Environmental drivers of tick density in UK dairy farms: implications for livestock health and agri-environment policy

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Environmental policies promoting woodland creation and habitat restoration are increasing habitat suitability for Ixodes ricinus but impacts on livestock tick-borne disease risk remain unclear. This study examined how landscape features influence tick distribution on UK dairy farms with a recent history of tick-borne disease. Methods Questing ticks were sampled on 72 pastures in 12 dairy farms in southwest England (2,376 transects), stratified by distance from pasture boundaries and adjacency to woodland or non-woodland habitats. Environmental variables were measured at transect, boundary, and pasture scales. Generalised linear mixed models identified predictors of tick presence in pastures, and nymph density at pasture boundaries. Farm-level associations between tick abundance, woodland cover, and cattle pathogen prevalence were assessed descriptively. Results A total of 1,701 ticks were collected (91.3% nymphs). Ticks were detected on 20% of transects and in 89% of pastures, with densities strongly aggregated at pasture boundaries. The proportion of woodland cover within 50m buffers was the dominant environmental driver at both boundary and pasture scales, with greater cover associated with higher nymph densities and increased probability of tick presence. Boundaries adjacent to water also supported significantly higher nymph densities. Conclusions Local landscape features, particularly woodland cover and small water bodies at boundaries, strongly influence tick distribution in UK dairy pastures. Woodland expansion through environmental schemes may thus be expected to increase tick distribution and densities in farmed landscapes, with implications for livestock exposure and public health. Figures Figure 1 Figure 2 Figure 3 Background The distribution of vector-borne diseases is changing globally, with significant implications for human and animal health. These changes have been associated with multiple, interacting factors that influence vector, pathogen and host dynamics, including climate change, land use change and the encroachment of agriculture into natural ecosystems ( 1 – 4 ). In turn, environmental governance and land management policies are shaping land use transitions, raising concerns that policy-driven landscape change may inadvertently contribute to the emergence and spread of vector borne diseases ( 5 , 6 ). Ticks are among the most important arthropod vectors in Europe, transmitting multiple pathogens of both medical and veterinary concern. Ixodes ricinus , the most widespread tick species on the continent, is responsible for the highest burden of tick-borne disease in humans and livestock ( 7 , 8 ). The abundance and distribution of I. ricinus varies greatly across different landscapes, reflecting a dependence on suitable microclimates for survival, and availability of hosts to provide blood meals. Microclimate, including a relative humidity of greater than 80% for off-host developmental phases and the availability of vertebrate hosts are both strongly influenced by habitat and landscape structure (9–12). Small mammals serve as the primary hosts for nymphs and larvae ( 13 ), while adult ticks feed most frequently on large herbivores such as deer and cattle. Densities of I. ricinus are commonly highest in woodland and ecotonal habitats, that support the requisite microclimate and host abundance, but can also survive in open areas such as rough grasslands if conditions are favourable ( 14 – 17 ). Across Europe, environment and climate policies promoting afforestation, natural regeneration, and habitat connectivity, such as the EU Biodiversity Strategy for 2030 ( 18 ), are implicated in creating environments favourable for I. ricinus survival and influencing tick dispersal through changes in wildlife host distributions ( 19 – 21 ). Transitions toward sustainable agricultural practices are also central to these strategies, aligning with global initiatives to integrate biodiversity conservation and restoration into farming systems ( 22 ). Although the biodiversity and climate benefits of these landscape changes are widely acknowledged, their implications for livestock health remain poorly understood, particularly where altered habitats are likely to increase proximity between livestock, wildlife and tick vectors. For cattle, the two most important tick-borne diseases in northern Europe are bovine babesiosis, primarily caused by Babesia divergens , and tick-borne fever, caused by Anaplasma phagocytophilum , both transmitted by I. ricinus . Babesiosis is recognised as an emerging infectious disease in Europe, with outbreaks linked to expansion of the vector range and changing host dynamics ( 23 ). In adult cattle disease can be severe, characterised by fever, haemoglobinuria and depression, and often fatal ( 24 ). Outbreaks, often associated with previously unexposed cattle or sporadic exposure to infection, result in significant