Wildlife risk mitigation protocols reduce risk species visits and pathogen marker detection in open-air farms

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However, due to the implicit closer interaction with wildlife, outdoor farms are also more at risk for disease maintenance at the wildlife-livestock interface. Disease control options in outdoor farming systems include the development and application of wildlife risk mitigation protocols (RMPs). However, while the nature of the proposed mitigation actions and the degree of farmer uptake have repeatedly been assessed, only limited information exists on their effectiveness. In this study, we re-visited 14 farms of a pilot study to quantify the effect of applying RMPs on the detection rates of risk wildlife (assessed by means of camera traps; CTs) and of selected pathogen markers (using sponges for environmental nucleic acid detection; ENAD). The application of farm-specific RMPs resulted in a 30% reduction in farm visits by high-risk wildlife and an 18% reduction in the frequency of pathogen marker detection. High-risk species detection declined on 11 farms and increased on three, all of them small ruminant farms. Regarding pathogen markers, we observed frequency reductions for four and increases for two markers. These changes were statistically significant for the Salmonella spp. marker inv A. At the farm level, the reduction in the detection frequency of wild boar ( Sus scrofa ) correlated with the reduction in the detection frequency of the inv A marker. These findings may be relevant for assessing other interventions at the wildlife-livestock interface, regardless of the farmed species, farming system, and target pathogen. Camera trapping Cattle Environmental nucleic acid detection Pig Small ruminants Wildlife-livestock interface risk mitigation biosafety Figures Figure 1 Figure 2 Introduction Outdoor farming systems are regarded as sustainable since they are less input-dependent and generate less waste than most indoor farming systems [ 1 , 2 ]. Furthermore, some outdoor farming systems may favor the dilution effect of biodiversity on pathogen exposure risk [ 3 , 4 ] and contribute to biodiversity conservation through the preservation of valuable habitats, for instance through the creation and maintenance of lentic water bodies in arid environments [ 5 ]. However, due to the implicit closer interaction with wildlife, outdoor farms are also more at risk for disease maintenance or emergence at the interface. This is especially relevant in the Iberian Peninsula due to the high prevalence of endemic multi-host infections shared between wildlife and livestock such as animal tuberculosis (TB) [ 6 ]. In extensive hoofstock farming systems, disease control strategies addressing a range of risk factors simultaneously are preferrable [ 7 ]. One such strategy consists in the development and application of wildlife risk mitigation protocols (RMPs) [ 8 ]. These RMPs focus mainly on avoiding or significantly reducing the direct and indirect interactions of a herd with other livestock species and wildlife. In the Iberian Peninsula, RMPs include a range of specific biosafety measures (BSMs), often targeting wildlife management (e.g., hunting) and interaction hotspots (risk points) such as water points and feeders [ 9 , 10 ]. Research has evidenced that most wildlife-livestock interactions are indirect [ 11 , 12 ] and take place at risk hotspots such as water [ 13 , 14 ] or feed [ 15 ]. Once implemented, RMPs should be assessed regarding their performance in terms of their efficacy in reducing wildlife-livestock interactions and improving health indicators, and the results need to be communicated to the farmers. Indeed, the willingness to implement BSMs is driven by the farmers’ perception of BSM efficacy [ 16 ]. In a recent pilot study, we showed that it is possible to monitor open-air farm biosafety including both hosts and pathogens. Through a ‘One Health’ approach, we developed an innovative interdisciplinary strategy to prevent and monitor animal diseases at the wildlife-livestock interface to bridge the sanitary gap between indoor and open-air farming systems while maximizing the contribution of animal farming to biodiversity conservation. In essence, the pilot study showed that (1) short-term camera trap (CT) deployment generates valuable information on farm biodiversity and on the rate of farm-visits by risk species, and that (2) environmental nucleic acid detection (ENAD) at risk points or on animals informs on pathogen marker presence in the farm environment [ 4 ]. Innovating for resilience in traditional, open-air, and sustainable animal production is one way for making animal farming compatible with biodiversity conservation [ 17 ]. This is of special relevance to the Iberian Peninsula which represents a biodiversity hotspot in Western Europe [ 18 ]. The RMPs developed for extensive farming systems represent an important step in this process. However, while the nature of BSMs proposed to farmers [ 9 , 10 ] and the degree of farmer acceptance and uptake of some of these BSMs [ 9 , 19 ] have repeatedly been assessed, only limited information exists on their effectiveness. For specific BSMs, such as small-scale barriers and waterhole modifications or feeder or food storage modifications, evidence has been collected on the reduction of wildlife visits [ 20 , 21 , 22 ] or on the improvement of herd health indicators [ 23 ]. However, quantitative information on the effectiveness of applying complete RMPs on the detection rates of risk wildlife and on pathogen detection remains lacking. In this study, we re-visited the farms of the Herrero-García et al. [ 4 ] pilot study to quantify the effect of applying RMPs on the detection rates of high-risk wildlife (assessed by means of CTs) and of selected pathogen markers (using ENAD). We focus on four mammals of relevance for different shared infections, namely red deer ( Cervus elaphus ), Eurasian wild boar ( Sus scrofa ), Red fox ( Vulpes vulpes ) and European badger ( Meles meles ), and on markers of six bacterial pathogens, namely the Mycobacterium tuberculosis complex, Mycobacterium avium subp. paratuberculosis , Coxiella burnetii , Escherichia coli , Salmonella spp., and Brucella spp. We show that RMPs effectively reduce both risk wildlife visits and pathogen marker detection. Material and methods Sampling sites and farm visits In this study, we revisited 14 of the 15 farms included in the pilot study of Herrero-García et al. [ 4 ]. The study farms (cattle n = 6; small ruminant n = 4; and pig n = 4) were distributed in five regions of mainland Spain: Madrid, cattle and small ruminants; Extremadura, pig; Castilla y León, cattle; Castilla La Mancha, small ruminants; and Murcia, pig. All farms were fenced but wildlife use of farm premises was recorded. The farms participating in this study were chosen as pilot points to run farm-specific wildlife RMPs [ 9 ]. The first round of farm visits (T1) took place in 2022 and included