BUSCADOR RECOLECTA

Appendix S1-S5 and References., Peer reviewed

Supplementary Tables (S1-S6) and Figures (S1-S3)., Peer reviewed

datasheet.xlsx Data used in the analyses, consists on two sheets: - boxplotindices: data with stability, synchrony and richness values for each plot or community, the approach used to calculate each index (long-term or year-to-year), and the treatment applied in each plot (control or fertilized). - matrixalldatacom: data used to carry out the analyses at the community level. For each community it is given: the number of the plot or community, stability, synchrony, richness, functional composition and diversity of the five traits studied, and treatment (control or fertilized). For each of these variables there are two columns, one with the variable calculated using the long-term approach (named "longterm_" or "cum_") and the other one for the variable calculated using the year-to-year approach (named "yeartoyear_" or "mean_"). - matrixalldataspp: data used to carry out the analyses at the species level. For each species it is given: species name, long-term and year-to-year stability, species values for the five traits studied, and treatment (control or fertilized). Missing values are indicated by NAs., Understanding the mechanisms underlying community stability has become an urgent need in order to protect ecosystems from global change and resulting biodiversity loss. While community stability can be influenced by richness, synchrony in annual fluctuations of species, species stability and functional traits, the relative contributions of these drivers to stability are still unclear. In semi-natural grasslands, land-use changes such as fertilization might affect stability by decreasing richness and influencing year-to-year fluctuations. In addition, they can promote long-term directional trends, shifting community composition and influencing grassland maintenance. Thus, it is important to consider how species and community stability vary year-to-year but also in the long term. Using a 14-year vegetation time series of a species-rich semi-natural Mediterranean grassland, we studied the relative importance of richness, synchrony, species stability and functional traits on community stability. To assess land-use change effects on stability, we applied a fertilization treatment. To distinguish stability patterns produced by year-to-year fluctuations from those caused by long-term trends, we compared the results obtained using a detrending approach from those without detrending. Stability is influenced by richness, synchrony and functional traits. Fertilization decreases species and community stability by promoting long-term trends in species composition, favouring competitive species and decreasing richness. Studying stability at the community and species level, and accounting for the effect of trends is essential to understand stability and its drivers more comprehensively., [Methods] Study site and experimental design: In 2003, 12 plots of 15x5 m (hereafter called macro-plots) were established inside an area of 5500 m2. Half of the macro-plots (six) were used as control plots and half were fertilized with sewage sludge in a single event in 2003, applying manually to the soil surface 5 kg m-2 . The sludge came from a municipal urban wastewater treatment plant located in Tudela (Navarra, Spain), and it was sludge previously dried to 28% dry matter by centrifugation. To accurately assess vegetation changes, a 1x1 m permanent plot was placed in the centre of each macro-plot. Every year for 14 consecutive years (from 2004 to 2017), at the end of June, vegetation was sampled by R. Ibáñez, who identified and recorded every vascular plant species present in each of the permanent plots. The 1x1 m permanent plots were divided into 100 10x10 cm subplots to measure species abundance (frequency) by counting the number of 10x10 cm subplots in which the species was present (presence was recorded if shoots overlapped with the sampling unit/subplot, not according to rooted plants). Richness, synchrony and stability measures: Species richness in each permanent plot was measured both as cumulative species richness, counting the number of species found at least once in a permanent plot during the 14 years of the study, and as mean species richness, averaging the number of species found in a permanent plot over the 14 years (Lepš et al., 2018). Community-level synchrony for each permanent plot was calculated using the log variance ratio index (“Logvar”), which is the log-transformation of the ratio of observed to expected variance (i.e. the ratio of variance of the total community abundance to the sum of variances of the abundance of each species; Lepš et al., 2018; Roscher et al., 2011). Stability at both the community and the species level was calculated as the inverse of the coefficient of variation (CV-1) across years of cumulative or individual species abundances in each permanent plot. In order to distinguish the patterns produced by long-term trends from those caused by year-to-year fluctuations, we also used the three-term local quadrat variance detrending method (T3; Hill, 1973), which consists in calculating the variance in three year time periods, to remove the effect of long-term trends both on synchrony and stability indices (Lepš, Götzenberger, et al., 2019). Thus, we calculated synchrony (log variance ratio) and stability (CV-1) indices using both the non-detrending (hereafter “long-term” synchrony and stability) and the T3-detrending