losses ( 25 , 26 ). Tick-borne fever is rarely fatal but contributes to considerable production losses through reduced milk yield, abortions and stillbirths ( 27 ). Despite these impacts, the environmental and farm-level drivers of the diseases remain poorly characterised. A small number of studies have reported associations between woodland proximity, ecotonal habitats and tick abundance in cattle pastures in France ( 28 – 30 ). However, these effects are determined by complex dynamics among habitat structure, host community composition, and local microclimatic conditions. Further research is needed to understand how these factors influence vector – host -pathogen dynamics across different landscapes and agricultural systems. The UK dairy sector provides a valuable case study in this context. Evidence suggests the distribution of I. ricinus is changing in the UK ( 31 ), and tick-borne pathogens are being detected in new areas ( 32 ). Tick-borne diseases rank among the top three health priorities for extensively grazed livestock in the UK, and are considered as “high-impact, low-prevalence” conditions by the farming industry ( 33 , 34 ). At the same time, national policy aims to establish nearly one million hectares of new woodland by 2050, with farmers expected to deliver much of this expansion through agri-environment schemes that incentivise the creation of wildlife-rich habitats ( 35 ). This study investigates the landscape-scale environmental factors influencing the abundance and distribution of ticks on grazing pastures within dairy farms with a recurrent history of bovine babesiosis or tick-borne fever outbreaks in southwest England. We distinguish between within- pasture micro-climate and vegetation effects and those associated with ecotonal habitats and woodland proximity surrounding pastures. A parallel study conducted on the same farms examined the prevalence of subclinical infections with B. divergens and A. phagocytophilum in the cattle and is reported elsewhere (Shanks et al., 2025 in review). Here we leverage that data to examine broad scale correlations between farm landscape features, tick abundance and pathogen prevalence in cattle. Finally, we consider the implications of our findings in the context of current agricultural and environmental policies driving landscape change, as well as their relevance for disease control in both livestock and humans. Methods Farm selection Twelve dairy farms were purposively recruited from the counties of Cornwall, Devon and Dorset in southwest England through local veterinary networks (Fig. 1 ). This region accounts for approximately 33% of the national herd and 39% of dairy cattle specifically ( 36 ), and has an established presence of I. ricinus and tick-borne disease in cattle ( 37 – 40 ). The area is predominantly rural, characterised by agricultural land, woodland, heathland, and coastal habitats that support a mixture of livestock farming systems including beef and dairy cattle ( 36 , 41 ). Farms were eligible for inclusion if they (i) did not routinely purchase replacement stock, (ii) had a veterinary diagnosed history of recurrent bovine babesiosis and/or tick-borne fever in the herd, and (iii) maintained a minimum of 50 breeding cows. Farm characteristics Preliminary discussions were conducted with farmers to gather information on general grazing and management practices, and to identify the pastures used for cattle grazing. Herd sizes ranged from 95 to 400 cattle. All farms grazed the milking herd on pasture for at least part of the day or night from spring to autumn and housed indoors during the winter months. Only dairy cattle were present on the study farms; no beef cattle were kept. Four farmers reported winter grazing of sheep, but specific information on which pastures and when grazing occurred was not available. All farmers reported regular deer presence, including sightings in cattle grazed pastures. Acaricides were used for tick control on all but one farm, however, none are licensed for this purpose in cattle in the UK and their use was therefore off-label. Sampling strategy A sampling framework was designed to capture variation in tick abundance across environmental gradients, incorporating pasture characteristics, boundary features, and fine-scale microhabitat conditions at drag locations. To investigate variability in tick distribution across a landscape gradient, the pastures for sampling were selected based on pasture grazing history and proximity to woodland. Specifically, pastures grazed by cows within the previous 12 months were categorised by whether a pasture boundary bordered woodland or not. Six pastures per farm were selected, three with a woodland border if available, and the remainder from non-woodland border pastures. Within each selected pasture, boundaries with the most and least dense bordering woodland were chosen for sampling; where no difference was apparent, two boundaries were randomly selected. Tick sampling was conducted using the blanket-dragging approach ( 42 ). A 1m 2 white blanket was pulled slowly across the