farmer interviews, camera trapping and ENAD [ 4 ]. In 2022, each farmer received a detailed report of the outcome of the farm-specific wildlife RMP including a list of recommended biosafety measures (BSMs). The second round (Time 2) included new rounds of camera trapping and ENAD but no new interviews. This second sampling event took place one year later, in 2023, after the implementation of general and specific risk mitigation actions (Fig. 1; Additional file 1: Tables S1 and S2 for details). Farmer feedback on BSM uptake was gathered during the second visit or telephonically. Camera trapping For wildlife monitoring, Browning CTs (Browning Strike Force HD ProX, Browning Arms Company®, Morgan, Utah, USA) were deployed on each farm at Time 1 (T1) and Time 2 (T2). Per farm and sampling time, 28–31 cameras were deployed, of which 18–21 were directed towards water or food points, identified as risk points in the previous study. The remaining 10 cameras were placed at random points on the farm. These cameras were set to be operating for 48 hours, releasing 3 shots when motion was detected, with a time lapse of one minute between consecutive activations. No baits or attractants were used. Environmental nucleic acid detection (ENAD) ENAD sampling was performed on 20 environmental surfaces collected from risk points and on 10 animal hocks per farm at T1 and at T2. The ready-to-use GPSponge® Kit (GPS genetic PCR solutions, Orihuela, Spain) pre-hydrated with an isotonic, surfactant, nucleic acid preservative liquid was used. For DNA extraction and purification, we used the GPSpin® Microbiome Fecal DNA Kit (GPS™, Orihuela, España) according to manufacturer instructions, starting from the pellet obtained after centrifuging 900 µL of the sample for 3 min at 13.000 rpm. We tested all samples for the following pathogen markers by real-time PCR: Mycobacterium tuberculosis complex -IS 6110 [ 24 ], M. avium subsp. paratuberculosis -IS 900 [ 25 ]; Brucella sp. -IS 711 [ 26 ], Coxiella burnetii -IS 1111 [ 27 ], Salmonella enterica - inv A [ 28 ], and Escherichia coli - uid A [ 29 , 30 ]. Statistical analysis To statistically analyze our results, we used Fisher's exact statistical test, which allows us to determine whether there is a significant association between two categorical variables when the sample size is small. In this case, it would be the presence/absence of risk species or pathogen markers on each farm at T1 and T2. It was assessed using 2x2 contingency tables, considering a confidence level of 95%. Results After implementing farm-specific RMPs we managed to reduce the observed frequency of farm visits by risk species and the observed frequency of pathogen marker detection. The following paragraphs describe these results. Considering the detection frequency of the four risk mammals for the set of study sites, we observed reductions in presence between 18% (red deer) and 72% (badger) in total (Table 2). The differences were statistically significant for wild boar, fox and badger (Figure 2a). At the individual farm level, red deer appeared on seven of 14 farms and in five of them the detection frequency decreased at T2. Wild boar appeared on 12 farms and decreased on eight of them, including the two pig farms where wild boar had been detected at T2. Foxes appeared on all farms and decreased on ten of them, including all pig farms and five of six cattle farms. However, fox presence increased at T2 in three of four small ruminant farms. Finally, badgers were detected on ten farms, decreasing on eight of them including five of the six cattle farms. Overall risk species detection declined on 11 farms and increased on three, all of them small ruminant farms. This yielded an average decline of -30% and this decline was most pronounced in cattle farms (-43%) (Table 2). For the six pathogen markers considered, we observed frequency reductions for four and increases for two, namely the M. tuberculosis complex markerIS 6110 and the Brucella spp. marker IS 711 . These changes were significant for the Salmonella spp. marker inv A and marginally significant for the generic E. coli marker uid A (Figure 2b). The increase in IS 6110 detection was mostly due to new detections on two previously negative pig farms. The detection of uid A and inv A declined consistently at T2 in all three livestock species. At the farm level, we observed significant reductions for the uid A marker on one cattle and on one small ruminant farm, and for inv A on two cattle farms and one small ruminant farm. Detection of the Coxiella burnetii marker IS 1111 occurred consistently at both times in all three positive small ruminant farms and the Brucella spp. marker IS 711 was detected on two small ruminant farms. The mean decline in pathogen marker detection was -18% when all markers were considered. This decline was most pronounced in small ruminant farms (-23%) (Table 1). Still at the individual farm level, we found a significant correlation between changes in the detection frequency of risk hosts and pathogen markers. Specifically, we observed a positive correlation between the percentage of reduction in the detection frequency of wild boars and the percentage of reduction in the detection frequency of the inv A marker (r S =0.68, p <0.05). Discussion The application of farm-specific RMPs resulted in a 30% reduction in farm visits by risk wildlife and an 18% reduction in the frequency of pathogen marker detection. By combining short-time CT deployments with sponge-based ENAD, this innovative farm biosafety monitoring protocol enabled us to assess wildlife presence and pathogen marker detection at the same time and by noninvasive means. The apparent impact of the RMPs was not uniform. For instance, high-risk species detections, the combined detection frequency of red deer, wild boar, fox and badger declined consistently on all six cattle farms and on all four pig farms after running the RMPs. By contrast, this indicator declined only on one small ruminant farm while it increased in the remaining three. This could suggest that cattle farmers and pig farmers were more likely to take up the mitigation actions proposed in the RMPs, possibly due to their knowledge regarding the risk posed by endemic animal TB [6] and due to the fear to African Swine Fever emergence, respectively. Indeed, the apparent effect of the RMPs on wild boar detection was most evident on pig farms (-80%; Table 2). Comparatively, small ruminant farmers face less pressure regarding animal health and might therefore be less prone to implement time-consuming and sometimes costly mitigation actions. An alternative explanation to the low impact of RMPs on small ruminants is that implementing certain easy risk mitigation actions, such as elevating feeders and water troughs to avoid wild boar or badgers, is not as viable for small ruminants as it is for cattle given the smaller difference in body size. The large apparent impact of the RMPs on badger detection, especially on cattle farms (-81%) would indicate