BUSCADOR RECOLECTA

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

Appendix S1: Questionnaire used in this study. Farmer's perceptions of vulture attacks on livestock. Appendix S2: Number of farmers who have responded each question and number of responses obtained. Appendix S3. Livestock type and characteristics (percentages) involved in the 616 complaints reported from 2008–2020: cattle (n = 471), horses (n = 92) and sheep/goats (n = 53). Appendix S4: Number of farms for each livestock type managed by the 133 farmers interviewed, and number of farms reporting attacks and characteristics (%) of the livestock involved. Some farmers provided multiple responses regarding the characteristics of the livestock involved in the attacks. Appendix S5: Models (GLMs) determining the number of complaints for vulture attacks on livestock. We present the variables included in the set of models (model description); the number of estimated parameters (K); the Akaike Information Criteria corrected for small sample sizes (AICc); and the Akaike weight (wi) by the models evaluated (32 models; n = 110). Abbreviations relating to variables correspond to: ‘extensive livestock density’ (Ext); ‘distance to nearest landfill site’ (Dland); ‘number of griffon vulture pairs’ (Gpair); ‘griffon vulture abundance’ (Gabun); and ‘distance to nearest supplementary feeding station’ (DSFS). Selected models are shown in bold. Appendix S6: Relative importance of the variables estimated from the Akaike weight of the 32 models evaluated in the analysis of the determinants of the number of complaints for vulture attacks on livestock. Values close to 1 indicate variables that appear in most models and that show the highest likelihood given the data, i.e., those that show the highest Akaike weight values., Peer reviewed

6 pages. -- Supplementary Figure 1: Endogenous PEX5 in WT cell lines had no influence on the diffusion of labelled PEX5L. -- Supplementary Figure 2: Schematic representation of the protein constructs used in this work with their calculated molecular weights. -- Supplementary figure 3: Disordered regions from HsPEX5 and TbPEX5 amino acid sequences were predicted using the prDOS server (http://prdos.hgc.jp)., Peer reviewed

Table S1. Data for the 21 wild boar monitored by GPS-collars in this study. Table S2. Questionnaire for the characterization of fences, boundaries and perimeters of the estates in the study area. Fig. S1. Main type of fences presents in our study area. Type I (simple [A] or reinforced livestock-type fence [B]), type II (poorly-maintained big game-proof fence), type III (moderately-maintained big game-proof fence) and type IV (well-maintained big game- proof fence). Fig. S2. Fences crosses by wild boar family groups (data from photo-trapping) and some images of holes in fences obtained during the walking tour to quantify the permeability index (number of holes per km of fence). Table S3. Type of fence by main land use in the study area. Each fence (n=189) featured one or two land use. We included: length in m and percentage of type of fence for each of the possible combinations between land use. Table S4. Model selection results from the analysis of the factors influencing wild boar crossing success across fences. The corrected Akaike Information Criterion (AICc) and delta AICc (ΔAICc), i.e. the difference in AICc score relative to the model with the lowest value (most parsimonious model), are showed. As predictors were used: Type of fence (I-IV), Sex (males, females), Period (FSP, Hunting, FAP; see text for details) and their interactions (Sex*Period, Sex*Type and Type*Period). Individual (ID) was considered as a random effect factor. Fig. S3. Average daily activity of the wild boar monitored in this study. The grey band represent the inactivity period which will be excluded for the estimation of the crossing success. Fig. S4. Temporal and seasonal patterns of the interactions between animals and fences. (A) Number of crosses and bounces per month (from 1=January to 12=December). (B) Number of crosses and bounces per period (FSP= food shortage period, Hunting= hunting season, FAP= food abundance period). Appendix 1- Fence Behaviour Analysis., Peer reviewed

4 pages. -- PDF file includes supplementary information about the main article. -- 1. Estimating fishing exposure at the sampled sites. -- 2. Detailed results of the statistical analysis. -- Table 1: Effects size for all the explanatory variables considered. -- Table 2: Fish density (fish/km2) at each of the 15 sampling sites, as estimated from the model above., Peer reviewed

Data File S1. Serum proteomics analysis; Data File S2. Analysis of immunoglobulin proteins underrepresented and overrepresented in infected cohorts when compared to PCR– individuals; Data File S3. Human-autoantibody general survey in PCR– and PCR+ cohorts; Data File S4. Analysis of selected non-immunoglobulin proteins underrepresented and overrepresented in infected cohorts when compared to PCR– individuals., Peer reviewed

