmental Sciences, Szent István University, Gödöllő 2100, Hungary

<sup>ap</sup> Department of Natural Resources, Environment and Landscape, School of Agriculture, University of Lisbon, 1349-017 Lisbon, Portugal

<sup>aq</sup> Muséum National d'Histoire Naturelle, Laboratoire Biologie des Organismes et Ecosystèmes Aquatiques, BOREA (MNHN, CNRS, Sorbonne Université, Université de Caen, IRD, Université de Guadeloupe Antilles), Station Marine de Dinard, CRESCO, 35800 Dinard, France

<sup>ar</sup> Laboratory of Ichthyology, Scientific and Practical Center for Bioresources, National Academy of Sciences of Belarus, Minsk 220072, Belarus

<sup>as</sup> Department of Biology, Faculty of Science, Muğla Sıtkı Koçman University, 48000 Menteşe, Muğla, Turkey

<sup>at</sup> Department of Biological Sciences, College of Science, Research Center for the Natural and Applied Sciences, Graduate School, University of Santo Tomas, Manila, 1008, Metro Manila, Philippines

<sup>au</sup> Department of Applied Ecology, University of Dubrovnik, 20000 Dubrovnik, Croatia

<sup>av</sup> Fisheries and Oceans Canada, Maurice Lamontagne Institute, Mont-Joli, Quebec G5H 3Z4, Canada

<sup>aw</sup> Arctic and Aquatic Research Division, Freshwater Institute, Fisheries and Oceans Canada, Winnipeg, Quebec MB R3T 2N6, Canada

<sup>ax</sup> GoConsult, 22763 Hamburg, Germany

<sup>ay</sup> Scientific Direction, French Research Institute for Exploitation of the Sea (IFREMER), 44980 Nantes, France

<sup>az</sup> Senckenberg Research Institute and Natural History Museum Frankfurt, Department of River Ecology and Conservation, 63571 Gelnhausen, Germany

<sup>ba</sup> Nature and Environment Management Operators s.r.l., 50121 Florence, Italy

<sup>bb</sup> Department of Biology, University of Florence, 50121 Florence, Italy

<sup>bc</sup> The Key Laboratory of Aquatic Biodiversity and Conservation, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430071, China

<sup>bd</sup> DST/NRF Research Chair in Inland Fisheries and Freshwater Ecology, South African Institute for Aquatic Biodiversity, Grahamstown 6140, South Africa

<sup>be</sup> GroundTruth, Water, Wetlands and Environmental Engineering, Hilton, KwaZulu-Natal 3245, South Africa

<sup>bf</sup> Behavioural Ecology Group, Department of Systematic Zoology and Ecology, ELTE Eötvös Loránd University, H-1117 Budapest, Hungary

<sup>bg</sup> Faculty of Fisheries, Ege University, 35100 Bornova, Izmir, Turkey

<sup>bh</sup> Tomsk State University, Tomsk 634050, Russia

<sup>bl</sup> Institute of Systematics and Ecology of Animals, Siberian Branch of the Russian Academy of Sciences, Novosibirsk 630090, Russia

<sup>bn</sup> Novosibirsk branch of Russian Federal Research Institute of Fisheries and Oceanography, Novosibirsk 630090, Russia

<sup>bk</sup> Department of Ecology, Faculty of Natural Sciences, Comenius University, 841 04 Bratislava, Slovakia

<sup>bl</sup> Institute of Marine Research, Flødevigen Research Station, NO-7485 His, Norway

<sup>bm</sup> Norwegian Institute for Nature Research, NO-7485 Trondheim, Norway

<sup>bn</sup> Department of Ecology and Biogeography, Faculty of Biological and Veterinary Sciences, Nicolaus Copernicus University, 87-100 Toruń, Poland

<sup>bo</sup> Faculty of Environment and Resource Studies, Mahidol University, Salaya 73170, Thailand

<sup>bp</sup> College of Biological Sciences and Biotechnology, Chungnam National University, Daejeon 305-764, Republic of Korea

<sup>bq</sup> Department of Biology, Faculty of Arts and Science, Düzce University, 81620 Düzce, Turkey

<sup>br</sup> Department of Zoology and Fisheries, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, 165 00 Praha, Czechia

<sup>bs</sup> Department of Fisheries, Institute of Animal Science, Ss Cyril and Methodius University, Skopje 1000, Macedonia

<sup>bt</sup> Institute of Marine Biological Resources & Inland Waters, Hellenic Centre for Marine Research, Anavissos, 19013, Attica, Greece
bu Department of Aquatic Biology & Fisheries, University of Kerala, Thiruvananthapuram, Kerala 695034, India

bv REF Division, ICAR-Central Inland Fisheries Research Institute, Kolkata, West Bengal 700120, India

bw Fishery Research Laboratory, Kyushu University, Fukutsu, Fukuoka 811-3304, Japan

bx Department of Evolution, Ecology and Behaviour, Faculty of Health and Life Sciences, University of Liverpool, Liverpool L69 7TX, England, United Kingdom

by Marine Research Centre, Finnish Environment Institute, 00790 Helsinki, Finland

bz Hierofalcon Research Group, 95124 Catania, Italy

ca Department of Animal Ecology and Physiology, Institute for Water and Wetland Research, Radboud University & Netherlands Centre of Expertise on Exotic Species, 6500 GL Nijmegen, the Netherlands

cb Natural History Research Center, Shanghai Natural History Museum, Branch of Shanghai Science & Technology Museum, Shanghai 200041, China

cc Laboratory of Hydrobiology, Scientific and Practical Center for Bioresources, National Academy of Sciences of Belarus, Minsk 220072, Belarus

cd Aquatic Science Institute, Tibet Academy of Agriculture and Animal Husbandry Science, Lhasa 850009, China

ce Lloyd Environmental Pty Ltd, Somers, Victoria 3927, Australia

cf School of Health and Life Sciences, Federation University Australia, Ballarat, Victoria 3350, Australia

cg Department of Chemistry, Biology and Biotechnologies, University of Perugia, 06123 Perugia, Italy

ch Facultad de Ciencias Biológicas, Universidad Autónoma de Nuevo León, Nuevo León 66455, Mexico

c New Mexico Biopark Society, Albuquerque, NM 87102, USA

c South African Institute for Aquatic Biodiversity, Grahamstown 6140, South Africa

ck University of Battambang, 02360 Battambang, Cambodia

d Norwegian Scientific Committee for Food and Environment (VKM), NO-0213 Oslo, Norway

cm Department of Earth and Environmental Sciences, University of Pavia, 27100 Pavia, Italy

cn Laboratoire interdisciplinaire des environnements continentaux, Centre national de la recherche scientifique, Université de Lorraine, 57000 Metz, France

co Freshwater Fish Ecology Laboratory, Ecosystem Science and Management Program, University of Northern British Columbia, Prince George, British Columbia V2N 4Z9, Canada

cp Northwest Atlantic Fisheries Centre, Fisheries and Oceans Canada, St. John's, Newfoundland and Labrador A1A 5J7, Canada

cq Faculty of Natural and Agricultural Sciences, Ovidius University of Constanta, Constanta 900527, Romania

cr Marine Organism Investigations, Marina Village, Ballina, Killaloe, Clare V94 767X, Ireland

cs Marine Research Institute, Klaipėda University, 92294 Klaipėda, Lithuania

ct Department of Biodiversity and Ecosystems Management, Environmental Sciences Research Institute, Shahid Beheshti University, 1983963113 Tehran, Iran

cu South African National Biodiversity Institute, Biological Invasions Directorate, Pretoria 0001, South Africa

cv Department of Biology, Sefako Makgatho Health Sciences University, Gauteng 0208, South Africa

cw Young Water Professionals, South African Chapter, Limpopo 1685, South Africa

c Institute of Zoology, Ilia State University, Tbilisi 0162, Georgia

cy Swedish University of Agricultural Sciences, Department of Aquatic Resources, Division of Coastal Research, SE-453 30 Oregrund, Sweden

cz Department of Biology, Faculty of Sciences, Ege University, 35040 Izmir, Turkey

da Department of Biodiversity Conservation and Sustainable Use of Natural Resources, Danube Delta National Institute for Research and Development, Tulcea 820112, Romania

db Ontario Ministry of Natural Resources and Forestry, Peterborough, Ontario K9J 8M5, Canada

dc Department of Life and Environmental Sciences, Faculty of Science and Technology, Bournemouth University, Talbot Campus, Poole BH12 5BB, UK

dd University of Tartu, 80012 Pärnu, Estonia

de National Institute of Aquatic Resources, Technical University of Denmark, 2800 Lyngby, Denmark

df School of Zoology, Tel Aviv University, Tel Aviv 6997801, Israel

dg The Inter-University Institute for Marine Sciences in Eilat, Coral Beach, Eilat 8810302, Israel

dh Texas Parks and Wildlife Department, Coastal Fisheries, 4200 Smith School Rd., Austin, TX 78744, USA

di CNR-IRSA Water Research Institute, 28922 Verbania, Italy

dj CIBIO, Research Center in Biodiversity and Genetic Resources, InBIO Associate Laboratory and Faculty of Sciences and Technologies, University of the Azores, 9500-321 Ponta Delgada, Portugal