ground in 10m transects. After each transect, the blanket was inspected, and all life stages of ticks were counted and removed. At each selected pasture boundary, twelve standardised drags were carried out, stratified by distance from the boundary: 0m (immediately adjacent to the physical boundary feature such as hedgerow, fence, or ditch, n = 3), 2m (n = 3), 4m (n = 3), and 15m (n = 3), with the latter representing semi-open pasture. An additional nine semi-randomised drags were carried out in each pasture at a distance greater than 15m from all boundaries to represent open pasture. This design captured spatial variation in tick presence relative to pasture edges, where ticks have been found to be concentrated in grazing pastures in a prior study from France ( 29 ). Sampling took place in May and June 2024, with all drags in a given pasture completed on the same day under dry weather conditions. Questing adults and nymphs were collected from the vegetation. In total, 33 drags were carried out per pasture giving 2376 drags across the study. Questing ticks were assumed to be I. ricinus based on previous surveillance confirming this as the predominant questing species in southern England ( 38 ). Dermacentor reticulatus has also been found in the region ( 43 ) however any questing Dermacentor spp. would have been adults, readily distinguished by their larger size and ornate scutum. The nymphs of D. reticulatus are reportedly nidicolous, whereas adults exhibit exophilic behaviour and are the only stages likely to be collected by blanket-dragging ( 44 ). Environmental predictors A hierarchical framework was used to investigate environmental factors driving tick presence/absence and nymph density across three nested spatial scales: transect scale (microhabitat), pasture boundary scale (ecotonal habitat), and pasture scale (landscape context) (Table 1 ). Table 1 Environmental variables measured at three spatial scales to investigate tick presence/absence and nymph density. Subcategories for each variable are listed. Scale Variable Subcategories Summary values across all transects (n = 2376) Mean ± SD Range Frequency (%) Transect Location relative to pasture boundary 0 m, 2 m, 4 m, 15 m, open pasture – - - Vegetation height (cm) - 17.96 ± 12.81 0–100 - Vegetation density (score 0-100) - 7.35 ± 7.78 0–65 - Number of cattle dung piles - 0.54 ± 1.08 0–8 - Dominant vegetation type (% of transects) a Grass Herbaceous/wildflowers Bare ground Bramble/fern Rushes - - 92.0 4.1 1.7 1.5 0.7 Boundary Structural boundary elements (present/absent) b Hedgerow Visible fence Ditch – - 81.0 61.9 5.3 Dominant habitat type within 10 m outside pasture boundary (% of transects) c Pasture Trees Track/Road Scrubland Water – - 47.5 26.3 18.3 4.2 3.7 Woodland within 50 m buffer (%) (boundary) - 8.79 ± 15.20 0–70 - Pasture Saturation deficit - 6.90 ± 2.33 2.30–13.94 - Woodland within 50 m buffer (%) (entire pasture) - 16.22 ± 16.48 0–61 - a Up to three vegetation types were initially recorded for each transect as % cover. For analysis, only the single dominant type was retained as a categorical variable. b Structural elements were recorded as present/absent; multiple could be present per recording c Habitat type within 10 m of the pasture boundary was recorded as a single category (pasture, woodland, track/road, scrubland, or water). At the finest resolution, vegetation type, height and density were recorded for each transect to characterize microhabitat features influencing tick survival and questing behaviour ( 16 , 45 – 47 ). Ground vegetation height and density were measured using a sward stick, with density expressed as the number of 5cm increments on the stick obscured by vegetation. The three dominant vegetation types and their proportions were recorded for each transect, categorised into five classes: (i) herbaceous plants and wildflowers, (ii) bracken and bramble, (iii) rushes, (iv) grass, and (v) bare ground. Cattle dung piles were counted within each transect to provide an index of cattle abundance, following approaches applied to other host species ( 48 ). Cattle can redistribute ticks across pastures through movement, potentially influencing local tick distribution patterns ( 46 ). In addition, dung piles could influence tick survival and questing activity by physically covering or obstructing ticks. At the pasture boundary scale, structural and contextual features hypothesized to influence tick abundance along pasture edges were recorded. Pasture boundaries were classified by the presence or absence of structural elements (hedgerow, visible fence, ditch) that may provide refuges or pathways for tick hosts ( 28 ) or modify micro-climate conditions; more than one element could be present in the same boundary. The dominant habitat within 10m on the opposite side of the boundary at the location of 0m transects was recorded as a single observed category (pasture, trees, water body, scrubland, track/road) to characterise the immediate landscape context influencing ecotonal conditions and host behaviour ( 29 , 30 ). The ‘trees’ category