Tracking pathogens with eDNA in natural areas: how environmental gradients shape surveillance strategies

Tracking pathogens with eDNA in natural areas: how environmental gradients shape surveillance strategies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Tracking pathogens with eDNA in natural areas: how environmental gradients shape surveillance strategies Alberto Perelló, Camilla Smoglica, Carlos González-Crespo, Marta Pérez-Sancho, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7642008/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Apr, 2026 Read the published version in Veterinary Research → Version 1 posted You are reading this latest preprint version Abstract The wildlife-livestock-environment interface is a complex system with implications for biodiversity and diseases. Environmental nucleic acid detection (ENAD) is a non-invasive method for monitoring pathogens via DNA/RNA. However, how environmental variables influence ENAD remains poorly explored in heterogeneous geographic contexts. In this study, 18 sites were evaluated in Iberian Peninsula, collecting 10 surface sponge samples per site and a total of 146 environmental fecal samples. Differences in pathogen ENAD were assessed among sponge sampling methods and between sponge and fecal samples. The relationship between ENAD results and environmental, mammal community and wildlife health variables was investigated. The results show that environmental characteristics influence pathogen ENAD at larger geographic scales, with greater pathogen diversity and richness observed at higher latitudes. Most markers found in feces were also detectable in surface sponges. Combining different sponge sampling methodologies provides the best overall coverage of detectable pathogen markers. A predictive map linking pathogen ENAD in sponges to environmental factors was developed. Environmental DNA zoonosis disease surveillance non-invasive integrated wildlife monitoring episystem natural areas Figures Figure 1 Figure 2 INTRODUCTION The wildlife-livestock-environment interface is a complex system with profound implications for biodiversity conservation and pathogen circulation [ 1 , 2 ]. Biodiversity and disease dynamics are influenced by multiple factors, including pathogen specificity, transmission routes, and habitat characteristics [ 3 ]. Current literature presents two key hypotheses regarding the interaction between biodiversity and pathogens: the amplification effect and the dilution effect, both of which vary based on the pathogens involved and the geographic regions [ 4 , 5 ]. In this context, it is crucial to explore the roles that wildlife and livestock play in pathogen maintenance within the environment, based on the concept known as episystem. This approach integrates the interactions between pathogens, hosts, and environmental factors across specific spatial and temporal scales [ 6 ]. In this framework, environmental nucleic acid detection (ENAD) emerges as a powerful tool to monitor pathogens, including those introduced or spread by invasive species [ 7 ]. To date, ENAD has primarily been applied in aquatic environments, using PCR, qPCR, or metabarcoding to detect a broad range of organisms, from microbes (bacteria, viruses, protists) to macroorganisms [ 7 ]. Its potential has been recognized by the World Organization for Animal Health (WOAH), which includes ENAD in its guidelines for detecting Gyrodactylus salaris in salmonids [ 8 ]. Recent ENAD surveys have yielded highly promising results for monitoring terrestrial mammals and their associated pathogens [ 9 ]. For example, ENAD methodologies has been applied to detect African swine fever virus (ASFV) from turbid water and soil samples, as well as ASFV in pig breeding facilities or Mycobacterium tuberculosis complex (MTC) in cattle farms and European bison populations using dry sponges pre-hydrated with a specific surfactant applied to the animals' skin and contaminated surfaces [10—13]. These sponges have also recently been used to detect other pathogen markers related to M. avium subp. paratuberculosis , Coxiella burnetii , Brucella spp., Salmonella enterica and Escherichia coli on surfaces at risk points on farms, such as waterers and feeders, and on animals [ 14 ]. In addition, ENAD has also been applied to substrates such as equine enrichment toys [ 15 ], honey [ 16 ], invertebrates [17—19], as well as fecal samples from amphibians, reptiles, and livestock [14—20]. Similarly, ENAD from non-invasive sampling of wildlife feces is recognized as an effective tool for infectious disease surveillance in natural areas providing valuable data for assessing disease circulation, zoonotic risks, and ecosystem health without the need for direct animal handling [ 21 ]. Indeed, ENAD offers advantages in terms of sampling efficiency, reduced processing time, and lower costs—particularly in challenging contexts involving elusive or low-density animal populations [ 7 , 22 ]. When combined with spatial and epidemiological data, ENAD can also offer a comprehensive understanding of pathogen dynamics across a geographic area [ 7 ]. For instance, a pilot study on non-invasive monitoring in outdoor farming systems integrated vertebrate richness data and ENAD-derived health markers, revealing a negative correlation between pathogen marker richness and farm vertebrate richness [ 14 ]. Similarly, a recent study carried out in Poland demonstrated the application of ENAD for detecting MTC in European bison populations within regions with historical records of tuberculosis [ 11 ]. These studies have highlighted the effectiveness of ENAD approaches—based on fecal and sponge sampling integrated with ecological data—in detecting pathogens in both livestock and wild animal populations, whether in outdoor farming environments or natural ecosystems [ 11 , 14 ]. However, these investigations have been generally limited to single, environmentally homogeneous areas. Consequently, the extent to which environmental variables influence ENAD performance remains poorly explored, particularly in heterogeneous geographic contexts. Exploring this variability is essential to validate ENAD as a robust surveillance tool adaptable to different geographic and ecological conditions. In this regard, the present study aims to evaluate the relationship between ENAD-detected pathogens, mammal community characteristics, and environmental factors across 18 sites on the Iberian Peninsula. Environmental DNA (eDNA) from sponges and fecal samples were analyzed for pathogen-specific health markers, providing insight into pathogen detection across heterogeneous geographic areas. MATERIAL AND METHODS Study area. This work has been developed in 18 pilot monitoring sites throughout the Iberian Peninsula, 15 Spanish and 3 Portuguese, which participated in a pilot network for integrated wildlife monitoring (Wildlife Health Surveillance Plan -WHSP- of Spain) [ 23 , 24 ]. The sites were selected to ensure they were representative of the five bioregions: Atlantic Spain (1); Cereal plains (2); Continental Mediterranean ecosystems (3); Inland mountains (4); and Southern and eastern coast (5); recognized in the WHSP of Spain and Portugal [ 25 , 26 ]. Surface sponges eDNA sampling. Two sponges from surfaces were taken in the 50 m 2 in front of each of 5 randomly selected camera traps (CTs) of each of the 18 CT grids (180 in total) deployed for wildlife species monitoring purposes (Wildlife Health Surveillance Plan -WHSP- of Spain). In front of each CT, one sponge sampled objects such as stones and tree trunks (O-sponge) and a second sponge sampled the surface of bare soil including feces or rootings if present (S-sponge). A total of ten sponges were taken per study site. All sponge samples were tested for PCR inhibition by including an internal control in each PCR reaction, discarding four of 180 (2.22%; all soil samples). All sampling points within a study site were collected on the same day between 5:30 AM and 10:30 AM during April-June 2023. Sampling was conducted systematically from south to north to ensure consistent conditions across all locations. Fecal eDNA sampling. Fecal material found on the walked transects was identified as belonging to Eurasian wild boar ( Sus scrofa ) or wild ruminant species (mostly red deer, Cervus elaphus , and roe deer, Capreolus capreolus ). If fresh samples were found, 5–10 g portions collected into sterile bags and preserved refrigerated until reaching the laboratory, where samples were stored frozen at -20ºC until DNA extraction. All fecal samples were checked for inhibition, discarding seven of 146 (4.79%; all of them ruminant samples). Nucleic acid extraction. The extraction and purification of environmental DNA (eDNA) from surface sponge samples was performed using the QIAamp Fast DNA Stool Mini Kit (Qiagen Hilden; Germany), starting from the pellet obtained after centrifuging 900 µL of the sample for 3 minutes at 13.000 rpm. Genomic DNA from fecal samples was isolated from approximately 200 mg of each fecal sample using the same Qiagen kit, in accordance with the manufacturer’s instructions, except for the step where samples were mixed with the InhibitEX buffer, where the incubation time was changed to 10 min at 95°C. Extracted and purified DNA samples were eluted in 200 µL of PCR-grade water and stored at 4°C until further molecular analysis. Molecular detection. The pathogen markers analyzed using PCR technics (Table 1 ) include bacteria: E. coli ( uidA , stx1 , stx2 and eae ), MTC (IS 6110 and mpb 70), Salmonella spp. ( invA ), C. burnetii (IS 1111 ), Brucella spp. (IS 711 ), M. avium subp. paratuberculosis (IS 900 ); and parasites: Balantioides coli, Blastocystis sp. , Cryptosporidium spp, Encephalitozoon spp, Enterocytozoon bieneusi, Giardia duodenalis , and Toxoplasma gondii . More details on PCR protocols are specified in Additional file 1 (supplementary methods section). Environmental factors influencing pathogen diversity detected on surfaces in natural areas. Sponge molecular marker diversity index. A Shannon diversity index (H’) was calculated to quantify and standardize the diversity of molecular markers detected in sponge samples across study sites. This analysis was performed using the dplyr (v1.1.4) and vegan (v2.7-1) R packages [ 54 , 55 ]. For each study area, five sampling points were established, and the abundance value for each molecular marker was determined by the number of positive sampling points detected within each study area (range: 0–5). Study area classification. To investigate the influence of environmental factors on pathogen diversity detected through ENAD on surfaces in natural areas, study areas were classified using a hierarchical clustering on principal components (HCPC; see supplementary Fig. 1). This analysis was implemented using the FactoMineR (v2.11) and factoextra (v1.0.7) R packages [ 56 , 57 ]. The optimal number of clusters was determined using a combination of environmental variables (land cover and climatic parameters), mammal community characteristics (see Additional file 1 —supplementary methods section— for more information on the parameters used), and wildlife health parameters (described in Perelló et al. -manuscript under review-) [ 58 ]. This classification allowed the comparison of pathogen diversity (H’) detected in sponge samples and the assessment of factors potentially driving molecular detection in natural areas. Spatial distribution modeling of clusters in the Iberian Peninsula. To predict the spatial distribution of identified clusters across the Iberian Peninsula, a random forest (RF) classification model was performed using the caret (v7.0-1) and randomForest (v4.7-1.2) packages in R [ 59 , 60 ]. The model was based on environmental factors (land cover and climatic variables) as predictors and the HCPC-defined clusters as response categories. All analyses were conducted with a fixed random seed to ensure reproducibility. Model performance was evaluated using a leave-one-out cross-validation (LOOCV), wherein each observation was sequentially used as validation data while the remaining observations served as training data. The cross-validation parameters were established using the trainControl function with LOOCV specified as the resampling method. Class probabilities were calculated for each iteration, and predictions were systematically stored for subsequent analysis. The RF model was trained using the train function, with cluster designation as the response variable. Predictor variables incorporated multiple land cover classifications (according to the classifications made by Barroso et al. (2023) from Corine LandCover, only shrubland, forest coverage, bare land, grassland and urban land uses were taken into account) [ 23 , 61 ] and selected bioclimatic variables established by the United States Geological Survey (i.e. total precipitation during the driest quarter, annual mean temperature, maximum temperature during the warmest month, mean temperature of the warmest quarter and mean temperature of the coldest quarter) [ 62 ]. Model optimization was performed by tuning the mtry parameter (number of variables randomly sampled as candidates at each split) across three values. Performance was quantitatively assessed via confusion matrix analysis, which provided classification accuracy metrics and Cohen's Kappa statistics. The optimal mtry value was determined based on cross-validation results, with overall accuracy and Kappa values recorded