Data S1. Serum proteomics analysis; Data S2. Analysis of immunoglobulin proteins differentially represented in TB+ elephants when compared to TB− animals; Data S3. Gene Ontology (GO) annotations for non-immunoglobulin proteins; Data S4. Most significant pathways identified by Reactome analysis of non-immunoglobulin proteins; Data S5. Protein sequence alignment between M. tuberculosis P9WNK5|ESXB_MYCTU ESAT-6-like protein EsxB and identified mycobacterial proteins with predicted antigen binding regions to immunoglobulin proteins., Peer reviewed

[EN] Using EURING data and geolocation, we describe migration routes and nonbreeding range of Red-rumped Swallows breeding in the Western Palearctic. One bird ringed in southern Spain and recovered in southern Morocco indicates southwestern migration; geolocator data from five birds from central and eastern Iberian Peninsula confirm migration to various nonbreeding sites in sub-Saharan west Africa between Senegal/Mauritania and Ghana. Two swallows showed non-breeding site itinerancy by using more than one nonbreeding site per season. Despite wide ranges in departure for autumn (August- October) and spring migration (February-March), all birds arrived at nonbreeding and breeding sites within ±1-week from each other., [DE] In dieser Studie beschreiben wir Zugrouten und Überwinterungsgebiete westpaläarktischer Rötelschwalben basierend auf EURING- und Geolokations-Daten. Eine Rötelschwalbe, die in Südspanien beringt und im südlichen Marokko wiedergefunden wurde, spricht für einen südwestlichen Zug. Geolokalisation von fünf Vögeln der zentralen und östlichen Iberischen Halbinsel zeigen Überwinterungsorte im sub-Saharischen Westafrika zwischen Senegal/Mauretanien und Ghana. Zwei der getrackten Rötelschwalben nutzten mehrere Überwinterungsplätze pro Saison. Trotz der großen Schwankungsbreite der Abzugszeiten im Herbst (August-Oktober) und im Frühjahr (Februar-März) erreichten die getrackten Vögel ihre Nichtbrut- bzw. Brutplätze innerhalb von 1–2 Wochen., Peer reviewed

Transects, up to 99 m in length (dependent on the field's length), were surveyed in linear stable landscape features (field, track or ditch margins) to estimate vole abundance from November 2011 until September 2017. Each transect was divided into 3 m sections (33 in total) and the presence or absence of one or more signs of vole activity (i.e., latrines by burrows, fresh vegetation clippings, and recent burrow excavations) in each section was noted. The proportion of sections with signs of vole presence per transect was then used as the abundance index. The number of surveys carried out at any time varied adaptively with the perceived risk of an outbreak (according to changes in estimated abundance in previous monitoring surveys). The response variable typically used in all models is proportional growth rate (r_{t,i}, where is the abundance index for site at time (Royama 1992; Berryman 2002). A benefit of using r_{t,i}, rather than ln(N_{t,i}), is that any multiplicative effects of site quality are cancelled out, provided they are constant over time. To calculate r_{t,i}, vole abundance indices are required at the same location in successive time periods (i.e., N_{t,i} and N_{t+1,i}). Given that exact transect locations were rarely reused in successive months, and all transect measurements took place throughout the year rather than discrete seasons, the data had to be aggregated to consistent locations and times to allow growth rate to be calculated. As such, transects were temporally aggregated into a respective yearly quarter (e.g., January to March 2014). Transects were spatially aggregated by sequentially selecting an unassigned transect as a reference point for the ith centroid and assigning all unassigned transects within a 5 km radius to the ith centroid, and repeating until all transects had been allocated (see Figure 2 for a summary of the number of transects assigned to each centroid, centroid locations, and time series of growth rate of each centroid). Once complete, the mean Julian day, X and Y UTM (Universal Transverse Mercator) and the mean index was calculated for all transects assigned to each centroid for each time period. Where a centroid had successive values of N_{t,i} and N_{t+1,i} available, the corresponding proportional growth rate was calculated. A constant of 3.03 was added to N_{t,i} to avoid zero entries (3.03 was the lowest non-zero value of N observed). The final dataset consisted of 3,751 observations., Peer reviewed

Figure S1: Showing the differentiation of human HL60 into neutrophils; Figure S2: Showing DNA levels of A. phagocytophilum in infected HL60 cells; Figure S3:A representative MPO levels measurement in the four treatments (X-VIVO control, E. coli, PMA, and A. phagocytophilum); Dataset S1: showing previously published proteomics data., Peer reviewed