included both isolated trees and boundaries adjoining larger woodland patches. To assess broader landscape influences, the proportion of woodland cover (coniferous and deciduous) was quantified within a 50 m buffer at two spatial scales: (i) around individual pasture boundaries and (ii) around entire pastures. The 50 m buffer was chosen to capture the effect of surrounding woodland ( 30 , 49 ), while minimising dilution by more distant open habitats and overlap between neighbouring pastures. Data on land cover were extracted from the UK Centre for Ecology and Hydrology Land Cover Map (tiff 2022) ( 50 ) using R version 4.4.1( 51 ) and QGIS version 3.40 ‘Bratislava’( 52 ). At the pasture scale, saturation deficit was included as a climatic variable influencing tick desiccation risk and questing activity ( 53 ). Temperature and humidity were measured at vegetation height immediately before the first drag and after the last drag in each pasture. Saturation deficit was calculated from temperature and humidity using the formula: SD = (1 − RH/100) × 4.9463 × e (0.0621 T ) where T is temperature in °C and RH is relative humidity (%) ( 54 ). Statistical analysis All statistical analyses were performed using RStudio 2024.09.01. Generalised Linear Mixed Models (GLMMs) were fitted using the glmmTMB package ( 55 ) to investigate the influence of environmental factors on tick presence/absence and nymph density. Model assumptions, including overdispersion, zero-inflation, and outliers were assessed using simulation-based residuals from the DHARMa package ( 56 ). Collinearity among fixed effects was checked using Variance Inflation Factors (VIF) and model convergence was verified. Environmental factors influencing tick presence at transect level To examine environmental effects on the presence or absence of questing ticks (nymphs and adults), a binomial GLMM with logit link was fitted to the full dataset of 2376 transects. At the transect level, fixed effects included vegetation height, vegetation density, distance from pasture boundary, dominant vegetation type and the number of cow dung piles. At the pasture level, fixed effects included saturation deficit and the proportion of woodland in a 50m pasture buffer. No boundary-level predictors were included in this model. Random intercepts were fitted for farm (n = 12) and pasture (n = 72). Backward stepwise model selection was performed using the drop1 function, retaining predictors based on minimisation of Akaike’s Information Criterion (AIC) ( 57 ). The relative contribution of retained predictors was assessed from their effect sizes, confidence intervals and changes in AIC. Model performance was evaluated using conditional (including random effects) and marginal (fixed effects only) area under the receiver operating characteristic curve (AUC) values, calculated with pROC ( 58 ), and the true skill statistic (TSS) ( 59 ). Environmental factors influencing nymph density at pasture boundaries To identify environmental risk factors and pasture boundary characteristics associated with nymph density, a negative binomial GLMM was fitted to nymph counts recorded on 0m transects. At the transect level, fixed effects included vegetation height, vegetation density, distance from pasture boundary, dominant vegetation type, and number of cow dung piles. At the boundary level, fixed effects included presence or absence of structural boundary features, the adjacent habitat type and the proportion of woodland in a 50m boundary buffer. At the pasture level, saturation deficit was included. Random intercepts were fitted for farm (n = 12), pasture (n = 72), and boundary (n = 144). The same AIC-based backward stepwise selection was applied. Model fit was assessed using marginal and conditional R² ( 60 ), and root mean squared error (RMSE) based on conditional (including random effects) and marginal (fixed effects only) predictions. Two-way interaction terms between the proportion of woodland in a 50m boundary buffer and the adjacent habitat type were also evaluated to test whether effects of woodland differed by adjacent habitat. Competing models with and without these interactions were compared using AIC and marginal and conditional R 2 values, calculated with the MuMIn package ( 61 ). Cattle pathogen prevalence and associations with tick abundance Broad scale, farm-level associations between cattle A. phagocytophilum and B. divergens prevalence and mean tick abundance and woodland cover across sampled grazing pastures per farm were examined using Pearson correlation coefficients. Herd-level prevalence data for adult cattle were obtained from a parallel prevalence study conducted on the same farms during the same grazing season (Shanks et al., 2025 in review). Full details of cattle recruitment, blood sampling, DNA extraction, and PCR assays are reported in that study. For this analysis, PCR results were summarised as the proportion of adult cattle per farm testing positive for A. phagocytophilum and B. divergens . Mean tick abundance was calculated as the mean number of ticks per 0 m transect per