dk Department of Fisheries, Regional Unit of Thesprotia, Epirus, 46 100, Igoumenitsa, Greece

dl Department of Fisheries, Apiculture, Wildlife Management and Special Zoology, University of Zagreb Faculty of Agriculture, 10000 Zagreb, Croatia

dm Pôle écohydraulique OFB-IMFT-P, French Agency for Biodiversity, 31400 Toulouse, France

dn Sahel Square, Tehran 1493889881, Iran

do Sari Agricultural Sciences and Natural Resources University, Sari, 4816118771, Mazandaran, Iran

dp National Centre for Biological Sciences, Bangalore 560065, India

dq National Fish and Wildlife Foundation, Baton Rouge, LA 70808, USA

dr Institute of Biology, Faculty of Natural Sciences and Mathematics, Ss Cyril and Methodius University, 10000 Skopje, Macedonia

ds Modelling, Evidence and Policy Group, School of Natural and Environmental Resources, Newcastle University, Newcastle NE1 7RU, UK

dt Nature and Biodiversity Conservation Agency, Vietnam Environment Administration, Ministry of Natural Resources and Environment, 10 Ton That Thuyet, Nam Tu Liem District, Hanoi, Viet Nam

du Flora and Fauna Division, Department of Environment and Natural Resources, Palmerston, Northern Territory 0828, Australia

dv School of Agriculture, Lagos State University, Epe Campus, 106101 Epe, Lagos State, Nigeria

dw Department of Aquatic Science, Faculty of Science, Burapha University, Chon Buri 20130, Thailand

dx Faculty of Biology & Institute for Biological Research "Siniša Stanković", University of Belgrade, Belgrade 11000, Serbia

dy University of Gdańsk, Faculty of Oceanography and Geography, Institute of Oceanography, Professor Krzysztof Skóra Hel Marine Station, 84-150 Hel, Poland

dz Dadoda S.r.ls, 70029 Santeramo in colle, Italy

ea APEM Ltd, A17 Embankment, Business Park, Heaton Mersey, Manchester, Cheshire SK4 3GN, UK

eb Division of Mariculture, Central Marine Fisheries Research Institute, Cochin, Kerala 682018, India

ec Department of Biology, University of Pisa, 56126 Pisa, Italy

ed Pacific Biological Station, Fisheries and Oceans Canada, Nanaimo, British Columbia V9T 6N7, Canada

ee Institute of Oceanography, Federal University of Rio Grande, 96203-900 Rio Grande, Brazil

ef Institute of Oceanography, Hellenic Centre for Marine Research, Attica, Anavyssos 19013, Greece

eg Laboratory for Ichthyology and Coastal Fisheries, Institute of Oceanography and Fisheries, 21000 Split, Croatia

eh Skretting Turkey, Güllük Milas, 48670, Muğla, Turkey

el Norwegian Research Centre, 5007 Bergen, Norway

el Department of Biological Sciences, University of Bergen, 5007 Bergen, Norway

ek Research Institute for Nature and Forest (INBO), B-1630 Linkebeek, Belgium

el Institute of Botany, Faculty of Biology, Jagiellonian University, 30-387 Kraków, Poland

em Pearl River Fisheries Research Institute, Chinese Academy of Fishery Science, Guangzhou 510380, China

en Key Laboratory of Recreational fisheries Research, Ministry of Agriculture and Rural Affairs, Guangzhou 510380, China

eo Centre for Invasion Biology, South African Institute for Aquatic Biodiversity, Makhanda 6139, South Africa

ep Department of Biodiversity Studies and Bioeducation, Faculty of Biology and Environmental Protection, University of Lodz, 90-237 Lodz, Poland

eq College of Fisheries, Huazhong Agricultural University, Wuhan 430070, China

er PML Applications Ltd, Plymouth, Devon PL1 3DH, UK

es Aquaculture Technology Promotion Station of Nantong, Nantong, China
## HIGHLIGHTS

* A global approach is required to identify invasive species posing high risk impact.

* 195 assessors screened 819 non-native species from 15 groups of aquatic organisms.

* Risk thresholds were identified for 14 aquatic organism groups.

* The resulting risk thresholds and rankings will help management and conservation.

## GRAPHICAL ABSTRACT

<figure><img src="image_4.png" /></figure>

## ARTICLE INFO

**Article history:**

Received 13 April 2021

Received in revised form 13 May 2021

Accepted 14 May 2021

Available online 20 May 2021

Editor: Damia Barcelo

**Keywords:**

Decision support tools

AS-ISK

Hazard identification

Non-native species

Risk analysis

Climate change

## ABSTRACT

The threat posed by invasive non-native species worldwide requires a global approach to identify which introduced species are likely to pose an elevated risk of impact to native species and ecosystems. To inform policy, stakeholders and management decisions on global threats to aquatic ecosystems, 195 assessors representing 120 risk assessment areas across all six inhabited continents screened 819 non-native species from 15 groups of aquatic organisms (freshwater, brackish, marine plants and animals) using the Aquatic Species Invasiveness Screening Kit. This multi-lingual decision-support tool for the risk screening of aquatic organisms provides assessors with risk scores for a species under current and future climate change conditions that, following a statistically based calibration, permits the accurate classification of species into high-, medium- and low-risk categories under current and predicted climate conditions. The 1730 screenings undertaken encompassed wide geographical areas (regions, political entities, parts thereof, water bodies, river basins, lake drainage basins, and marine regions), which permitted thresholds to be identified for almost all aquatic organismal groups screened as well as for tropical, temperate and continental climate classes, and for tropical and temperate marine ecoregions. In total, 33 species were identified as posing a 'very high risk' of being or becoming invasive, and the scores of several of these species under current climate increased under future climate conditions, primarily due to their wide thermal tolerances. The risk thresholds determined for taxonomic groups and climate zones provide a basis against which area-specific or climate-based calibrated thresholds may be interpreted. In turn, the risk rankings help decision-makers identify which species require an immediate 'rapid' management action (e.g. eradication, control) to avoid or mitigate adverse impacts, which require a full risk assessment, and which are to be restricted or banned with regard to importation and/or sale as ornamental or aquarium/fishery enhancement.

© 2021 The Author(s). 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 threat posed by invasive non-native species (NNS) worldwide to native species and ecosystems requires a global approach to identify species that pose a high risk of becoming invasive across varied geographies and climates (Gordon et al., 2008). Use of the same risk screening protocols worldwide can provide the larger-scale information with which to inform the decisions of policy makers and environmental managers in dealing with species invasions (Early et al., 2016; Shackleton et al., 2019). Indeed, reliable, evidence-based risk assessment (RA) methods are vital to decision-making in biosecurity management at national and international levels in order to prevent or mitigate unwanted biological invasions (Kaiser and Burnett, 2010). Ideally, the RA outcomes should compare and prioritise NNS according to their potential invasiveness under current and future climate conditions (Barney and DiTomaso, 2010), which aids in identifying the underlying factors associated with invasion hotspots (O'Donnell et al., 2012; Chapman et al., 2020).

The first step in the NNS risk analysis process is risk screening (i.e. hazard identification), which aims to identify the NNS that are likely to pose an elevated threat to native species and ecosystems and, therefore, warrant more comprehensive (i.e. full) RA (Copp et al., 2005b). Aquatic species that are likely to carry a high risk of becoming invasive,

hence posing a threat to native species, usually possess life-history traits including frequent reproduction with a high incidence of ovoviviparity, large body size and a long life span, the ability to exploit opportunistically available food resources, a history of invasion success, and a close climate matching with the RA area (Statzner et al., 2008; Chan et al., 2021). In addition, these species often tolerate higher salinity, broader environmental temperatures and higher levels of organic pollution than native species (Leuven et al., 2009). Importantly, the identification of species posing a high risk of being (or becoming) invasive in a certain RA area provides a basis for advice to policy, decision-makers and other stakeholders regarding management options for existing and potential future invasive NNS (Copp et al., 2005b, 2016b; Mumford et al., 2010; David et al., 2013; David and Gollasch, 2018, 2019).

The objectives of the present study were to: (i) construct a global database of risk screenings that span the broadest range of aquatic organisms possible, given available resources, across the widest possible geographical spread; (ii) subject the global database of screenings to calibration and accuracy analysis; and (iii) generate global-scale risk thresholds at the organism group and climate class/marine ecoregion levels under both current and future climate conditions. The global-scale thresholds identified will provide a basis against which thresholds calibrated for specific RA areas may be interpreted and will also allow the 'rapid risk screening' of individual species for a certain RA area
whenever specific calibration is not possible. In addition, these global-scale thresholds will place RA area-specific risk screening calibrations within a broader, global context, also accounting for climate change predictions. In turn, this will enhance the value and scope of more localised calibrations to inform environmental policy and decision-makers of the relative risk rankings of aquatic NNS so as to facilitate the cost-effective allocation of management resources.

