BUSCADOR RECOLECTA

Table S1. Compounds studied and standard supplier. Table S2. LC-MS/MS conditions and quality parameters for the analysis of pharmaceuticals (ordered by retention time: R.T.), indicating quantification (Q) and confirmation (q) transitions, Collision Energy (C.E.), the response Factor (F) of the calibration curve, Instrument Detection Limit (IDL), Method Detection Limit (MDL), intra-day precision (%, n=5), inter-day precision (%, n=5) and percentage recovery with standard deviation (%R±SD). Coefficient of determination (R2) was 0.99 for all compounds over a concentration range of 0.001-0.6 ng/µL. Table S3. LC-MS/MS conditions and quality parameters for the analysis of pesticides and OPEs (*) (ordered by retention time: R.T.), indicating quantification (Q) and confirmation (q) transitions used, Collision Energy (C.E.), the response Factor (F) of the calibration curve, Instrument Detection Limit (IDL), Method Detection Limit (MDL), intra-day precision (%, n=5), inter-day precision (%, n=5) and percentage recovery with standard deviation (%R±SD). Coefficient of determination (R2) was 0.99 for all compounds over a concentration range of 0.001-0.6 ng/µL. Table S4. LC-MS/MS conditions and quality parameters for the analysis of PFAS (ordered by retention time: R.T.), indicating quantification (Q) and confirmation (q) transitions used, Collision Energy (C.E.), the response Factor (F) of the calibration curve, Instrument Detection Limit (IDL), Method Detection Limit (MDL), intra-day precision (%, n=5), inter-day precision (%, n=5) and percentage recovery with standard deviation (%R±SD). Coefficient of determination (R2) was 0.99 for all compounds over a concentration range of 0.001-0.6 ng/µL. Figure 1. UPLC-MS/MS chromatogram of a pharmaceuticals standard at 0.6 ng/µL. Figure 2. UPLC-MS/MS chromatogram of a pesticide and organophosphoshate ester standard at 0.6 ng/µL. Figure 3. UPLC-MS/MS chromatogram of a perfluoroalkyl substances standard at 0.6 ng/µL., Peer reviewed

S1. Hunting bag trends of ungulate species and red fox at regional scale from Lleida Province. S2. Hunting bag trends of ungulate species and red fox at local scale from the Controlled Hunting Area (Tornafort study zone). S3. Data of capercaillie males counted in the monitored leks in the study zones. S4. Carcass experimental protocol., Peer reviewed

77 FFPE tissue samples from metastatic colorectal cancer (mCRC) tumours, Existing immune markers and tumor mutational burden signatures have modest predictive accuracies of immune-check-point inhibitors (ICI) therapeutic efficacy. In this study, we developed an immune metabolic signature suitable for personalized ICI therapies. A classifier using an immune-metabolic signature (IMMETCOLS) was developed on a training set of 75 metastatic colorectal cancer (mCRC) samples and validated on 4,200 tumors from the TCGA database belonging to 11 types. Here we reveal that IMMETCOLS signature classifies tumors into 3 distinct immune-metabolic clusters. Cluster 1 displays enhanced glycolysis-Warburg, hexosamine biosynthesis and epithelial-to-mesenchymal biomarkers. On multivariate analysis, Cluster 1 tumors were enriched in pro-immune signature but not Immunophenoscore, and were associated with the poorest median survival. It’s predicted tumor metabolic features suggest an acidic-lactate-rich tumor microenvironment (TME) geared to an immunosuppressive setting, enriched in fibroblasts. Cluster 2 displays features of gluconeogenesis ability, which is needed for glucose-independent survival and preferential use of alternative carbon sources, including glutamine and lipid uptake/β-oxidation. Its metabolic features suggest a hypoxic and hypoglycemic TME, associated with poor tumor-associated antigen presentation. Finally, cluster 3 is highly glycolytic, but also has a solid mitochondrial function, with concomitant up-regulation of glutamine and essential amino acids transporters and the pentose phosphate pathway leading to glucose exhaustion in the TME and immunosuppression. Together these findings suggest that IMMETCOLS signature provides a classifier of tumors from diverse origins, yielding three clusters with distinct immune-metabolic profiles, representing a new predictive tool for patient selection for specific immune-metabolic therapeutic approaches, Peer reviewed

Table S1a. Summary of main findings of the 34 studies reviewed that assessed the relationship between European Turtle Dove abundance (in the broad sense, including density, variation in numbers, etc.) and occurrence with habitat. Table S1b. Summary of main findings of the 34 studies reviewed that assessed the relationship between European Turtle Dove abundance (in the broad sense; some studies assessed density, others used measures of relative abundance, trends, etc.) and occurrence with habitat. Results of direct comparison between habitats, and favourable elements in each major habitat type. Table S2. Plant taxa whose seeds have been reported as ingested by turtle dove. Table S3. Relative importance of the 40 environmental predictors of the eight Species Distribution Models for Streptopelia turtur in the second European Breeding Bird Atlas, EBBA2 (Keller et al. 2020) and their weighted Ensemble Prediction. Variable importance ranges between 0 (no importance) to 100 %., Peer reviewed

