BUSCADOR RECOLECTA

GENERAL INFO: Generalinformation about each species assessed in the research Species: English name (Scientific name) Species: Species ID Origin: Continent of origin of the species Pathway: Main pathway of introduction into Great Britain Habitat: Major habitat invaded ORIGINAL SCORES: Original scores provided by assessors that range from 0-very low likelihood, to 4-very high likelihood. PROBABILITIES USING MUMFORD ET AL: Original scores are transformed to 0-1 probabilities using the cut-offs of Mumford et al. 2010: 0.05, 0.22, 0.50, 0.78 and 0.95. Q1-Q34 Number of questions in risk assessments used to populate the fault tree. See correspondence between questions and events in Supplementary information. ORIGINAL UNCERTAINTY: Original uncertainty scores range from 0-certaint to 3-high uncertainty TRANSFORMED UNCERTAINTY: Original uncertainty scores are transformed to probabilties as: 0, 0.2, 0.4 and 0.6. See more information in methods., Originala data used in "Applying Fault Tree Analysis to biological invasions identifies optimal targets for effective biosecurity" by Drs. Belinda Gallardo et al. Published in the Journal of Applied Ecology (2022), the Biosecurity Research Initiative at St Catharine’s (BioRISC, http://www.biorisc.com), Peer reviewed

jpe14256-sup-0001-supinfo1 = Supplementary Information 1: Tabes S1-S2 and Figure S1.-- jpe14256-sup-0002-supinfo2 = Supplementary Information 2: Description of the Fault Tree., Peer reviewed

Data type: Tables., Explanation note: Table S1. Species evaluated with impact assessments. Table S2. Classification of the impact questions into the different impact types. Table S3. GProt-Spp per impact assessment. Inter-rater reliability using all impact questions of the protocol. Table S4. Summary of the principal and sensitivity analyses performed to study the influence of different factors on the consistency of responses in protocol questions. Table S5. Queries used to search scientific articles in Web of Science. Table S6. Models used to evaluate the influence of the protocol and taxonomic group in assessor consistency. Table S7. Saturated models for the two nested model to unravel the influence of impact types and their potential interaction with the taxonomic groups. Table S8. The 10 regression models with the lowest AICc to evaluate the influence of the protocol and the taxonomic groups. Table S9. Tukey post-hoc for the variable protocol in the model including the variable taxonomic group. Table S10. Tukey post-hoc for the variable protocol in the model including the number of assessors. Table S11. Consensus Tukey post-hoc for the variable impact type. Table S12. Variance partitioning of the models to unravel the shared variance of the variable protocol with the number of responses per protocol question and impact types. Table S13. GProt-Quest per protocol question. Inter-rater reliability per question when considering the impact scores of all species of the same taxonomic group., This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited., Peer reviewed

Data type: R objects., Explanation note: An R list object containing the used impact assessments in the study An R function to calculate the coefficient G (inter-rater reliability metric)., This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited., Peer reviewed

Data type: Figure., Explanation note: Consistency in impact assessments of invasive species is generally high and depends on protocols and impact types., This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited., Peer reviewed

Table S1: Fatty acid composition of linseed, sunflower, and avocado oils, expressed as percentage of methyl ester; Figure S1: Desirability function for OG formulation (a: OGL; b: OGS; c: OGA); Figure S2: Typical HPLC chromatogram of curcuminoids. Component 1: curcumin, component 2: demethoxycurcumin, component 3: bisdemethoxycurcumin; Figure S3: G′ and G″ as a function of frequency for OG with curcumin (0.2% w/w; OGLCur, OGSCur, and OGACur); Figure S4: G′ and G″ of OGLCur, OGSCur, and OGACur as a function of temperature during heating from 10 °C to 70 °C; Figure S5: Cell viability of CaCo-2 (a) and ThP-1 (b) after 4 h and 24 h, respectively, of incubation with micellar phases from digested samples with curcumin (0.2% w/w)., Peer reviewed

-General information for the experimental section, crystallographic data, computational details, catalytic reaction profiles, and NMR spectra (PDF). -Cartesian coordinates of the optimized structures (XYZ)., Peer reviewed