## 2. Materials and methods

### 2.1. Risk screening procedure

For the purposes of this study, 'invasive species' are defined, as per Copp et al. (2005c, p. 244), as those NNS "that spread, with or without the aid of humans, in natural or semi-natural habitats, producing a significant change in composition, structure, or ecosystem processes, or cause severe economic losses to human activities". Species were evaluated for their potential to become invasive in the assessor(s)-defined RA area using the Aquatic Species Invasiveness Screening Kit (AS-ISK: free download at www.cefas.co.uk/nns/tools/). This is a decision-support tool (Copp et al., 2016b, 2021) adapted from the Fish Invasiveness Screening Kit (Copp et al., 2009, 2005a), which itself was derived from the globally-applied Weed Risk Assessment of Pheloung et al. (1999). The AS-ISK comprises questions from the generic screening module of the European Non-native Species in Aquaculture Risk Analysis Scheme (Copp et al., 2016a) and incorporates the 'minimum requirements' (Roy et al., 2018) for the assessment of invasive NNS with regard to the 2014 EU Regulation 1143/2014 (European Union, 2014). As a taxon-generic toolkit, the AS-ISK is applicable to any aquatic species (other than parasites and pathogens) in virtually any climatic/marine ecoregion zone (Copp et al., 2016b; Table 1), and allows the screening of 27 groups of aquatic organisms in total (taxonomy after Ruggiero et al., 2015): mammals, birds, reptiles, amphibians, fishes (freshwater, brackish, marine), tunicates, lancelets, invertebrates (freshwater, brackish, marine), 'other' animals (freshwater, brackish, marine), plants (freshwater, brackish, marine), protists (freshwater, brackish, marine), fungi (freshwater, brackish, marine), and bacteria (freshwater, brackish, marine).

The screening protocol consists of 55 questions (Copp et al., 2016b). The first 49 questions comprise the Basic Risk Assessment (BRA), which are concerned with the biogeographical and biological aspects of the species being screened. The last six questions address the Climate

<table><caption>**Table 1**
Published initial applications of the Aquatic Species Invasiveness Screening Kit (AS-ISK) by aquatic organismal group.</caption><thead><tr><th>Aquatic<br>organismal group</th><th>Reference(s)</th></tr></thead><tbody><tr><td>Reptiles</td><td>Ruykys et al. (2021)</td></tr><tr><td>Amphibians</td><td>Ruykys et al. (2021)</td></tr><tr><td>Freshwater fishes</td><td>Glamuzina et al. (2017), Li et al. (2017), Tarkan et al. (2017a), Tarkan et al. (2017b), Dodd et al. (2019), Suresh et al. (2019), Interesova et al. (2020), Moghaddas et al. (2020), Zięba et al. (2020), Glamuzina et al. (2021), Haubrock et al. (2021), Kumar et al. (2021), Moghaddas et al. (2021), Radočaj et al. (2021), Ruykys et al. (2021), Wei et al. (2021)</td></tr><tr><td>Brackish fishes</td><td>Castellanos-Galindo et al. (2018), Clarke et al. (2020)</td></tr><tr><td>Marine fishes</td><td>Filiz et al. (2017a), Filiz et al. (2017b), Bilge et al. (2019), Clarke et al. (2020), Lyons et al. (2020), Uyan et al. (2020)</td></tr><tr><td>Tunicates</td><td>Clarke et al. (2020)</td></tr><tr><td>Freshwater<br>invertebrates</td><td>Paganelli et al. (2018), Ruykys et al. (2021), Semenchenko<br>et al. (2018)</td></tr><tr><td>Brackish<br>invertebrates</td><td>Clarke et al. (2020), Ruykys et al. (2021)</td></tr><tr><td>Marine<br>invertebrates</td><td>Clarke et al. (2020), Killi et al. (2020), Stasolla et al. (2020),<br>Ruykys et al. (2021)</td></tr><tr><td>Freshwater plants</td><td>Ruykys et al. (2021)</td></tr><tr><td>Marine plants</td><td>Clarke et al. (2020), Ruykys et al. (2021)</td></tr><tr><td>Marine protists</td><td>Clarke et al. (2020)</td></tr></tbody></table>

Change Assessment (CCA), which require the assessor to evaluate how future predicted climate conditions are likely to affect the BRA with respect to risks of introduction, establishment, dispersal and impact. To achieve a valid screening, for each question the assessor must provide a response, a level of confidence in the response, and a justification. In all cases, the assessor is a specialist in the biology/ecology of the aquatic organism under screening for the RA area under study. Upon completion of the screening, the species receives both a BRA score and a BRA + CCA (composite) score (ranging from -20 to 68 and from -32 to 80, respectively). Scores < 1 suggest that the species is unlikely to become invasive and is therefore classified as 'low risk' (Pheloung et al., 1999). Higher scores classify the species as posing either a 'medium risk' or a 'high risk' of becoming invasive. Distinction between medium-risk and high-risk levels depends upon setting a 'threshold' value (see Section 2.2 Data processing and analysis).

The ranked levels of confidence (1 = low; 2 = medium; 3 = high; 4 = very high) associated with each question-related response mirror the confidence rankings recommended by the International Programme on Climate Change (IPCC, 2005; see also Copp et al., 2016b). Based on the confidence level (CL) allocated to each response, a confidence factor (CF) is computed as:

$$CF = \sum (CL_{Qi})/(4 \times 55) (i = 1, \dots, 55)$$

where CL<sub>Qi</sub> is the confidence level for the ith Question (Qi), 4 is the maximum achievable value for confidence (i.e. very high: see above) and 55 is the total number of questions. Based on the 49 Qs comprising the BRA and the six Qs comprising the CCA, the CL<sub>BRA</sub> and CL<sub>CCA</sub> are also computed (out of the CL<sub>Total</sub> for all 55 Qs).

### 2.2. Data processing and analysis

Data consisted of: (i) individual contributions to the present study by assessors invited to screen one (or more) NNS belonging to one or more aquatic organismal groups of choice (i.e. falling within their expertise) for a certain RA area; and (ii) datasets from more comprehensive screening studies of NNS for a certain RA area, both published (see Table 1) and unpublished. For each species screened, the scientific name used in the original contribution or study was updated to the most recent taxonomy after the World Register of Marine Species (www.marinespecies.org), else after the Integrated Taxonomic Information System (www.itis.gov/) or FishBase (www.fishbase.org). This was followed by 'cross-checking' for the existence of at least one peer-reviewed publication that used the updated scientific name in case of a change in taxonomy. A notable exception was the retention of the original name Crassostrea gigas instead of the recently proposed Magallana gigas for the Pacific oyster (see Bayne et al., 2017). Taxonomic details of the corresponding Order and Family were also retrieved for each species screened.

Except for marine regions, for each RA area the corresponding Köppen-Geiger climate class (i.e. Tropical, Dry, Temperate, Continental, Polar: Peel et al., 2007) was identified, noting that in several cases more than one climate class applied to the same RA area. For marine regions, the classification by Spalding et al. (2007) was used including: (i) Arctic, (ii) Temperate Northern Atlantic and Temperate Northern Pacific (grouped in the present study into 'Temperate marine'), and (iii) Central Indo-Pacific, Tropical Atlantic, Tropical Eastern Pacific and Western Indo-Pacific (grouped in the present study into 'Tropical marine').

The shape of the global distribution of the BRA and BRA + CCA scores was tested in R x64 v3.6.3 (R Development Core Team, 2020) using the package 'moments' v0.14 (Komsta and Novomestky, 2015), with normality, skewness and kurtosis evaluated by the Jarque-Bera (JB), D'Agostino and Anscombe tests, respectively. Computation of risk outcomes was based on receiver operating characteristic (ROC) curve analysis (Bewick et al., 2004). An ROC curve is a graph of sensitivity vs
1 - specificity for each threshold value, where in the present context sensitivity and specificity will be the proportion of a priori invasive and non-invasive species, respectively, correctly identified as such. For ROC curve analysis to be implemented, the species selected for screening must be categorised a priori as non-invasive or invasive using independent literature sources.

The a priori categorisation was as follows (see also Clarke et al., 2020): (i) a first search was made of FishBase for any reference to the species' threat, with the species categorised as non-invasive if listed as 'harmless', categorised as invasive if listed as 'potential pest', or scored as absent if either not evaluated or not listed in the above database; (ii) a second search was made of the Centre for Agriculture and Bioscience International Invasive Species Compendium (CABI ISC: www.cabi.org/ISC) and the Global Invasive Species Database (GISD: www.iucngisd.org), with the species categorised as invasive if it appeared in any of such lists or scored as absent if not listed; (iii) a third search was made of the Invasive and Exotic Species of North America list (www.invasive.org), with the species categorised as invasive if it appeared in any of such lists or scored as absent if not listed; (iv) except for those species categorised as invasive in any (or all) of the previous three steps, a Google Scholar (literature) search was performed to check whether at least one peer-reviewed reference is found that 'demonstrates' (hence, not 'assumes') invasiveness/impact. The latter was then taken as 'sufficient evidence' for categorising the species as invasive; whereas, if no evidence was found, then the species was categorised as non-invasive. Overall, the advantage of this method is that, by virtue of its meta-analytical foundation, it draws upon and combines previous approaches into a multi-tiered protocol. This maximises the amount of information collectable about the NNS under screening, thereby increasing the accuracy of the screening outcomes (Vilizzi, Copp and Hill, unpublished).