farm. At the farm level, woodland cover was summarised as the mean proportion of woodland within 50 m buffers around all sampled pastures. Results Distribution of questing ticks within dairy pastures A total of 1,701 questing ticks were collected over the study period. Overall, ticks were found on 483 transects (20.3%). Nymphs were the most common life stage, comprising 91.3% of the total ticks collected (n = 1553), present in 461 (19.4%) transects, followed by adult males (n = 91, 5.3%), present in 66 (2.8%) transects and adult females (n = 57, 5.4%), present in 47 (2.0%) transects. The maximum number of nymphs recorded in a single transect was 36. Across all transects in the study, the mean nymph density was 0.65 nymphs per transect (SD = 2.19). At the pasture boundary (0m distance from pasture boundary, n = 432 transects across all farms), the overall mean nymph density was 2.28 nymphs per transect (SD = 4.1, max = 36), with mean densities declining at increasing distances into the pasture (Fig. 3 ). In total, ticks were collected from 64 of the 72 sampled pastures (88.9%), with 75% of all ticks collected from 12 (32.4%) pastures. The maximum number of ticks collected in a single pasture was 104. Environmental variables Grass was the dominant vegetation type recorded in 92% of transects across all farms, with herbaceous/wildflowers (4.1%), bare ground (1.7%), bramble/fern (1.5%), and rushes (0.7%) less frequently observed. Hedgerows were recorded as a boundary feature at 81% of 0m transects, visible fences at 61.9% and ditches at 5.3%. More than one of these features was present at 48.1% of 0 m transects (n = 208). The dominant habitat within 10m on the opposite side of the boundary at 0m transects was pasture (47.5%), followed by trees (26.3%), track/road (18.3%), scrubland (4.2%), and water (3.7%). Environmental factors influencing tick presence at transect level The presence of questing ticks at the transect level was significantly associated with distance from the pasture boundary, vegetation height, and the proportion of woodland within a 50 m pasture buffer (Table 2 ). The probability of detecting ticks decreased with increasing distance from the boundary (-0.14, ± 0.01, p < 0.001), increased with vegetation height (0.06 ± 0.01, p < 0.001) and increased with proportion of woodland within a 50 m buffer around the entire pasture (0.05 ± 0.01, p < 0.001); values are model estimates ± SE. Vegetation density, dominant vegetation type, number of cow dung piles and saturation deficit did not have significant impact on presence of questing ticks at transect level (i.e. were dropped during the model selection process, without appreciable increase in AIC (see Additional file 1). Random effects indicated additional variation at the farm (SD = 0.17) and pasture (SD = 1.20) levels. The final GLMM showed good model performance, having a conditional AUC of 0.900 (95% CI: 0.886–0.915) and a marginal AUC of 0.833 (95% CI: 0.814–0.852), with a maximum TSS of 0.66. Table 2 Final binomial GLMM results for the presence of questing ticks (nymphs and adults) across all transects. Fixed effects Estimate ± SE z- value p-value Delta AIC Intercept − 2.84 ± 0.29 -9.87 < 0.001 - Vegetation height 0.06 ± 0.01 9.60 < 0.001 34.4 Distance from the boundary − 0.14 ± 0.01 -13.68 < 0.001 241.4 Wood 50m pasture buffer 0.05 ± 0.01 5.34 < 0.001 25.2 Random effects Variance ± SD Farm 0.03 ± 0.17 - - - Pasture 1.44 ± 1.20 - - - Fixed-effect estimates (logit scale) with standard error (SE), z, p, and Delta AIC from the final model; random-effect variances and standard deviation (SD) are reported for farm and pasture. Predictors were retained based on minimisation of AIC; p-values are reported for inference only. Environmental factors influencing nymph density at pasture boundaries Nymph density in transects located 0m from pasture boundaries was positively associated with the proportion of woodland within a 50 m boundary buffer (3.87 ± 0.79, p < 0.001) and to a lesser extent with boundaries adjacent to water (1.21 ± 0.38, p = 0.001), although water was present in relatively few pastures sampled which may limit the strength of this association (Table 3 ). Boundaries adjacent to track/road showed a marginally negative impact on nymph density (–0.56 ± 0.31, p = 0.07), while scrubland and tree adjacent habitats were not significantly different in impact from pasture. Vegetation height, vegetation density, presence of a hedgerow, and dung piles were not significant predictors of nymph density. Random effects indicated additional unmeasured factors affecting nymph density at the farm (SD = 0.53), pasture (SD = 0.70), and boundary (SD = 0.79) levels. The final GLMM explained 27.2% of the variance in nymph density through fixed effects alone (marginal R²), and 78.4% when random effects were included (conditional R²; lognormal method). Prediction error was lower for the full model (conditional RMSE = 2.12) compared with fixed effects alone (marginal RMSE = 3.80). Interaction terms between the proportion of woodland within a 50m boundary buffer and adjacent