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

2021· article· en· W3164086783 on OpenAlexaff
Lorenzo Vilizzi, Gordon H. Copp, Jeffrey E. Hill, Б. В. Адамович, Luke Aislabie, Daniel R. Akin, Abbas J. Al-Faisal, David Almeida, Mohamad Noor Amal Azmai, Rigers Bakiu, Adriana Bellati, Renée Bernier, Jason M. Bies, Gökçen Bilge, Paulo Branco, Thuyet D. Bui, João Canning‐Clode, Henrique Anatole Cardoso Ramos, Gustavo A. Castellanos‐Galindo, Nuno Castro, Ratcha Chaichana, Paula Chaínho, Joleen Chan, Almir Manoel Cunico, Amélia Curd, Punyanuch Dangchana, Dimitriy Dashinov, Phil I. Davison, Mariele Pasuch de Camargo, Jennifer A. Dodd, Allison L. Durland Donahou, Lennart Edsman, Fitnat Güler Ekmekçı, Jessica Elphinstone-Davis, Tibor Erős, Charlotte Evangelista, Gemma V. Fenwick, Árpád Ferincz, Éric Feunteun, Halit Filiz, Sandra Carla Forneck, H. S. Gajduchenko, João Gama Monteiro, Ignácio Gestoso, Daniela Giannetto, Allan S. Gilles, Francesca Gizzi, Branko Glamuzina, Luka Glamuzina, Jesica Goldsmit, Stephan Gollasch, Philippe Goulletquer, Joanna Grabowska, Rogan Harmer, Phillip J. Haubrock, Dekui He, Jeffrey W. Hean, Gábor Herczeg, Katie E. Howland, Ali İlhan, Е. А. Интересова, Katarína Jakubčinová, Anders Jelmert, Stein Ivar Johnsen, Tomasz Kakareko, Kamalaporn Kanongdate, Nurçin Killi, Jeong-Eun Kim, Şerife Gülsün Kırankaya, Dominika Kňazovická, Oldřich Kopecký, Vasil Kostov, Nicholas Koutsikos, Sebastian Kozic, Tatia Kuljanishvili, Lohith Kumar, Yoshihisa Kurita, Irmak Kurtul, Lorenzo Lazzaro, Laura Lee, Maiju Lehtiniemi, Giovanni Leonardi, R.S.E.W. Leuven, Shan Li, Tatsiana Lipinskaya, Fei Liu, Lance Lloyd, Massimo Lorenzoni, Sergio Luna, Timothy J. Lyons, Kit Magellan, Martin Malmstrøm, Agnese Marchini, Sean M. Marr, G. Masson, Laurence Masson, Cynthia H. McKenzie, Daniyar Memedemin, Roberto Mendoza, Dan Minchin, Laurence Miossec, Seyed Daryoush Moghaddas, Moleseng C. Moshobane, Levan Mumladze, Rahmat Naddafi, Elnaz Najafi-Majd, Aurel Năstase, NĂVODARU Ion, J. Wesley Neal, Sarah Nienhuis, Matura Nimtim, Emma T. Nolan, Anna Occhipinti‐Ambrogi, Henn Ojaveer, Sergej Olenin, Karin H. Olsson, Norio Onikura, Kathryn A. O’Shaughnessy, Daniele Paganelli, Paola Parretti, Jiří Patoka, Richard Thomas B. Pavia, Daniele Pellitteri‐Rosa, Michèle Pelletier-Rousseau, Elfritzson M. Peralta, Costas Perdikaris, Dariusz Pietraszewski, Marina Piria, Sophie Pitois, Laura Pompei, Nicolas Poulet, Cristina Preda, Riikka Puntila-Dodd, Ali Turk Qashqaei, Tena Radočaj, Hossein Rahmani, Smrithy Raj, David B. Reeves, Milica Ristovska, Viktor Rizevsky, D. Ross Robertson, Peter Robertson, Laura Ruykys, Abdulwakil Olawale Saba, José Maria Santos, Hasan M. Sarı, Pedro Segurado, Vitaliy Semenchenko, Wansuk Senanan, Nathalie Simard, Predrag Simonović, Michał E. Skóra, Kristína Slovák Švolíková, Evangelia Smeti, Tereza Šmídová, Ivan Špelić, Greta Srėbalienė, Gianluca Stasolla, Paul Stebbing, Barbora Števove, Vettath Raghavan Suresh, Bettina Szajbert, Kieu Anh T. Ta, Ali Serhan Tarkan, Jonathan Tempesti, Thomas W. Therriault, Hannah J. Tidbury, Nildeniz Top‐Karakuş, Elena Tricarico, Débora Fernanda Avila Troca, Konstantinos Tsiamis, Quenton M. Tuckett, Pero Tutman, Umut Uyan, E. Uzunova, Leonidas Vardakas, Gaute Velle, Hugo Verreycken, Lizaveta Vintsek, Hui Wei, András Weiperth, Olaf L. F. Weyl, Emily R. Winter, Radosław Włodarczyk, Louisa E. Wood, Ruibin Yang, Sercan Yapıcı, Shayne S.B. Yeo, Baran Yoğurtçuoğlu, Anna L. E. Yunnie, Yunjie Zhu, Grzegorz Zięba, Kristína Žitňanová, Stacey A. Clarke

Bibliographic record

VenueThe Science of The Total Environment · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine Ecology and Invasive Species
Canadian institutionsDiscovery Air (Canada)Ministry of Natural Resources and ForestryFisheries and Oceans CanadaUniversity of Northern British ColumbiaTrent University
FundersWydział Biologii i Ochrony Środowiska, Uniwersytet ŁódzkiCentre for Environment, Fisheries and Aquaculture Science
KeywordsInvasive speciesCurrent (fluid)Scale (ratio)EcologyEnvironmental scienceIntroduced speciesClimate changeGeographyBiologyEnvironmental resource managementOceanographyGeologyCartography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.246
Teacher spread0.235 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations147
Published2021
Admission routes1
Has abstractyes

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