A measure of the accuracy of the calibration analysis is the Area Under the Curve (AUC), which ranges from 0 to 1: a model whose predictions are 100% correct has an AUC of 1, one whose predictions are 100% wrong has an AUC of 0. In the former case there are neither 'false positives' (a priori non-invasive species classified as high risk, hence false invasive) nor 'false negatives' (a priori invasive species classified as low or medium risk, hence false non-invasive); in the latter case, the test cannot discriminate between 'true positives' (a priori invasive species classified as high risk, hence true invasive) and 'true negatives' (a priori non-invasive species classified as low or medium risk, hence true non-invasive). Following ROC curve analysis, the best threshold value that maximises the true positive rate and minimises the false positive rate was determined using Youden's J statistic.

Because of sample size constraints (see Vilizzi et al., 2019), group-specific thresholds for both the BRA and BRA + CCA were fitted to those groups of aquatic organisms for which >10 species were screened. Consequently, for mammals and birds for which there were low numbers of taxa a combined threshold was computed by pooling together the screened species for these groups with those screened for reptiles and amphibians. This rendered the respective thresholds statistically significant, permitting their use for distinguishing between high-risk and low-to-medium risk species until such time that RA-area-specific calibrations can be undertaken for those taxonomic groups. To highlight 'very high risk' species for the aquatic organismal groups with large enough sample sizes, ad hoc thresholds for the BRA and BRA + CCA were set weighted according to the range of scores for the high-risk species (see Clarke et al., 2020) and with the constraint that the species was screened for a 'representative' number of RA areas (i.e. weighted according to the corresponding organismal group). Additionally, climate class-specific thresholds were computed for freshwater fishes, and marine ecoregion-specific thresholds for marine fishes and invertebrates - the aquatic organismal groups with large enough sample size for successful computation of such thresholds. In all cases, ROC curve analysis was carried out with the package 'pROC' (Robin et al., 2011) for R x64 v3.6.3 using 2000 bootstrap replicates for the

confidence intervals of specificities, which were computed along the entire range of sensitivity points (i.e. 0 to 1, at 0.1 intervals).

Following Smith et al. (1999), three measures of accuracy were defined:

1) for a priori invasive species: $A_i = (I_r/I_t) \times 100$, where $I_r$ is the number of a priori invasive species rejected, and $I_t$ the total number of a priori invasive species screened;

2) for a priori non-invasive species: $A_n = (N_a/N_t) \times 100$, where $N_a$ is the number of a priori non-invasive species accepted and $N_t$ the total number of a priori non-invasive species screened;

3) overall: $A_o = (N_a + I_r)/(N_t + I_t)$.

In all cases, values above 50% are indicators of the accuracy of the screening tool.

### 3. Results

In total, 1730 screenings were conducted by 195 assessors (the co-authors of this study) on 819 taxa comprising 798 species, nine sub-species, three hybrids, and nine genera (Supplementary data Table S1). Of these taxa (hereafter, loosely termed 'species'), 562 (68.6%) were categorised a priori as non-invasive and 257 (31.4%) as invasive (Supplementary data Table S1) and were screened relative to 120 RA areas (Supplementary data Tables S2 and S3) across all six inhabited continents (Fig. 1). The RA areas consisted of extensive geographical areas, regions, countries, parts of countries, states, other political entities, water bodies, river basins, lake drainage basins, and marine regions (Supplementary data Table S2). Screenings encompassed 15 groups of aquatic organisms (Fig. 2) in 104 Orders (Supplementary data Table S2), with 24 species assigned to two different groups depending on the RA area's aquatic habitat (Supplementary data Table S4).

The BRA scores ranged from -15.0 to 55.0, with a mean = 18.6, a median = 18.0, and 5th and 95th percentiles = -4.0 and 42.5. Their distribution was not normal (JB = 36.664, P < 0.001), not skewed (skewness = 0.076, z = 1.230, P = 0.195), but platykurtic (kurtosis = 2.305, z = -9.359, P < 0.001) (Fig. 3a). The BRA + CCA scores ranged from -27.0 to 67.0, with a mean = 22.3, a median = 22.0, and 5th and 95th percentiles = -5.6 and 51.1. Their distribution was not normal (JB = 16.378, P < 0.001), not skewed (skewness = -0.039, z = -0.675, P = 0.499), but platykurtic (kurtosis = 2.531, z = -5.210, P < 0.001) (Fig. 3b). The majority of delta values (i.e. differences between BRA + CCA and BRA scores, hence accounting for climate change predictions) were equal to 0, 4, 6 and 10 (>10% of the total in all cases), and overall the proportion of the positive differences was much larger than that of the negative differences (68.8% vs 16.7%) (Fig. 3c). Across all species, the mean CL values were: $CL_{Total} = 2.73 \pm 0.01$, $CL_{BRA} = 2.78 \pm 0.01$, and $CL_{CCA} = 2.25 \pm 0.02$, indicating in all cases medium to high confidence (Supplementary data Table S5).

Thresholds were computed for all screened groups of aquatic organisms except those represented by ≤10 species (Table 2). For reptiles, amphibians, freshwater and marine fishes, tunicates, freshwater and brackish invertebrates and marine protists, the BRA threshold was lower than the BRA + CCA one, whereas the opposite was true for brackish fishes, marine invertebrates, and freshwater and marine plants. Except for marine protists (BRA), the mean AUC values (in Table 2) were in all cases > 0.5 - this confirmed the ability of the toolkit to differentiate between a priori invasive and non-invasive species. After pooling, BRA and BRA + CCA thresholds could be computed for mammals and birds, and in both cases the BRA threshold was lower than the BRA + CCA one (Table 2).

Based on the aquatic organismal group-specific thresholds (excluding the pooled ones), all three measures of accuracy had a mean value ≥50% for all groups except tunicates (BRA + CCA only), marine plants and marine protists (both BRA and BRA + CCA) - a result of the relatively small sample sizes (Table 3). The number (and proportion) of true positives
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Map of the risk assessment areas for which species were screened with the Aquatic Species Invasiveness Screening Kit (AS-ISK; see also Supplementary data Table S2).</figcaption></figure>

was consistently larger than that of the false negatives, which in all cases accounted for only 0–5.6% of the screened species for each group (Table 4). Similarly, the proportion of false positives was in most cases smaller than that of the true positives, and the proportion of the medium-risk species was always relatively high. In total, 33 species were identified as carrying a very high risk of invasiveness: 26 species based on both the BRA and BRA + CCA, four on the BRA only, and three on the BRA + CCA only (Fig. 4a, b).

Of the 82 non-marine region RA areas, 56 included one climate class. To these RA areas, an additional four were added for which the second climate class (namely, Continental) was only marginally represented (Supplementary data Table S2), whereas the only RA area represented

by the Dry climate was removed from further analysis (noting that the Polar climate was found only in combination with the Temperate and/ or Continental climates, hence could not be analysed separately regard- less of sample size). In total, 59 non-marine region RA areas were there- fore considered. For freshwater fishes in tropical, temperate and continental climates, both the BRA and BRA + CCA thresholds were higher for the tropical climate, lower for the temperate and even lower for the continental climate, whereas the BRA + CCA was similar to the BRA in all cases (Table 5). Of the 38 marine ecoregion RA areas, four fell within the Arctic ecoregion, 24 in the Temperate grouping (including 23 RA areas in the Temperate Northern Atlantic and one in the Temperate Northern Pacific ecoregions), and ten in the Tropical

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Number (and corresponding percentage) of species screened according to aquatic organismal group.</figcaption></figure>
<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /><figcaption>**Fig. 3.** (a) Frequency distribution of the Basic Risk Assessment (BRA) scores for the species screened with AS-ISK; (b) same for the BRA + CCA (Climate Change Component) scores; (c) same for delta CCA values (i.e. differences between BRA + CCA and BRA scores for each of the species screened) with corresponding percentage.</figcaption></figure>
<caption>**Table 2**</caption>

Basic Risk Assessment (BRA) and BRA + Climate Change Assessment (CCA) AS-ISK thresholds from receiver operating characteristic curve analysis for the groups of aquatic organisms screened in the present study (Marine bacteria added for completeness). For each aquatic organismal group, the following is provided: number of screened species (n), threshold (Thr) values for the BRA and BRA + CCA [the Area Under the Curve (AUC) values are >0.5, and therefore are statistically valid, including for Mammals, Birds, Reptiles and Amphibians – with the latter two groups pooled together with Mammals and Birds for computation of the thresholds], with corresponding mean, lower confidence interval (LCI) and upper confidence interval (UCI) for the AUC, very high risk threshold (VH Thr) and minimum number of risk assessment areas (RAAs) for selection of the very high risk species. Note that threshold values are given in increments of 0.05 and rounded to the first or second decimal (as applicable, following AS-ISK threshold notation).