habitat type were also evaluated to test whether the effect of nearby woodland varied across immediate habitat contexts. Inclusion of these interactions did not substantially improve model fit (ΔAIC < 2), increased marginal R² only slightly (from 0.26 to 0.30) while conditional R² remained almost unchanged (0.74–0.80). These terms were therefore not retained in the final model (Additional file 1). Table 3 Final negative binomial GLMM results for nymph density at pasture boundaries (0m transects). Final GLMM (fixed effects) Estimate ± SE z- value p-value Delta AIC Intercept -0.76 ± 0.32 -2.38 0.02 - Vegetation height -0.01 ± 0.01 -1.61 0.11 - Vegetation density 0.01 ± 0.01 1.60 0.11 - Hedgerow 0.30 ± 0.20 1.49 0.14 - Woodland within 50 m boundary buffer 3.87 ± 0.79 4.92 < 0.001 26.2 Cattle dung piles 0.12 ± 0.08 1.47 0.14 - Adjacent habitat: Scrubland 0.16 ± 0.47 0.33 0.74 - Adjacent habitat: Track/road -0.56 ± 0.31 -1.81 0.07 - Adjacent habitat: Water 1.21 ± 0.38 3.12 0.001 7.8 Adjacent habitat: Trees 0.19 ± 0.23 0.80 0.42 - Random effects Variance ± SD Farm 0.28 ± 0.53 - - - Pasture 0.48 ± 0.70 - - - Boundary 0.62 ± 0.79 - - - Fixed-effect estimates (log scale) with SE, z, p, and Delta AIC from the final GLMM are shown with random-effect variances for farm, pasture, and boundary. Predictors were retained based on minimisation of AIC; p-values are reported for inference only. Reference level for adjacent habitat = pasture. Cattle pathogen prevalence and associations with tick abundance Broad scale correlations at the farm level between herd-level (adult cows) B. divergens and A. phagocytophilum prevalence and the farm-level mean tick abundance at 0m transects or woodland cover within 50 m buffers were weak and not statistically significant. Correlation coefficients ranged from − 0.36 to 0.07, with wide confidence intervals (all p > 0.2). See Additional file 2. Discussion This is the first study to examine the landscape factors driving the abundance of the key tick vector, I. ricinus , within cattle grazing systems in the UK. By distinguishing the relative contributions of pasture-level vegetation and microclimate from those of ecotonal and broader habitat composition, it establishes a foundation for understanding how environmental structure influences tick hazard in livestock systems. By identifying the habitats and landscape features that sustain high tick densities, the findings provide a critical first step in anticipating how tick-borne disease risks may shift under environmental and climate policies across the UK and European Union that promote woodland expansion, habitat restoration, and landscape connectivity. Tick distribution was highly heterogenous, with strong spatial aggregation both between and within pastures. Densities were concentrated at pasture margins and declined sharply into open pastures, consistent with patterns reported across European agricultural landscapes ( 15 , 28 – 30 ). The proportion of woodland cover within 50m buffers around pasture boundaries and entire pastures were the dominant predictors of tick abundance, whereas hedgerow boundaries and immediate adjacency to tree habitats within 10m of pasture boundaries showed no association. This contrasts with studies in UK arable systems and French cattle pastures where hedgerows and tree cover along pasture perimeters have been associated with increased tick presence or abundance ( 15 , 28 , 30 ). Hedgerows bordered most pasture edges in our study system (81%), reducing statistical power to detect independent effects. In addition, the hedgerows were recorded only as a binary variable, which did not capture structural variation such as width, density and composition, known to influence tick populations ( 28 , 30 ). In hedgerow-dominated dairy landscapes, such binary measures may therefore provide poor indicators of vegetated microsites used by tick hosts. The absence of an effect of adjacency to tree habitats within 10m of pasture boundaries suggests that processes influencing I. ricinus distribution in grazing pastures operated across broader spatial scales than local boundary features. Woodland extent in 50m buffers captures landscape features such as connectivity and patch size that are known to influence tick populations ( 13 , 62 ). Modelling studies suggest woodlands are a source of ticks for pastures, with migration across ecotones required to sustain the presence of ticks in pastures ( 63 ). While evidence for deer transporting ticks into pastures is mixed ( 64 ), regular reported deer sightings in and around cattle pastures in our study are consistent with this potential mechanism. Small mammals also contribute to tick abundance at the woodland – ecotone - pasture interface, although their dispersal role is complex and species-specific ( 17 ). While hedgerows can contribute to habitat connectivity and facilitate host movement between woodland and pastures, they were not detected as important habitats for sustaining tick populations along pasture boundaries in our study landscape. It is important to study hedgerow effects on