a strong effect of reducing access to feed after applying the risk mitigation actions. This coincides with previous reports from the UK [20]. However, we cannot exclude the possibility of some (undeclared) culling taking place, too. It is also interesting to note the strong effects of RMPs on fox detections, especially on cattle farms (-50%). This is relevant since foxes are potentially implicated in the cycles of several pathogens shared with livestock [31]. The apparent impact of the RMPs on pathogen marker detection was even more variable, since two markers increased while four others decreased at T2. Markers detected on a few occasions such as IS 711 are not as relevant in terms of percentual changes as frequently detected markers including IS 6110 , uid A and inv A. In fact, IS 711 was detected in two samples at T2 and in one at T1. The IS 6110 marker showed a more uniform increase from T1 to T2. This marker was detected on 11 of 14 farms and its detection frequency increased at T2 in seven of them. Notably, IS 6110 positive sponge samples were detected in two pig farms for the first time at T2. In turn, uid A and inv A, the generic E. coli and Salmonella spp. markers, were both the most detected markers and the only ones with significant reductions in frequency of detection at T2 (Table 1). These markers might constitute good general indicators of farm hygiene or farm exposure to pathogens [4]. The correlation between wild boar detection and inv A detection suggests a link between a high-risk host, (i.e., the wild boar), and a pathogen marker, (i.e., Salmonella spp.). In this regard, the participation of the wild boar in Salmonella maintenance is well established [32,33]. This study has several limitations. The first one is that we did not perform a new full risk assessment at T2, as was done at T1 [4]. Rather, we relied on the farmer’s feedback to assess the degree of uptake of the mitigation actions proposed in the RMPs (Table S2). It is expected that most of the cheap changes to the water points and feeders were applied, while few of the expensive new fencings were implemented. Some interventions depend on third parties (neighbors, hunters, municipal and environmental authorities) and may sometimes have been applied without consulting all relevant parties. In a similar survey focused on cattle farms we estimated the degree of application of BSMs at one third [9]. Another limitation is the number and timing of the CTs and ENAD. The number of CTs is limited by the time needed by a team of two people to set up/collect the cameras while sparing time for ENAD and travel. While ENAD generally took place in the same months (October-November 2022 and 2023, respectively), CTs were generally deployed in winter-spring in 2022 and in autumn 2023. While this variation could have affected wildlife abundance or farm visitation rates, we do not expect huge differences since we avoided the dry summer, i.e., the main limiting season in Mediterranean habitats [5]. In summary, we managed to assess the apparent impact of RMPs on the biosafety of open-air hoofstock farms and found significant reductions in wildlife farm visits and in pathogen marker detection, although with variability among livestock species, wildlife species, and pathogen markers. These findings can be generalized to other interventions at the wildlife livestock interface, regardless of the farmed species, farming system, and target pathogen. We propose that combining massive, short-term CT deployments with sponge-based ENAD enables farmers, field veterinarians or veterinary services to monitor farm biosafety in a broad range of settings and in a noninvasive manner. Declarations Declaration of competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Ethics statement The University of Castilla-La Mancha (UCLM) research ethics committee granted a formal waiver of ethics approval, since only routine veterinary care was involved in this study. In addition, Ethics approval was unnecessary according to Spanish national regulations (Real Decreto 53/2013). CRediT authorship contribution statement A.M.R.: Writing – review & editing, Visualization, Validation, Investigation, Formal analysis, Data curation. G.H.G.: Writing – review & editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation. P.B.: Writing – review & editing, Visualization, Validation, Investigation, Formal analysis, Data curation. C.H.B.: Writing – review & editing, Validation, Investigation, Data curation. D.R.: Writing – review & editing, Validation, Investigation, Data curation. T.G.S.: Writing – review & editing, Visualization, Validation, Supervision, Resources, Methodology, Conceptualization. A.P.: Writing – review & editing, Validation, Investigation, Data curation. A.D.G.: Writing – review & editing, Validation, Resources, Methodology, Investigation, Data curation. P.P.: Writing – review & editing, Visualization, Validation, Formal analysis, Data curation. A.B.: Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. L.D.: Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization. C.G.: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. M.P.S.: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Methodology, Investigation, Conceptualization. Funding This work was supported by the Spanish MCIN/AEI/10.130 39/501100011033/ and the European Union NextGeneration EU/ PRTR [grant number PLEC2021-008113]; and the MCIN/AEI/10.13039 /501100011033/FEDER, EU [grant numbers PID2022-141906OB-C21, PID2022-141906OB-C22]. G.H.G. was funded by Junta de Castilla y León and FSE [grant number LE036-20]. A.M.R. by Universidad de Castilla-La Mancha and Ministerio de Ciencia e Innovación [FPI grant number PREP2022-000546]. P.B. by Juan de la Cierva Formación 2021, NextGenerationEU, PRTR [grant number DC2022–049103-I]. A.P. holds a predoctoral research contract at UCLM (2023-UNIVERS-11983), co-funded by the European Social Fund Plus (ESF+). P.P. by Juan de la Cierva Formación 2021, funded by the Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación and NextGenerationEU/PRTR [grant number FJC2021–046805-I]. Acknowledgments The authors express their sincere gratitude to the teams at LABOCOR, MAEVA, SaBio-IREC, VISAVET, and the University of León for their valuable collaboration and support throughout this study. We are especially grateful to the farmers, farm owners, and veterinarians, whose involvement and cooperation were essential for the successful completion of this work. References Van Wagenberg CPA, de Haas Y, Hogeveen H, van Krimpen MM, Meuwissen MPM, van Middelaar CE, Rodenburg TB (2017) Animal Board Invited Review: Comparing conventional and organic livestock production systems on different aspects of sustainability. 