as performance indicators. For spatial prediction classification, a custom function ( get_class_none ) to the probability outputs was applied. This function assigns class membership based on the maximum predicted probability. To account for classification uncertainty, observations with maximum probabilities below 0.6 were designated as "non-classified", while observations exceeding this threshold were assigned to their respective highest probability class (clusters 1, 2 or 3). The spatial prediction was implemented using the calc function, and the resulting classification was visualized through raster mapping using the raster R package (v3.6-32) [ 63 ]. Statistical analysis. Statistical differences were assessed using various tests depending on the comparison. Differences between sponge sample types, feces species origin, and between fecal and sponge samples were evaluated using Fisher’s exact test and chi-square test, implemented in R software version 4.4.1. Additionally, the Wilcoxon Mann-Whitney test was used for PCR CT-values comparisons, performed using the dplyr (v1.1.4) and rstatix (v0.7.2) R packages [ 55 , 64 ]. For the variables used in the HCPC analysis, statistical differences between clusters were also assessed using the Kruskal-Wallis and Wilcoxon Mann-Whitney tests, again utilizing the dplyr and rstatix R packages [ 55 , 64 ]. RESULTS Our findings revealed that methodology, particularly sampling design, influences the efficacy of ENAD for non-invasive pathogen monitoring in natural areas. A clear influence of environmental factors on pathogen detection on natural surfaces was found, underscoring the importance of optimized sampling protocols for reliable environmental surveillance. Surface sponges ENAD. Surface sampling with sponges showed capability to detect E. coli , MTC, Salmonella spp., C. burnetii , G. duodenalis and T. gondii molecular markers in natural areas. However, methodology in sponge sampling (O-sponges or S-sponges) influenced specific marker detectability. In this study, S-sponges showed higher utility for detecting E. coli markers, since a significantly higher proportion of S-sponges tested positive for the uidA gene (40.69%) compared to the O-sponges (15.55%) (Fig. 1 a). Regarding other E. coli markers, stx1 was detected only in S-sponges and stx2 tended to be detected more frequently in S-sponges. The PCR CT-values also tended to be lower in S-sponges for the three E. coli markers ( p = 0.01 for uidA CT-values; supplementary table 1 ). For the MTC markers IS 6110 and mpb 70, no significant differences in proportion of positives were found between O-sponges and S-sponges, however, for IS 6110 the PCR CT-values were significantly lower in the S-sponges ( p = 0.01; supplementary table 1 ). Regarding other bacteria, the Salmonella spp. invA marker was detected only in S-sponges, while no differences were found between sponge samples for the C. burnetii IS 1111 marker, despite PCR CT-values tended to be lower in S-sponges (supplementary table 1 ). Parasite ENAD showed no significant differences between sponge samples, but a trend to a higher detectability of G. duodenalis . and T. gondii in the O-sponges was observed (supplementary table 1 ). The highest proportion of positive sampling points was found for the uidA marker (48.80%), detected in 100% of the study sites, followed by the MTC marker IS 6110 (42.22% of sampling points) detected in 77.77% of the locations. The parasites were the second group of markers with more positive sampling points (12.22% for T. gondii and 8.89% for G. duodenalis ) and study sites presence (50% for T. gondii and 33.30% for G. duodenalis ). Other markers showed a proportion of positive sampling points ranging from 2.22 to 5.55% and study sites from 11.11 to 16.66% (supplementary tables 1 and 2). Fecal ENAD. Several bacteria and parasites were detected in wild boar and wild ruminant fecal samples. These included E. coli markers, MTC IS 6110 , Salmonella spp., G. duodenalis , Blastocysitis sp., B. coli , E. bieneusi and Encephalitozoon cuniculi . From a general perspective, no apparent differences were found between wild boar feces and ruminant feces, except for specific pathogens. The E. coli markers showed no apparent differences between wild boar and wild ruminant feces. However, a trend to higher proportion of positives for uidA , stx1 , stx2 and eae markers in wild boar feces was found (supplementary table 1 ). Regarding the PCR CT-values, significant differences were found for uidA ( p = 0.003, with lower values in wild boar feces) and stx1 ( p = 0.03, with lower values in wild ruminant feces). The MTC IS 6110 marker was detected both in wild boar and wild ruminant feces with no significant differences for positivity and CT-values. The Salmonella spp. invA was found in 4.34% of the wild boar feces (supplementary table 1 ). Parasite markers showed no difference between ruminant and wild boar feces. G. duodenalis , Blastocysits sp., and Enc. cuniculi were found in both fecal samples, while B. coli and E. bieneusi were found only in wild boar feces (supplementary table 1 ). The highest proportion of positive study sites in feces samples were for E. coli markers ( uidA -100%-, stx2 -44.44%-, eae -44.44%- and stx1 -22.22%) followed by MTC IS 6110 (33.33%) and three parasite markers ( G. duodenalis -22.22%-, Blastocystis sp.-22.22%- and Enc. cuniculi -16.66%-). Finally, B. coli and invA were positive in 11.11% and E. bieneusi in 5.56% of the study sites (supplementary table 2). Comparing surface sponges and fecal ENAD. Among the pathogens analyzed in both sponge and fecal samples, significant differences were found in opposite senses for E. coli and MTC markers. All E. coli markers were more positive in fecal samples ( uidA p < 0.001; stx1 p = 0.02; stx2 p = 0.03; eae was detected only in fecal samples; Fig. 1 a). Consistently, the PCR CTs for uidA were lower in fecal samples ( p = 0.01; supplementary table 1 ). By contrast, the MTC marker IS 6110 was detected in 25.57% of sponge samples, while only 5.04% of fecal samples tested positive ( p < 0.001) (Fig. 1 a). The IS 6110 CT-values were also lower in sponge samples ( p = 0.005; supplementary table 1 ). The mpb 70 marker was also detected in sponge samples. Other bacteria like C. burnetii (IS 1111 ) were detected only in sponge samples, too (supplementary table 1 ). Regarding parasites, there were no significant differences in Giardia detectability between surface or fecal ENAD, however, a higher positivity trend in feces was observed (Fig. 1 a; supplementary table 1 ). The CT-values for G. duodenalis were significantly lower in fecal samples ( p = 0.004). A comparison of the proportion of sites positive to pathogen markers between sponge and fecal samples is shown in Fig. 1 b. Significant differences were found in the number of positive study sites for eae and IS 6110 markers ( p = 0.003 and p = 0.02, respectively; supplementary table 2). Factors driving pathogen ENAD in sponge samples. Pathogen detection in sponge samples was strongly associated with climatic variables (latitude influence) rather than biological factors typically linked to pathogen circulation. Notably, pathogen ENAD showed a negative relationship with serological sanitary data and wild ungulate abundance and a positive relationship with grazing ruminant livestock (see “Correlations with pathogen marker diversity index” section and cluster description). The subsequent analysis examines the specific factors influencing pathogen ENAD in surfaces. A study site clustering was performed to assess how land cover, climate, mammal community and health variables influence pathogen detection (supplementary table 3). Three groups (clusters) of locations were established, seven locations belonging to cluster 1, six to cluster 2 and five to cluster 1 (supplementary Fig. 1). Clusters were mainly differentiated by the influence of latitude. Cluster 1, the southern one and representing Mediterranean climate, presents higher temperatures and lower precipitation rates than cluster 2 with intermediate climatic characteristics and cluster 3, with lower temperatures and higher precipitation rates, typical of Atlantic climate (supplementary table 3). Despite no significant differences when comparing the three clusters, a positive trend from southern locations to northern locations was seen for forest, grassland and urban land use (supplementary table 3). Regarding mammal communities, the relative weight of red deer in the mammal community was significantly higher in cluster 1, while carnivores had significantly more weight in clusters 2 and 3. Ruminant livestock and wild boar tended to have more weight in cluster 3. Co-exposure rates showed significant differences between clusters, being higher in cluster 1 and lower in cluster 3 (supplementary table 3). The diversity of pathogen markers (H’) detected in sponges (ENAD) showed a non-significant trend among the three clusters ( p = 0.11; supplementary table 3), with highest values in cluster 3 (1.26 ± 0.23), followed by cluster 2 (1.13 ± 0.31) and cluster 1 (0.77 ± 0.47). However, pairwise comparisons revealed significant differences between cluster 1 and cluster 3 (Wilcoxon Mann-Whitney test: W = 5; p = 0.04). The comparisons between clusters 2 and 3 (Wilcoxon Mann-Whitney test: W = 12; p = 0.66) and clusters 1 and 2 (Wilcoxon Mann-Whitney test: W = 11; p = 0.18) showed no significant differences. Pathogen marker richness in sponge samples followed the same pattern, with highest values in cluster 3 (range: 3–6; mean: 4.20 ± 1.30), followed by cluster 2 (range: 2–5; mean: 3.50 ± 1.05) and cluster 1 (range: 1–5; mean: 2.57 ± 1.27; p = 0.09; supplementary table 3). Correlations with pathogen marker diversity. Pathogen marker diversity detected on surfaces showed marginally significant positive correlations with forest cover (r²=0.42; p = 0.08) and grassland (r²=0.41; p = 0.09). Latitude demonstrated a strong positive correlation with marker diversity (r²=0.65; p = 0.003). For the climatic variables, precipitation during the driest quarter was positively correlated with pathogen marker diversity (r²=0.57; p = 0.01), while temperature variables showed significant negative correlations, including annual mean temperature (r²=-0.71; p < 0.001), maximum temperature during the warmest month (r²=-0.66; p = 0.003), mean temperature of the warmest quarter (r²=-0.74; p < 0.001), and mean temperature of the coldest quarter (r²=-0.59; p = 0.01). Additionally, pathogen marker diversity showed a significant positive correlation with relative weight of ruminant livestock in the mammal community (r²=0.62; p = 0.006) and a negative correlation with co-exposure rates (r²=-0.49; p = 0.04). Non-significant results were established for carnivore, red deer and wild boar relative weights in the community (r²=0.36 p = 0.14; r²=-0.28 p = 0.26; r²=-0.15 p = 0.55, respectively). Single pathogen markers analysis. Cluster 3 locations had higher positivity to all analyzed markers in surface ENAD, except for T. gondii with higher positivity in cluster 2 locations (Table 2 ). Spatial distribution of de clusters in the Iberian Peninsula. Based on the factors driving molecular detection of pathogen markers in sponge samples, a cluster distribution map was created using land cover and climatic data from open data sources. This map distributes the three defined clusters across the Iberian Peninsula, each one characterized by different environmental conditions that influence pathogen ENAD. The confusion matrix of the predicted clustering map demonstrates balanced performance metrics, with an AUC, F1 score, precision, and recall all measuring 0.89. This indicates that errors are evenly distributed across classes, with equal rates of false positives and false negatives for each class (supplementary tables 4 and 5). This map (Fig. 2 and supplementary Fig. 2) provides an initial reference for expected pathogen diversity and richness in sponge samples across the Iberian Peninsula. DISCUSSION This study identifies the determinants that modulate pathogen environmental nucleic acid detection (ENAD). Our findings provide relevant guidelines concerning the collection of fecal samples and the use of sponges for surface sampling, thus facilitating methodological decision-making and improved sampling designs. Through a comparative analysis of diverse episystems, we show that climatic variables, in partial conjunction with land use patterns, constitute the predominant factors that drive pathogen detection on surfaces in natural ecosystems. The detection of different molecular markers in sponge samples taken from surfaces revealed the potential of this non-invasive methodology for studying the presence of pathogens in natural areas. When differentiating between the two types of sponge samples (O-sponge and S-sponge), significant differences were observed. The detection of the markers uidA , stx1 , stx2 ( E. coli ), IS 6110 ( Mycobacterium spp.), and invA ( Salmonella spp.) was consistently higher in S-sponges. Conversely, the detection of mpb 70 (MTC), IS 1111 ( C. burnetii ), G. duodenalis , and T. gondii was slightly higher in O-sponges. This result suggests that it might be a good strategy to combine O- and S-sponge samples to cover the full range of detectable pathogen markers. When comparing the results of sponge samples from this study, collected in natural areas, with those reported by Herrero-García et al. (2024) [ 14 ] from hoofstock