tick abundance and hazard in other farmland contexts, sampling across different boundary types and integrating more detailed metrics of hedgerow structure. By contrast, adjacency to water predicted higher nymph densities in boundary transects. To our knowledge, this is the first study to examine the influence of local water bodies on tick abundance in UK grazing pastures. Studies in other European landscapes have reported I. ricinus abundance to be high along rivers in wet, humid canopies supporting dense undergrowth with high relative humidity ( 62 ). Riparian zones support a high proportion of wildlife ( 65 ), increasing opportunities for host - vector contact and tick dispersion into pasture edges. Our results indicate that cattle exposure to ticks is likely to be concentrated at pasture edges, particularly those bordering woodland and water bodies. Restricting cattle access to these areas could reduce exposure to ticks, but these areas also provide shade, shelter, and valuable grazing, creating welfare and economic trade-offs ( 66 ). Furthermore, complete avoidance of tick exposure is neither feasible nor advised in pasture-based systems within endemic regions. Low-level exposure to infected ticks is thought to support protective immunity to B. divergens , and controlled exposure remains a recommended strategy for herd management in endemic areas ( 24 , 67 ). However, key uncertainties remain regarding the spatial distribution of infected ticks within pastures (e.g. whether hazard mirrors patterns in tick abundance), the development and persistence of protective immunity, and how controlled exposure could be implemented at farm level. Tick pathogen data was not available in this study, however, B. divergens prevalence in questing ticks is typically extremely low across UK and European landscapes ( 68 – 71 ), while A. phagocytophilum prevalence is higher on average ( 68 , 72 , 73 ). Consequently, while questing nymph density is a useful proxy for cattle exposure, due to the diverse hosts and pathways involved in transmission of B. divergens and A. phagocytophilum ( 74 ), infection risk could remain low or inconsistent even in habitats with high nymph densities, limiting the predictive value for assessing farm-level risk. No significant associations were detected between farm-level tick abundance or woodland cover and pathogen prevalence in cattle, though statistical power was limited by the low sample size and the purposive recruitment of herds with a recurrent history of bovine babesiosis or tick-borne fever that likely reduced variability between farms. In addition, infection risk is influenced by multiple processes, including tick pathogen prevalence, multi-year transmission cycles, host immunity, and farm management practices such as acaricide use and grazing patterns that were not captured in this study. Our findings suggest potential unintended consequences of agri-environment policies that promote woodland expansion and connectivity in farmed landscapes. Increasing woodland extent within grazing systems may enhance habitat suitability for ticks and host species, increasing the likelihood of livestock exposure at the woodland - pasture interface. Multi-year studies integrating tick and cattle sampling, pathogen screening, host activity, and detailed farm management data are required to determine whether landscape changes driven by agri-environment schemes will contribute to measurable increases in tick-borne disease risk, and to inform evidence-based recommendations for disease control at farm level. In the interim, strengthened farm surveillance of tick-borne diseases will be essential to guide management and policy decisions ( 75 ). Finally, the implications of this study extend beyond livestock health. High nymph densities in ecotonal habitats within grazing pastures also pose a risk to humans, given I. ricinus is the primary UK vector of Borrelia burgdorferi sensu lato, the causative pathogen of Lyme disease, and tick-borne encephalitis virus, both detected in southern England ( 76 , 77 ). Farmers and recreational users of farmland may therefore be exposed to infected nymphs at pasture margins, reinforcing the continued importance of awareness campaigns promoting personal protective measures. Conclusions This study provides the first systematic analysis of the influence of local landscape features on tick hazard in UK dairy systems. Tick densities were highly aggregated across pastures and in ecotonal habitats. Woodland cover was the dominant predictor of tick abundance, and adjacency to water bodies also contributed to elevated nymph densities at pasture edges. Hedgerows were not significant predictors of nymph density in our study farms. In a policy context, the findings highlight a critical tension. Woodland creation and connectivity are central to EU and UK environmental targets, delivering clear biodiversity and climate benefits, yet woodland established adjacent to grazed pastures could inadvertently increase livestock exposure to ticks. Translating ecological risk factors into policy