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Microbiol Immunol 59:433–442. https://doi.org/10.1111/1348-0421.12275 Tolhurst BA, Ward AI, Delahay RJ (2011) A study of fox (Vulpes vulpes) visits to farm buildings in Southwest England and the implications for disease management. Eur J Wildl Res 57:1227–1230. https://doi.org/10.1007/s10344-011-0523-0 Methner U, Heller M, Bocklisch H (2010) Salmonella enterica subspecies enterica serovar Choleraesuis in a wild boar population in Germany. European Journal of Wildlife Research, 56 (4), 493–502. https://doi.org/10.1007/s10344-009-0339-3 Ortega N, Fanelli A, Serrano A, Martínez-Carrasco C, Escribano F, Tizzani P, Candela MG (2020) Salmonella seroprevalence in wild boar from Southeast Spain depends on host population density. Res Vet Sci 132:400–403. https://doi.org/10.1016/j.rvsc.2020.07.026 Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Tables.docx Cite Share Download PDF Status: Published Journal Publication published 27 Nov, 2025 Read the published version in Veterinary Research → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7236055","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":508460226,"identity":"5f012340-379a-42f4-8307-99d07e861aad","order_by":0,"name":"Ángela Marín-Rojo","email":"","orcid":"","institution":"SaBio Instituto de Investigación en Recursos Cinegéticos (IREC) CSIC-UCLM-JCCM","correspondingAuthor":false,"prefix":"","firstName":"Ángela","middleName":"","lastName":"Marín-Rojo","suffix":""},{"id":508460227,"identity":"e1ad391f-cc29-4632-9f48-085c9a33a8d2","order_by":1,"name":"Gloria Herrero-García","email":"","orcid":"","institution":"Universidad de León","correspondingAuthor":false,"prefix":"","firstName":"Gloria","middleName":"","lastName":"Herrero-García","suffix":""},{"id":508460228,"identity":"f3fdcf70-8f40-491f-9ee0-1680b3e6ce81","order_by":2,"name":"Carmen Herranz-Benito","email":"","orcid":"","institution":"Complutense University of Madrid","correspondingAuthor":false,"prefix":"","firstName":"Carmen","middleName":"","lastName":"Herranz-Benito","suffix":""},{"id":508460229,"identity":"40d056e8-acfc-4993-aef1-d97d6134db19","order_by":3,"name":"Patricia Barroso","email":"","orcid":"","institution":"Universidad de León","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"","lastName":"Barroso","suffix":""},{"id":508460230,"identity":"39263037-6df8-4848-a7cf-d3dcb20d80f6","order_by":4,"name":"David Relimpio","email":"","orcid":"","institution":"SaBio Instituto de Investigación en Recursos Cinegéticos (IREC) CSIC-UCLM-JCCM","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Relimpio","suffix":""},{"id":508460231,"identity":"17dfae37-9022-45f4-a6fc-f73d701304e0","order_by":5,"name":"Teresa García-Seco","email":"","orcid":"","institution":"Complutense University of Madrid","correspondingAuthor":false,"prefix":"","firstName":"Teresa","middleName":"","lastName":"García-Seco","suffix":""},{"id":508460232,"identity":"917c3673-2126-475e-a05c-88cc3b403f7a","order_by":6,"name":"Alberto Perelló","email":"","orcid":"","institution":"SaBio Instituto de Investigación en Recursos Cinegéticos (IREC) CSIC-UCLM-JCCM","correspondingAuthor":false,"prefix":"","firstName":"Alberto","middleName":"","lastName":"Perelló","suffix":""},{"id":508460233,"identity":"eb962a8f-acca-4dfe-8410-f50e82964caf","order_by":7,"name":"Alberto Díez-Guerrier","email":"","orcid":"","institution":"Complutense University of Madrid","correspondingAuthor":false,"prefix":"","firstName":"Alberto","middleName":"","lastName":"Díez-Guerrier","suffix":""},{"id":508460234,"identity":"fe0b31b4-72bb-4588-95ef-4161328bebf5","order_by":8,"name":"Pilar Pozo","email":"","orcid":"","institution":"Complutense University of Madrid","correspondingAuthor":false,"prefix":"","firstName":"Pilar","middleName":"","lastName":"Pozo","suffix":""},{"id":508460235,"identity":"de4125d1-2e6c-4d63-aec2-cf533457ccd5","order_by":9,"name":"Ana Balseiro","email":"","orcid":"","institution":"Universidad de León","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Balseiro","suffix":""},{"id":508460236,"identity":"5c33d8e0-55ea-4e67-8cf5-a2e792d46783","order_by":10,"name":"Lucas Domínguez","email":"","orcid":"","institution":"Complutense University of Madrid","correspondingAuthor":false,"prefix":"","firstName":"Lucas","middleName":"","lastName":"Domínguez","suffix":""},{"id":508460237,"identity":"55400f01-63b1-4811-85f6-2d6bae26be87","order_by":11,"name":"Marta Pérez-Sancho","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYDADftK1SDaQrMXgALEqddvPHnxcUVFnb3wj+ekGhoo6wlrMzuQlG545w5a47Uaa2Q2GM4eJ0HIgx0yysY0nwexGDtsNxjYinGd2/o35z8Z/EvbGM0Ba/hHjsBs5ZoyNDQaMGyRAWhqYidHyLlmy4VhC4owzz8xuJBwjxi/ncw9+bKips+dvT35240MNEQ5jYOBBYicQowFVyygYBaNgFIwCbAAASYM7c/Acv/QAAAAASUVORK5CYII=","orcid":"","institution":"Complutense University of Madrid","correspondingAuthor":true,"prefix":"","firstName":"Marta","middleName":"","lastName":"Pérez-Sancho","suffix":""},{"id":508460238,"identity":"439889e6-4c9b-4ecf-9cec-6f90126d575b","order_by":12,"name":"Christian Gortázar","email":"","orcid":"","institution":"SaBio Instituto de Investigación en Recursos Cinegéticos (IREC) CSIC-UCLM-JCCM","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"","lastName":"Gortázar","suffix":""}],"badges":[],"createdAt":"2025-07-28 16:53:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7236055/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7236055/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13567-025-01671-0","type":"published","date":"2025-11-27T15:57:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90578398,"identity":"8a6d6317-6a46-4983-98b2-516c094bb1fb","added_by":"auto","created_at":"2025-09-04 09:44:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":185519,"visible":true,"origin":"","legend":"\u003cp\u003eImages captured by a camera trap at a small, unintended water point formed by a broken hose on one of the small ruminant farms. From left to right, top to bottom (in chronological order): red deer (\u003cem\u003eCervus elaphus\u003c/em\u003e), wild boar (\u003cem\u003eSus scrofa\u003c/em\u003e), Iberian magpies (\u003cem\u003eCyanopica cooki\u003c/em\u003e), domestic sheep (\u003cem\u003eOvis aries\u003c/em\u003e), a red deer and a red fox (\u003cem\u003eVulpes vulpes\u003c/em\u003e) simultaneously, and a wild boar bathing in the water. The broken hose redirected water away from the intended trough, creating an artificial water source that attracted a variety of wild and domestic species. Three of the four risk species for pathogen transmission identified in this study were recorded at this site. This situation illustrates how minor infrastructure failures can lead to indirect contact between species, highlighting the importance of implementing basic biosecurity measures to prevent such interactions.