farm premises, as shown in Table 3 , differences can be observed. Sample positivity was always higher on farm premises than in natural habitats except for IS 6110 . This difference could be explained by the existence of livestock sanitation campaigns for livestock and by the possible exclusion of other wild ungulates by livestock [ 65 , 66 ]. However, it is relevant to note that all markers detected on farm environments were also detected in natural areas. In natural environments, significantly higher positivity rates for molecular markers of pathogens were obtained in feces collected from the environment compared to sponges. However, this difference appears to be driven by the greater detection of markers associated with E. coli ( uidA , stx1 , stx2 , and eae ), which presumably could be more easily found in fecal samples than in surface samples in natural areas. Thus, given that most markers detected in feces can also be detected in surface sponges, the latter represents the best choice. Diversity and richness of pathogen markers detected in sponges was mainly driven by climatic factors. This might explain why counterintuitively, pathogen ENAD showed a negative relationship with serological indicators of pathogen exposure. This does not fit with established knowledge regarding vertebrate community influences on pathogen dynamics and host health [ 23 ]. In this study, we observed that in areas at higher latitudes, which have higher precipitation rates and lower temperatures, the detection of pathogen markers in surface samples is higher. We found the greatest diversity of markers in sponges from cluster 3, which is also the cluster with the highest precipitation rates, lower/moderate temperatures, and greater forest and grassland cover. All these factors might contribute to a higher proportion of shaded areas, higher humidity, and less extreme temperatures. These conditions are important for the survival of certain pathogens. For example, Fine et al. (2011) [ 67 ] found that Mycobacterium bovis has a lower survival rate in the environment during spring/summer, when temperatures and UV light incidence are highest. These authors also report that shade influences the survival times of this pathogen. This is consistent with our findings, which reflect a marginally significant positive effect of forest and grassland cover on the diversity of markers detected in sponge samples. Our results also demonstrate that the diversity of detected markers correlates positively with precipitation indices and negatively with temperature. Similarly, the findings of Williams et al. (2005) [ 68 ] show that E. coli O157 has higher survival rates in more humid environments and at lower temperatures. It is important to emphasize that the previously referenced studies address the survival of the microorganism itself, whereas in the present study, genetic material from both non-viable and viable bacteria may be detected [ 69 ]. The results could indicate that environmental conditions are likely to influence the preservation of microbial DNA in the environment. It is noteworthy that while no significant differences were found in the diversity index of markers detected across different clusters, significant differences emerged when pairwise comparisons were made between cluster 1 and cluster 3. From this, it can be inferred that cluster 2 represents a transitional environment with relatively intermediate climatic and habitat characteristics, since the pairwise comparison of this cluster with the others does not have significant differences. The balanced distribution of errors between false positives and false negatives indicates that the model does not systematically favor any specific cluster, reinforcing the reliability of our predictions for different geographic regions of the Iberian Peninsula. The generated predictive map represents a valuable tool for guiding sampling strategies in future studies. Identifying areas with a higher probability of detecting genetic material from pathogenic bacteria will optimize sampling resources and efforts. However, it is important to consider that this model constitutes a first approximation that will need to be refined by incorporating additional variables and increasing the sample size, among other considerations. Altogether, we identify habitat and climate characteristics as the main driver of pathogen marker detection in studies at larger geographic scales. This study has several limitations, primarily due to the limited number of study sites, which restricts our ability to evaluate the influence of environmental factors on pathogen ENAD. Additionally, some markers were analyzed only in fecal or sponge samples due to limitations in the remaining sample volumes. Furthermore, the detectability of fecal samples from different species is determined by multiple factors, such as animal density and study site characteristics including type of vegetation and topography. There is also a limitation, especially in the wild ruminant group, in correctly differentiating feces of one species from another. Finally, a clear limitation for molecular analysis and the standardization of results on fecal samples is its variable conservation status. Overall, our results suggest that combining O- and S-sponge samples may be an effective strategy to maximize the detection of pathogen markers. However, since most markers identified in fecal samples were also detectable in surface sponges, surface sponges seem to be the most practical and efficient option. Furthermore, our findings highlight habitat and climate characteristics as key drivers of pathogen marker detection at broader geographic scales. Declarations ETHICS APPROVAL STATEMENT. This study involved the collection of environmental DNA (eDNA) samples without direct interaction with or disturbance to living organisms. As such, no ethical approval or animal care protocols were required in accordance with current Spanish and European Union regulations. All sampling activities complied with relevant local and national guidelines and did not involve endangered or protected species. CONSENT FOR PUBLICATION. Not applicalble. AVAILABILITY OF DATA AND MATERIALS. The datasets generated and/or analysed during the current study are available in the Zenodo repository: https://doi.org/10.5281/zenodo.17099159. Further supplementary information and data are available are available from the corresponding author on reasonable request. AP: [email protected] (ORCID 0000-0001-6238-9048) and CG: [email protected] (ORCID 0000-0003-0012-4006). COMPETING INTERESTS. The authors declare that they have no competing interests. FUNDING. This work was supported by the EcoEpi project (SBPLY/23/180225/000008), funded by the EU through the ERDF and by the JCCM through INNOCAM. It was also supported by the project 220418CONV, a management assignment agreement by which the Ministry of Agriculture, Fisheries and Food (MAPA) entrusted the University of Castilla–La Mancha (UCLM) with tasks related to wildlife health management in Spain. AUTHOR CONTRIBUTIONS. AP, CS, CGC, and CG conceived the study. Methodology was developed by AP, CGC, MPS, DGB, CH, ARV, EF, LD, BML, and CG, with all authors contributing to validation. AP, CS, and CG performed the formal analyses; AP and CG carried out the investigation; and AP, CS, and CG curated the data. AP prepared the original draft with input from CS and CG. All authors revised and approved the manuscript. Visualization was provided by AP. Project administration was coordinated by AP and CG. Supervision was provided by EF, BML, and CG. Funding was secured by MPS, LD, and CG. All the authors read and approved the final version of the manuscript. ACKNOWLEDGEMENTS. We are grateful for the support provided by hunting estate staff and park rangers, regional administrations, and veterinary services. AP holds a predoctoral research contract at UCLM (2023-UNIVERS-11983), co-funded by the European Social Fund Plus (ESF+). References Morand S (2020) Emerging diseases, livestock expansion and biodiversity loss are positively related at global scale. Biol. Conserv. 248, 108707, 1-9. https://doi.org/10.1016/j.biocon.2020.108707. Thompson L, Rowntree J, Windisch W, Waters SM, Shalloo L, Manzano P (2023) Ecosystem management using livestock: Embracing diversity and respecting ecological principles. Anim Front. 13(2), 28-34. https://doi.org/10.1093/af/vfac094 Barroso P, Gortázar C (2024) The coexistence of wildlife and livestock. Anim Front. 14(1), 5-12. https://doi.org/10.1093/af/vfad064. 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J Appl Microbiol 98(5), 1075–1083. https://doi.org/10.1111/j.1365-2672.2004.02530.x Taberlet P, Bonin A, Zinger L, Coissac E (2018) Terrestrial ecosystems. In: Taberlet, P., Bonin, A., Zinger, L., & Coissac, E (eds) Environmental DNA: For biodiversity research and monitoring. Oxford, pp 114-116. Tables Table 1. Pathogens analyzed, their target genes, and the referenced PCR techniques used. Bacteria Pathogen Targeted gen PCR technique References E. coli uidA Real-time PCR [27,28] Shiga toxin-producing E. coli stx1, stx2 and eae Real-time PCR Foodproof STEC Screening Lyokit, Biotecon diagnosis GmbH, Postdam, Germany M. tuberculosis complex IS 6110 and mpb 70 Real-time PCR [29,30] Salmonella spp. invA Real-time PCR [31] C. burnetii IS 1111 Real-time PCR Sponges [32] and feces [33]. Brucella spp. IS 711 Real-time PCR [34] M. avium subp. paratuberculosis IS 900 Real-time PCR [35] Parasites Pathogen Targeted gen PCR technique References B. coli ITS1–5.8 s-rRNA–ITS2 region and the ssu -rRNA gene Direct PCR [36] Blastocystis sp . ssu rRNA gene Direct PCR [37] Cryptosporidium spp. a ssu rRNA gene Nested PCR [38] E. bieneusi b ssu rRNA gene RT-PCR [39,40] Encephalitozoon spp. c ssu rRNA gene RT-PCR [39,41] G. duodenalis d ssu rRNA gene Real-time PCR (qPCR) [42] T. gondii 200- to 300-fold repetitive 529 bp DNA fragment in the parasite genome Direct PCR [43,44] a Specific subtyping tools targeting the partial 60-kDa glycoprotein gene (gp60) were used in those samples that tested positive for Cryptosporidium spp. by ssu -PCR for C. canis [45]. b Samples identified as E. bieneusi positive were genotyped using a nested PCR protocol to amplify a fragment of the internal transcribed spacer (ITS) region as well as portions of the flanking large and small subunits of the ribosomal RNA ( ssu rRNA) gene, as previously described [46]. c Samples identified as Encephalitozoon spp. positive were genotyped using a nested PCR protocol to amplify the ITS marker as previously described by Katzwinkel-Wladarsch et al. (1996) [47], allowing, in turn, the identification of species. d Giardia -positive isolates that yielded cycle threshold (CT) values ≤ 34 in RT-PCR were subsequently reassessed by a nested PCR to amplify a fragment of the ssu rRNA gene [48,49] to determine the molecular diversity of the parasite at the assemblage level. Samples that tested positive by ssu -PCR were re-amplified at the genes codifying the glutamate dehydrogenase ( gdh ), β-giardin ( bg ) and triose phosphate isomerase ( tpi ) proteins to determine the molecular diversity of the parasite at the sub-assemblage level. A semi-nested PCR was used to amplify a fragment of the gdh gene [50] and nested PCRs were used to amplify fragments of the bg and tpi genes, respectively [51—53]. Table 1 should be placed after line 145. Table 2. Proportion (%) of positive communities in each cluster per pathogen marker analyzed. Pathogen marker Cluster 1 (% locations) Cluster 2 (% locations) Cluster 3 (% locations) IS 6110 MTC 71.43 66.67 100 mpb 70 MTC 0 16.67 40 uidA Escherichia coli 100 100 100 stx1 Escherichia coli 14.29 0 20 stx2 Escherichia coli 14.29 16.67 20 invA Salmonella spp. 0 0 40 IS 1111 Coxiella burnetii 14.29 16.67 20 Giardia duodenalis 28.57 33.33 40 Toxoplasma gondii 14.29 100 40 MTC – Mycobacterium tuberculosis complex. Table 2 should be placed after line 346. Table 3 . Comparison of PCR-CT values and % of positive sponge samples between previous studies done in farms by Herrero-García et al. (2024) [14] and this study. Pathogen marker Herrero-García et al. (2024) 14 This study % of positive samples PCR-CT values % of positive samples PCR-CT values Mean Max. Min. Mean Max. Min. IS 6110 17.73 39.08 43.90 36.04 25 37.14 39.97 31.57 IS 1111 5.88 34.19 37.80 31.61 1.67 37.80 39.51 35.35 uidA 81.56 35.19 43.84 23.93 27.22 35.92 39.41 26.20 stx1 2.13 41.24 43.28 40.03 1.67 35.89 38.87 33.38 stx2 2.84 35.50 37.94 33.36 2.78 32.41 35.16 30.09 eae 2.84 30.29 32.33 28.87 0.00 0.00 0.00 0.00 invA 8.51 38.42 41.58 33.61 1.11 36.17 36.29 36.05 Max. and Min. refers to maximum and minimum CT-values per each marker. Table 3 should be placed after line 387. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Additional file 1 — Supplementary data and methods. This file provides further information regarding PCR results, clustering of the study sites and map performance metrics. 