<table><thead><tr><th rowspan="2">Aquatic organismal group</th><th rowspan="2">n</th><th colspan="6">BRA</th><th colspan="6">BRA + CCA</th></tr><tr><th>Thr</th><th>Mean</th><th>LCI</th><th>UCI</th><th>VH Thr</th><th>RAAs</th><th>Thr</th><th>Mean</th><th>LCI</th><th>UCI</th><th>VH Thr</th><th>RAAs</th></tr></thead><tbody><tr><td>Mammals</td><td>2</td><td>25.5</td><td>0.7180</td><td>0.5834</td><td>0.8525</td><td>-</td><td>-</td><td>31.6</td><td>0.6639</td><td>0.5223</td><td>0.8056</td><td>-</td><td>-</td></tr><tr><td>Birds</td><td>4</td><td>25.5</td><td>0.7180</td><td>0.5834</td><td>0.8525</td><td>-</td><td>-</td><td>31.6</td><td>0.6639</td><td>0.5223</td><td>0.8056</td><td>-</td><td>-</td></tr><tr><td>Reptiles</td><td>30</td><td>28.5</td><td>0.6550</td><td>0.4176</td><td>0.8924</td><td>-</td><td>2</td><td>36.5</td><td>0.5975</td><td>0.3489</td><td>0.8461</td><td>-</td><td>2</td></tr><tr><td>Amphibians</td><td>24</td><td>15.5</td><td>0.7983</td><td>0.6144</td><td>0.9822</td><td>-</td><td>3</td><td>19.5</td><td>0.7815</td><td>0.5934</td><td>0.9696</td><td>-</td><td>3</td></tr><tr><td>Freshwater fishes</td><td>259</td><td>14.7</td><td>0.8446</td><td>0.7957</td><td>0.8936</td><td>30</td><td>10</td><td>17.7</td><td>0.8213</td><td>0.7691</td><td>0.8735</td><td>36</td><td>10</td></tr><tr><td>Brackish fishes</td><td>17</td><td>38</td><td>0.7917</td><td>0.5542</td><td>1.0000</td><td>-</td><td>-</td><td>29.5</td><td>0.6875</td><td>0.3969</td><td>0.9781</td><td>-</td><td>-</td></tr><tr><td>Marine fishes</td><td>127</td><td>12.75</td><td>0.8254</td><td>0.7089</td><td>0.9420</td><td>-</td><td>3</td><td>19</td><td>0.7819</td><td>0.6541</td><td>0.9096</td><td>-</td><td>3</td></tr><tr><td>Tunicates</td><td>22</td><td>22.5</td><td>0.6417</td><td>0.3943</td><td>0.8890</td><td>-</td><td>2</td><td>36.75</td><td>0.5792</td><td>0.3119</td><td>0.8464</td><td>-</td><td>2</td></tr><tr><td>Freshwater invertebrates</td><td>144</td><td>13.25</td><td>0.8243</td><td>0.7185</td><td>0.9301</td><td>30</td><td>4</td><td>25.75</td><td>0.8199</td><td>0.7076</td><td>0.9322</td><td>36</td><td>4</td></tr><tr><td>Brackish invertebrates</td><td>11</td><td>15</td><td>0.6111</td><td>0.0247</td><td>1.0000</td><td>-</td><td>-</td><td>26.9</td><td>0.7222</td><td>0.4142</td><td>1.0000</td><td>-</td><td>-</td></tr><tr><td>Marine invertebrates</td><td>151</td><td>15.1</td><td>0.8842</td><td>0.8333</td><td>0.9351</td><td>30</td><td>3</td><td>14.25</td><td>0.8483</td><td>0.7859</td><td>0.9107</td><td>36</td><td>3</td></tr><tr><td>Freshwater plants</td><td>15</td><td>24.5</td><td>0.8611</td><td>0.6697</td><td>1.0000</td><td>-</td><td>-</td><td>12.5</td><td>0.9028</td><td>0.7522</td><td>1.0000</td><td>-</td><td>-</td></tr><tr><td>Marine plants</td><td>15</td><td>32</td><td>0.6161</td><td>0.3098</td><td>0.9224</td><td>-</td><td>-</td><td>27.25</td><td>0.6250</td><td>0.3197</td><td>0.9303</td><td>-</td><td>-</td></tr><tr><td>Marine protists</td><td>18</td><td>34</td><td>0.4545</td><td>-</td><td>0.1639</td><td>-</td><td>-</td><td>42.75</td><td>0.5779</td><td>0.2899</td><td>0.8659</td><td>-</td><td>-</td></tr><tr><td>Marine bacteria</td><td>1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>

grouping (including three RA areas in the Central Indo-Pacific, four in the Tropical Atlantic, one in the Tropical Eastern Pacific, and two in the Western Indo-Pacific ecoregions) (Supplementary data Table S2). However, owing to low sample sizes, thresholds could not be computed for any aquatic organismal group in the Arctic ecoregion. For marine fishes, the BRA and BRA + CCA thresholds were higher for the temperate relative to the tropical ecoregion grouping, and the BRA + CCA threshold was higher than the BRA threshold in both cases (Table 5). For marine invertebrates, the BRA and BRA + CCA thresholds were higher for the tropical relative to the temperate ecoregion grouping, and the BRA threshold was similar to the BRA + CCA for the temperate ecoregion grouping but lower for the tropical ecoregion grouping (Table 5). In all cases, the mean AUC values were well above 0.5.

Based on the climate/marine ecoregion-specific thresholds, all three measures of accuracy had a mean value well above 50% in all cases and for both the BRA and BRA + CCA (Table 6). The number (and proportion) of true positives was consistently larger than that of the false negatives, which always accounted for only 0–1.6% of the screened species for each combination (Table 7). Similarly, the proportion of false positives was in most cases smaller than that of the true positives, and the proportion of medium-risk species was always relatively high.

<caption>**Table 3**</caption>

Accuracy measures for screenings on the groups of aquatic organisms for which BRA and BRA + CCA thresholds were directly computed (cf. Table 2). $A_i$ = accuracy for a priori invasive species; $A_n$ = accuracy for a priori non-invasive species; $A_0$ = overall accuracy (see text for details). In italics, values < 50%.

<table><thead><tr><th rowspan="2">Aquatic organismal group</th><th colspan="3">BRA</th><th colspan="3">BRA + CCA</th></tr><tr><th>A<sub>i</sub></th><th>A<sub>n</sub></th><th>A<sub>0</sub></th><th>A<sub>i</sub></th><th>A<sub>n</sub></th><th>A<sub>0</sub></th></tr></thead><tbody><tr><td>Reptiles</td><td>70.0</td><td>75.0</td><td>73.3</td><td>70.0</td><td>75.0</td><td>73.3</td></tr><tr><td>Amphibians</td><td>100.0</td><td>70.6</td><td>79.2</td><td>50.0</td><td>50.0</td><td>50.0</td></tr><tr><td>Freshwater fishes</td><td>83.9</td><td>74.7</td><td>78.0</td><td>77.4</td><td>71.7</td><td>73.7</td></tr><tr><td>Brackish fishes</td><td>62.5</td><td>100.0</td><td>82.4</td><td>62.5</td><td>88.9</td><td>76.5</td></tr><tr><td>Marine fishes</td><td>88.9</td><td>75.2</td><td>77.2</td><td>72.2</td><td>79.8</td><td>78.7</td></tr><tr><td>Tunicates</td><td>80.0</td><td>58.3</td><td>68.2</td><td>40.0</td><td>91.7</td><td>68.2</td></tr><tr><td>Freshwater invertebrates</td><td>76.9</td><td>83.9</td><td>82.6</td><td>65.4</td><td>95.8</td><td>90.3</td></tr><tr><td>Brackish invertebrates</td><td>88.9</td><td>50.0</td><td>81.8</td><td>66.7</td><td>100.0</td><td>72.7</td></tr><tr><td>Marine invertebrates</td><td>86.4</td><td>78.3</td><td>81.5</td><td>83.1</td><td>75.0</td><td>78.1</td></tr><tr><td>Freshwater plants</td><td>83.3</td><td>100.0</td><td>88.9</td><td>58.3</td><td>100.0</td><td>72.2</td></tr><tr><td>Marine plants</td><td>42.9</td><td>87.5</td><td>66.7</td><td>28.6</td><td>75.0</td><td>53.3</td></tr><tr><td>Marine protists</td><td>42.9</td><td>36.4</td><td>38.9</td><td>71.4</td><td>27.3</td><td>44.4</td></tr></tbody></table>

# 4. Discussion

## 4.1. Risk screening extent

In this study, fishes and invertebrates represented the largest proportion of screened aquatic species, thus reflecting the composition of introduced animal species recorded for e.g. European waters (Alcaraz et al., 2005; Gherardi et al., 2009; Katsanevakis et al., 2013) but also the relative number of experts (cf. assessors) in the various aquatic organismal groups. After freshwater fishes, freshwater and marine invertebrates comprised the second most-widely screened group of aquatic organisms, with the marine invertebrates including a large proportion of Decapoda – an Order that comprises several of the world’s worst invasive species (Lowe et al., 2000; Souty-Grosset et al., 2006). The large number of screenings for freshwater fishes in this study can be attributed to the importance of inland waters as providers of ecosystem services for human societies (e.g. Wilson and Carpenter, 1999) and to the fact that these habitats are under high human-induced pressure, including NNS introductions (e.g. Hughes et al., 1998; Rahel, 2000). The broad geographical spread of most of the screened freshwater fish species reflects the increasing homogenisation of aquatic fauna and flora as a result of worldwide introductions (e.g. McKinney, 1998; Rahel, 2000). Further, the taxonomic Orders that were more frequently screened are those usually ecologically flexible, able to withstand adverse ecological conditions, generally widespread over large spatial scales, and often of economic importance (e.g. Hulme, 2009).