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7236055/v1/2a6ab047d0c6340cf40bf42c.png"},{"id":90578399,"identity":"320b86a2-0f07-402f-b37b-5af4c6bfda8f","added_by":"auto","created_at":"2025-09-04 09:44:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":34516,"visible":true,"origin":"","legend":"\u003cp\u003eEffects of applying risk mitigation protocols to 14 open-air hoofstock farms in Spain. a) changes in the frequency of detection of risk host species (red deer, wild boar, red fox and badger), in %; b) changes in the frequency of detection of pathogen markers (IS\u003cem\u003e6110, \u003c/em\u003eIS\u003cem\u003e900, \u003c/em\u003eIS\u003cem\u003e1111\u003c/em\u003e, \u003cem\u003euidA\u003c/em\u003e, \u003cem\u003einvA\u003c/em\u003e and IS\u003cem\u003e711\u003c/em\u003e), in %. Significant (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05) and marginally significant (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.1) changes are indicated. MTC: \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex; MAP: \u003cem\u003eM. avium\u003c/em\u003e \u003cem\u003eparatuberculosis\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7236055/v1/5c70cc71ebba87807751a0e9.png"},{"id":97178364,"identity":"ceacd3f6-3233-4fe3-9da0-e276c110aa94","added_by":"auto","created_at":"2025-12-01 16:09:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":734126,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7236055/v1/38e5e809-5e27-49a6-964c-a6d72b7f9599.pdf"},{"id":90578401,"identity":"d5e588a6-c8d6-428e-884f-1649bb7c341e","added_by":"auto","created_at":"2025-09-04 09:44:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20526,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7236055/v1/b69a66bd1b28bee21cbcdf28.docx"},{"id":90578400,"identity":"69b05291-5b64-406c-90c0-19e436baf2c9","added_by":"auto","created_at":"2025-09-04 09:44:46","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":42234,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7236055/v1/2b469aeca33bec6cb1aa34fa.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Wildlife risk mitigation protocols reduce risk species visits and pathogen marker detection in open-air farms","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOutdoor farming systems are regarded as sustainable since they are less input-dependent and generate less waste than most indoor farming systems [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Furthermore, some outdoor farming systems may favor the dilution effect of biodiversity on pathogen exposure risk [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and contribute to biodiversity conservation through the preservation of valuable habitats, for instance through the creation and maintenance of lentic water bodies in arid environments [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, due to the implicit closer interaction with wildlife, outdoor farms are also more at risk for disease maintenance or emergence at the interface. This is especially relevant in the Iberian Peninsula due to the high prevalence of endemic multi-host infections shared between wildlife and livestock such as animal tuberculosis (TB) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn extensive hoofstock farming systems, disease control strategies addressing a range of risk factors simultaneously are preferrable [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. One such strategy consists in the development and application of wildlife risk mitigation protocols (RMPs) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These RMPs focus mainly on avoiding or significantly reducing the direct and indirect interactions of a herd with other livestock species and wildlife. In the Iberian Peninsula, RMPs include a range of specific biosafety measures (BSMs), often targeting wildlife management (e.g., hunting) and interaction hotspots (risk points) such as water points and feeders [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Research has evidenced that most wildlife-livestock interactions are indirect [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and take place at risk hotspots such as water [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] or feed [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOnce implemented, RMPs should be assessed regarding their performance in terms of their efficacy in reducing wildlife-livestock interactions and improving health indicators, and the results need to be communicated to the farmers. Indeed, the willingness to implement BSMs is driven by the farmers\u0026rsquo; perception of BSM efficacy [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In a recent pilot study, we showed that it is possible to monitor open-air farm biosafety including both hosts and pathogens. Through a \u0026lsquo;One Health\u0026rsquo; approach, we developed an innovative interdisciplinary strategy to prevent and monitor animal diseases at the wildlife-livestock interface to bridge the sanitary gap between indoor and open-air farming systems while maximizing the contribution of animal farming to biodiversity conservation. In essence, the pilot study showed that (1) short-term camera trap (CT) deployment generates valuable information on farm biodiversity and on the rate of farm-visits by risk species, and that (2) environmental nucleic acid detection (ENAD) at risk points or on animals informs on pathogen marker presence in the farm environment [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eInnovating for resilience in traditional, open-air, and sustainable animal production is one way for making animal farming compatible with biodiversity conservation [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This is of special relevance to the Iberian Peninsula which represents a biodiversity hotspot in Western Europe [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The RMPs developed for extensive farming systems represent an important step in this process. However, while the nature of BSMs proposed to farmers [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and the degree of farmer acceptance and uptake of some of these BSMs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] have repeatedly been assessed, only limited information exists on their effectiveness. For specific BSMs, such as small-scale barriers and waterhole modifications or feeder or food storage modifications, evidence has been collected on the reduction of wildlife visits [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] or on the improvement of herd health indicators [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, quantitative information on the effectiveness of applying complete RMPs on the detection rates of risk wildlife and on pathogen detection remains lacking.\u003c/p\u003e\u003cp\u003eIn this study, we re-visited the farms of the Herrero-Garc\u0026iacute;a et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] pilot study to quantify the effect of applying RMPs on the detection rates of high-risk wildlife (assessed by means of CTs) and of selected pathogen markers (using ENAD). We focus on four mammals of relevance for different shared infections, namely red deer (\u003cem\u003eCervus elaphus\u003c/em\u003e), Eurasian wild boar (\u003cem\u003eSus scrofa\u003c/em\u003e), Red fox (\u003cem\u003eVulpes vulpes\u003c/em\u003e) and European badger (\u003cem\u003eMeles meles\u003c/em\u003e), and on markers of six bacterial pathogens, namely the \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex, \u003cem\u003eMycobacterium avium\u003c/em\u003e subp. \u003cem\u003eparatuberculosis\u003c/em\u003e, \u003cem\u003eCoxiella burnetii\u003c/em\u003e, \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eSalmonella\u003c/em\u003e spp., and \u003cem\u003eBrucella\u003c/em\u003e spp. We show that RMPs effectively reduce both risk wildlife visits and pathogen marker detection.