approach (hereafter “year-to-year” synchrony and stability; for which the original variance used in the log variance ratio index or in the inverse of the coefficient of variation was replaced by the three-term local-quadrat variance) (Valencia, de Bello, Lepš, et al., 2020). All synchrony and stability indices were calculated using the calc_sync function of the package “tempo” in R (Lepš, Götzenberger, et al., 2019). Plant functional traits and indices: We obtained data for five functional traits (plant height, Leaf Dry Matter Content or LDMC, Specific Leaf Area or SLA, Leaf Area or LA and Seed Mass or SM) for most of the species present in the 1x1 m permanent plots (data available for 98%, 78%, 84%, 98% and 85% of the species, respectively). Trait data were collected in-situ. Although the number of individuals measured for each trait varied, traits were measured following the protocols provided by Cornelissen et al. (2003). Missing data were obtained from BROT, a trait database for Mediterranean Basin species (Tavşanoğlu & Pausas, 2018), or from TRY database (Kattge et al., 2020). Mean trait values and abundance data for each species were used to calculate community functional composition and functional diversity for the five traits studied. Functional composition was measured as the Community Weighted Mean (CWM; Garnier et al., 2004) and functional diversity as the Rao Quadratic Index (Rao, 1982), which is the sum of pairwise functional distances between species weighted by relative abundance (Mouchet et al., 2010). To calculate these indices, we used the function dbFD of the “FD” package in R (Laliberté & Legendre, 2010; Laliberté et al., 2014). Data analysis: To study how fertilization influenced stability, synchrony and species richness at the community level, and to test if the values displayed by these indices and their response to fertilization changed depending on the approach used to calculate them (e.g. long-term or year-to-year), we carried out three multiple linear regression models using as response variables the stability, synchrony or species richness index in each plot, respectively. As explanatory variables we used the treatment applied in each plot (i.e. control or fertilized), the approach used to calculate the index (i.e. long-term or year-to-year stability and synchrony, and cumulative or mean species richness), and the interaction between both. In order to discover the main drivers of community stability, we used different linear regression models to test for relationships between community stability (long-term and year-to-year) and synchrony, richness, functional composition and diversity of the five traits studied and treatment. The explanatory variables were calculated in different ways depending on if we studied long-term or year-to-year stability, so that the variables would as well reflect “accumulated” or “yearly” values. For long-term stability we used long-term synchrony and cumulative species richness, and the functional composition and diversity indices were calculated using the cumulative abundance of each species across all years. By contrast, for year-to-year stability we used year-to-year synchrony and mean species richness across years, and the functional composition and diversity were calculated separately for each plot and year and then averaged across all years. This way, the first type of models was more focused on detecting processes acting across years and promoting trends, while the second type was focused on year-to-year processes. We build linear regression models by first running simple regression models for each explanatory variable and then applied multiple regression models with synchrony, richness, treatment and the functional indices selected as significant or marginally significant in the simple models. We then studied stability at the species level. Species stability was averaged over control and over fertilized plots. As we found some extreme values corresponding to highly stable species (Brachypodium retusum (Pers.) P.Beauv. and Aphyllanthes monspeliensis L.), we log transformed average species stability. We tested for relationships between log-transformed species stability (long-term and year-to-year) and the five functional traits studied. We first used simple models for each trait and when a certain trait was significant we did multiple models using trait, treatment and the interaction between both as explanatory variables. On the other hand, we also studied how fertilization influenced species stability, checking if results changed when using the long-term or year-to-year approach. We calculated the Pearson’s correlation coefficient and applied a paired t-test to test for differences in species stability between control and fertilized plots and between the long-term and year-to-year approach. All analyses were carried out with the lm, cor.test and t.test functions in R software (v. 4.0.3; R Core Team, 2020)., Fundación Caja Navarra, Award: Ref. 10833 (Programa “Tú Eliges, Tú Decides”), University of Navarra, Award: Project “Biodiversity Data Analytics and Environmental Quality”, University of Navarra, Award: Project “Red de Observatorios de la Biodiversidad de Navarra (ROBIN)”, Departamento de Educación, Award: Ayudas predoctorales para la realización de programas de doctorado de interés para Navarra; Plan de Formación y de I+D 2018, Ministerio de Ciencia e Innovación, Award: Ref. RTI2018-096884-B-C31 (Project FORMAL), Czech Academy of Sciences, Award: No. RVO 67985939, Peer reviewed

