BUSCADOR RECOLECTA

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

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

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

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

Two datasets are provided: general data on breeders (onset and end of the pre-breeding period) and newborn general information and biochemical data, Seahorses (Hippocampus spp.) are exceptional marine species considering their reproductive patterns and other features. Due to the iconic characteristics of these fishes, aquarium trade and research efforts have increased in the last years. Consequently, novel rearing techniques have been developed; however, there is a need for improvements on a series of issues, namely reproduction success enhancement. The tropical species Hippocampus reidi is the most traded seahorse but many aspects of breeding and its impact on the quality of neonates are still poorly understood. In the present study, we assessed the effects of two pre-breeding diets on newborn quality and viability considering biochemical characteristics, energetic status and ultrastructural aspects of muscular tissue. During the whole pre-breeding season (5 months), the breeders were fed on one of the following diets: M0 (adult non-enriched Artemia) and M5 (adult non-enriched Artemia + mysidaceans). From the onset of the reproduction period, all breeders were fed for 6 months on diet M5. Breeding success and energetic status (ATP, total adenylic nucleotides, AEC and NAD) of newborns resulted considerably enhanced in treatment M5. However, initial differences in neonates quality did not affect further newborn performance (survival and growth until day 7 after male’s pouch release) while gaining access to high-quality preys (copepods). Besides, morphological alterations in muscle tissue were not observed. The reproduction in the species followed a capital–income continuum pattern characterized by an initial mixed capital-income period (until 70-100 days since the onset of the breeding season) followed by an income breeding period with progressive exhaustion of body reserves, especially in M0-newborns. Interestingly, the effects of pre-breeding diets were also noticed in the second half of the breeding period. Our results seemed to indicate that the requirements in essential fatty acids in H. reidi are lower than in other seahorse species (e.g., H. guttulatus). Globally, the results achieved revealed that high-quality pre-breeding diets enhanced reproduction success and would likely result advantageous to improve newborn endurance in conditions of moderate starvation or sub-optimal feeding, Peer reviewed

5 tables, 3 figures, Supplementary Table 1. Conversion factors applied to δ15N and δ13C for lipid normalization in taxa associated to seaweeds on Cíes Archipelago. (-) normalization not required.-- Supplementary Table 2. Relative abundances (%) in epifauna identified in 2017-2018 (spring, summer and autumn) on canopy-forming seaweeds in Cíes Archipelago. * indicates <0.01%.-- Supplementary Table 3. Summary table for Multipatt results (p-value) showing taxa significantly associated to Year (2017 – 2018) and Season (Sp – Spring; Su – Summer; Au – Autumn).-- Supplementary Table 4. Taxa richness (S), Shannon diversity (H’), Simpson dominance (D’), and Pielou’s evenness (J’) in epifaunal assemblages associated to Codium spp. and Gongolaria baccata.-- Supplementary Table 5. Summary table for Multipatt results (p-value) showing taxa significantly associated to seaweed (Codium spp and G. baccata) assemblages seasonally sampled from Summer-2017 (Su17) to Autumn-2018 (Au18).-- Supplementary Table 6. Syngnathid specimens (recaptured specimens not included) collected in 2017 and 2018 (spring, summer and autumn) by UVC on Cíes Archipelago.-- Supplementary Figure 1. Two-dimensional non-metric multidimensional scaling (NMDS; Bray–Curtis similarities) plot of the variation in stable isotopes (δ13C and δ15N) in syngnathids (H. guttulatus, E. aequoreus and S. acus) collected in spring, summer and autumn (2017 and 2018).-- Supplementary Figure 2. Two-dimensional convex hulls of the variation in δ13C and δ15N in epifauna community and syngnathids, considering Year (2017 – 2018), functional groups (FG), and main taxa (Taxa).-- Supplementary Figure 3. Seasonal fluctuations of epifaunal taxa (bars; relative abundance in taxa contributing to syngnathid feeding regimes) in canopy-forming assemblages (Codium spp. and Gongolaria baccata), and syngnathids abundance (dotted lines) on Cíes Archipelago in surveys carried out in 2017 – 2018, Peer reviewed

4 files, 1. Understanding the responses of aquatic animals to temperature variability is essential to predict impacts of future climate change and to inform conservation and management. Most ectotherms such as fish are expected to adjust their behaviour to avoid extreme temperatures and minimize acute changes in body temperature. In coastal Skagerrak, Norway, sea surface temperature (SST) ranges seasonally from 0 to over 20 °C, representing a challenge to the fish community which includes both cold-, cool- and warm-water affinity species. 2. By acoustically tracking 111 individuals of Atlantic cod (Gadus morhua), pollack (Pollachius pollachius) and ballan wrasse (Labrus bergylta) in 2015 - 2018, we examined how coexisting species within a fish community adjusted their behaviour (i.e. vertical distribution in the water column and habitat selection) to cope with the thermal variation. 3. Mixed-effect models showed that thermal preference was a main driver of behaviour and habitat use of the fish community in a southern Norwegian fjord. Cod used colder waters, compared with pollack and ballan wrasse. Increases in SST during summer were associated with the use of deeper, colder waters by cod, especially by larger individuals, and conversely with the occupancy of shallower areas by pollack and ballan wrasse. During winter, when SST dropped and the thermal stratification reversed, pollack and ballan wrasse moved to deeper, relatively warmer areas, while cod selected shallower, colder habitats. Though habitat selection was affected by temperature, species-specific habitat selection was observed even when temperature was similar throughout habitats. 4. This study shows how cohabiting fish species respond to thermal heterogeneity, suggesting that i) temperature regulates the access to the different depths and habitats and ii) behavioural plasticity may be an important factor for coping with temperature variability and potentially for adaptation to climate change, The Research Council of Norway, Award: 294926; European Union’s Horizon 2020, Award: 793627; Regionale forskningsfond Oslofjordfondet, Award: 272090, Peer reviewed

6 figures, 3 tables, Supplementary material for the article https://doi.org/10.1016/j.marenvres.2021.105315, Figure S1. Results of Vtg mRNA expression in females after normalization process with a different number of reference genes.-- Figure S2. Results of Vtg mRNA expression in males after normalization process with a different number of reference genes.-- Figure S3. Individual observation of RT-qPCR data for female and male different Vtg domains normalized with different number of reference genes.-- Figure S4. Bioanalizer profiles of three samples of RNA selected to assess RNA quality.-- Figure S5. Melt curve analysis of reference genes and vitellogenin primer pairs.-- Figure S6. Results of 1% agarose gel electrophoresis of PCR product using all primer pairs tested.-- Table S1. Equations of standard curves for primers pair efficiency.-- Table S2. Power analysis showing the effect size that could be confidently detected (% change in comparison with control values) in our RT-qPCR analyses results using a sample size of 3, and the averaged observed standard deviation (SD) in our samples.-- Table S3. Results of Two-Way ANOVA performed in females and males respectively to evaluate the effect of factor "time” (t4 and t24), factor “chemical” (C, SC and EE2) and the interaction of the two factors on Vtg mNRA normalized expression levels with different number of reference genes.-- Zip mmc2. Sequences.-- Zip mmc3. Alignments, Peer reviewed

5 files, Supplementary data for the article https://doi.org/10.1098/rstb.2020.0165, MGR_conserved_miRNAs.fas.-- PFU_conserved_miRNAs.fas.-- RPH_conserved_miRNAs.fas.-- SBR_conserved_miRNAs.fas.-- TGR_conserved_miRNAs.fas, Peer reviewed