3 pages. -- Figures and tables. -- Table S1. List of Posidonia oceanica rhizome samples analyzed for the different organic components of ultra-violet filters (UVFs) and paraben conservatives. -- Table S2. Limits of detection and quantification, and precision (relative standard deviation, RSD%) of the analyses of the different ultra-violet filters (UVFs) and paraben conservatives. -- Figure S1. Map of the study site indicating the location of the sampling points. -- Figure S2. Morphology of the seagrass Posidonia oceanica, detailing the structure of the peeled rhizome., Peer reviewed

Table A.1: Most important crops (> 1000 ha) in the V (mid-west) administrative region of Navarra (130,000 ha) in 2007, 2019 and their change (in ha and %) along the survey period, broken down by each crop’s rain-fed and irrigated surface. Table A.2: Number of irrigated and non-irrigated points sampled each year during the survey period. Figure A.1: Species-level occurrence probabilities: a) before irrigation (excluding control sampling locations); and b) after irrigation was implemented. Species are organized by habitat classification: farmland, shrubland, (R) rocky habitat specialist species, (Fo) forest habitat specialist species, (W) wetland habitats specialist species, (U) urban habitat specialist species, and non-specialist. The short horizontal black lines show means, the boxes show 50% credible intervals (CI) and the vertical lines show the 95% CI. Figure A.2: Effect of arable land surface on species occurrence probabilities organized by habitat classification: farmland, shurbland, (R) rocky habitat specialist species, (Fo) forest habitat specialist species, (W) wetland habitats specialist species, (U) urban habitat specialist species, and non-specialist. The short horizontal black lines show the mean value (across MCMC iterations), the boxes show the 50% credible intervals (CI), and the vertical lines delineate the 95% CI. Light grey indicates no effect of arable land on occurrence probability, orange (negative effect) and blue (positive effect) indicate that the 50% CI does not overlap zero but the 95% CI does overlap zero, and red (negative effect) and dark blue (positive effect) indicate that the 95% CI does not overlap zero. Figure A.3: Detection probability by: a) hour; and b) date. The black lines show the mean values across all species analysed (with shaded 50% and 95% credible intervals). Light grey lines show the detection probabilities of each species included in the analysis. JAGS model code: Multi-species hierarchical occurrence model with a BACI design., Peer reviewed

The datasets are reported in the supplementary material Tables S1-S4., Table S1. Summary of the distributions of the proportion of the population harvested each year and supporting data. Figure S1. Histogram and probability density of the estimated proportion of the population harvested each year, autocorrelation and trace plots. Table S2. Summary of the estimated distributions of abundance and supporting data at each site. The Geweke test was -0.557 (P=0.577) for the wild boar and 1.085 (P=0.278) for the red deer models. Figure S2. Histogram and probability density of the abundance estimated at each site, autocorrelation and trace plots of the Markov Chain Monte Carlo simulations. Table S3. Summary of the estimated distributions and supporting references for the sensitivity and specificity of the diagnostic tests. Figure S3. Histogram and probability density of the estimated sensitivity and specificity of the diagnostic tests employed, autocorrelation and trace plots of the Markov Chain Monte Carlo simulations. Table S4. Summary of the estimated distributions of the true prevalence by host species and supporting data at each study site. Figure S4. Histogram and probability density of the estimated true prevalence at each site, autocorrelation and trace plots of the Markov Chain Monte Carlo simulations. Figure S5. Histogram and probability density of the estimated probability of Mycobacterium tuberculosis complex excretion from infected wild boar and red deer, autocorrelation and trace plots of the Markov Chain Monte Carlo simulations. Table S5. Summary of the estimated distributions of the R0 by host species at each study site. Figure S6. Histogram and probability density of the estimated R0 by host species at each study site, autocorrelation and trace plots of the Markov Chain Monte Carlo simulations. For intelligibility, only R0 estimates <200 were plotted. Table S6. Probability of animal tuberculosis being a single-host or multi-host disease at each site. Probabilities derived from the 100,000 estimates of the R0 of Mycobacterium tuberculosis complex in each host species. Single maintenance host: only one species with R0>1; Obligatory multi-host: R0 both species<1; Facultative multi-host: R0 both species >1. Highest probability for each site is highlighted in bold. Figure S7. Diagnostic plots of the generalised linear models. Diagnostic plots for the wild boar, red deer, and multi-host models with the whole dataset and after removing one influential outlier (site 15). Table S7. Summary of the generalised linear model selection. Models with ΔAICc<2 from the most supported model. Figure S8. Basic reproduction number estimated for the wild boar and red deer in each of the study sites. Kernel density of 100 estimates of the R0 of Mycobacterium tuberculosis complex in the wild boar and red deer drawn randomly from the 100,000 estimates at each site. Axes in square root scale, R0=1 as blue horizontal and vertical lines., Peer reviewed