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10:33:33","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":263669,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7642008/v1/452f78e80222e9bbb0ad92e5.html"},{"id":92937243,"identity":"6aaf9eb5-d794-4783-a661-16d9347a793e","added_by":"auto","created_at":"2025-10-07 10:33:33","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":118834,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the proportion of total positive samples between O-sponges and S-sponges, as well as between feces and sponges (\u003cstrong\u003ea\u003c/strong\u003e). Section \u003cstrong\u003eb\u003c/strong\u003e shows the comparison of the proportion of positive sites between sponges and feces. Pathogen markers: \u003cem\u003eEscherichia coli \u003c/em\u003e(\u003cem\u003euidA\u003c/em\u003e, \u003cem\u003estx1\u003c/em\u003e, \u003cem\u003estx2\u003c/em\u003e and \u003cem\u003eeae\u003c/em\u003e), MTC (IS\u003cem\u003e6110\u003c/em\u003e and \u003cem\u003empb\u003c/em\u003e70), \u003cem\u003eSalmonella\u003c/em\u003e spp. (\u003cem\u003einvA\u003c/em\u003e), \u003cem\u003eCoxiella burnetii\u003c/em\u003e (IS\u003cem\u003e1111\u003c/em\u003e), \u003cem\u003eGiardia\u003c/em\u003e \u003cem\u003eduodenalis\u003c/em\u003e, and \u003cem\u003eToxoplasma gondii\u003c/em\u003e. Statistical significance is indicated as follows: \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05 (*), \u003cem\u003ep\u003c/em\u003e\u0026lt;0.01 (**), and \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001 (***).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7642008/v1/473ce77c8cca337cbdc34bf6.jpg"},{"id":92937245,"identity":"0fd49560-9e98-40f8-9267-45a9add7491d","added_by":"auto","created_at":"2025-10-07 10:33:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89723,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distributions of de clusters in the Iberian Peninsula with varying pathogen marker diversity index (H’) and richness (supplementary table 3; supplementary figure 2). In the bar chart is represented the mean and the standard deviation of markers diversity and richness per cluster.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7642008/v1/453725b665f4be30cbac39b2.jpg"},{"id":108438048,"identity":"21ea4e7e-f861-4117-a52f-f2807b65a8eb","added_by":"auto","created_at":"2026-05-04 16:05:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":761132,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7642008/v1/f4adbad0-61ad-4cab-87f5-708b4d8ba9ee.pdf"},{"id":92937249,"identity":"0d57d44e-9fbe-4b9b-b68a-b73f6aabc0f0","added_by":"auto","created_at":"2025-10-07 10:33:33","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":347357,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional file 1 — Supplementary data and methods. \u003c/strong\u003eThis file provides further information regarding PCR results, clustering of the study sites and map performance metrics.\u003c/p\u003e","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7642008/v1/218e03ace9bd6a2792531e2b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Tracking pathogens with eDNA in natural areas: how environmental gradients shape surveillance strategies","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe wildlife-livestock-environment interface is a complex system with profound implications for biodiversity conservation and pathogen circulation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Biodiversity and disease dynamics are influenced by multiple factors, including pathogen specificity, transmission routes, and habitat characteristics [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Current literature presents two key hypotheses regarding the interaction between biodiversity and pathogens: the amplification effect and the dilution effect, both of which vary based on the pathogens involved and the geographic regions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In this context, it is crucial to explore the roles that wildlife and livestock play in pathogen maintenance within the environment, based on the concept known as episystem. This approach integrates the interactions between pathogens, hosts, and environmental factors across specific spatial and temporal scales [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this framework, environmental nucleic acid detection (ENAD) emerges as a powerful tool to monitor pathogens, including those introduced or spread by invasive species [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. To date, ENAD has primarily been applied in aquatic environments, using PCR, qPCR, or metabarcoding to detect a broad range of organisms, from microbes (bacteria, viruses, protists) to macroorganisms [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Its potential has been recognized by the World Organization for Animal Health (WOAH), which includes ENAD in its guidelines for detecting \u003cem\u003eGyrodactylus salaris\u003c/em\u003e in salmonids [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Recent ENAD surveys have yielded highly promising results for monitoring terrestrial mammals and their associated pathogens [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. For example, ENAD methodologies has been applied to detect African swine fever virus (ASFV) from turbid water and soil samples, as well as ASFV in pig breeding facilities or \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex (MTC) in cattle farms and European bison populations using dry sponges pre-hydrated with a specific surfactant applied to the animals' skin and contaminated surfaces [10\u0026mdash;13]. These sponges have also recently been used to detect other pathogen markers related to \u003cem\u003eM. avium\u003c/em\u003e subp. \u003cem\u003eparatuberculosis\u003c/em\u003e, \u003cem\u003eCoxiella burnetii\u003c/em\u003e, \u003cem\u003eBrucella\u003c/em\u003e spp., \u003cem\u003eSalmonella enterica\u003c/em\u003e and \u003cem\u003eEscherichia coli\u003c/em\u003e on surfaces at risk points on farms, such as waterers and feeders, and on animals [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition, ENAD has also been applied to substrates such as equine enrichment toys [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], honey [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], invertebrates [17\u0026mdash;19], as well as fecal samples from amphibians, reptiles, and livestock [14\u0026mdash;20]. Similarly, ENAD from non-invasive sampling of wildlife feces is recognized as an effective tool for infectious disease surveillance in natural areas providing valuable data for assessing disease circulation, zoonotic risks, and ecosystem health without the need for direct animal handling [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Indeed, ENAD offers advantages in terms of sampling efficiency, reduced processing time, and lower costs\u0026mdash;particularly in challenging contexts involving elusive or low-density animal populations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. When combined with spatial and epidemiological data, ENAD can also offer a comprehensive understanding of pathogen dynamics across a geographic area [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For instance, a pilot study on non-invasive monitoring in outdoor farming systems integrated vertebrate richness data and ENAD-derived health markers, revealing a negative correlation between pathogen marker richness and farm vertebrate richness [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Similarly, a recent study carried out in Poland demonstrated the application of ENAD for detecting MTC in European bison populations within regions with historical records of tuberculosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These studies have highlighted the effectiveness of ENAD approaches\u0026mdash;based on fecal and sponge sampling integrated with ecological data\u0026mdash;in detecting pathogens in both livestock and wild animal populations, whether in outdoor farming environments or natural ecosystems [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, these investigations have been generally limited to single, environmentally homogeneous areas. Consequently, the extent to which environmental variables influence ENAD performance remains poorly explored, particularly in heterogeneous geographic contexts. Exploring this variability is essential to validate ENAD as a robust surveillance tool adaptable to different geographic and ecological conditions.\u003c/p\u003e\u003cp\u003eIn this regard, the present study aims to evaluate the relationship between ENAD-detected pathogens, mammal community characteristics, and environmental factors across 18 sites on the Iberian Peninsula. Environmental DNA (eDNA) from sponges and fecal samples were analyzed for pathogen-specific health markers, providing insight into pathogen detection across heterogeneous geographic areas.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cp\u003e\u003cb\u003eStudy area.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis work has been developed in 18 pilot monitoring sites throughout the Iberian Peninsula, 15 Spanish and 3 Portuguese, which participated in a pilot network for integrated wildlife monitoring (Wildlife Health Surveillance Plan -WHSP- of Spain) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The sites were selected to ensure they were representative of the five bioregions: Atlantic Spain (1); Cereal plains (2); Continental Mediterranean ecosystems (3); Inland mountains (4); and Southern and eastern coast (5); recognized in the WHSP of Spain and Portugal [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eSurface sponges eDNA sampling.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTwo sponges from surfaces were taken in the 50 m\u003csup\u003e2\u003c/sup\u003e in front of each of 5 randomly selected camera traps (CTs) of each of the 18 CT grids (180 in total) deployed for wildlife species monitoring purposes (Wildlife Health Surveillance Plan -WHSP- of Spain). In front of each CT, one sponge sampled objects such as stones and tree trunks (O-sponge) and a second sponge sampled the surface of bare soil including feces or rootings if present (S-sponge). A total of ten sponges were taken per study site. All sponge samples were tested for PCR inhibition by including an internal control in each PCR reaction, discarding four of 180 (2.22%; all soil samples).\u003c/p\u003e\u003cp\u003eAll sampling points within a study site were collected on the same day between 5:30 AM and 10:30 AM during April-June 2023. Sampling was conducted systematically from south to north to ensure consistent conditions across all locations.\u003c/p\u003e\u003cp\u003e\u003cb\u003eFecal eDNA sampling.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFecal material found on the walked transects was identified as belonging to Eurasian wild boar (\u003cem\u003eSus scrofa\u003c/em\u003e) or wild ruminant species (mostly red deer, \u003cem\u003eCervus elaphus\u003c/em\u003e, and roe deer, \u003cem\u003eCapreolus capreolus\u003c/em\u003e). If fresh samples were found, 5\u0026ndash;10 g portions collected into sterile bags and preserved refrigerated until reaching the laboratory, where samples were stored frozen at -20\u0026ordm;C until DNA extraction. All fecal samples were checked for inhibition, discarding seven of 146 (4.79%; all of them ruminant samples).\u003c/p\u003e\u003cp\u003e\u003cb\u003eNucleic acid extraction.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe extraction and purification of environmental DNA (eDNA) from surface sponge samples was performed using the QIAamp Fast DNA Stool Mini Kit (Qiagen Hilden; Germany), starting from the pellet obtained after centrifuging 900 \u0026micro;L of the sample for 3 minutes at 13.000 rpm. Genomic DNA from fecal samples was isolated from approximately 200 mg of each fecal sample using the same Qiagen kit, in accordance with the manufacturer\u0026rsquo;s instructions, except for the step where samples were mixed with the InhibitEX buffer, where the incubation time was changed to 10 min at 95\u0026deg;C. Extracted and purified DNA samples were eluted in 200 \u0026micro;L of PCR-grade water and stored at 4\u0026deg;C until further molecular analysis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMolecular detection.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe pathogen markers analyzed using PCR technics (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) include bacteria: \u003cem\u003eE. coli\u003c/em\u003e (\u003cem\u003euidA\u003c/em\u003e, \u003cem\u003estx1\u003c/em\u003e, \u003cem\u003estx2\u003c/em\u003e and \u003cem\u003eeae\u003c/em\u003e), MTC (IS\u003cem\u003e6110\u003c/em\u003e and \u003cem\u003empb\u003c/em\u003e70), \u003cem\u003eSalmonella\u003c/em\u003e spp. (\u003cem\u003einvA\u003c/em\u003e), \u003cem\u003eC. burnetii\u003c/em\u003e (IS\u003cem\u003e1111\u003c/em\u003e), \u003cem\u003eBrucella\u003c/em\u003e spp. (IS\u003cem\u003e711\u003c/em\u003e), \u003cem\u003eM. avium\u003c/em\u003e subp. \u003cem\u003eparatuberculosis\u003c/em\u003e (IS\u003cem\u003e900\u003c/em\u003e); and parasites: \u003cem\u003eBalantioides coli, Blastocystis sp.\u003c/em\u003e, \u003cem\u003eCryptosporidium\u003c/em\u003e spp, \u003cem\u003eEncephalitozoon\u003c/em\u003e spp, \u003cem\u003eEnterocytozoon bieneusi, Giardia duodenalis\u003c/em\u003e, and \u003cem\u003eToxoplasma gondii\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eMore details on PCR protocols are specified in Additional file 1 (supplementary methods section).\u003c/p\u003e\u003cp\u003e\u003cb\u003eEnvironmental factors influencing pathogen diversity detected on surfaces in natural areas.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSponge molecular marker diversity index.