This work was funded by Ministerio de Ciencia e Innovación (PID2021-126489OB-I00, MCIN/AEI/10.13039/501100011033, “FEDER, Una manera de hacer Europa”); ACRONYM: NUPROVI Alba Martín gratefully acknowledges receipt of a pre-doctoral grant (PRE2019-089288) from Ministerio de Ciencia e Innovación (MCIN/AEI/10.13039/501100011033, “El FSE invierte en tu futuro”); CSIC is acknowledged for supporting Open Access publication., No

3 tables, 30 figures, Supplementary Material for the article https://doi.org/10.1016/j.ecolind.2020.107141, Table S1. Summary of the model structure fitted to each species’ body size data.-- Table S2. Summary table indicating the body size reference points and source of information for each species’ length at maturity.-- Figure S1. Percentage of annual change for the body size of each species at catch for the period 2000-2018 in ICES division 9.a (lower panel) and ICES division 8.c (upper panel).-- Figure S2. Time series of the estimated indices of abundance for the 20 species analysed from 2000 to 2018 in the Galician coast (NE Atlantic) taken from Alonso-Fernández et al. (2019) and updated up to year 2018.-- Figure S3. Percentage of change by year for each species index of abundance for the period 2000-2018 in ICES division 9.a (lower panel) and ICES division 8.c (upper panel).-- Figure S4. Time series of the skewness of the body size frequency distribution for the 20 species analysed over the period 2000 to 2018.-- Figure S5. Slope of the linear trend of body size skewness for each species in ICES division 9.a (lower panel) and ICES division 8.c (upper panel).-- Figure S6. Residual check for the model fitted to Trisopterus luscus body size data.-- Figure S7. Residual check for the model fitted to Pollachius pollachius body size data.-- Figure S8. Residual check for the model fitted to Mullus surmuletus body size data.-- Figure S9. Residual check for the model fitted to Dicentrarchus labrax body size data.-- Figure S10. Residual check for the model fitted to Conger conger body size data.-- Figure S11. Residual check for the model fitted to Labrus bergylta body size data.-- Figure S12. Residual check for the model fitted to Diplodus sargus body size data.-- Figure S13. Residual check for the model fitted to Scophthalmus maximus body size data.-- Figure S14. Residual check for the model fitted to Scophthalmus rhombus body size data.-- Figure S15. Residual check for the model fitted to Solea solea body size data.-- Figure S16. Residual check for the model fitted to Solea senegalensis body size data.-- Figure S17. Residual check for the model fitted to Pegusa lascaris body size data.-- Figure S18. Residual check for the model fitted to Platichthys flesus body size data.-- Figure S19. Residual check for the model fitted to Scyliorhinus canicula body size data.-- Figure S20. Residual check for the model fitted to Raja undulata body size data.-- Figure S21. Residual check for the model fitted to Sepia officinalis body size data.-- Figure S22. Residual check for the model fitted to Octopus vulgaris body size data.-- Figure S23. Residual check for the model fitted to Loligo vulgaris body size data.-- Figure S24. Residual check for the model fitted to Maja brachydactyla body size data.-- Figure S25. Residual check for the model fitted to Necora puber body size data.-- Table S3. Values for all explanatory variables used for predictions for each species' model (Fig. 4 and Fig. 5 in the main text and Fig. S26).-- Figure S26. Estimated (±95 C.I.) variation in body size at catch with depth for the 20 species.-- Figure S27. Plots of the DFA model fitted to the predicted body size at catch for each species in ICES division 8.c in the Galician coast (NE Atlantic).-- Figure S28. Plots of the DFA model fitted to the predicted body size at catch for each species in ICES division 9.a in the Galician coast (NE Atlantic).-- Figure S29. Relationship between (a) the rate of change in body size (% · year-1) and (b) the rate of change in relative abundance (% · year-1) with the average proportion of immature individuals caught (in number, ImC).-- Figure S30. Relationship between the rate of change in body size (% · year-1) with the time trend of body size skewness, Peer reviewed

Supporting Information includes Table S1 and Figures S1–S5 containing the NMR signals of flazin and HPLC and HPLC-MS chromatograms of flazin in the reactions and foods., Peer reviewed