BUSCADOR RECOLECTA

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

Table S1. Supporting data for Figure 1; Table S2. Supporting data for Figure 2; Table S3. Supporting data for Figure 3; Table S4. Supporting data for Figure 4; Table S5. Supporting data for Figure 5; Table S6. Supporting data for Figure 6; Table S7. Supporting data for Figure 7; Table S8. Supporting data for Figure 8; Table S9. Supporting data for Figure 9., Peer reviewed

Peer reviewed

Table S1. Formulations and initial cooling rates of laminating fats (LF1–LF4) containing olive pomace oil (OPO) and prepared in batches of 1300 g. Table S2. Crystallization and melting peak enthalpies measured by DSC for the different laminating fats (LFs) containing olive pomace oil (OPO) tested at the beginning and end of the chilling storage period in comparison to the ingredients PS and cocoa butter and the controls CB and CLF. Figure S1. Effect of frequency on complex modulus (G*) for laminating fats after 60 days of chilling storage in comparison with controls CB and CLF, and examples of potential fits to the weak gel model. CB, control commercial butter; CLF, control commercial laminating fat; LF1–LF4, laminating fats formulated with olive pomace oil (OPO). Figure S2. Conical penetration work (spreadability) values at 20 °C for the different laminating fats tested during chilling storage. CB, control commercial butter; CLF, control commercial laminating fat; LF1–LF4, laminating fats formulated with olive pomace oil (OPO). Figure S3. Color values (L*, a*, and b*) for the different laminating fats tested during chilling storage. CB, control commercial butter; CLF, control commercial laminating fat; LF1–LF4, laminating fats formulated with olive pomace oil (OPO). Figure S4. Lipid oxidation (TBARS) for the different laminating fats LF1–LF4 formulated with olive pomace oil (OPO) tested during chilling storage in comparison with control commercial butter (CB) and control commercial laminating fat (CLF)., 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

Figure S1: Effect of INSTANT CASCARA (IC) beverage on gastrointestinal transit of male and female rats evaluated radiographically 24 h after initiation of IC exposure (cohort 2). Figure S2: Effect of INSTANT CASCARA (IC) beverage on the morphometric and densitometric radiographic analysis of gastrointestinal organs 24 h after IC administration (cohort 2). Figure S3: Effect of INSTANT CASCARA (IC) beverage on the wet and dry weight of the feces of male and female rats. Feces were collected from the cages throughout the X-ray session (cohorts 1 and 2). Table S1: Effect of INSTANT CASCARA (IC) beverage on the macroscopic characteristics of the gastrointestinal organs and epididymal/periovarian and retroperitoneal fat at sacrifice. Table S2: Distribution of female animals (%) according to their estrous cycle in the different studies performed., Peer reviewed

XRF, biogeochemical and pollen results of B4 depression sediments, Sanetti Plateau (Bale Mountains, Ethiopia), Peer reviewed

All_region_-_community_-_familes_by_samples.xlsx = The sample-by-taxa spreadsheet used in the all-region whole community analysis, i.e. "taxaxsamples" in the Dryad file "Example script to calculate biological metrics in biomonitoR.R", All_region_-_community_-_env._variables_and_bio._metrics.xlsx = A spreadsheet listing - for all samples used in the all-region whole community analysis - methods details, environmental variables and biological response variables, the latter calculated in biomonitoR, All_region_-_highRR_-_familes_by_samples.xlsx = The sample-by-taxa spreadsheet used in the all-region 'high RR' analysis, All_region_-_highRR_-_env._variables_and_bio._metrics.xlsx = A spreadsheet listing - for all samples used in the all-region 'high RR' analysis - methods details, environmental variables and biological response variables, All_region_-_community_-_STAR_ICMi_only.xlsx = A spreadsheet listing - for East Mediterranean samples used in the whole community analyses - the region-specific biomonitoring indices STAR_ICMi and its ASPT (average score per taxon) as calculated following Buffagni et al. (2006), All_region_-_highRR_-_STAR_ICMi_only.xlsx = A spreadsheet listing - for East Mediterranean samples used in the 'high RR' analyses - the region-specific biomonitoring indices STAR_ICMi and its ASPT (average score per taxon) as calculated following Buffagni et al. (2006), Fuzzy_coding_of_traits.csv = A spreadsheet showing the calculation of fuzzy-coded scores for each trait. The final column for each trait (e.g. G for "Maximum potential size") is based on the preceding columns for that trait (e.g. E and F for "Maximum potential size"). For example, in row 10, no individuals have a maximum potential size ≤ .25 cm (0*4, where 4 is the trait weight shown in row B) and 75% of individuals have a maximum potential size > 0.25-0.5 cm (0.75*4); therefore (0*4)+(0.75*4)=3., Genus-level_analyses.xlsx = A multi-tab spreadsheet showing the sample-by-taxa matrix and community metrics for each region/dataset used in the genus-level analyses described in Appendix S1.4, 1. Rivers are dynamic ecosystems in which both human impacts and climate-driven drying events are increasingly common. These anthropogenic and natural stressors interact to influence the biodiversity and functioning of river ecosystems. Disentangling ecological responses to these interacting stressors is necessary to guide management actions that support ecosystems adapting to global change., 2. We analysed the independent and interactive effects of human impacts and natural drying on aquatic invertebrate communities—a key biotic group used to assess the health of European freshwaters. We calculated biological response metrics representing communities from 406 rivers in eight European countries: taxonomic richness, functional richness and redundancy, and two biomonitoring indices that indicate ecological status. We analysed metrics based on the whole community and a group of taxa with traits promoting resistance and/or resilience (‘high RR’) to drying. We also examined how responses vary across Europe in relation to climatic aridity., 3. Most community metrics decreased independently in response to impacts and drying. A richness-independent biomonitoring index (the average score per taxon; ASPT) showed particular potential for use in biomonitoring, and should be considered alongside new metrics representing high RR diversity, to promote accurate assessment of ecological status., 4. High RR taxonomic richness responded only to impacts, not drying. However, these predictors explained little variance in richness and other high RR metrics, potentially due to low taxonomic richness. Metric responsiveness could thus be enhanced by developing region-specific high RR groups comprising sufficient taxa with sufficiently variable impact sensitivities to indicate ecological status., 5. Synthesis and applications. Our results inform recommendations guiding the development of metrics to assess the ecological status of dynamic river ecosystems—including those that sometimes dry—thus identifying priority sites requiring further investigation to identify the stressors responsible for environmental degradation. We recommend concurrent consideration of richness-independent biomonitoring indices (such as an ASPT) and new high RR richness metrics that characterize groups of resistant and resilient taxa for region-specific river types. Interactions observed between aridity, impacts and drying evidence that these new metrics should be adaptable, promoting their ability to inform management actions that protect river ecosystems responding to climate change., European Cooperation in Science and Technology, Award: CA15113, Peer reviewed