\u003c/span\u003e\u003c/p\u003e\u003cp\u003eA Shannon diversity index (H\u0026rsquo;) was calculated to quantify and standardize the diversity of molecular markers detected in sponge samples across study sites. This analysis was performed using the \u003cem\u003edplyr\u003c/em\u003e (v1.1.4) and \u003cem\u003evegan\u003c/em\u003e (v2.7-1) R packages [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. For each study area, five sampling points were established, and the abundance value for each molecular marker was determined by the number of positive sampling points detected within each study area (range: 0\u0026ndash;5).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eStudy area classification.\u003c/span\u003e\u003c/p\u003e\u003cp\u003eTo investigate the influence of environmental factors on pathogen diversity detected through ENAD on surfaces in natural areas, study areas were classified using a hierarchical clustering on principal components (HCPC; see supplementary Fig.\u0026nbsp;1). This analysis was implemented using the \u003cem\u003eFactoMineR\u003c/em\u003e (v2.11) and \u003cem\u003efactoextra\u003c/em\u003e (v1.0.7) R packages [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe optimal number of clusters was determined using a combination of environmental variables (land cover and climatic parameters), mammal community characteristics (see Additional file 1 \u0026mdash;supplementary methods section\u0026mdash; for more information on the parameters used), and wildlife health parameters (described in Perell\u0026oacute; et al. -manuscript under review-) [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. This classification allowed the comparison of pathogen diversity (H\u0026rsquo;) detected in sponge samples and the assessment of factors potentially driving molecular detection in natural areas.\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSpatial distribution modeling of clusters in the Iberian Peninsula.\u003c/span\u003e\u003c/p\u003e\u003cp\u003eTo predict the spatial distribution of identified clusters across the Iberian Peninsula, a random forest (RF) classification model was performed using the \u003cem\u003ecaret\u003c/em\u003e (v7.0-1) and \u003cem\u003erandomForest\u003c/em\u003e (v4.7-1.2) packages in R [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. The model was based on environmental factors (land cover and climatic variables) as predictors and the HCPC-defined clusters as response categories. All analyses were conducted with a fixed random seed to ensure reproducibility.\u003c/p\u003e\u003cp\u003eModel performance was evaluated using a leave-one-out cross-validation (LOOCV), wherein each observation was sequentially used as validation data while the remaining observations served as training data. The cross-validation parameters were established using the \u003cem\u003etrainControl\u003c/em\u003e function with LOOCV specified as the resampling method. Class probabilities were calculated for each iteration, and predictions were systematically stored for subsequent analysis.\u003c/p\u003e\u003cp\u003eThe RF model was trained using the \u003cem\u003etrain\u003c/em\u003e function, with cluster designation as the response variable. Predictor variables incorporated multiple land cover classifications (according to the classifications made by Barroso et al. (2023) from Corine LandCover, only shrubland, forest coverage, bare land, grassland and urban land uses were taken into account) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] and selected bioclimatic variables established by the United States Geological Survey (i.e. total precipitation during the driest quarter, annual mean temperature, maximum temperature during the warmest month, mean temperature of the warmest quarter and mean temperature of the coldest quarter) [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Model optimization was performed by tuning the \u003cem\u003emtry\u003c/em\u003e parameter (number of variables randomly sampled as candidates at each split) across three values.\u003c/p\u003e\u003cp\u003ePerformance was quantitatively assessed via confusion matrix analysis, which provided classification accuracy metrics and Cohen's Kappa statistics. The optimal \u003cem\u003emtry\u003c/em\u003e value was determined based on cross-validation results, with overall accuracy and Kappa values recorded as performance indicators.\u003c/p\u003e\u003cp\u003eFor spatial prediction classification, a custom function (\u003cem\u003eget_class_none\u003c/em\u003e) to the probability outputs was applied. This function assigns class membership based on the maximum predicted probability. To account for classification uncertainty, observations with maximum probabilities below 0.6 were designated as \"non-classified\", while observations exceeding this threshold were assigned to their respective highest probability class (clusters 1, 2 or 3). The spatial prediction was implemented using the \u003cem\u003ecalc\u003c/em\u003e function, and the resulting classification was visualized through raster mapping using the \u003cem\u003eraster\u003c/em\u003e R package (v3.6-32) [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis.\u003c/h2\u003e\u003cp\u003eStatistical differences were assessed using various tests depending on the comparison. Differences between sponge sample types, feces species origin, and between fecal and sponge samples were evaluated using Fisher\u0026rsquo;s exact test and chi-square test, implemented in R software version 4.4.1. Additionally, the Wilcoxon Mann-Whitney test was used for PCR CT-values comparisons, performed using the \u003cem\u003edplyr\u003c/em\u003e (v1.1.4) and \u003cem\u003erstatix\u003c/em\u003e (v0.7.2) R packages [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eFor the variables used in the HCPC analysis, statistical differences between clusters were also assessed using the Kruskal-Wallis and Wilcoxon Mann-Whitney tests, again utilizing the \u003cem\u003edplyr\u003c/em\u003e and \u003cem\u003erstatix\u003c/em\u003e R packages [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eOur findings revealed that methodology, particularly sampling design, influences the efficacy of ENAD for non-invasive pathogen monitoring in natural areas. A clear influence of environmental factors on pathogen detection on natural surfaces was found, underscoring the importance of optimized sampling protocols for reliable environmental surveillance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSurface sponges ENAD.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSurface sampling with sponges showed capability to detect \u003cem\u003eE. coli\u003c/em\u003e, MTC, \u003cem\u003eSalmonella\u003c/em\u003e spp., \u003cem\u003eC. burnetii\u003c/em\u003e, \u003cem\u003eG. duodenalis\u003c/em\u003e and \u003cem\u003eT. gondii\u003c/em\u003e molecular markers in natural areas. However, methodology in sponge sampling (O-sponges or S-sponges) influenced specific marker detectability. In this study, S-sponges showed higher utility for detecting \u003cem\u003eE. coli\u003c/em\u003e markers, since a significantly higher proportion of S-sponges tested positive for the \u003cem\u003euidA\u003c/em\u003e gene (40.69%) compared to the O-sponges (15.55%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Regarding other \u003cem\u003eE. coli\u003c/em\u003e markers, \u003cem\u003estx1\u003c/em\u003e was detected only in S-sponges and \u003cem\u003estx2\u003c/em\u003e tended to be detected more frequently in S-sponges. The PCR CT-values also tended to be lower in S-sponges for the three \u003cem\u003eE. coli\u003c/em\u003e markers (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01 for \u003cem\u003euidA\u003c/em\u003e CT-values; supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For the MTC markers IS\u003cem\u003e6110\u003c/em\u003e and \u003cem\u003empb\u003c/em\u003e70, no significant differences in proportion of positives were found between O-sponges and S-sponges, however, for IS\u003cem\u003e6110\u003c/em\u003e the PCR CT-values were significantly lower in the S-sponges (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01; supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Regarding other bacteria, the \u003cem\u003eSalmonella\u003c/em\u003e spp. \u003cem\u003einvA\u003c/em\u003e marker was detected only in S-sponges, while no differences were found between sponge samples for the \u003cem\u003eC. burnetii\u003c/em\u003e IS\u003cem\u003e1111\u003c/em\u003e marker, despite PCR CT-values tended to be lower in S-sponges (supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Parasite ENAD showed no significant differences between sponge samples, but a trend to a higher detectability of \u003cem\u003eG. duodenalis\u003c/em\u003e. and \u003cem\u003eT. gondii\u003c/em\u003e in the O-sponges was observed (supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe highest proportion of positive sampling points was found for the \u003cem\u003euidA\u003c/em\u003e marker (48.80%), detected in 100% of the study sites, followed by the MTC marker IS\u003cem\u003e6110\u003c/em\u003e (42.22% of sampling points) detected in 77.77% of the locations. The parasites were the second group of markers with more positive sampling points (12.22% for \u003cem\u003eT. gondii\u003c/em\u003e and 8.89% for \u003cem\u003eG. duodenalis\u003c/em\u003e) and study sites presence (50% for \u003cem\u003eT. gondii\u003c/em\u003e and 33.30% for \u003cem\u003eG. duodenalis\u003c/em\u003e). Other markers showed a proportion of positive sampling points ranging from 2.22 to 5.55% and study sites from 11.11 to 16.66% (supplementary tables 1 and 2).\u003c/p\u003e\u003cp\u003e\u003cb\u003eFecal ENAD.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSeveral bacteria and parasites were detected in wild boar and wild ruminant fecal samples. These included \u003cem\u003eE. coli\u003c/em\u003e markers, MTC IS\u003cem\u003e6110\u003c/em\u003e, \u003cem\u003eSalmonella\u003c/em\u003e spp., \u003cem\u003eG. duodenalis\u003c/em\u003e, \u003cem\u003eBlastocysitis\u003c/em\u003e sp., \u003cem\u003eB. coli\u003c/em\u003e, \u003cem\u003eE. bieneusi\u003c/em\u003e and \u003cem\u003eEncephalitozoon cuniculi\u003c/em\u003e. From a general perspective, no apparent differences were found between wild boar feces and ruminant feces, except for specific pathogens.\u003c/p\u003e\u003cp\u003eThe \u003cem\u003eE. coli\u003c/em\u003e markers showed no apparent differences between wild boar and wild ruminant feces. However, a trend to higher proportion of positives for \u003cem\u003euidA\u003c/em\u003e, \u003cem\u003estx1\u003c/em\u003e, \u003cem\u003estx2\u003c/em\u003e and \u003cem\u003eeae\u003c/em\u003e markers in wild boar feces was found (supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Regarding the PCR CT-values, significant differences were found for \u003cem\u003euidA\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, with lower values in wild boar feces) and \u003cem\u003estx1\u003c/em\u003e (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03, with lower values in wild ruminant feces). The MTC IS\u003cem\u003e6110\u003c/em\u003e marker was detected both in wild boar and wild ruminant feces with no significant differences for positivity and CT-values. The \u003cem\u003eSalmonella\u003c/em\u003e spp. \u003cem\u003einvA\u003c/em\u003e was found in 4.34% of the wild boar feces (supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Parasite markers showed no difference between ruminant and wild boar feces. \u003cem\u003eG. duodenalis\u003c/em\u003e, \u003cem\u003eBlastocysits\u003c/em\u003e sp., and \u003cem\u003eEnc. cuniculi\u003c/em\u003e were found in both fecal samples, while \u003cem\u003eB. coli\u003c/em\u003e and \u003cem\u003eE. bieneusi\u003c/em\u003e were found only in wild boar feces (supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe highest proportion of positive study sites in feces samples were for \u003cem\u003eE. coli\u003c/em\u003e markers (\u003cem\u003euidA\u003c/em\u003e-100%-, \u003cem\u003estx2\u003c/em\u003e-44.44%-, \u003cem\u003eeae\u003c/em\u003e-44.44%- and \u003cem\u003estx1\u003c/em\u003e-22.22%) followed by MTC IS\u003cem\u003e6110\u003c/em\u003e (33.33%) and three parasite markers (\u003cem\u003eG. duodenalis\u003c/em\u003e-22.22%-, \u003cem\u003eBlastocystis\u003c/em\u003e sp.-22.22%- and \u003cem\u003eEnc. cuniculi\u003c/em\u003e-16.66%-). Finally, \u003cem\u003eB. coli\u003c/em\u003e and \u003cem\u003einvA\u003c/em\u003e were positive in 11.11% and \u003cem\u003eE. bieneusi\u003c/em\u003e in 5.56% of the study sites (supplementary table 2).\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparing surface sponges and fecal ENAD.