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Experimental appendix including photophysical and computational methods and X-ray crystallography details (2299209–2299212); NMR spectra and X-ray diffraction figures for structural characterization; theoretical calculations; and photophysical properties., Cartesian coordinates of the DFT-optimized structures for complexes 1, 2, 1-Cl, 1-I, and [{Pt(bzq)(μ-S∧N)2}] (HS∧N = 2-mercaptopyrimidine)., Peer reviewed

-Additional experimental details, NMR data, and methods and computational details (PDF). -Cartesian coordinates of computed structures (XYZ)., Peer reviewed

1 figure, 3 tables, Supporting information for the article https://doi.org/10.1016/j.wasman.2022.01.035, Figure S1 - Box plots and kernel density plots before and after normalization. The density plots are based on all samples. Data transformation: Log Normalization; Data scaling: Autoscaling.-- Table S1 – Amino acids content from substrates of the five study groups: control group (a), 25% replacement (b), 50% replacement (c), 75% replacement (d) and 100% replacement (e)) of expired fish feed. (g/100 g total amino acids). OHPro: hydroxyproline. Errors are the confidence intervals for n=2 (replicates of analysis) and α =0.05.-- Table S2 – Amino acids content of Black Soldier Fly larvae (g/100 g total amino acids). Errors are the confidence intervals for n=2 (replicates of analysis) and α=0.05.-- Table S3 - Fatty acid composition from substrates lipid extracts of the five study groups: control group (a), 25% replacement (b), 50% replacement (c), 75% replacement (d) and 100% replacement (e) of expired fish feed. Abundances are expressed in relative abundance (%) and values are means of five samples (n = 5) ± standard deviation, Peer reviewed

Electronic supplementary material, Figure S1. Box plot of the α-diversity index Observed richness for the bacterial community at each sampling time (0, 15, 30, 50 days after bacterization) of control (CON), V. dahliae-inoculated (Vd), Pseudomonas simiae–treated (PICF7), P. simiae PICF7/V. dahliae-inoculated (PICF7_Vd), P. polymyxa-treated (PIC73) and P. polymyxa/V. dahliae-inoculated (PIC73_Vd) plants. The statistical differences resulted by the Dunn post-hoc test are represented by asterisks (level of significance: *p < 0.05; **p < 0.01; ***p < 0.001). Figure S2. Box plot of the α-diversity indices Observed richness for the fungal community at each sampling time (0, 15, 30, 50 days after bacterization) of control (CON), V. dahliae-inoculated (Vd), Pseudomonas simiae–treated (PICF7), P. simiae/ V. dahliae-inoculated (PICF7_Vd), P. polymyxa-treated (PIC73) and P. polymyxa/ V. dahliae-inoculated (PIC73_Vd) plants. The statistical differences resulted by the Dunn post-hoc test are represented by asterisks (level of significance: *p < 0.05; **p < 0.01; ***p < 0.001). Figure S3. PCoA analysis with the Weighted-Unifrac distance of control (CON), V. dahliae-inoculated (Vd), Pseudomonas simiae–treated (PICF7), P. simiae/ V. dahliae-inoculated (PICF7_Vd), P. polymyxa-treated (PIC73) and P. polymyxa/ V. dahliae-inoculated (PIC73_Vd) plants. Figure S4. PCoA analysis of the bacterial community with the Weighted-Unifrac distance for the bacterial community at each sampling time (0, 15, 30, 50 days after bacterization) of control (CON), V. dahliae-inoculated (Vd), Pseudomonas simiae–treated (PICF7), P. simiae/ V. dahliae-inoculated (PICF7_Vd), P. polymyxa-treated (PIC73) and P. polymyxa/ V. dahliae-inoculated (PIC73_Vd) plants. Figure S5. PCoA analysis with the Weighted-Unifrac distance for the factor “time” considering all the sampling times: zero (0), fifteen (15), thirty (30) and fifty (50) days after bacterization. Figure S6. PCoA analysis with Bray Curtis distance for the factor “time” considering all the sampling times: zero (0), fifteen (15), thirty (30) and fifty (50) days after bacterization. Figure S7. Taxonomy profiles at phylum level of the olive ‘Picual’ root-associated microbiota upon treatment with two biological control agents and in the absence or presence (subsequent inoculation) of Verticillium dahliae. Bacterial (A) and fungal (B) communities at phylum level for the different treatments examined: control (CON), Paenibacillus polymyxa-treated (PIC73), P. polymyxa/V. dahliae-inoculated (PIC73_Vd), Pseudomonas simiae-treated (PICF7), P. simiae/V. dahliae-inoculated (PICF7_Vd) and V. dahliae-inoculated (Vd) plants. Only the phyla with relative abundance > 1% for bacteria and 0.1% for fungi are shown (n = 10). Asterisks indicate the phyla that showed significant differences (ANCOMB p < 0.05) by the taxonomical analysis (see the main text). Figure S8. Taxonomy profiles at phylum level of the olive ‘Picual’ root-associated microbiota upon treatment with two biological control agents and in the absence or presence (subsequent inoculation) of Verticillium dahliae at each sampling time (0, 15, 30, 50 days after bacterization). Bacterial (A) and fungal (B) communities at phylum level for the different treatments examined: control (CON), Paenibacillus polymyxa-treated (PIC73), P. polymyxa/V. dahliae-inoculated (PIC73_Vd), Pseudomonas simiae-treated (PICF7), P. simiae/V. dahliae-inoculated (PICF7_Vd) and V. dahliae-inoculated (Vd) plants. Only the genera with relative abundance > 1% for bacteria and 0.1% for fungi are shown (n = 10). Figure S9. Taxonomic profiles at genus level of the bacterial community at each sampling time (0, 15, 30, 50 days after bacterization) of control (CON), Paenibacillus polymyxa-treated (PIC73), P. polymyxa/V. dahliae-inoculated (PIC73_Vd), Pseudomonas simiae-treated (PICF7), P. simiae/V. dahliae-inoculated (PICF7_Vd) and V. dahliae-inoculated (Vd) plants. Only the genera with a relative abundance > 2% are shown (n = 10). Asterisks indicate the genera that showed significant differences (ANCOMB p < 0.05) by the taxonomical analysis (see the main text). Figure S10. Taxonomic profiles at genus level of the fungal community at each sampling time (0, 15, 30, 50 days after bacterization) of control (CON), Paenibacillus polymyxa-treated (PIC73), P. polymyxa/V. dahliae-inoculated (PIC73_Vd), Pseudomonas simiae-treated (PICF7), P. simiae/V. dahliae-inoculated (PICF7_Vd) and V. dahliae-inoculated (Vd) plants. Only the genera with a relative abundance > 2% are shown (n = 10). Asterisk indicates the genus that showed significant differences (ANCOMB p < 0.05by the taxonomical analysis (see the main text)., Peer reviewed