The folder contains a metadata sheet describing the datasets included in the LOTVS collection, and a proposal template to be used in data requests. Metadata will be regularly updated., Peer reviewed

The folder contains a metadata sheet describing the datasets included in the LOTVS collection, and a proposal template to be used in data requests. Metadata will be regularly updated., Peer reviewed

Supplementary data 1. Crystallographic data for the structural analysis have been deposited on the Cambridge Crystallographic data Center (CCDC 1514267), and copies of the data can be obtained free of charge at www.ccdc.cam.ac.uk/conts/retrieving.html. Additional experimental details, structural characterization data (Projection along the a-axis showingthe polymeric an 1-D endless inorganic chain, projection of the structure of [C6H16N2O]2+ cation along the a-axis showing the chair conformation of piperazinium entities, contributions to the Hirshfeld surface area for C6H16N2OPbCl4 and model for the formation and recombination of the exciton in the title compound are given in Fig S1–S4 and selected bond distances and angles in [C6H16N2O]PbCl4 and surface composition (in atomic %) are given in Table S1 and S2.), Peer reviewed

This file describes meteorological and limnological methodologies used by the author to obtain data from Las Madres lake and its environment in 1991-2021., Peer reviewed

This file compiles supplementary materials of the book on the long-term limnology of Las Madres lake (Spain) for 1991-2021., Peer reviewed

[Description of methods used for collection/generation of data] Auditory function (ABR data): Auditory brainstem responses (ABR) recordings were performed on a TDT ABR and DPOAE acquisition system, with a RZ6 processor (Tucker‐Davis Technologies, Alachua, FL, USA). In brief, mice were anesthetized with ketamine (100 mg/kg; Imalgene 1000; Merial, Lyon, France) and xylazine (10 mg/kg; Rompun 2%; Bayer, Leverkusen, Germany) by intraperitoneal injection and the ABR tests were performed in a sound‐attenuating chamber. Two different sound stimuli, clicks and tone bursts, were generated with SigGenRZ software (TDT). Stimuli were calibrated using SigCalRZ software and a PCB 377C01 precision condenser microphone, with a 426B03 preamplifier and a 480C02 signal conditioner. Click (duration 0.1 ms) and tone burst (duration 5 ms, 2.5 ms each for rise and decay, without plateau) at 4, 8, 16, 24, 32 and 40 kHz stimuli were delivered by a MF1 open field magnetoelectrostatic speaker (TDT) at 30 (click) or 50 (tone bursts) pulses per second, and from 90 to 10 dB SPL, in 5–10 dB steps. The evoked response was collected with stainless steel needle electrodes placed at the vertex (active), ventrolateral to the right ear (reference) and tail base (ground), promediated, and analyzed with BioSigRZ software (TDT). Cochlear gene expresión: Inner ear dissection was performed and samples were frozen in RNAlater® solution (Ambion, Foster City, CA, USA). Cochlear RNA was extracted using the RNeasy Plus Mini kit (Qiagen, Hilden, Germany) automated on the Qiacube (Qiagen, Hilden, Germany). Quality determination and cDNA generation from pooled cochlear RNA extracts (3 cochlea from different animals per group) were performed. Quantitative amplification was performed in triplicate on a Quant Studio 7 Flex PCR System (Applied Biosystems, Foster City, CA, USA) using either commercial TaqMan probes (Nox4, Nox3, Nrf2, Nlrp3, Il1b, Tnfa). Data were collected after each amplification step and analyzed with QuantStudio™ Real-Time PCR software 1.3 (Applied Biosystems). Hprt1 gene was used as a housekeeping gene, and the n-fold differences were calculated using the 2−ΔΔCt method., The objective of the study was to explore the role of NOX4 in agen-related hearing loss. Hearing evaluation (with auditory brainstem responses, ABR) and cochlear gene expresión in Nox4 knockout mice compared to wild type mice, along age (2-15 months). ABR data were obtained from Nox4 knockout and wild type mice fom 2 to 15 month of age. Cochlear expression of Nox3, Nox4, Nrf2, Nlrp3, Il1b and Tnfa genes were determined by RT-qPCR in pooled simples (3 cochleae from 3 independent mice) in Nox4 knockout and wild type of 4 and 8 months of age., THEARPY: bases genéticas y moleculares de la sordera neurosensorial y del daño auditivo: exploración de nuevas dianas y estrategias terapéuticas”. Convocatoria 2020 Proyectos de I+D+i - RTI Tipo B (PID2020-115274RB-I00). FEDER/MICIN, 2021-2024., ABR DATA: - Folder with .arf files, raw data from Auditory Brainstem Response test performed by a Tucker Davis Technologies Workstation in Nox4 knockout and wiild type littermates at different ages from 2 to 15 months. - Folder with cvs files, processed data with ABR waves analysis. - SPSS file, including all the data used for statistics. GENE EXPRESIION DATA: -pdf file with RNA integrity data. Samples consisted on pools of 3 cochleae per genotype (Nox4 knockout and wiild type) and age (4 and 8 months) -Excel file with gene expresión data, using two endogenous genes (Rplp0 and Hprt1)., Peer reviewed

