BUSCADOR RECOLECTA

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

Table S1: HPLC retention time (Rt), maximum absorption (λmax) and m/z of the identified major bioactive compounds from Opuntia stricta var. dillenii according to Gómez-López et al. (2021) [18]; Table S2: antioxidant activities (ORAC and LOX-FL) of Opuntia stricta var. dillenii’s whole fruit, tissues (peel, and pulp) and by-product (bagasse); Table S3: effects of 10, 25, 50 and 100 µg/mL of extracts from Opuntia stricta var. dillenii whole fruit, peel, pulp and bagasse tissues on triglycerides content (%) of 3T3-L1 mature adipocytes treated for 24 h. Figure S1: HPLC-DAD chromatogram of betalains (at 480 and 535 nm) and phenolic compounds (at 280 and 370 nm) in Opuntia stricta var. dillenii from (a) whole fruit, (b) peel, (c) pulp, and (d) bagasse from the industrialisation. Numbers correspond to the identified compounds indicated in Table S1; Figure S2: structural presentation of the main betalains and phenolic compounds identified in Opuntia stricta var. dillenii., Peer reviewed

1 zip file, containing: -Morphological data.xlsx: Mean values of each species for the four quantitative traits (LA, LW, ILB and LMA) and the five qualitative traits (Leaf Habit, Pubescence, Shape, Apex, Margin) LA: Leaf Area, expressed in cm2. LW: Lenght-Width ratio, dimensionless. ILB: Index of Lobulation, expressed as (Perimeter/square root of Area), dimensionless. LMA: dry Leaf Mass per Area, expresed as (dry weight/leaf area), expressed in g/m2. Leaf Habit: Deciduous or Evergreen Pubescence: Glabrous, Pubescent or Densely Pubescent Shape: Elliptic, Obovate, Oblong, Lanceolate, Ovate or Circular. Apex: Acute, Acuminate, Rounded or Straight Margin: Entire, Lobated, Serrate, Dentate, Undulate, Spinose or Crenate. -Climatic data.csv: Average Worldclim variables for each species used in the climatic PCA. See https://www.worldclim.org/data/bioclim.html for further details. "bio20" was calculated ex profeso for the paper as a result of BIO16 minus BIO17. -Supplementary Tables.xlsx: Supplementary material containing the five supplementary tables for the main manuscript., Files in this folder include raw data as well as supplementary files corresponding to the paper: "Oak leaf morphology may be more strongly shaped by climate than by phylogeny" Rubén Martín-Sánchez, Domingo Sancho-Knapik, David Alonso-Forn, Ana López-Ballesteros, Juan Pedro Ferrio, Andrew L. Hipp, José Javier Peguero-Pina, Eustaquio Gil-Pelegrín. Annals of Forest Science. 2024. https://doi.org/10.1186/s13595-024-01232-z In addition, you can also find the five supplementary tables generated for the same paper. Corresponding author: Rubén Martín Sánchez (rmartin@cita-aragon.es), Peer reviewed

21 files, The present work describes LC-ESI-MS/MS analyses of tryptic digestion peptides from phages that infect Staphylococcus aureus-causing mastitis, and isolated from dairy products. A total of 1935 non-redundant peptides belonging to 1282 proteins were identified and analyzed. Among them, 80 staphylococcal peptides from phages were confirmed. These peptides belong to proteins such as phage repressors, structural phage proteins, uncharacterized phage proteins and complement inhibitors. Moreover, of the phage origin peptides found, eighteen of them were specific to S. aureus strains. These diagnostic peptides could be useful for the identification and characterization of S. aureus strains that cause mastitis. Furthermore, a study of bacteriophage phylogeny and the relationship among the identified phage peptides and the bacteria they infect was also performed. The results show the specific peptides which are present in closely related phages, and the existing links between bacteriophage phylogeny and the respective Staphylococcus spp. infected, Excel_dataset_in_Supplemental_Data.xlsx.-- README.txt.-- SA_41_mcarrera_O1205_343.RAW.-- SA_92_mcarrera_O1205_192.RAW.-- SA_280_mcarrera_O1205_190.RAW.-- SA_286_mcarrera_O1205_188.RAW.-- SA_507_mcarrera_O1205_345.RAW.-- SA_587_mcarrera_O1205_184.RAW.-- SA_617_mcarrera_O1205_335.RAW.-- SA_640_mcarrera_O1205_337.RAW.-- SA_700_mcarrera_O1205_182.RAW.-- SA_844_mcarrera_O1205_196.RAW.-- SA_894_mcarrera_O1205_347.RAW.-- SA_ATCC9144_mcarrera_O1205_200.RAW.-- SA_ATCC29213_mcarrera_O1205_333.RAW.-- SA_ATCC35845_mcarrera_O1205_194.RAW.-- SA_CA19_mcarrera_O1205_341.RAW.-- SA_GP2_mcarrera_O1205_349.RAW.-- SA_GP17_mcarrera_O1205_351.RAW.-- SA_OV18_mcarrera_O1205_339.RAW.-- SA_PE1_mcarrera_O1205_198.RAW.-- SA_U17_mcarrera_O1205_186.RAW, Peer reviewed