Despite the large number of aquatic species screened in this study, bacteria were represented by only one species and no screenings for fungi were contributed. Risk screenings of these groups of aquatic organisms would require the participation of experts in the fields of microbiology and mycology, respectively. This points to the need for greater multi-disciplinarity in future risk identification/assessment studies, which is particularly important as both aquatic bacteria and fungi are known to exert in some cases severe ecological impacts once established and spread in their invasive range (Litchman, 2010), similar to their terrestrial counterparts (Alderman, 1996; Loo, 2008). Regardless, it must be noted that pathogenic and parasitic organisms are normally evaluated separately from other NNS using risk assessment protocols specific to infectious agents (e.g. Peeler et al., 2007; D'hondt et al., 2015; Copp et al., 2016a). In addition, the diminutive size of these taxa could cause their presence to go un-noticed, thereby limiting knowledge of their spread and extent of invasiveness.
<caption>**Table 4**</caption>

Risk outcomes (given as number of screened species and corresponding percentage) for the BRA and BRA + CCA for the groups of aquatic organisms for which risk thresholds were directly computed. Species are categorised a priori as either Non-invasive or Invasive (see Supplementary data Table S2). Medium-risk and high-risk outcomes for each group are based on the thresholds given in Table 2, whereas low-risk outcomes are based on a 'default' threshold of 1.

<table><thead><tr><th rowspan="3">Aquatic organismal group</th><th colspan="4">BRA</th><th colspan="4">BRA + CCA</th></tr><tr><th colspan="2">Non-invasive</th><th colspan="2">Invasive</th><th colspan="2">Non-invasive</th><th colspan="2">Invasive</th></tr><tr><th>n</th><th>%</th><th>n</th><th>%</th><th>n</th><th>%</th><th>n</th><th>%</th></tr></thead><tbody><tr><td><strong>Reptiles</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>0</td><td>0.0</td><td>1</td><td>3.3</td><td>0</td><td>0.0</td><td>1</td><td>3.3</td></tr><tr><td>Medium</td><td>15</td><td>50.0</td><td>2</td><td>6.7</td><td>15</td><td>50.0</td><td>2</td><td>6.7</td></tr><tr><td>High</td><td>5</td><td>16.7</td><td>7</td><td>23.3</td><td>5</td><td>16.7</td><td>7</td><td>23.3</td></tr><tr><td><strong>Amphibians</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>1</td><td>4.2</td><td>0</td><td>0.0</td><td>12</td><td>50.0</td><td>0</td><td>0.0</td></tr><tr><td>Medium</td><td>11</td><td>45.8</td><td>0</td><td>0.0</td><td>5</td><td>20.8</td><td>7</td><td>29.2</td></tr><tr><td>High</td><td>5</td><td>20.8</td><td>7</td><td>29.2</td><td>17</td><td>70.8</td><td>7</td><td>29.2</td></tr><tr><td><strong>Freshwater fishes</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>43</td><td>16.6</td><td>2</td><td>0.8</td><td>50</td><td>19.3</td><td>3</td><td>1.2</td></tr><tr><td>Medium</td><td>81</td><td>31.3</td><td>13</td><td>5.0</td><td>69</td><td>26.6</td><td>18</td><td>6.9</td></tr><tr><td>High</td><td>42</td><td>16.2</td><td>78</td><td>30.1</td><td>47</td><td>18.1</td><td>72</td><td>27.8</td></tr><tr><td><strong>Brackish fishes</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td></tr><tr><td>Medium</td><td>9</td><td>52.9</td><td>3</td><td>17.6</td><td>8</td><td>47.1</td><td>3</td><td>17.6</td></tr><tr><td>High</td><td>0</td><td>0.0</td><td>5</td><td>29.4</td><td>1</td><td>5.9</td><td>5</td><td>29.4</td></tr><tr><td><strong>Marine fishes</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>36</td><td>28.3</td><td>1</td><td>0.8</td><td>33</td><td>26.0</td><td>1</td><td>0.8</td></tr><tr><td>Medium</td><td>46</td><td>36.2</td><td>1</td><td>0.8</td><td>54</td><td>42.5</td><td>4</td><td>3.1</td></tr><tr><td>High</td><td>27</td><td>21.3</td><td>16</td><td>12.6</td><td>22</td><td>17.3</td><td>13</td><td>10.2</td></tr><tr><td><strong>Tunicates</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td></tr><tr><td>Medium</td><td>7</td><td>31.8</td><td>2</td><td>9.1</td><td>11</td><td>50.0</td><td>6</td><td>27.3</td></tr><tr><td>High</td><td>5</td><td>22.7</td><td>8</td><td>36.4</td><td>1</td><td>4.5</td><td>4</td><td>18.2</td></tr><tr><td><strong>Freshwater invertebrates</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>14</td><td>9.7</td><td>0</td><td>0.0</td><td>1</td><td>0.7</td><td>0</td><td>0.0</td></tr><tr><td>Medium</td><td>85</td><td>59.0</td><td>6</td><td>4.2</td><td>112</td><td>77.8</td><td>9</td><td>6.3</td></tr><tr><td>High</td><td>19</td><td>13.2</td><td>20</td><td>13.9</td><td>5</td><td>3.5</td><td>17</td><td>11.8</td></tr><tr><td><strong>Brackish invertebrates</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td></tr><tr><td>Medium</td><td>1</td><td>9.1</td><td>1</td><td>9.1</td><td>2</td><td>18.2</td><td>3</td><td>27.3</td></tr><tr><td>High</td><td>1</td><td>9.1</td><td>8</td><td>72.7</td><td>0</td><td>0.0</td><td>6</td><td>54.5</td></tr><tr><td><strong>Marine invertebrates</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>15</td><td>9.9</td><td>0</td><td>0.0</td><td>16</td><td>10.6</td><td>0</td><td>0.0</td></tr><tr><td>Medium</td><td>57</td><td>37.7</td><td>8</td><td>5.3</td><td>53</td><td>35.1</td><td>10</td><td>6.6</td></tr><tr><td>High</td><td>20</td><td>13.2</td><td>51</td><td>33.8</td><td>23</td><td>15.2</td><td>49</td><td>32.5</td></tr><tr><td><strong>Freshwater plants</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>1</td><td>5.6%</td><td>0</td><td>0.0%</td><td>4</td><td>22.2%</td><td>1</td><td>5.6%</td></tr><tr><td>Medium</td><td>5</td><td>27.8%</td><td>2</td><td>11.1%</td><td>2</td><td>11.1%</td><td>4</td><td>22.2%</td></tr><tr><td>High</td><td>0</td><td>0.0%</td><td>10</td><td>55.6%</td><td>0</td><td>0.0%</td><td>7</td><td>38.9%</td></tr><tr><td><strong>Marine plants</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td></tr><tr><td>Medium</td><td>7</td><td>46.7</td><td>4</td><td>26.7</td><td>6</td><td>40.0</td><td>5</td><td>33.3</td></tr><tr><td>High</td><td>1</td><td>6.7</td><td>3</td><td>20.0</td><td>2</td><td>13.3</td><td>2</td><td>13.3</td></tr><tr><td><strong>Marine protists</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td></tr><tr><td>Medium</td><td>4</td><td>22.2</td><td>4</td><td>22.2</td><td>3</td><td>16.7</td><td>2</td><td>11.1</td></tr><tr><td>High</td><td>7</td><td>38.9</td><td>3</td><td>16.7</td><td>8</td><td>44.4</td><td>5</td><td>27.8</td></tr></tbody></table>

## 4.2. Risk outcomes under current and future climate conditions

The high proportion of positive 'delta' values (i.e. after accounting for climate change predictions: Fig. 3c) is in line with findings that climate change is likely to exacerbate the risk of introduction, establishment, dispersal and impact of several NNS, though some species might respond negatively to changes in climate conditions (Kernan, 2015). With predicted warmer temperatures, reduced lake ice cover, altered flow regimes, increased salinity due to changes in precipitation and

saltwater intrusion, and increased environmental disturbances, climate change is likely to favour the spread of NNS along their invasion pathways as these conditions present opportunities for enhanced survival and lower invasion resistance of the invaded habitats (Rahel and Olden, 2008). Further, climate change may result in altered transport and/or introduction mechanisms or reduced effectiveness of control strategies (Hellmann et al., 2008).

Several of the top (i.e. 'very high risk') species under current climate conditions achieved an even higher score under conditions of climate change (Fig. 4). Of these species, most are either primarily warm-water/tropical or have wide thermal tolerances. For example, the red-eared slider *Trachemys scripta scripta* is a very common semi-aquatic turtle native to the south-eastern USA (Florida to south-eastern Virginia: Powel et al., 1991) and in its introduced range occurs in a wide variety of habitats, including slow-flowing rivers, floodplain swamps, marshes, seasonal wetlands and permanent ponds (Scriber et al., 1986). Both goldfish *Carassius auratus* and gibel carp *Carassius gibelio* are known to establish across a wide geographical and climatic range. These species are widespread and locally invasive both in Europe's more northerly parts, such as Finland and Poland (e.g. Grabowska et al., 2010; Puntila et al., 2013), across the Mediterranean region (e.g. Crivelli, 1995; Tarkan et al., 2012), and further afield in Australia (e.g. Beatty et al., 2017) and the Americas (Magalhães and Jacobi, 2013; Halas et al., 2018). The common lionfish *Pterois miles* is another highly invasive species, especially since its invasion of the western Atlantic and Mediterranean Sea, which has been unprecedentedly rapid (Bariche et al., 2017; Schofield, 2010). The channelled applesnail *Pomacea canaliculata* is native to South America and has been introduced as an ornamental species in Europe and the Mediterranean area, but also elsewhere in the world through aquaculture (https://www.cabi.org/isc/datasheet/68490). *Phyllorhiza punctata* is native to the tropical Western Pacific, i.e. from Australia to Japan (Rippingale and Kelly, 1995) and has been reported across the Mediterranean Region (Abed-Navandi and Kikinger, 2007; Boero et al., 2009; Çevik et al., 2011; Gueroun et al., 2014; Deidun et al., 2017). An in-depth, species-specific description of all the 'very high risk' species identified in this study (Fig. 4) is provided in the Supplementary data, Appendix A1.