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong the pathogens analyzed in both sponge and fecal samples, significant differences were found in opposite senses for \u003cem\u003eE. coli\u003c/em\u003e and MTC markers. All \u003cem\u003eE. coli\u003c/em\u003e markers were more positive in fecal samples (\u003cem\u003euidA p\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003estx1 p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02; \u003cem\u003estx2 p\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03; \u003cem\u003eeae\u003c/em\u003e was detected only in fecal samples; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Consistently, the PCR CTs for \u003cem\u003euidA\u003c/em\u003e were lower in fecal samples (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01; supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). By contrast, the MTC marker IS\u003cem\u003e6110\u003c/em\u003e was detected in 25.57% of sponge samples, while only 5.04% of fecal samples tested positive (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). The IS\u003cem\u003e6110\u003c/em\u003e CT-values were also lower in sponge samples (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005; supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The \u003cem\u003empb\u003c/em\u003e70 marker was also detected in sponge samples. Other bacteria like \u003cem\u003eC. burnetii\u003c/em\u003e (IS\u003cem\u003e1111\u003c/em\u003e) were detected only in sponge samples, too (supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Regarding parasites, there were no significant differences in \u003cem\u003eGiardia\u003c/em\u003e detectability between surface or fecal ENAD, however, a higher positivity trend in feces was observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea; supplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The CT-values for \u003cem\u003eG. duodenalis\u003c/em\u003e were significantly lower in fecal samples (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\u003cp\u003eA comparison of the proportion of sites positive to pathogen markers between sponge and fecal samples is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb. Significant differences were found in the number of positive study sites for \u003cem\u003eeae\u003c/em\u003e and IS\u003cem\u003e6110\u003c/em\u003e markers (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003 and \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02, respectively; supplementary table 2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFactors driving pathogen ENAD in sponge samples.\u003c/b\u003e\u003c/p\u003e\u003cp\u003ePathogen detection in sponge samples was strongly associated with climatic variables (latitude influence) rather than biological factors typically linked to pathogen circulation. Notably, pathogen ENAD showed a negative relationship with serological sanitary data and wild ungulate abundance and a positive relationship with grazing ruminant livestock (see \u0026ldquo;Correlations with pathogen marker diversity index\u0026rdquo; section and cluster description). The subsequent analysis examines the specific factors influencing pathogen ENAD in surfaces.\u003c/p\u003e\u003cp\u003eA study site clustering was performed to assess how land cover, climate, mammal community and health variables influence pathogen detection (supplementary table 3). Three groups (clusters) of locations were established, seven locations belonging to cluster 1, six to cluster 2 and five to cluster 1 (supplementary Fig.\u0026nbsp;1). Clusters were mainly differentiated by the influence of latitude. Cluster 1, the southern one and representing Mediterranean climate, presents higher temperatures and lower precipitation rates than cluster 2 with intermediate climatic characteristics and cluster 3, with lower temperatures and higher precipitation rates, typical of Atlantic climate (supplementary table 3). Despite no significant differences when comparing the three clusters, a positive trend from southern locations to northern locations was seen for forest, grassland and urban land use (supplementary table 3). Regarding mammal communities, the relative weight of red deer in the mammal community was significantly higher in cluster 1, while carnivores had significantly more weight in clusters 2 and 3. Ruminant livestock and wild boar tended to have more weight in cluster 3. Co-exposure rates showed significant differences between clusters, being higher in cluster 1 and lower in cluster 3 (supplementary table 3).\u003c/p\u003e\u003cp\u003eThe diversity of pathogen markers (H\u0026rsquo;) detected in sponges (ENAD) showed a non-significant trend among the three clusters (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.11; supplementary table 3), with highest values in cluster 3 (1.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23), followed by cluster 2 (1.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31) and cluster 1 (0.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47). However, pairwise comparisons revealed significant differences between cluster 1 and cluster 3 (Wilcoxon Mann-Whitney test: W\u0026thinsp;=\u0026thinsp;5; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04). The comparisons between clusters 2 and 3 (Wilcoxon Mann-Whitney test: W\u0026thinsp;=\u0026thinsp;12; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.66) and clusters 1 and 2 (Wilcoxon Mann-Whitney test: W\u0026thinsp;=\u0026thinsp;11; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.18) showed no significant differences. Pathogen marker richness in sponge samples followed the same pattern, with highest values in cluster 3 (range: 3\u0026ndash;6; mean: 4.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.30), followed by cluster 2 (range: 2\u0026ndash;5; mean: 3.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05) and cluster 1 (range: 1\u0026ndash;5; mean: 2.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.27; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09; supplementary table 3).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCorrelations with pathogen marker diversity.\u003c/span\u003e\u003c/p\u003e\u003cp\u003ePathogen marker diversity detected on surfaces showed marginally significant positive correlations with forest cover (r\u0026sup2;=0.42; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.08) and grassland (r\u0026sup2;=0.41; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.09). Latitude demonstrated a strong positive correlation with marker diversity (r\u0026sup2;=0.65; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). For the climatic variables, precipitation during the driest quarter was positively correlated with pathogen marker diversity (r\u0026sup2;=0.57; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), while temperature variables showed significant negative correlations, including annual mean temperature (r\u0026sup2;=-0.71; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), maximum temperature during the warmest month (r\u0026sup2;=-0.66; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), mean temperature of the warmest quarter (r\u0026sup2;=-0.74; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and mean temperature of the coldest quarter (r\u0026sup2;=-0.59; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01). Additionally, pathogen marker diversity showed a significant positive correlation with relative weight of ruminant livestock in the mammal community (r\u0026sup2;=0.62; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006) and a negative correlation with co-exposure rates (r\u0026sup2;=-0.49; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04). Non-significant results were established for carnivore, red deer and wild boar relative weights in the community (r\u0026sup2;=0.36 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.14; r\u0026sup2;=-0.28 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.26; r\u0026sup2;=-0.15 \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.55, respectively).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSingle pathogen markers analysis.\u003c/span\u003e\u003c/p\u003e\u003cp\u003eCluster 3 locations had higher positivity to all analyzed markers in surface ENAD, except for \u003cem\u003eT. gondii\u003c/em\u003e with higher positivity in cluster 2 locations (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSpatial distribution of de clusters in the Iberian Peninsula.\u003c/span\u003e\u003c/p\u003e\u003cp\u003eBased on the factors driving molecular detection of pathogen markers in sponge samples, a cluster distribution map was created using land cover and climatic data from open data sources. This map distributes the three defined clusters across the Iberian Peninsula, each one characterized by different environmental conditions that influence pathogen ENAD. The confusion matrix of the predicted clustering map demonstrates balanced performance metrics, with an AUC, F1 score, precision, and recall all measuring 0.89. This indicates that errors are evenly distributed across classes, with equal rates of false positives and false negatives for each class (supplementary tables 4 and 5). This map (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and supplementary Fig.\u0026nbsp;2) provides an initial reference for expected pathogen diversity and richness in sponge samples across the Iberian Peninsula.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study identifies the determinants that modulate pathogen environmental nucleic acid detection (ENAD). Our findings provide relevant guidelines concerning the collection of fecal samples and the use of sponges for surface sampling, thus facilitating methodological decision-making and improved sampling designs. Through a comparative analysis of diverse episystems, we show that climatic variables, in partial conjunction with land use patterns, constitute the predominant factors that drive pathogen detection on surfaces in natural ecosystems.\u003c/p\u003e\u003cp\u003eThe detection of different molecular markers in sponge samples taken from surfaces revealed the potential of this non-invasive methodology for studying the presence of pathogens in natural areas. When differentiating between the two types of sponge samples (O-sponge and S-sponge), significant differences were observed. The detection of the markers \u003cem\u003euidA\u003c/em\u003e, \u003cem\u003estx1\u003c/em\u003e, \u003cem\u003estx2\u003c/em\u003e (\u003cem\u003eE. coli\u003c/em\u003e), IS\u003cem\u003e6110\u003c/em\u003e (\u003cem\u003eMycobacterium\u003c/em\u003e spp.), and \u003cem\u003einvA\u003c/em\u003e (\u003cem\u003eSalmonella\u003c/em\u003e spp.) was consistently higher in S-sponges. Conversely, the detection of \u003cem\u003empb\u003c/em\u003e70 (MTC), IS\u003cem\u003e1111\u003c/em\u003e (\u003cem\u003eC. burnetii\u003c/em\u003e), \u003cem\u003eG. duodenalis\u003c/em\u003e, and \u003cem\u003eT. gondii\u003c/em\u003e was slightly higher in O-sponges. This result suggests that it might be a good strategy to combine O- and S-sponge samples to cover the full range of detectable pathogen markers.\u003c/p\u003e\u003cp\u003eWhen comparing the results of sponge samples from this study, collected in natural areas, with those reported by Herrero-Garc\u0026iacute;a et al. (2024) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] from hoofstock farm premises, as shown in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, differences can be observed. Sample positivity was always higher on farm premises than in natural habitats except for IS\u003cem\u003e6110\u003c/em\u003e. This difference could be explained by the existence of livestock sanitation campaigns for livestock and by the possible exclusion of other wild ungulates by livestock [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. However, it is relevant to note that all markers detected on farm environments were also detected in natural areas.\u003c/p\u003e\u003cp\u003eIn natural environments, significantly higher positivity rates for molecular markers of pathogens were obtained in feces collected from the environment compared to sponges. However, this difference appears to be driven by the greater detection of markers associated with \u003cem\u003eE. coli\u003c/em\u003e (\u003cem\u003euidA\u003c/em\u003e, \u003cem\u003estx1\u003c/em\u003e, \u003cem\u003estx2\u003c/em\u003e, and \u003cem\u003eeae\u003c/em\u003e), which presumably could be more easily found in fecal samples than in surface samples in natural areas.\u003c/p\u003e\u003cp\u003eThus, given that most markers detected in feces can also be detected in surface sponges, the latter represents the best choice.\u003c/p\u003e\u003cp\u003eDiversity and richness of pathogen markers detected in sponges was mainly driven by climatic factors. This might explain why counterintuitively, pathogen ENAD showed a negative relationship with serological indicators of pathogen exposure. This does not fit with established knowledge regarding vertebrate community influences on pathogen dynamics and host health [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we observed that in areas at higher latitudes, which have higher precipitation rates and lower temperatures, the detection of pathogen markers in surface samples is higher. We found the greatest diversity of markers in sponges from cluster 3, which is also the cluster with the highest precipitation rates, lower/moderate temperatures, and greater forest and grassland cover. All these factors might contribute to a higher proportion of shaded areas, higher humidity, and less extreme temperatures. These conditions are important for the survival of certain pathogens. For example, Fine et al. (2011) [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e] found that \u003cem\u003eMycobacterium bovis\u003c/em\u003e has a lower survival rate in the environment during spring/summer, when temperatures and UV light incidence are highest. These authors also report that shade influences the survival times of this pathogen. This is consistent with our findings, which reflect a marginally significant positive effect of forest and grassland cover on the diversity of markers detected in sponge samples. Our results also demonstrate that the diversity of detected markers correlates positively with precipitation indices and negatively with temperature. Similarly, the findings of Williams et al. (2005) [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] show that \u003cem\u003eE. coli\u003c/em\u003e O157 has higher survival rates in more humid environments and at lower temperatures. It is important to emphasize that the previously referenced studies address the survival of the microorganism itself, whereas in the present study, genetic material from both non-viable and viable bacteria may be detected [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The results could indicate that environmental conditions are likely to influence the preservation of microbial DNA in the environment.