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

File "input_data_myimon.csv" Description: Temporal occurrence data for Myiopsitta monachus in the Iberian Peninsula from 1975 to 2016. Include geographic coordinates (WGS84), month and year of detection and the source for each register. Data sources: GAE-SEO: Cuaderno de aves exóticas http://grupodeavesexoticas.blogspot.com.es/ Reservoir birds: Reservoir Birds http://www.reservoirbirds.com/index.asp ico-ornitho.cat: Data collected on citizen science web portal www.ornitho.cat Accessed from GBIF.org (06 June 2017) GBIF occurrence download https://doi.org/10.15468/dl.vvfrwk SPEA: Noticiáro Ornitológico SPEA (Sociedade Portuguesa para o Estudio das Aves) GBIF 2020: GBIF.org (03 June 2020) GBIF occurrence download https://doi.org/10.15468/dl.mwj59n Abellán et al. 2016: Abellán, P., Carrete, M., Anadón, J. D., Cardador, L., & Tella, J. L. (2016). Non-random patterns and temporal trends (1912-2012) in the transport, introduction and establishment of exotic birds in Spain and Portugal. Diversity and Distributions, 22(3), 263–273. https://doi.org/10.1111/ddi.12403 Aves Extremadura: Aves de Extremadura Vol. 5 2009-2014. http://extremambiente.juntaex.es/files/biblioteca_digital/Aves%20de%20Extremadura_ Vol-5_a.pdf, File "input_data_psikra.csv" Description: Temporal occurrence data for Psittacula krameri in the Iberian Peninsula from 1970 to 2016. Include geographic coordinates (WGS84), month and year of detection and the source for each register. Data sources: GAE-SEO: Cuaderno de aves exóticas http://grupodeavesexoticas.blogspot.com.es/ Reservoir birds: Reservoir Birds http://www.reservoirbirds.com/index.asp SPEA: Noticiáro Ornitológico SPEA (Sociedade Portuguesa para o Estudio das Aves) GBIF 2020: GBIF.org (03 June 2020) GBIF occurrence download https://doi.org/10.15468/dl.5a4ax6 Abellán et al. 2016: Abellán, P., Carrete, M., Anadón, J. D., Cardador, L., & Tella, J. L. (2016). Non-random patterns and temporal trends (1912-2012) in the transport, introduction and establishment of exotic birds in Spain and Portugal. Diversity and Distributions, 22(3), 263–273. https://doi.org/10.1111/ddi.12403, File "myimon_1y2sampl_data_with_vars.csv" Description: Detection history, sampling effort, site covariates and testing data for Myiopsitta monachus in the period 1991-2013 according to survey seasons of 1 year with two replicate observation periods. Fields referring to detection data begins with "det" followed by numbers indicating the survey season (from 1 to 23, 1 corresponding to 1991) and obsevation period (1 or 2). Fields referring to sampling effort begins with "eff" followed by numbers indicating the survey season (from 1 to 23) and obsevation period (1 or 2). "NA" corresponds to non-sampled locations. Other fields: "x": Longitude of 10-km sampling sites in Iberian Peninsula (in ETRS89 LAEA) "y": Latitude of 10-km sampling sites in Iberian Peninsula (in ETRS89 LAEA) "messEv": Climatic similarity to native areas "urban": Percentage of urban land "hii": Global human influence index "agri": Percentage of farmalnd "test": Test data for one extra survey season (corresponds to year 2014) to validate occupancy models, File "myimon_2y2sampl_data_with_vars.csv" Description: Detection history, sampling effort, site covariates and testing data for Myiopsitta monachus in the period 1991-2013 according to survey seasons of 2 years with two replicate observation periods. Fields referring to detection data begins with "det" followed by numbers indicating the survey season (from 1 to 11, note that each survey season include data for two years, starting from 1992-1993) and obsevation period (1 or 2). Fields referring to sampling effort begins with "eff" followed by numbers indicating the survey season (from 1 to 11) and obsevation period (1 or 2)."NA" corresponds to non-sampled locations. Other fields: "x": Longitude of 10-km sampling sites in Iberian Peninsula (in ETRS89 LAEA) "y": Latitude of 10-km sampling sites in Iberian Peninsula (in ETRS89 LAEA) "messEv": Climatic similarity to native areas "urban": Percentage of urban land "hii": Global human influence index "agri": Percentage of farmalnd "test": Test data for one extra survey season (corresponds to cummulative values of years 2014-2015) to validate occupancy models, File "myimon_3y2sampl_data_with_vars.csv": Description: Detection history, sampling effort, site covariates and testing data for Myiopsitta monachus in the period 1991-2013 according to survey seasons of 3 years with two replicate observation periods. Fields referring to detection data begins with "det" followed by numbers indicating the survey season (from 1 to 8, note that each survey season include data for three years, starting from 1991-1993) and obsevation period (1 or 2). Fields referring to sampling effort begins with "eff" followed by numbers indicating the survey season (from 1 to 8) and obsevation period (1 or 2)."NA" corresponds to non-sampled locations. Other fields: "x": Longitude of 10-km sampling sites in Iberian Peninsula (in ETRS89 LAEA) "y": Latitude of 10-km sampling sites in Iberian Peninsula (in ETRS89 LAEA) "messEv": Climatic similarity to native areas "urban": Percentage of urban land "hii": Global human influence index "agri": Percentage of farmalnd "test": Test data for one extra survey season (corresponds to cummulative values of years 2015-2016) to validate occupancy models, File "psikra_1y2sampl_data_with_vars.csv" Description: Detection history, sampling effort, site covariates and testing data for Psittacula krameri in the period 1991-2013 according to survey seasons of 1 year with two replicate observation periods. Fields referring to detection data begins with "det" followed by numbers indicating the survey season (from 1 to 23, 1 corresponding to 1991) and obsevation period (1 or 2). Fields referring to sampling effort begins with "eff" followed by numbers indicating the survey season (from 1 to 23) and obsevation period (1 or 2)."NA" corresponds to non-sampled locations. Other fields: "x": Longitude of 10-km sampling sites in Iberian Peninsula (in ETRS89 