Overall, the responses to climate-change questions tended to increase scores as well as thresholds for most taxonomic groups in most climatic regions. Species with broad distributions tended to possess broad thermal tolerances, suggesting that those species are likely to be able to expand their ranges, and thus impacts, poleward under future climate conditions. For example, six freshwater fish species not native to Great Britain were predicted to benefit from the forecasted future climate conditions, thus offering the potential to expand their ranges (Britton et al., 2010), which in Great Britain would be northward. Such poleward shifts in marine species distributions were initially deemed likely (VanDerWal et al., 2013), based in part on shifts observed in previous warm periods (Drinkwater, 2006), and they have been predicted for some freshwater fishes, e.g. channel catfish *Ictalurus punctatus* (McCaulley and Beitinger, 1992). Indeed, poleward shifts have been documented in a recent meta-analysis of marine species distributions (Chaudhary et al., 2021). As such, tropical species are likely to expand poleward into temperate regions (e.g. Quero, 1998; Scavia et al., 2002), and temperate species to expand poleward into continental regions (Root et al., 2003; Hickling et al., 2006). Whereas, species with more limited thermal tolerances are likely to undergo shifts in their distributions (and thus impacts), which are characterised by range recessions and range expansions in a poleward direction (Roessig et al., 2004; Rahel and Olden, 2008; Eissa and Zaki, 2011; Renaud et al., 2012). Wide thermal tolerances may be enhanced by local adaptation, such as is apparent in the cold-adapted population of eastern mosquitofish *Gambusia holbrooki*, which has established a self-sustaining population in Normandy, France (Beaudouin et al., 2008). This reflects the wide geographical distribution of *Gambusia holbrooki* in its native range, which
<figure><img src="image_10.png" /><figcaption>**Fig. 4.** (a) Very high risk species based on the BRA score (±SE) and screened for a representative number of risk assessment areas; (b) Same for the BRA + CCA. In dark gray, very high risk species for both the BRA and BRA + CCA. Black circle (●): listing in the Centre for Agriculture and Bioscience International Invasive Species Compendium (CABI ISC); black square (■): listing in the Global Invasive Species Database (GISD). R = Reptiles; A = Amphibians; FF = Freshwater fishes; MF = Marine fishes; T = Tunicates; FI = Freshwater invertebrates; MI = Marine invertebrates. Within each aquatic organismal group, species are ordered according to decreasing score. See Table 2 for very high risk thresholds and number of risk assessment areas criteria.</figcaption></figure>

extends along the Mississippi River basin from the Gulf of Mexico northward to midway up the states of Indiana and Illinois (Aislabie et al., 2019).

Thus, in many cases, increased scores for BRA + CCA for freshwater species are warranted. Further, this phenomenon, which is based on the interaction of climate and physiology, should pertain also to species in different aquatic environments such as brackish and marine systems.

For example, red lionfish *Pterois volitans* scores in Florida (USA) increased slightly from the BRA to the BRA + CCA, with modest increases mainly due to a greater potential for the species to survive inshore in the northern Gulf of Mexico during winter, thereby extending annual impacts (Lyons et al., 2020). Conversely, within tropical climate zones, warmer conditions under climate-change scenarios are not likely to
<caption>**Table 5**</caption>

BRA and BRA + CCA thresholds from receiver characteristic curve analysis for freshwater fishes relative to three climate classes (after Peel et al., 2007) and for marine fishes and invertebrates relative to two marine ecoregion groupings (modified after Spalding et al., 2007). Abbreviations and notation as in Table 2.

<table><thead><tr><th rowspan="2">Climate/marine ecoregion</th><th rowspan="2">n</th><th colspan="4">BRA</th><th colspan="4">BRA + CCA</th></tr><tr><th>Thr</th><th>Mean</th><th>LCI</th><th>UCI</th><th>Thr</th><th>Mean</th><th>LCI</th><th>UCI</th></tr></thead><tbody><tr><td colspan="10"><strong>Freshwater fishes</strong></td></tr><tr><td>Tropical</td><td>63</td><td>18.4</td><td>0.9153</td><td>0.8370</td><td>0.9936</td><td>19.6</td><td>0.8986</td><td>0.8088</td><td>0.9885</td></tr><tr><td>Temperate</td><td>200</td><td>15.9</td><td>0.8685</td><td>0.8197</td><td>0.9174</td><td>16.0</td><td>0.8493</td><td>0.7974</td><td>0.9012</td></tr><tr><td>Continental</td><td>58</td><td>12.9</td><td>0.7844</td><td>0.6579</td><td>0.9110</td><td>13</td><td>0.7387</td><td>0.5992</td><td>0.8782</td></tr><tr><td colspan="10"><strong>Marine fishes</strong></td></tr><tr><td>Temperate</td><td>46</td><td>19.5</td><td>0.8083</td><td>0.6422</td><td>0.9745</td><td>31.5</td><td>0.8208</td><td>0.6589</td><td>0.9828</td></tr><tr><td>Tropical</td><td>83</td><td>12.5</td><td>0.8521</td><td>0.6994</td><td>1.0000</td><td>23.4</td><td>0.8016</td><td>0.6390</td><td>0.9643</td></tr><tr><td colspan="10"><strong>Marine invertebrates</strong></td></tr><tr><td>Temperate</td><td>97</td><td>15.1</td><td>0.9236</td><td>0.8643</td><td>0.9829</td><td>15.6</td><td>0.8871</td><td>0.8153</td><td>0.9588</td></tr><tr><td>Tropical</td><td>63</td><td>35.75</td><td>0.7386</td><td>0.6085</td><td>0.8688</td><td>23.25</td><td>0.7279</td><td>0.5895</td><td>0.8662</td></tr></tbody></table>

incite large alterations to the spread, abundance, or impacts of tropical species. The same can be said for continental climate zones with regard to cold-water species due to their existing adaptations to that climate types. Whereas range recession of a species' distribution could occur should a future temperature regime exceed the species' thermal tolerances (Scavia et al., 2002).

Despite the general emphasis on range expansion and greater impacts of invasive species due to warmer conditions (Rahel and Olden, 2008; Bradley et al., 2010), climate change is a complex issue for NNS risk assessment. When providing responses to CCA questions and ranking their confidence in those responses, the assessor must consider a great breadth of information, including climate-match model predictions (e.g. Britton et al., 2010), if available for their RA area, as well as emissions scenarios, climate-model outputs, and time frames (Kennedy, 1990). The IPCC (2014) presents a variety of scenarios based on future emissions levels, with the extremes represented by RCP8.5 and RCP2.6. Further, there are numerous climate models that may be used for guidance, though these may differ in profound ways from one another in terms of predicted future temperature and precipitation regimes (e.g. Kirtman et al., 2017). Other anomalies such as Arctic warming, which are expected to lead to harsh, cold winters in midlatitude areas of North America and Asia (Cohen et al., 2014; Kug et al., 2015), or non-analogue climates (Fitzpatrick and Hargrove, 2009) occur. Extreme events can set back or cancel species range expansion (Canning-Clode et al., 2011; Rehage et al., 2016) and thus may influence future risk estimates. Multi-directional range shifts are not only possible, but likely (VanDerWal et al., 2013). The time frame for such predictions is an important variable as well, given that the potential outcomes of range expansion, contraction, or oscillation in size are relative to current NNS ranges. In view of the complexity of climate change interactions with biologically important factors such as physiology,

<caption>**Table 6**</caption>

Accuracy measures for screenings on freshwater fishes relative to three climate classes and for marine fishes and invertebrates relative to two marine ecoregion groupings (see Table 5). Abbreviations and notation as in Table 3.