9 pages. -- Figure S1: SDS‐PAGE of the various APE1 variants. -- Figure S2: (A) Far‐UV spectra of the different APE1 variants: unlabelled (black), Cy3‐APE1 (magenta), Cy5‐APE1 (cyan) and doubly labelled (purple). The protein concentration was 4 mM in buffer 20 mM potassium phosphate pH 7.0, 50 mM NaCl, 5 mM MgCl2. (B) Thermal denaturation profiles as based on the change in ellipticity at 222 nm in the absence (solid line) and presence (broken line) of an equimolar amount of product DNA. The scan rate was 1ºC / min. -- Figure S3: Binding of labelled APE1 to the oligonucleotides mimicking the abasic product (P) and substrate (S). -- Figure S4: Incision kinetics of 2 mM substrate DNA by the various APE1 variants as followed by 18% polyacrylamide‐urea gel electrophoresis. -- Figure S5: Sequence of the oligonucleotides used as model of the abasic APE1 product (left) and substrate (right), highlighting the two labelled thymines. X stands for tetrahydrofurane. -- Figure S6: Original prediction as obtained with AlphaFold of human APE1 (UniProt entry P27695), represented as a cartoon, and aligned to the DNA part (grey sticks) of crystal structure 5DFF [17]. -- Figure S7: Emission spectra of doubly labelled APE1 (black line) and Cy5‐APE1 (cyan solid line) upon excitation at 547 nm. Spectrum of Cy5‐APE1 with exc 647 nm (cyan broken line). -- Figure S8: Charge distribution in full‐length APE1 bound to DNA., Peer reviewed

Supplementary contents: Appendix A: Additional results of carnivore environmental favourability in Andalusia. Appendix B: Data on drivers threatening terrestrial carnivores in Andalusia used in General Linear Modelling. Appendix C: Additional results of multiple drivers influence changes in carnivore environmental favourability in Andalusia. Supplementary references., Peer reviewed

Supplementary Table S1 Number of birds that were mist netted as a part of the study by species and site. Supplementary Table S2 Operating conditions of the Agilent 7700x ICP-MS spectrometer. Supplementary Table S3 Results of the analysis of TORT-3 (Lobster hepatopancreas), ERM-BB186 (Pig kidney) and BCR-185 (Bovine liver) certified reference materials. Units in mg kg-1 dry weight. Supplementary Table S4 Results of the analysis of SRM-1577c (Bovine liver) certified reference material in µg kg -1 (*) or mg kg-1 (**) d. w. Supplementary Table S5 Results of the analysis of SRM-1643f (Trace elements in water), in µg L-1. Supplementary Table S6 Analyzed concentrations of trace elements in feathers of birds (in μg g-1 of d. w.) by site and species. Supplementary Table S7 Mean (± SD) stable carbon (δ13C) and nitrogen (δ15N) isotope ratios (‰) of bird feathers by species, with sample size (N) and species characteristics (G – granivores, I – insectivores, O – omnivores, P – piscivores, M – migratory, R – resident). Supplementary Fig. S1 The results of the PCA: Scree plot (A) showing the proportion of variance in the principal components and graphs showing the proportion of each variable in PC1 (B), PC2 (C), and PC3 (D). Supplementary Fig. S2 Biplot showing stable isotope values in bird feathers (δ13C versus δ15N). Data points represent mean values and error bars represent standard error of the mean (SEM). Food groups are indicated by color: granivorous birds are dark red, omnivorous birds are yellow, insectivorous birds are green, and piscivorous birds are blue. Supplementary Table S8 Estimated marginal of trace elements by feeding habits of birds. Means and standard errors (μg g-1 of d. w.) were calculated based on GLM models with Al as a covariate. The results were averaged over the levels of study sites, age of feathers, and migration pattern. The intervals were back-transformed from the log scale and the tests were performed using Tukey method on the log scale. Supplementary Table S9 Estimated marginal means of trace elements by study site. Means and standard errors (μg g-1 of d. w.) were calculated based on GLM models with Al as a covariate. The results were averaged over the levels of feeding habits, age of feathers, and migration pattern. The intervals were back-transformed from the log scale and the tests were performed using Tukey method on the log scale. Supplementary Table S10 Estimated marginal means (± SEM) of trace in new and old feathers. Means and standard errors (μg g-1 of d. w.) were calculated based on GLM models with Al as a covariate. The results were averaged over the levels of feeding habits, study site, and migration pattern. The intervals were back-transformed from the log scale and the tests were performed on the log scale. Supplementary Table S11 Estimated marginal means (± SEM) of trace elements in feathers of birds by migration pattern. Means and standard errors (μg g-1 of d. w.) were calculated based on GLM models with Al as a covariate. The results were averaged over the levels of feeding habits, study site, and age of feathers. The intervals were back-transformed from the log scale and the tests were performed on the log scale., Peer reviewed