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eSampling sites and farm visits\u003c/p\u003e\u003cp\u003eIn this study, we revisited 14 of the 15 farms included in the pilot study of Herrero-Garc\u0026iacute;a et al. [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The study farms (cattle n\u0026thinsp;=\u0026thinsp;6; small ruminant n\u0026thinsp;=\u0026thinsp;4; and pig n\u0026thinsp;=\u0026thinsp;4) were distributed in five regions of mainland Spain: Madrid, cattle and small ruminants; Extremadura, pig; Castilla y Le\u0026oacute;n, cattle; Castilla La Mancha, small ruminants; and Murcia, pig. All farms were fenced but wildlife use of farm premises was recorded.\u003c/p\u003e\u003cp\u003eThe farms participating in this study were chosen as pilot points to run farm-specific wildlife RMPs [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The first round of farm visits (T1) took place in 2022 and included farmer interviews, camera trapping and ENAD [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In 2022, each farmer received a detailed report of the outcome of the farm-specific wildlife RMP including a list of recommended biosafety measures (BSMs). The second round (Time 2) included new rounds of camera trapping and ENAD but no new interviews. This second sampling event took place one year later, in 2023, after the implementation of general and specific risk mitigation actions (Fig.\u0026nbsp;1; Additional file 1: Tables S1 and S2 for details). Farmer feedback on BSM uptake was gathered during the second visit or telephonically.\u003c/p\u003e\u003cp\u003eCamera trapping\u003c/p\u003e\u003cp\u003eFor wildlife monitoring, Browning CTs (Browning Strike Force HD ProX, Browning Arms Company\u0026reg;, Morgan, Utah, USA) were deployed on each farm at Time 1 (T1) and Time 2 (T2). Per farm and sampling time, 28\u0026ndash;31 cameras were deployed, of which 18\u0026ndash;21 were directed towards water or food points, identified as risk points in the previous study. The remaining 10 cameras were placed at random points on the farm. These cameras were set to be operating for 48 hours, releasing 3 shots when motion was detected, with a time lapse of one minute between consecutive activations. No baits or attractants were used.\u003c/p\u003e\u003cp\u003eEnvironmental nucleic acid detection (ENAD)\u003c/p\u003e\u003cp\u003eENAD sampling was performed on 20 environmental surfaces collected from risk points and on 10 animal hocks per farm at T1 and at T2. The ready-to-use GPSponge\u0026reg; Kit (GPS genetic PCR solutions, Orihuela, Spain) pre-hydrated with an isotonic, surfactant, nucleic acid preservative liquid was used. For DNA extraction and purification, we used the GPSpin\u0026reg; Microbiome Fecal DNA Kit (GPS\u0026trade;, Orihuela, Espa\u0026ntilde;a) according to manufacturer instructions, starting from the pellet obtained after centrifuging 900 \u0026micro;L of the sample for 3 min at 13.000 rpm. We tested all samples for the following pathogen markers by real-time PCR: \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e complex -IS\u003cem\u003e6110\u003c/em\u003e [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], \u003cem\u003eM. avium\u003c/em\u003e subsp. \u003cem\u003eparatuberculosis\u003c/em\u003e -IS\u003cem\u003e900\u003c/em\u003e [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]; \u003cem\u003eBrucella\u003c/em\u003e sp. -IS\u003cem\u003e711\u003c/em\u003e [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], \u003cem\u003eCoxiella burnetii\u003c/em\u003e -IS\u003cem\u003e1111\u003c/em\u003e [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], \u003cem\u003eSalmonella enterica\u003c/em\u003e -\u003cem\u003einv\u003c/em\u003eA [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and \u003cem\u003eEscherichia coli\u003c/em\u003e -\u003cem\u003euid\u003c/em\u003eA [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eTo statistically analyze our results, we used Fisher's exact statistical test, which allows us to determine whether there is a significant association between two categorical variables when the sample size is small. In this case, it would be the presence/absence of risk species or pathogen markers on each farm at T1 and T2. It was assessed using 2x2 contingency tables, considering a confidence level of 95%.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAfter implementing farm-specific RMPs we managed to reduce the observed frequency of farm visits by risk species and the observed frequency of pathogen marker detection. The following paragraphs describe these results.\u003c/p\u003e\n\u003cp\u003eConsidering the detection frequency of the four risk mammals for the set of study sites, we observed reductions in presence between 18% (red deer) and 72% (badger) in total (Table 2). The differences were statistically significant for wild boar, fox and badger (Figure 2a). At the individual farm level, red deer appeared on seven of 14 farms and in five of them the detection frequency decreased at T2. Wild boar appeared on 12 farms and decreased on eight of them, including the two pig farms where wild boar had been detected at T2. Foxes appeared on all farms and decreased on ten of them, including all pig farms and five of six cattle farms. However, fox presence increased at T2 in three of four small ruminant farms. Finally, badgers were detected on ten farms, decreasing on eight of them including five of the six cattle farms. Overall risk species detection declined on 11 farms and increased on three, all of them small ruminant farms. This yielded an average decline of -30% and this decline was most pronounced in cattle farms (-43%) (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the six pathogen markers considered, we observed frequency reductions for four and increases for two, namely the \u003cem\u003eM. tuberculosis\u003c/em\u003e complex markerIS\u003cem\u003e6110\u003c/em\u003e and the \u003cem\u003eBrucella\u003c/em\u003e spp. marker IS\u003cem\u003e711\u003c/em\u003e. These changes were significant for the \u003cem\u003eSalmonella\u003c/em\u003e spp. marker \u003cem\u003einv\u003c/em\u003eA and marginally significant for the generic \u003cem\u003eE. coli\u003c/em\u003e marker \u003cem\u003euid\u003c/em\u003eA (Figure 2b). The increase in IS\u003cem\u003e6110\u003c/em\u003e detection was mostly due to new detections on two previously negative pig farms. The detection of \u003cem\u003euid\u003c/em\u003eA and \u003cem\u003einv\u003c/em\u003eA declined consistently at T2 in all three livestock species. At the farm level, we observed significant reductions for the \u003cem\u003euid\u003c/em\u003eA marker on one cattle and on one small ruminant farm, and for \u003cem\u003einv\u003c/em\u003eA on two cattle farms and one small ruminant farm. Detection of the \u003cem\u003eCoxiella burnetii\u003c/em\u003e marker IS\u003cem\u003e1111\u003c/em\u003e occurred consistently at both times in all three positive small ruminant farms and the \u003cem\u003eBrucella\u003c/em\u003e spp. marker IS\u003cem\u003e711\u003c/em\u003e was detected on two small ruminant farms. The mean decline in pathogen marker detection was -18% when all markers were considered. This decline was most pronounced in small ruminant farms (-23%) (Table 1). Still at the individual farm level, we found a significant correlation between changes in the detection frequency of risk hosts and pathogen markers. Specifically, we observed a positive correlation between the percentage of reduction in the detection frequency of wild boars and the percentage of reduction in the detection frequency of the \u003cem\u003einv\u003c/em\u003eA marker (r\u003csub\u003eS\u003c/sub\u003e=0.68, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe application of farm-specific RMPs resulted in a 30% reduction in farm visits by risk wildlife and an 18% reduction in the frequency of pathogen marker detection. By combining short-time CT deployments with sponge-based ENAD, this innovative farm biosafety monitoring protocol enabled us to assess wildlife presence and pathogen marker detection at the same time and by noninvasive means.\u003c/p\u003e\n\u003cp\u003eThe apparent impact of the RMPs was not uniform. For instance, high-risk species detections, the combined detection frequency of red deer, wild boar, fox and badger declined consistently on all six cattle farms and on all four pig farms after running the RMPs. By contrast, this indicator declined only on one small ruminant farm while it increased in the remaining three. This could suggest that cattle farmers and pig farmers were more likely to take up the mitigation actions proposed in the RMPs, possibly due to their knowledge regarding the risk posed by endemic animal TB [6] and due to the fear to African Swine Fever emergence, respectively. Indeed, the apparent effect of the RMPs on wild boar detection was most evident on pig farms (-80%; Table 2). Comparatively, small ruminant farmers face less pressure regarding animal health and might therefore be less prone to implement time-consuming and sometimes costly mitigation actions. An alternative explanation to the low impact of RMPs on small ruminants is that implementing certain easy risk mitigation actions, such as elevating feeders and water troughs to avoid wild boar or badgers, is not as viable for small ruminants as it is for cattle given the smaller difference in body size.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe large apparent impact of the RMPs on badger detection, especially on cattle farms (-81%) would indicate a strong effect of reducing access to feed after applying the risk mitigation actions. This coincides with previous reports from the UK [20]. However, we cannot exclude the possibility of some (undeclared) culling taking place, too. It is also interesting to note the strong effects of RMPs on fox detections, especially on cattle farms (-50%). This is relevant since foxes are potentially implicated in the cycles of several pathogens shared with livestock [31].\u003c/p\u003e\n\u003cp\u003eThe apparent impact of the RMPs on pathogen marker detection was even more variable, since two markers increased while four others decreased at T2. Markers detected on a few occasions such as IS\u003cem\u003e711\u003c/em\u003e are not as relevant in terms of percentual changes as frequently detected markers including IS\u003cem\u003e6110\u003c/em\u003e, \u003cem\u003euid\u003c/em\u003eA and \u003cem\u003einv\u003c/em\u003eA. In fact, IS\u003cem\u003e711\u003c/em\u003e was detected in two samples at T2 and in one at T1. The IS\u003cem\u003e6110\u003c/em\u003e marker showed a more uniform increase from T1 to T2. This marker was detected on 11 of 14 farms and its detection frequency increased at T2 in seven of them. Notably, IS\u003cem\u003e6110\u003c/em\u003e positive sponge samples were detected in two pig farms for the first time at T2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn turn, \u003cem\u003euid\u003c/em\u003eA and \u003cem\u003einv\u003c/em\u003eA, the generic \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eSalmonella\u003c/em\u003e spp. markers, were both the most detected markers and the only ones with significant reductions in frequency of detection at T2 (Table 1). These markers might constitute good general indicators of farm hygiene or farm exposure to pathogens [4]. The correlation between wild boar detection and \u003cem\u003einv\u003c/em\u003eA detection suggests a link between a high-risk host, (i.e., the wild boar), and a pathogen marker, (i.e., \u003cem\u003eSalmonella\u0026nbsp;\u003c/em\u003espp.). In this regard, the participation of the wild boar in \u003cem\u003eSalmonella\u003c/em\u003e maintenance is well established [32,33].\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. The first one is that we did not perform a new full risk assessment at T2, as was done at T1 [4]. Rather, we relied on the farmer’s feedback to assess the degree of uptake of the mitigation actions proposed in the RMPs (Table S2). It is expected that most of the cheap changes to the water points and feeders were applied, while few of the expensive new fencings were implemented. Some interventions depend on third parties (neighbors, hunters, municipal and environmental authorities) and may sometimes have been applied without consulting all relevant parties. In a similar survey focused on cattle farms we estimated the degree of application of BSMs at one third [9]. Another limitation is the number and timing of the CTs and ENAD. The number of CTs is limited by the time needed by a team of two people to set up/collect the cameras while sparing time for ENAD and travel. While ENAD generally took place in the same months (October-November 2022 and 2023, respectively), CTs were generally deployed in winter-spring in 2022 and in autumn 2023. While this variation could have affected wildlife abundance or farm visitation rates, we do not expect huge differences since we avoided the dry summer, i.e., the main limiting season in Mediterranean habitats [5].\u003c/p\u003e\n\u003cp\u003eIn summary, we managed to assess the apparent impact of RMPs on the biosafety of open-air hoofstock farms and found significant reductions in wildlife farm visits and in pathogen marker detection, although with variability among livestock species, wildlife species, and pathogen markers. These findings can be generalized to other interventions at the wildlife livestock interface, regardless of the farmed species, farming system, and target pathogen. We propose that combining massive, short-term CT deployments with sponge-based ENAD enables farmers, field veterinarians or veterinary services to monitor farm biosafety in a broad range of settings and in a noninvasive manner.