LAEA) "y": Latitude of 10-km sampling sites in Iberian Peninsula (in ETRS89 LAEA) "messEv": Climatic similarity to native areas "urban": Percentage of urban land "hii": Global human influence index "agri": Percentage of farmalnd "test": Test data for one extra survey season (corresponds to year 2014) to validate occupancy models, File "psikra_2y2sampl_data_with_vars.csv" Description: Detection history, sampling effort, site covariates and testing data for Psittacula krameri in the period 1991-2013 according to survey seasons of 2 years with two replicate observation periods. Fields referring to detection data begins with "det" followed by numbers indicating the survey season (from 1 to 11, note that each survey season include data for two years, starting from 1992-1993) and obsevation period (1 or 2). Fields referring to sampling effort begins with "eff" followed by numbers indicating the survey season (from 1 to 11) and obsevation period (1 or 2)."NA" corresponds to non-sampled locations. Other fields: "x": Longitude of 10-km sampling sites in Iberian Peninsula (in ETRS89 LAEA) "y": Latitude of 10-km sampling sites in Iberian Peninsula (in ETRS89 LAEA) "messEv": Climatic similarity to native areas "urban": Percentage of urban land "hii": Global human influence index "agri": Percentage of farmalnd "test": Test data for one extra survey season (corresponds to cummulative values of years 2014-2015) to validate occupancy models, File "psikra_3y2sampl_data_with_vars.csv" Description: Detection history, sampling effort, site covariates and testing data for Psittacula krameri in the period 1991-2013 according to survey seasons of 3 years with two replicate observation periods. Fields referring to detection data begins with "det" followed by numbers indicating the survey season (from 1 to 8, note that each survey season include data for three years, starting from 1991-1993) and obsevation period (1 or 2). Fields referring to sampling effort begins with "eff" followed by numbers indicating the survey season (from 1 to 8) and obsevation period (1 or 2)."NA" corresponds to non-sampled locations. Other fields: "x": Longitude of 10-km sampling sites in Iberian Peninsula (in ETRS89 LAEA) "y": Latitude of 10-km sampling sites in Iberian Peninsula (in ETRS89 LAEA) "messEv": Climatic similarity to native areas "urban": Percentage of urban land "hii": Global human influence index "agri": Percentage of farmalnd "test": Test data for one extra survey season (corresponds to cummulative values of years 2015-2016) to validate occupancy models, [Abstract] Concern about the impacts of biological invasions has generated a great deal of interest in understanding factors that determine invasion success. Most of our current knowledge comes from static approaches that use spatial patterns as a proxy of temporal processes. These approaches assume that species are present in areas where environmental conditions are the most favourable. However, this assumption is problematic when applied to dynamic processes such as species expansions when equilibrium has not been reached. In our work, we analyse the roles played by human activities, climatic matching, and spatial connectivity on the two main underlying processes shaping the spread of invasive species (i.e., colonisation and extinction) using a dynamic modelling approach. For this, we used a large dataset that has recorded the occurrence of two invasive bird species -the ring-necked and the monk parakeets- in the Iberian Peninsula from 1991 to 2016., [Methods] Temporal occurrence data for the monk and ring-necked parakeets were obtained from a comprehensive database of exotic birds in mainland Spain and Portugal, which compiled records of exotic species observed in the wild in both countries from 1912 to 2012 through a systematic review of scientific and grey literature and observations from local experts [1]. This dataset was updated until 2016 using the same methodology and complemented with ‘human observation’ data from the Global Biodiversity Information Facility [2,3]. Locations were incorporated to a Geographic Information System (GIS) using a cylindrical equal-area projection at 10 km resolution to fit the maximum daily distances covered by the species. We used as sampling sites for analyses the 10-km grid cells in the Iberian Peninsula. The occurrence data in each sampling sites was classified in surveys seasons and replicate observation periods within seasons using the date of the records. To account for potential variation related to the criteria used to classified the data, we considered three alternative sampling schemes: (1) survey seasons of one calendar year with two replicate observation periods (Jan-Jun and Jul-Dec), (2) survey seasons of two calendar years with two replicate observation periods (each of 1 calendar year) and (3) survey seasons of three calendar years with two replicate observation periods (each of 1.5 years). To account for potential detection biases related to an uneven sampling effort across time and space, we included an estimate of sampling effort as a survey-specific covariate of detection probability in models. This variable was computed as the cumulative value of observation records of both native and alien bird individuals retrieved from GBIF (‘human observation’ category [4]) in a particular sampling site and observation period considered. As sampling site covariates, we calculated the climatic similarity between each of the sites in the study area and the species native ranges using multivariate environmental similarity surfaces (MESS) and compiled information on three variables describing human-transformed environments: (i) the Global Human Influence Index [5] and two more specific descriptors of anthropogenic habitats known to affect invasions, the percentage of ii) urban environments (including urban and built-up areas) and iii) farmland. These two land-use variables were derived from data provided by the USGS Land Cover Institute (LCI) (https://landcover.usgs.gov/) at 500m resolution using ArcMap 10.5. More detailed description of methods can be found in the manuscript., Peer reviewed