<table><thead><tr><th rowspan="2">Climate/marine ecoregion</th><th colspan="3">BRA</th><th colspan="3">BRA + CCA</th></tr><tr><th>A<sub>1</sub></th><th>A<sub>n</sub></th><th>A<sub>0</sub></th><th>A<sub>1</sub></th><th>A<sub>n</sub></th><th>A<sub>0</sub></th></tr></thead><tbody><tr><td colspan="7"><strong>Freshwater fishes</strong></td></tr><tr><td>Tropical</td><td>84.6</td><td>94.6</td><td>90.5</td><td>84.6</td><td>89.2</td><td>87.3</td></tr><tr><td>Temperate</td><td>82.5</td><td>75.0</td><td>78.0</td><td>87.5</td><td>64.2</td><td>73.5</td></tr><tr><td>Continental</td><td>81.1</td><td>66.7</td><td>75.9</td><td>83.8</td><td>57.1</td><td>74.1</td></tr><tr><td colspan="7"><strong>Marine fishes</strong></td></tr><tr><td>Temperate</td><td>80.0</td><td>77.8</td><td>78.3</td><td>80.0</td><td>83.3</td><td>82.6</td></tr><tr><td>Tropical</td><td>91.7</td><td>81.7</td><td>83.1</td><td>66.7</td><td>88.7</td><td>85.5</td></tr><tr><td colspan="7"><strong>Marine invertebrates</strong></td></tr><tr><td>Temperate</td><td>88.5</td><td>90.1</td><td>89.7</td><td>84.6</td><td>85.9</td><td>85.6</td></tr><tr><td>Tropical</td><td>38.6</td><td>100.0</td><td>57.1</td><td>65.9</td><td>78.9</td><td>69.8</td></tr></tbody></table>

dispersal, demography, species interactions, and evolution, not all changes in climate may result in greater spread or heighten NNS impacts (Urban et al., 2016).

### 4.3. Management implications

The very low proportion and, in most cases, near or total absence of false negatives across the representative groups of aquatic organisms screened in the present study is an indicator of the accuracy of the risk screenings (cf. Kumschick and Richardson, 2013). The management consequences of this elevated accuracy could be that of a large number

<caption>**Table 7**</caption>

Risk outcomes (given as number of screened species and corresponding percentage) for the BRA and BRA + CCA for freshwater fishes relative to three climate classes and for marine fishes and invertebrates relative to two marine ecoregion groupings (see Table 5). See Table 4 for details.

<table><thead><tr><th rowspan="2">Climate/marine ecoregion</th><th colspan="2">BRA</th><th colspan="2">Invasive</th><th colspan="2">BRA + CCA</th></tr><tr><th>n</th><th>%</th><th>n</th><th>%</th><th>n</th><th>%</th></tr></thead><tbody><tr><td colspan="7"><strong>Freshwater fishes</strong></td></tr><tr><td>Tropical</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>10</td><td>15.9</td><td>1</td><td>1.6</td><td>8</td><td>12.7</td></tr><tr><td>Medium</td><td>25</td><td>39.7</td><td>3</td><td>4.8</td><td>25</td><td>39.7</td></tr><tr><td>High</td><td>2</td><td>3.2</td><td>22</td><td>34.9</td><td>4</td><td>6.3</td></tr><tr><td>Temperate</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>31</td><td>15.5</td><td>0</td><td>0.0</td><td>38</td><td>19.0</td></tr><tr><td>Medium</td><td>59</td><td>29.5</td><td>14</td><td>7.0</td><td>39</td><td>19.5</td></tr><tr><td>High</td><td>30</td><td>15.0</td><td>66</td><td>33.0</td><td>43</td><td>21.5</td></tr><tr><td>Continental</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>8</td><td>13.8</td><td>0</td><td>0.0</td><td>8</td><td>13.8</td></tr><tr><td>Medium</td><td>6</td><td>10.3</td><td>7</td><td>12.1</td><td>4</td><td>6.9</td></tr><tr><td>High</td><td>7</td><td>12.1</td><td>30</td><td>51.7</td><td>9</td><td>15.5</td></tr><tr><td>Marine fishes</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Temperate</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>1</td><td>2.2</td><td>0</td><td>0.0</td><td>1</td><td>2.2</td></tr><tr><td>Medium</td><td>27</td><td>58.7</td><td>2</td><td>4.3</td><td>29</td><td>63.0</td></tr><tr><td>High</td><td>8</td><td>17.4</td><td>8</td><td>17.4</td><td>6</td><td>13.0</td></tr><tr><td>Tropical</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>33</td><td>39.8</td><td>1</td><td>1.2</td><td>31</td><td>37.3</td></tr><tr><td>Medium</td><td>25</td><td>30.1</td><td>0</td><td>0.0</td><td>32</td><td>38.6</td></tr><tr><td>High</td><td>13</td><td>15.7</td><td>11</td><td>13.3</td><td>8</td><td>9.6</td></tr><tr><td>Marine invertebrates</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Temperate</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>15</td><td>15.5</td><td>0</td><td>0.0</td><td>12</td><td>12.4</td></tr><tr><td>Medium</td><td>49</td><td>50.5</td><td>3</td><td>3.1</td><td>49</td><td>50.5</td></tr><tr><td>High</td><td>7</td><td>7.2</td><td>23</td><td>23.7</td><td>10</td><td>10.3</td></tr><tr><td>Tropical</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Low</td><td>0</td><td>0.0</td><td>0</td><td>0.0</td><td>4</td><td>6.3</td></tr><tr><td>Medium</td><td>19</td><td>30.2</td><td>27</td><td>42.9</td><td>11</td><td>17.5</td></tr><tr><td>High</td><td>0</td><td>0.0</td><td>17</td><td>27.0</td><td>4</td><td>6.3</td></tr></tbody></table>
of species ultimately warranting comprehensive (full) RA. In this study, accuracy was measured explicitly and represented the 'pragmatic' approach, given that a full (comprehensive) RA, which might follow for species identified as potentially posing a high risk of invasiveness, would normally involve a major economic commitment. This is contrary to the 'idealistic' approach, which would involve assessing both medium- and high-risk species, given the even higher economic commitment of accounting for both. An even more pragmatic approach could be therefore to base management decisions (or species' rankings) on the risk screening outcomes until such time that a full RA can be undertaken. This approach has been employed by the UK's Alien Species Group in its 'impact' ranking of aquatic NNS with regard to waterbody classification under the EU Water Framework Directive (https://ec.europa.eu/environment/water/water-framework/index_en.html). Within this context, aquatic NNS are listed provisionally as being of low, medium or high impact (UK-TAG ASG, 2021) pending the outcome of a full, and in some cases rapid, RA commissioned by the Great Britain Non-native Species Secretariat (www.nonnativespecies.org). The categorisation of the species is then subsequently confirmed or changed according to the outcome of the full or rapid RA.

As shown in the present study, invasive and non-invasive species could be distinguished accurately across aquatic organisms to a greater degree than would be expected by chance alone. Calibrated thresholds could be computed for several taxonomic groups, and for freshwater and marine fishes and for marine invertebrates also based on climate/ecoregion. In RA areas for which no calibration is possible, e.g. due to a statistically insufficient number of screenings and/or to the requirement of screening only one target species (or small group thereof), these generalised thresholds (i.e. at the organism group or climate class level) can be reliably used in future risk screening applications for distinguishing between species that pose a high risk of being invasive from those posing a low-to-medium risk. Use of these thresholds may be therefore of particular relevance in cases of individual species being risk screened (e.g. Castellanos-Galindo et al., 2018; Zięba et al., 2020), including 'rapid risk assessment' studies, or for RA areas where the number of NNS is too limited for a valid calibration to be undertaken (e.g. Filiz et al., 2017b; Paganelli et al., 2018; Semchenko et al., 2018; Dodd et al., 2019; Lyons et al., 2020).

As is common in NNS risk analysis (e.g. Caley et al., 2006; Barry et al., 2008), the available scientific information (both peer-reviewed and gray literature) about the species being screened was reflected in the confidence rankings assessors attributed to their responses. As such, given the robust confidence levels, the present study provides a means for existing risk rankings to be adjusted and a stronger evidence base to identify: (i) which species require an immediate 'rapid' management action (e.g. eradication, control) to avoid or mitigate adverse impacts; (ii) which to subject to a full RA; and (iii) which to restrict or ban with regard to importation and/or sale as ornamental or aquarium/fishery enhancement. To this end, biological monitoring programmes may explicitly search for high-scoring species because the sooner these species are found in new areas the more likely are eradication programmes to be successful. Such monitoring could be supported by developing techniques such as DNA metabarcoding (Brown et al., 2016) and eDNA surveys (Holman et al., 2019). Risk identification therefore plays an important role in the provision of advice to policy makers, for the development of appropriate legislation, and associated regulation and management pertaining to NNS. In this perspective, the present study has also provided a means of fine-tuning NNS risk analysis procedures in countries that encompass more than one climatic class by the computation of generalised thresholds. In conclusion, the present study provides a comprehensive baseline to help identify (through risk screening using AS-ISK) for management priority high risk species across a range of taxonomic groups and geographical/climatic regions, even where existing information on such species invasiveness/impact is limited.

## CRediT authorship contribution statement

LV and GHC designed the concept, co-wrote and edited the manuscript; LV analysed the data, and all other authors contributed data to and participated in the composition and editing of the manuscript.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

This study is dedicated to our co-author, the late Prof. Olaf Weyl, whose sudden passing during field work in November 2020 was received by us with great sadness. This study represents a contribution to the term of reference 'd' ("Advance knowledge base to further develop indicators to evaluate the status and impact of non-indigenous species in marine environments") of the ICES working group on Introductions and Transfers of Marine Organisms. The participation of GHC was supported by the Cefas Science Excellence fund. AW and ÅF were supported by TKP2020-NKA-16 project.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.scitotenv.2021.147868.

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