Suppl. Fig S1. Comparison of length and number of substitutions versus p-value in the calculation of the Ka/Ks ratio. Genes have been colored according to the group to which they belong. A regression line has been added, together with its correlation coefficient and associated p-value. Suppl. Fig S2. Distribution of Ka/Ks along the length of genes S, M, N and E (black line). The normalized Shannon entropy obtained from Nextstrain database is shown for comparison (https://nextstrain.org/ncov/gisaid/global/6m). Pfam domains have been included (below): S → bCovS1N (PF16451, Betacoronavirus-like spike glycoprotein S1, N-terminal), bCoV_S1_RBD (PF09408, Betacoronavirus spike glycoprotein S1, receptor binding), CoV_S1_C (PF19209, Coronavirus spike glycoprotein S1, C-terminal), CoV_S2 (PF01601, Coronavirus spike glycoprotein S2); M → CoVM (PF01635, Coronavirus M matrix/glycoprotein); N → bCoV_lipid_BD (PF09399, Betacoronavirus lipid binding protein), bCoV_Orf14 (PF17635, Betacoronavirus uncharacterised protein 14), CoV_nucleocap (PF00937, Coronavirus nucleocapsid); E → CoVE (PF02723, Coronavirus small envelope protein E). The blue line marks the Ka/Ks value of 1. Suppl. Table S1. Genomes used in this work. Suppl. Table S2. Ka/Ks ratio obtained for each SARS-CoV-2 gene, together with the associated p-value. Blue color highlights structural genes, red color highlights non-structural genes, and gray color highlights accessory factors., The SARS-CoV-2 virus pandemic that emerged in 2019 has been an unprecedented event in international science, as it has been possible to sequence millions of genomes, tracking their evolution very closely. This has enabled various types of secondary analyses of these genomes, including the measurement of their sequence selection pressure. In this work we have been able to measure the selective pressure of all the described SARS-CoV-2 genes, even analyzed by sequence regions, and we show how this type of analysis allows us to separate the genes between those subject to positive selection (usually those that code for surface proteins or those exposed to the host immune system) and those subject to negative selection because they require greater conservation of their structure and function. We have also seen that when another gene with an overlapping reading frame appears within a gene sequence, the overlapping sequence between the two genes evolves under a stronger purifying selection than the average of the non-overlapping regions of the main gene. We propose this type of analysis as a useful tool for locating and analyzing all the genes of a viral genome, when an adequate number of sequences are available., Peer reviewed