PDF file contains: Tables S1-S5 and Figures S1-S4.-- © Author(s) 2022. CC BY 4.0 License. The copyright of individual parts of the supplement might differ from the article licence., Peer reviewed

R scripts showing how to use climwin package with tree-ring width and anatomy chronologies. The databases needed to use the scripts are included., [FILES] 1. climwin with dendro and anatomy.R R script in which climwin is used to study the growth/anatomy-climate relationships of 5 species with weekly time resolution. 2. climwin with the river flow.R R script in which climwin is used to study the growth-river flows relationships of 2 sites with monthly time resolution. 3. Pinus sylvestris model.R R script in which climwin is used to fit a multiple linear regression. 4. RingWidths.csv Database of detrended growths and anatomical variables needed to run the R scripts. Abbreviations: LA - lumen area CWT - cell wall thickness ew - earlywood lw - latewood Ps - Pinus sylvestris (Corbalán site) Aa - Abies alba (Paco Ezpela site) VA1 - Valdelinares (Pinus uncinata) AL - Alcalá de la Selva (Pinus sylvestris) CO - Olmedilla (Pinus nigra) AC - Alto de Cabra (Pinus pinaster) VH - Valbona (Pinus halepensis) 5. climate.rds Database of climate needed to run the R scripts. Abbreviations: T - Temperature Tmax - Maximum temperature Tmin - Minimum temperature P - Precipitation spei - Standardized Evapotranspiration Precipitation Index using a range of time scales (1, 3, 6, 9, 12, 24, 36 and 48 months) over which water deficits and surplus accumulate are considered. 6. Fraxinus.csv Database of detrended growths of Fraxinus needed to run the R scripts. 7. River flow.csv Database of river flow needed to run the R scripts. 8. readme.txt txt file explaining the details of the data. (2021-07-01), [METHODOLOGY] We aim to identify the most likely climate variables driving the growth and wood anatomy of the species using climwin package. We used the weekly resolved climate data and a randomization technique to find, for each climate variable, the most relevant period of the year in which climate was most related to growth according to climwin. To identify the most likely climate predictors of the growth and wood anatomy features and the most relevant time window (the most influential period of the year for individual climate variables), we fitted simple linear regressions with the growth/anatomy variables as the response variables and the climate variables as predictors. The mean of each factor in each time window considered was used as the aggregate statistics. For each factor all possible window lengths (periods of year) at weekly resolution (but monthly resolution for the flow river) was calculated and the one with the lowest ΔAICc compared to the null model (i.e., including the intercept only) was selected. Finally, randomization tests were calculated using 1000 repetitions to calculate pΔAICc (the likelihood that a climatic signal is real). October 1 of the previous year was established as the threshold for the beginning of the windows and November 31 of the year of growth as the limit for the end of the windows. A minimum length of two weeks was pre-defined. A multiple linear regression were fitted using P. sylvestris pine lumen area chronology, without distinguishing between earlywood and latewood, as the response variable and including the climate variables found to be statistically significant. For building the model with climwin we followed this procedure: (i) among the simple linear models calculated with climwin for the response variable, the model with the lowest ∆AICc was selected; (ii) using this model as baseline model, we introduced the rest of climatic variables one by one in order to fit all possible two-factor models, obtaining for each model ∆AICc, climate windows and p∆AICc; and (iii) the models with p∆AICc < 0.05 were selected. Finally, only a model with two climate variables met this condition. If more significant models with different climatic variables had been found, the whole process would have to be repeated including the model with two climatic factors with lower ∆AICc in the baseline model. Multicollinearity was avoided by controlling the variance inflation factor (VIF)., Ministerio de Economía y Competitividad: CGL2015-69186-C2-1-R Agencia Estatal de Investigación: RTI2018-096884-B-C31, Peer reviewed

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