BUSCADOR RECOLECTA

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

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

Supporting information A in S1 File. Glossary of additional key terms. Supporting information B in S1 File. Table reporting contrasting arguments and approaches used to define how alien taxa are considered and should be managed in accordance with different conservation values/motivations. As multiple values/motivations exist and determine which entities we are interested in (see also Supporting information A), distinct conservation targets can be identified. Note that here, we only consider conservation values/motivations that are expressed regardless of any nature’s instrumental (utilitarian) value, i.e., regardless of nature’s contributions to human well-being (see “nature for itself” framing [9]). Also, note that such contrasting arguments and approaches are not necessarily mutually exclusive and have been occasionally combined to find a middle ground to achieve broader conservation goals [10–13]. Supporting information C in S1 File. Circumstances under which the prevention/mitigation of a decreasing change is considered as a positive change under EICAT+. In EICAT+, we also consider as positive impacts (i.e., increasing changes) cases in which an alien species prevents/mitigates decreasing changes, e.g., when the performance of a native individual, the size of a native population, or the occupancy of a native species would have decreased, or decreased to a greater extent, if the alien species had not been introduced. Although some of these positive impacts can be inferred, the prevention of a decreasing change should be assessed under EICAT+ only when there is convincing evidence that a certain biodiversity attribute (e.g., population size) would have decreased, or decreased to a greater extent, in the absence of the alien species. In the case of extinction prevention, for instance, it must be clear that (i) the population was locally heading toward extinction before the introduction of the alien; and (ii) the alien taxon prevented, through a specific impact mechanism, an extinction that would have occurred in its absence [41,42] (Fig 2b). Other cases where an alien species may prevent or mitigate decreasing changes are, for instance, those in which the abundance (i.e., a proxy for population size) of a native species declined in the uninvaded (i.e., control) plots but not, or to a lesser extent, in the plots invaded by the alien. Note that positive impacts associated with the prevention/mitigation of a decreasing change will generally be more difficult to study and identify than those associated with actual increasing changes, as the former require extensive data regarding the temporal trend of individual performance, population size, or area of occupancy. Supporting information D in S1 File. EICAT+ mechanisms and submechanisms by which an alien taxon can cause positive impacts on native biodiversity attributes and examples of positive impacts sourced from the literature and assessed under EICAT+ (ML+ = Minimal positive impact, MN+ = Minor positive impact, MO+ = Moderate positive impact, MR+ = Major positive impact, MV+ = Massive positive impact). Rationales behind the formulation of the mechanisms and submechanisms can be found in the main text and in Supporting information G, H, and J. Supporting information E in S1 File. Table reporting examples sourced from the literature and classified as information that cannot be classified under EICAT+, but that contain information about mechanisms and might set the stage for future studies. Although these studies described the existence of mechanisms by which alien taxa may cause positive impacts on native taxa, such literature is considered as nonrelevant, as it did not measure, or provide information on, biodiversity attributes used in EICAT+ (e.g., performance of individuals or population size). Rationales behind the formulation of the mechanisms and submechanisms can be found in the main text and in Supporting information G, H, and J. Supporting information F in S1 File. How to attribute a confidence score in EICAT+. Supporting information G in S1 File. Additional information around the rationale behind the formulation of the EICAT+ mechanisms and submechanisms. Supporting information H in S1 File. Additional information about how alien species can cause positive impacts on native biodiversity through overcompensation. Supporting information J in S1 File. Additional information about how alien species can cause positive impacts on native biodiversity through hybridization. Supporting information K in S1 File. References used in the Supporting information., Peer reviewed

20 files, Mussels (Mytilus galloprovincialis) were exposed during 24 hours to a waterborne infection with 10E8 CFU/ml Vibrio splendidus (reference strain LGP32) in the tank water. Five biological replicates were used for each infected and control conditions, APDAPD3401230109.mgf.-- APDAPD3401230109.raw.-- APDAPD3401230110.mgf.-- APDAPD3401230110.raw.-- APDAPD3401230111.mgf.-- APDAPD3401230111.raw.-- APDAPD3401230112.mgf.-- APDAPD3401230112.raw.-- APDAPD3401230113.mgf.-- APDAPD3401230113.raw.-- APDAPD3401230114.mgf.-- APDAPD3401230114.raw.-- APDAPD3401230115.mgf.-- APDAPD3401230115.raw.-- APDAPD3401230116.mgf.-- APDAPD3401230116.raw.-- README.txt.-- checksum.txt.-- peptides.pep.xml, Peer reviewed

Supplementary methods, tables and figures., Peer reviewed

Supplementary Figures (1-15) and Tables (1-2). © The Authors under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited., Drought is one of the most difficult natural hazards to quantify and is divided into categories (meteorological, agricultural, ecological and hydrological), which makes assessing recent changes and future scenarios extremely difficult. This opinion piece includes a review of the recent scientific literature on the topic and analyses trends in meteorological droughts by using long-term precipitation records and different drought metrics to evaluate the role of global warming processes in trends of agricultural, hydrological and ecological drought severity over the last four decades, during which a sharp increase in atmospheric evaporative demand (AED) has been recorded. Meteorological droughts do not show any substantial changes at the global scale in at least the last 120 years, but an increase in the severity of agricultural and ecological droughts seems to emerge as a consequence of the increase in the severity of AED. Lastly, this study evaluates drought projections from earth system models and focuses on the most important aspects that need to be considered when evaluating drought processes in a changing climate, such as the use of different metrics and the uncertainty of modelling approaches.This article is part of the theme issue ‘Drought risk in the Anthropocene’., Peer reviewed