\u003c/p\u003e\u003cp\u003eIt is noteworthy that while no significant differences were found in the diversity index of markers detected across different clusters, significant differences emerged when pairwise comparisons were made between cluster 1 and cluster 3. From this, it can be inferred that cluster 2 represents a transitional environment with relatively intermediate climatic and habitat characteristics, since the pairwise comparison of this cluster with the others does not have significant differences.\u003c/p\u003e\u003cp\u003eThe balanced distribution of errors between false positives and false negatives indicates that the model does not systematically favor any specific cluster, reinforcing the reliability of our predictions for different geographic regions of the Iberian Peninsula. The generated predictive map represents a valuable tool for guiding sampling strategies in future studies. Identifying areas with a higher probability of detecting genetic material from pathogenic bacteria will optimize sampling resources and efforts. However, it is important to consider that this model constitutes a first approximation that will need to be refined by incorporating additional variables and increasing the sample size, among other considerations. Altogether, we identify habitat and climate characteristics as the main driver of pathogen marker detection in studies at larger geographic scales.\u003c/p\u003e\u003cp\u003eThis study has several limitations, primarily due to the limited number of study sites, which restricts our ability to evaluate the influence of environmental factors on pathogen ENAD. Additionally, some markers were analyzed only in fecal or sponge samples due to limitations in the remaining sample volumes. Furthermore, the detectability of fecal samples from different species is determined by multiple factors, such as animal density and study site characteristics including type of vegetation and topography. There is also a limitation, especially in the wild ruminant group, in correctly differentiating feces of one species from another. Finally, a clear limitation for molecular analysis and the standardization of results on fecal samples is its variable conservation status.\u003c/p\u003e\u003cp\u003eOverall, our results suggest that combining O- and S-sponge samples may be an effective strategy to maximize the detection of pathogen markers. However, since most markers identified in fecal samples were also detectable in surface sponges, surface sponges seem to be the most practical and efficient option. Furthermore, our findings highlight habitat and climate characteristics as key drivers of pathogen marker detection at broader geographic scales.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL STATEMENT.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involved the collection of environmental DNA (eDNA) samples without direct interaction with or disturbance to living organisms. As such, no ethical approval or animal care protocols were required in accordance with current Spanish and European Union regulations. All sampling activities complied with relevant local and national guidelines and did not involve endangered or protected species.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT FOR PUBLICATION.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicalble.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAVAILABILITY OF DATA AND MATERIALS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the Zenodo repository: https://doi.org/10.5281/zenodo.17099159.\u0026nbsp;Further supplementary information and data are available are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003eAP:\u0026nbsp;\u0026nbsp;[email protected] (ORCID 0000-0001-6238-9048) and\u0026nbsp;CG:\u0026nbsp;[email protected] (ORCID 0000-0003-0012-4006).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the EcoEpi project (SBPLY/23/180225/000008), funded by the EU through the ERDF and by the JCCM through INNOCAM. It was also supported by the project 220418CONV, a management assignment agreement by which the Ministry of Agriculture, Fisheries and Food (MAPA) entrusted the University of Castilla–La Mancha (UCLM) with tasks related to wildlife health management in Spain.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAP, CS, CGC, and CG conceived the study. Methodology was developed by AP, CGC, MPS, DGB, CH, ARV, EF, LD, BML, and CG, with all authors contributing to validation. AP, CS, and CG performed the formal analyses; AP and CG carried out the investigation; and AP, CS, and CG curated the data. AP prepared the original draft with input from CS and CG. All authors revised and approved the manuscript. Visualization was provided by AP. Project administration was coordinated by AP and CG. Supervision was provided by EF, BML, and CG. Funding was secured by MPS, LD, and CG. All the authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful for the support provided by hunting estate staff and park rangers, regional administrations, and veterinary services. AP holds a predoctoral research contract at UCLM (2023-UNIVERS-11983), co-funded by the European Social Fund Plus (ESF+).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMorand S (2020) Emerging diseases, livestock expansion and biodiversity loss are positively related at global scale. Biol. Conserv. 248, 108707, 1-9. https://doi.org/10.1016/j.biocon.2020.108707.\u003c/li\u003e\n\u003cli\u003eThompson L, Rowntree J, Windisch W, Waters SM, Shalloo L, Manzano P (2023) Ecosystem management using livestock: Embracing diversity and respecting ecological principles. 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R package version 3.6-32. https://CRAN.R-project.org/package=raster.\u003c/li\u003e\n\u003cli\u003eKassambara A (2023) \u003cem\u003erstatix\u003c/em\u003e: Pipe-friendly framework for basic statistical tests. R package version 0.7.2. https://CRAN.R-project.org/package=rstatix.\u003c/li\u003e\n\u003cli\u003eCastillo L, Fern\u0026aacute;ndez-Llario P, Mateos C, Carranza J, Ben\u0026iacute;tez-Medina JM, Garc\u0026iacute;a-Jim\u0026eacute;nez W, Bermejo-Mart\u0026iacute;n F, Hermoso de Mendoza J (2011) Management practices and their association with \u003cem\u003eMycobacterium tuberculosis \u003c/em\u003ecomplex prevalence in red deer populations in Southwestern Spain. Prev Vet Med 98(1), 58\u0026ndash;63. https://doi.org/10.1016/j.prevetmed.2010.11.008\u003c/li\u003e\n\u003cli\u003eChaikina NA, Ruckstuhl KE (2006) The effect of cattle grazing on native ungulates: the good, the bad and the ugly. Rangelands 28(3), 8-14. https://doi.org/10.2111/1551-501X(2006)28[8:TEOCGO]2.0.CO;2\u003c/li\u003e\n\u003cli\u003eFine AE, Bolin CA, Gardiner JC, Kaneene JB (2011) A Study of the Persistence of \u003cem\u003eMycobacterium bovis\u003c/em\u003e in the Environment under Natural Weather Conditions in Michigan, USA. Vet Med Int 2011, 765430. https://doi.org/10.4061/2011/765430\u003c/li\u003e\n\u003cli\u003eWilliams AP, Avery LM, Killham K, Jones DL (2005) Persistence of \u003cem\u003eEscherichia coli\u003c/em\u003e O157 on farm surfaces under different environmental conditions. J Appl Microbiol 98(5), 1075\u0026ndash;1083. https://doi.org/10.1111/j.1365-2672.2004.02530.x\u003c/li\u003e\n\u003cli\u003eTaberlet P, Bonin A, Zinger L, Coissac E (2018) Terrestrial ecosystems. In: Taberlet, P., Bonin, A., Zinger, L., \u0026amp; Coissac, E (eds)\u003cem\u003e \u003c/em\u003eEnvironmental DNA: For biodiversity research and monitoring. Oxford, pp 114-116.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003ePathogens analyzed, their target genes, and the referenced PCR techniques used.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBacteria\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.1604%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathogen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.5461%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTargeted gen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2833%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCR technique\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0102%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReferences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.1604%;\"\u003e\n \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.5461%;\"\u003e\n \u003cp\u003e\u003cem\u003euidA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2833%;\"\u003e\n \u003cp\u003eReal-time PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0102%;\"\u003e\n \u003cp\u003e[27,28]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.1604%;\"\u003e\n \u003cp\u003eShiga toxin-producing \u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.5461%;\"\u003e\n \u003cp\u003e\u003cem\u003estx1, stx2\u0026nbsp;\u003c/em\u003eand \u003cem\u003eeae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2833%;\"\u003e\n \u003cp\u003eReal-time PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0102%;\"\u003e\n \u003cp\u003eFoodproof STEC Screening Lyokit, Biotecon diagnosis GmbH, Postdam, Germany\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.1604%;\"\u003e\n \u003cp\u003e\u003cem\u003eM. tuberculosis\u0026nbsp;\u003c/em\u003ecomplex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.5461%;\"\u003e\n \u003cp\u003eIS\u003cem\u003e6110\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;mpb\u003c/em\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2833%;\"\u003e\n \u003cp\u003eReal-time PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0102%;\"\u003e\n \u003cp\u003e[29,30]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.1604%;\"\u003e\n \u003cp\u003e\u003cem\u003eSalmonella\u0026nbsp;\u003c/em\u003espp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.5461%;\"\u003e\n \u003cp\u003e\u003cem\u003einvA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2833%;\"\u003e\n \u003cp\u003eReal-time PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0102%;\"\u003e\n \u003cp\u003e[31]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.1604%;\"\u003e\n \u003cp\u003e\u003cem\u003eC. burnetii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.5461%;\"\u003e\n \u003cp\u003eIS\u003cem\u003e1111\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2833%;\"\u003e\n \u003cp\u003eReal-time PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0102%;\"\u003e\n \u003cp\u003eSponges [32] and feces [33].\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.1604%;\"\u003e\n \u003cp\u003e\u003cem\u003eBrucella\u0026nbsp;\u003c/em\u003espp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.5461%;\"\u003e\n \u003cp\u003eIS\u003cem\u003e711\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2833%;\"\u003e\n \u003cp\u003eReal-time PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0102%;\"\u003e\n \u003cp\u003e[34]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.1604%;\"\u003e\n \u003cp\u003e\u003cem\u003eM. avium\u003c/em\u003e subp. \u003cem\u003eparatuberculosis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.5461%;\"\u003e\n \u003cp\u003eIS\u003cem\u003e900\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2833%;\"\u003e\n \u003cp\u003eReal-time PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0102%;\"\u003e\n \u003cp\u003e[35]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParasites\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.1604%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathogen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.5461%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTargeted gen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2833%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCR technique\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0102%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReferences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.1604%;\"\u003e\n \u003cp\u003e\u003cem\u003eB. coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.5461%;\"\u003e\n \u003cp\u003eITS1\u0026ndash;5.8 s-rRNA\u0026ndash;ITS2 region and the \u003cem\u003essu\u003c/em\u003e-rRNA gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2833%;\"\u003e\n \u003cp\u003eDirect PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0102%;\"\u003e\n \u003cp\u003e[36]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.1604%;\"\u003e\n \u003cp\u003e\u003cem\u003eBlastocystis\u0026nbsp;\u003c/em\u003esp\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.5461%;\"\u003e\n \u003cp\u003e\u003cem\u003essu\u0026nbsp;\u003c/em\u003erRNA gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2833%;\"\u003e\n \u003cp\u003eDirect PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0102%;\"\u003e\n \u003cp\u003e[37]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.1604%;\"\u003e\n \u003cp\u003e\u003cem\u003eCryptosporidium\u003c/em\u003e