Table S1. Docking-based quantitative matrix for α-gliadin peptide library docked in HLA-DQ2.5; Table S2. Docking-based quantitative matrix for non-gliadin peptide library docked in HLA-DQ2.5; Table S3. Docking-based quantitative matrix for α-gliadin peptide library docked in HLA-DQ8.1. Figure S1a. Accuracy of predictions by the QMs for α-gliadin peptide (blue curve) and non-gliadin peptide (orange curve) at different cutoffs between binders and non-binders to HLA-DQ2.5. Figure S1b. Accuracy of predictions by the QM for HLA-DQ8.1 at different cutoffs between binders and non-binders. Figure S2. Intermolecular interactions between the most positively/negatively contributing peptide residues from α-gliadin and non-gliadin peptides binding into the corresponding pockets of HLA-DQ2.5 and HLA-DQ8.1. The hydrogen bonds are presented as orange discontinued lines; π-π-stacking with red or pale pink for weaker contacts, hydrophobic interactions with green or pale green for weaker interactions. The peptide backbone and sidechains are coloured in cyan. Hydrogens are not presented., Peer reviewed

Quantity (ng/μL) and quality assurance (ratio 260/280) of extracted RNA (Table S1); primer sequences and melting temperature (Tmelting) used for TJ protein quantification by qRT-PCR (Table S2); data (mean ± standard deviation) of transepithelial electrical resistance (TEER) (Ω cm2) from samples and control, before (t = 0 h) and after treatments (t = 4 h), and for Caco-2 and T24 cells; calculation of ΔTEER with respect to control is also included (Table S3); and PCR data of TJ proteins (occludin, Z0–1, and claudin-2) expression as fold change (mean ± standard deviation) with respect to the noninfected control (C), the effluent Ef, the infected control (UPEC), the effluent Ef in the infected model (UPEC + Ef), or urine (Urine) (Table S4)., Peer reviewed