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe University of Castilla-La Mancha (UCLM) research ethics committee granted a formal waiver of ethics approval, since only routine veterinary care was involved in this study. In addition, Ethics approval was unnecessary according to Spanish national regulations (Real Decreto 53/2013).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.M.R.: Writing – review \u0026amp; editing, Visualization, Validation, Investigation, Formal analysis, Data curation. G.H.G.: Writing – review \u0026amp; editing, Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation. P.B.: Writing – review \u0026amp; editing, Visualization, Validation, Investigation, Formal analysis, Data curation. C.H.B.: Writing – review \u0026amp; editing, Validation, Investigation, Data curation. D.R.: Writing – review \u0026amp; editing, Validation, Investigation, Data curation. T.G.S.: Writing – review \u0026amp; editing, Visualization, Validation, Supervision, Resources, Methodology, Conceptualization. A.P.: Writing – review \u0026amp; editing, Validation, Investigation, Data curation. A.D.G.: Writing – review \u0026amp; editing, Validation, Resources, Methodology, Investigation, Data curation. P.P.: Writing – review \u0026amp; editing, Visualization, Validation, Formal analysis, Data curation. A.B.: Writing – review \u0026amp; editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. L.D.: Writing – review \u0026amp; editing, Validation, Supervision, Resources, Project administration, Methodology, Funding acquisition, Conceptualization. C.G.: Writing – review \u0026amp; editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization. M.P.S.: Writing – review \u0026amp; editing, Writing – original draft, Visualization, Validation, Supervision, Resources, Methodology, Investigation, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Spanish MCIN/AEI/10.130 39/501100011033/ and the European Union NextGeneration EU/ PRTR [grant number PLEC2021-008113]; and the MCIN/AEI/10.13039 /501100011033/FEDER, EU [grant numbers PID2022-141906OB-C21, PID2022-141906OB-C22]. G.H.G. was funded by Junta de Castilla y León and FSE [grant number LE036-20]. A.M.R. by Universidad de Castilla-La Mancha and Ministerio de Ciencia e Innovación [FPI grant number PREP2022-000546]. P.B. by Juan de la Cierva Formación 2021, NextGenerationEU, PRTR [grant number DC2022–049103-I]. A.P. holds a predoctoral research contract at UCLM (2023-UNIVERS-11983), co-funded by the European Social Fund Plus (ESF+). P.P. by Juan de la Cierva Formación 2021, funded by the Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación and NextGenerationEU/PRTR [grant number FJC2021–046805-I].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their sincere gratitude to the teams at LABOCOR, MAEVA, SaBio-IREC, VISAVET, and the University of León for their valuable collaboration and support throughout this study. We are especially grateful to the farmers, farm owners, and veterinarians, whose involvement and cooperation were essential for the successful completion of this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVan Wagenberg CPA, de Haas Y, Hogeveen H, van Krimpen MM, Meuwissen MPM, van Middelaar CE, Rodenburg TB (2017) Animal Board Invited Review: Comparing conventional and organic livestock production systems on different aspects of sustainability. 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Res Vet Sci 132:400\u0026ndash;403. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.rvsc.2020.07.026\u003c/span\u003e\u003cspan address=\"10.1016/j.rvsc.2020.07.026\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\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":"Camera trapping, Cattle, Environmental nucleic acid detection, Pig, Small ruminants, Wildlife-livestock interface, risk mitigation, biosafety","lastPublishedDoi":"10.21203/rs.3.rs-7236055/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7236055/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOutdoor farming systems may favor the dilution effect of biodiversity on pathogen exposure and contribute to biodiversity conservation through the preservation of valuable habitats. However, due to the implicit closer interaction with wildlife, outdoor farms are also more at risk for disease maintenance at the wildlife-livestock interface. Disease control options in outdoor farming systems include the development and application of wildlife risk mitigation protocols (RMPs). However, while the nature of the proposed mitigation actions and the degree of farmer uptake have repeatedly been assessed, only limited information exists on their effectiveness. In this study, we re-visited 14 farms of a pilot study to quantify the effect of applying RMPs on the detection rates of risk wildlife (assessed by means of camera traps; CTs) and of selected pathogen markers (using sponges for environmental nucleic acid detection; ENAD). The application of farm-specific RMPs resulted in a 30% reduction in farm visits by high-risk wildlife and an 18% reduction in the frequency of pathogen marker detection. High-risk species detection declined on 11 farms and increased on three, all of them small ruminant farms. Regarding pathogen markers, we observed frequency reductions for four and increases for two markers. These changes were statistically significant for the \u003cem\u003eSalmonella\u003c/em\u003e spp. marker \u003cem\u003einv\u003c/em\u003eA. At the farm level, the reduction in the detection frequency of wild boar (\u003cem\u003eSus scrofa\u003c/em\u003e) correlated with the reduction in the detection frequency of the \u003cem\u003einv\u003c/em\u003eA marker. These findings may be relevant for assessing other interventions at the wildlife-livestock interface, regardless of the farmed species, farming system, and target pathogen.\u003c/p\u003e","manuscriptTitle":"Wildlife risk mitigation protocols reduce risk species visits and pathogen marker detection in open-air farms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-04 09:44:41","doi":"10.21203/rs.3.rs-7236055/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":"d7fc739a-0dc3-4878-9e10-f05b26d0999f","owner":[],"postedDate":"September 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:01:28+00:00","versionOfRecord":{"articleIdentity":"rs-7236055","link":"https://doi.org/10.1186/s13567-025-01671-0","journal":{"identity":"veterinary-research","isVorOnly":false,"title":"Veterinary Research"},"publishedOn":"2025-11-27 15:57:36","publishedOnDateReadable":"November 27th, 2025"},"versionCreatedAt":"2025-09-04 09:44:41","video":"","vorDoi":"10.1186/s13567-025-01671-0","vorDoiUrl":"https://doi.org/10.1186/s13567-025-01671-0","workflowStages":[]},"version":"v1","identity":"rs-7236055","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7236055","identity":"rs-7236055","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}