spp. \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.5461%;\"\u003e\n \u003cp\u003e\u003cem\u003essu\u003c/em\u003e rRNA gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2833%;\"\u003e\n \u003cp\u003eNested PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0102%;\"\u003e\n \u003cp\u003e[38]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.1604%;\"\u003e\n \u003cp\u003e\u003cem\u003eE. bieneusi\u0026nbsp;\u003c/em\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.5461%;\"\u003e\n \u003cp\u003e\u003cem\u003essu\u0026nbsp;\u003c/em\u003erRNA gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2833%;\"\u003e\n \u003cp\u003eRT-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0102%;\"\u003e\n \u003cp\u003e[39,40]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.1604%;\"\u003e\n \u003cp\u003e\u003cem\u003eEncephalitozoon\u0026nbsp;\u003c/em\u003espp. \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.5461%;\"\u003e\n \u003cp\u003e\u003cem\u003essu\u0026nbsp;\u003c/em\u003erRNA gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2833%;\"\u003e\n \u003cp\u003eRT-PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0102%;\"\u003e\n \u003cp\u003e[39,41]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.1604%;\"\u003e\n \u003cp\u003e\u003cem\u003eG. duodenalis\u0026nbsp;\u003c/em\u003e\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.5461%;\"\u003e\n \u003cp\u003e\u003cem\u003essu\u003c/em\u003e rRNA gene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2833%;\"\u003e\n \u003cp\u003eReal-time PCR (qPCR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0102%;\"\u003e\n \u003cp\u003e[42]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21.1604%;\"\u003e\n \u003cp\u003e\u003cem\u003eT. gondii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.5461%;\"\u003e\n \u003cp\u003e200- to 300-fold repetitive 529 bp DNA fragment in the parasite genome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.2833%;\"\u003e\n \u003cp\u003eDirect PCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0102%;\"\u003e\n \u003cp\u003e[43,44]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eSpecific subtyping tools targeting the partial 60-kDa glycoprotein gene (gp60) were used in those samples that tested positive for \u003cem\u003eCryptosporidium\u003c/em\u003e spp. by \u003cem\u003essu\u003c/em\u003e-PCR for \u003cem\u003eC. canis\u003c/em\u003e [45].\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u0026nbsp;\u003c/sup\u003eSamples identified as \u003cem\u003eE. bieneusi\u003c/em\u003e positive were genotyped using a nested PCR protocol to amplify a fragment of the internal transcribed spacer (ITS) region as well as portions of the flanking large and small subunits of the ribosomal RNA (\u003cem\u003essu\u003c/em\u003e rRNA) gene, as previously described [46].\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u0026nbsp;\u003c/sup\u003eSamples identified as \u003cem\u003eEncephalitozoon\u003c/em\u003e spp. positive were genotyped using a nested PCR protocol to amplify the ITS marker as previously described by Katzwinkel-Wladarsch et al. (1996) [47], allowing, in turn, the identification of species.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u0026nbsp;\u003c/sup\u003e\u003cem\u003eGiardia\u003c/em\u003e-positive isolates that yielded cycle threshold (CT) values \u0026le; 34 in RT-PCR were subsequently reassessed by a nested PCR to amplify a fragment of the \u003cem\u003essu\u003c/em\u003e rRNA gene [48,49] to determine the molecular diversity of the parasite at the assemblage level. \u0026nbsp;Samples that tested positive by \u003cem\u003essu\u003c/em\u003e-PCR were re-amplified at the genes codifying the glutamate dehydrogenase (\u003cem\u003egdh\u003c/em\u003e), \u0026beta;-giardin (\u003cem\u003ebg\u003c/em\u003e) and triose phosphate isomerase (\u003cem\u003etpi\u003c/em\u003e) proteins to determine the molecular diversity of the parasite at the sub-assemblage level. A semi-nested PCR was used to amplify a fragment of the \u003cem\u003egdh\u003c/em\u003e gene [50] and nested PCRs were used to amplify fragments of the \u003cem\u003ebg\u003c/em\u003e and \u003cem\u003etpi\u003c/em\u003e genes, respectively [51\u0026mdash;53].\u003c/p\u003e\n\u003cp\u003eTable 1 should be placed after line 145.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Proportion (%) of positive communities in each cluster per pathogen marker analyzed.\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6087%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathogen marker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(% locations)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(% locations)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(% locations)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6087%;\"\u003e\n \u003cp\u003eIS\u003cem\u003e6110\u0026nbsp;\u003c/em\u003eMTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e71.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e66.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6087%;\"\u003e\n \u003cp\u003e\u003cem\u003empb\u003c/em\u003e70 MTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6087%;\"\u003e\n \u003cp\u003e\u003cem\u003euidA Escherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6087%;\"\u003e\n \u003cp\u003e\u003cem\u003estx1 Escherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e14.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6087%;\"\u003e\n \u003cp\u003e\u003cem\u003estx2 Escherichia coli\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e14.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6087%;\"\u003e\n \u003cp\u003e\u003cem\u003einvA Salmonella\u0026nbsp;\u003c/em\u003espp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6087%;\"\u003e\n \u003cp\u003eIS\u003cem\u003e1111 Coxiella burnetii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e14.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e16.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6087%;\"\u003e\n \u003cp\u003e\u003cem\u003eGiardia duodenalis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e28.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e33.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 32.6087%;\"\u003e\n \u003cp\u003e\u003cem\u003eToxoplasma gondii\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e14.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.4638%;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMTC \u0026ndash; \u003cem\u003eMycobacterium tuberculosis\u0026nbsp;\u003c/em\u003ecomplex.\u003c/p\u003e\n\u003cp\u003eTable 2 should be placed after line 346.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e. Comparison of PCR-CT values and % of positive sponge samples between previous studies done in farms by Herrero-Garc\u0026iacute;a et al. (2024) [14] and this study.\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"566\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 95px;\"\u003e\n \u003cp\u003ePathogen\u003c/p\u003e\n \u003cp\u003emarker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 225px;\"\u003e\n \u003cp\u003eHerrero-Garc\u0026iacute;a et al. (2024)\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 246px;\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 66px;\"\u003e\n \u003cp\u003e% of positive samples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 159px;\"\u003e\n \u003cp\u003ePCR-CT values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 68px;\"\u003e\n \u003cp\u003e% of positive samples\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 178px;\"\u003e\n \u003cp\u003ePCR-CT values\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eMax.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003eMin.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eIS\u003cem\u003e6110\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e17.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e39.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e43.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e36.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e37.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e39.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e31.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eIS\u003cem\u003e1111\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e5.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e34.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e37.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e31.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e37.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e39.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e35.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cem\u003euidA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e81.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e35.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e43.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e23.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e27.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e35.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e39.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e26.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cem\u003estx1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e41.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e43.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e40.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e35.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e38.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e33.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cem\u003estx2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e35.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e37.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e33.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e32.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e35.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e30.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cem\u003eeae\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e30.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e32.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e28.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cem\u003einvA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e8.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e38.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e41.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e33.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e36.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e36.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e36.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eMax. and Min. refers to maximum and minimum CT-values per each marker.\u003c/p\u003e\n\u003cp\u003eTable 3 should be placed after line 387.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Environmental DNA, zoonosis, disease surveillance, non-invasive, integrated wildlife monitoring, episystem, natural areas","lastPublishedDoi":"10.21203/rs.3.rs-7642008/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7642008/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe wildlife-livestock-environment interface is a complex system with implications for biodiversity and diseases. Environmental nucleic acid detection (ENAD) is a non-invasive method for monitoring pathogens via DNA/RNA. However, how environmental variables influence ENAD remains poorly explored in heterogeneous geographic contexts.\u003c/p\u003e\u003cp\u003eIn this study, 18 sites were evaluated in Iberian Peninsula, collecting 10 surface sponge samples per site and a total of 146 environmental fecal samples. Differences in pathogen ENAD were assessed among sponge sampling methods and between sponge and fecal samples. The relationship between ENAD results and environmental, mammal community and wildlife health variables was investigated.\u003c/p\u003e\u003cp\u003eThe results show that environmental characteristics influence pathogen ENAD at larger geographic scales, with greater pathogen diversity and richness observed at higher latitudes. Most markers found in feces were also detectable in surface sponges. Combining different sponge sampling methodologies provides the best overall coverage of detectable pathogen markers. A predictive map linking pathogen ENAD in sponges to environmental factors was developed.\u003c/p\u003e","manuscriptTitle":"Tracking pathogens with eDNA in natural areas: how environmental gradients shape surveillance strategies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-07 10:33:28","doi":"10.21203/rs.3.rs-7642008/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"51b9adff-ce49-4c60-b837-0ba265fff0bd","owner":[],"postedDate":"October 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T16:05:48+00:00","versionOfRecord":{"articleIdentity":"rs-7642008","link":"https://doi.org/10.1186/s13567-026-01746-6","journal":{"identity":"veterinary-research","isVorOnly":false,"title":"Veterinary Research"},"publishedOn":"2026-04-28 15:58:29","publishedOnDateReadable":"April 28th, 2026"},"versionCreatedAt":"2025-10-07 10:33:28","video":"","vorDoi":"10.1186/s13567-026-01746-6","vorDoiUrl":"https://doi.org/10.1186/s13567-026-01746-6","workflowStages":[]},"version":"v1","identity":"rs-7642008","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7642008","identity":"rs-7642008","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}