Supplementary material table and figures, Table S1. Bacteria, oomycete, fungi, phages, plasmids and oligonucleotides used in this study. Figure S1. Halos of antibiosis of filter-sterilized supernatants of Pantoea agglomerans 9Rz4 grown in different culture media. Growth inhibition of the ascomycete yeast Schizosaccharomyces pombe (herbicolin A sensitive) with culture supernatants of P. agglomerans 9Rz4 and the herbicolin A deficient mutant NB1 grown overnight in LB, minimal medium (MM), YE and Strobel medium (Strobel et al., 1999) at 25 ºC. For the bioassays, a S. pombe top agar lawn was prepared in YE-agar and 300 µl of filter-sterilized supernatants were added to holes punched in the S. pombe bioassay plates. The size of the inhibition halos is indicative of the susceptibility of S. pombe to herbicolin A. The bioassays were repeated three times and representative pictures are shown. Picture were taken after 48 h of incubation at 30 ºC. The radius of the halos from three biological replicas is 7.0 ± 0.2 mm (LB), 4.1 ± 0.1 (MM), 6 ± 0.1 mm (YE) and 1.8 ± 0.05 mm (Strobel medium). Bars, 5 mm. Figure S2. Pantoea agglomerans 9Rz4 maize root colonization and its effect on plant growth. A, Maize plants 10 days after inoculation with 9RZ4. Non-inoculated plants were included as control. B, Root weight of maize plants shown in Fig. S2A. Shown are mean and standard deviation of six different plants. No statistically significant differences in root weight were observed between inoculated and non-inoculated plants. C, Maize root colonization assays of P. agglomerans 9Rz4 and its mutant strain NB1. Shown are mean and standard deviation of six different plants. In A-C, sterilization, germination and inoculation of maize seeds was carried out as described previously (Matilla et al., 2007), with minor modifications. Briefly, sterile maize seeds were incubated for 45 min at 30 ºC with a 107 CFU/mL of Pantoea agglomerans 9Rz4 strains. Thereafter, seeds were rinsed with sterile deionized water and planted in 50 mL tubes containing 40 g of sterile washed silica sand and 10% (v/w) plant nutrient solution supplemented with Fe-EDTA and micronutrients, as described previously (Matilla et al., 2007). Plants were maintained at 24 ºC with a daily light period of 16 h for 10 days. Figure S3. Effect of plasmid carriage on the antifungal properties of P. agglomerans 9Rz4. Bioactivity against Verticillium dahliae of 9Rz4 and a 9Rz4 variant (9Rz4-W) lacking plasmid p9Rz4_1. Pictures were taken after 96 h of incubation at 25 °C. Figure S4. Herbicolins A and B production is reduced in the quorum sensing mutant defective in the acyl-homoserine lactone synthase PagI. Abundance of herbicolins A and B relative to the wild type 9Rz4 in the supernatants of the P. agglomerans 9Rz4 strains. Data are the mean and standard deviations from three biological replicates and correspond to the intensity (area under the peak) of “extracted ion chromatograms” (EIC) shown in Fig. 6B. Note that the areas derived from the EICs are not comparable between compounds (e.g. herbicolin A vs herbicolin B) as these areas depend on the ionization efficiency of each compound. As shown in Fig. 1A, herbicolin B is found at trace levels in the 9Rz4 supernatants based on LC-HRMS analyses. Figure S5: Complementation of herbicolins A and B production in the quorum sensing mutant defective in the acyl-homoserine lactone synthase PagI. A, Growth inhibition of Schizosaccharomyces pombe with culture supernatants of Pantoea agglomerans strains. For the assays, P. agglomerans strains were inoculated at an initial OD660 of 0.05 in 10 mL of LB medium containing 1 mL supernatants of overnight cultures of P. agglomerans NB1 (herbicolin A defective; wild type in acyl-homoserine lactone production) or P. agglomerans PagI (defective in the synthesis of acyl-homoserine lactones) grown in LB medium. Then, bacterial cultures were grown overnight at 25 ºC, at which time the supernatants were collected, filter-sterilized and characterized chemically and biologically. For the bioassays, a Schizosaccharomyces pombe top agar lawn was prepared and 300 µL of filter-sterilized supernatants were added to holes punched in the S. pombe bioassay plates. Pictures were taken after 48 h of incubation at 30 ºC. Numerical values indicate the mean and standard deviation of the radius of the inhibition halo of three biological replicates. Bars, 5 mm. B, Extracted ion chromatograms (EIC) corresponding to an m/z of 659.385 ± 0.005 (theoretical value for [M+2H]2+ in herbicolin A) and to an m/z of 569.845 ± 0.005 (theoretical value for [M+2H]2+ in herbicolin B). C, Abundance of herbicolins A and B relative to the wild type strain 9Rz4 in the supernatants of the P. agglomerans 9Rz4 strains through the measurement of peak areas in the EICs shown in Fig. S5B. In B and C, note that the areas derived from the EICs are not comparable between compounds (e.g. herbicolin A vs herbicolin B) as these areas depend on the ionization efficiency of each compound. As shown in Fig. 1A, herbicolin B is found at trace levels in the 9Rz4 supernatants based on LC-HRMS analyses., 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