Contains: Table S1. GBIF occurrence downloads. Table S2. Model selection results for the size of neighbourhood of the autologistic term. Table S3. Model selection results for the detection (p) sub-model. Table S4. Model selection results for the colonisation (γ) sub-model for the monk parakeet. Table S5. Model selection results for the colonisation (γ) sub-model for the ring-necked parakeet. Table S6. Model selection results for the extinction (ε) sub-model for the monk parakeet. Table S7. Model selection results for the extinction (ε) sub-model for the ring-necked parakeet. Table S8. Model selection results for the best set of combined models for the monk parakeet. Table S9. Model selection results for the best set of combined models for the ring-necked parakeet. Table S10. Estimates of model coefficients for the monk parakeet. Table S11. Estimates of model coefficients for the ring-necked parakeet. Table S12. Estimates of model coefficients for the monk parakeet for models training with a subset of the data for years 2006-2013. Table S13. Estimates of model coefficients for the ring-necked parakeet for models training with a subset of the data for years 2006-2013. Figure S1. Temporal changes in the number of monk and ring-necked parakeet occurrences. Figure S2. Map of predictors used in analyses. Figure S3. Testing data and model predictions based on survey seasons of 1 year and two observation subperiods. Figure S4. Testing data and model predictions based on survey seasons of 3 years and two observation subperiods., Peer reviewed

This file contains Appendix S1 (including Figure A1 and Tables A1–A4), Appendix S2 (including Table A5), Appendix S3 and Appendix S4 (including Figures S1–S2 and Tables S1–S18) followed by supporting references., Contents: APPENDIX S1 Supporting analyses validating the analytical approaches.-- S1.1 Inclusion versus exclusion of samples taken in ponded conditions.-- S1.2 Spring and autumn versus all seasons.-- S1.3 Inclusion versus exclusion of perennial sites.-- S1.4 Genus and mixed subfamily-level analyses S1.5 Abundance versus presence–absence data.-- S1.6 Performance of biomonitoring indices in the all-region models.-- APPENDIX S2 Calculation of functional redundancy and richness.-- APPENDIX S3 Description of environmental conditions and communities in each dataset.-- APPENDIX S4 Supporting figures and tables.-- FIGURE S1 The contribution of 14 datasets to 16 models.-- FIGURE S2 NMDS ordinations of variability in community composition in each region.-- TABLE S1 Characteristics of each dataset.-- TABLE S2 Statements used to calculate the level of human impacts at each site.-- TABLE S3 Environmental variables included and excluded based on VIF analysis.-- TABLE S4 Rationale for inclusion of each biological trait.-- TABLE S5 Rationale for inclusion/exclusion of taxa in the trait assignment process.-- TABLE S6 Resistance/resilience scores of the top-33% ranked taxa in each dataset.-- TABLE S7 Summary of the invertebrate community in each dataset.-- TABLE S8 Model results – Atlantic region.-- TABLE S9 Model results – West Mediterranean region.-- TABLE S10 Model results – East Mediterranean region.-- TABLE S11 Model results – ES_S dataset.-- TABLE S12 Model results – ES_E dataset.-- TABLE S13 Model results – ES_NE2 dataset.-- TABLE S14 Model results – CY dataset.-- TABLE S15 The proportion of variance explained for each response metric.-- TABLE S16 Comparison of metric responses at family and genus level.-- TABLE S17 Summary of family-level community and high RR metric responses.-- TABLE S18 Model results – all-region model high RR group.-- References cited in the Supporting Information, Peer reviewed

PDF file contains: Appendix S1-S10., Peer reviewed

This document contains supplementary tables and figures., Peer reviewed