recommendations and farm-level disease control interventions is challenging. Further research and stakeholder engagement is needed to co-develop evidence-based recommendations that reconcile biodiversity and climate objectives with the protection of livestock and public health. Declarations Ethics approval and consent to participate Participating farms were informed that all information provided would remain strictly confidential, be securely disposed of in accordance with the data protection act and that all data would be summarised to ensure no farm would be individually identifiable. The work was carried out under the approval of the University of Liverpool Veterinary Research Ethics Committee: VREC1441 Consent for publication All participants consented to have their data published. Availability of data and materials The data supporting the conclusions of this article are included within the article and its additional files. Competing Interests The authors declare no competing interests Funding Funding for this study was provided by joint funding from the University of Liverpool Faculty of Health and Life Sciences PhD studentship scheme and the Animal and Plant Agency. Additional support for BP and RH was provided by the BBSRC-DEFRA OPTICK project funded by the Biotechnology and Biological Sciences Research Council (BBSRC), Natural Environment Research Council (NERC), UKRI Tackling Infections Strategic Theme and Department for Environment Food & Rural Affairs (Defra) through grant BB/X017974/1. Author contributions SS Sample collection, data analysis, wrote the first draft; RH data analysis, BP Supervision; NJ Funding acquisition, supervision; JD Supervision. CM, conceptualisation, funding acquisition, project management, supervision, data analysis, contribution to drafts. All authors edited the manuscript and approved the final version. Acknowledgements We thank the participating farmers for their time, the local veterinarians for their assistance with farm recruitment, Dr Rob Kelly for his clinical epidemiological advice and Abi Joyce for her help with field work. References Medlock JM, Leach SA. 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16:50:02","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":34001,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8076104/v1/bab1c9cff651975b199902fe.png"},{"id":97135536,"identity":"63eaa23f-f710-4d8b-ba4b-b8d4f153f34b","added_by":"auto","created_at":"2025-12-01 09:50:21","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":7750,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8076104/v1/def57993a7a86d09676be3f8.png"},{"id":96936934,"identity":"4cd6ca8b-135a-43c3-8d11-1de16b2c038f","added_by":"auto","created_at":"2025-11-27 16:50:02","extension":"xml","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":165513,"visible":true,"origin":"","legend":"","description":"","filename":"20657651206e4e3eb56d59a80d261be41structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8076104/v1/cefad7219a9786ff577be80c.xml"},{"id":96936933,"identity":"5784f718-4ab2-4bc8-adc3-3a85185ec460","added_by":"auto","created_at":"2025-11-27 16:50:02","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":178628,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8076104/v1/5719f8ff7ed7f99970c3fd04.html"},{"id":96936916,"identity":"e54dbf00-611d-4563-8045-21c689c1dfff","added_by":"auto","created_at":"2025-11-27 16:50:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":98982,"visible":true,"origin":"","legend":"\u003cp\u003eMap of southern England showing outlines of the relevant counties (Cornwall, Devon, and Dorset). Farm locations are indicated in green.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8076104/v1/a22452470dfbe6fd5617c077.png"},{"id":96936918,"identity":"c7dfaacd-1ed9-474a-97c0-ff32c318e1b1","added_by":"auto","created_at":"2025-11-27 16:50:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":189068,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic of within- pasture sampling design (panel a) and example field showing transect (white bars) placement (panel b). Distances are not to scale. Background imagery: Google Earth with the authors’ own modifications.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8076104/v1/d652d04f79eb21a7a0d8d448.png"},{"id":97135730,"identity":"4845d32d-530e-4aff-8981-1b6ca48e46e8","added_by":"auto","created_at":"2025-12-01 09:53:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":25574,"visible":true,"origin":"","legend":"\u003cp\u003eMean nymph density (nymphs per transect, averaged across all farms) by distance from pasture boundary. Error bars represent 95% confidence intervals.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8076104/v1/2498bbe29c4f9f0d182ff711.png"},{"id":106343375,"identity":"6e3dc814-612c-4638-b34e-6013a655ec2f","added_by":"auto","created_at":"2026-04-07 16:03:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1758352,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8076104/v1/1e75a76e-59a5-4ea7-9270-41921aee1837.pdf"},{"id":97135527,"identity":"529c8b89-3b4c-41bb-91dd-85c0b2b3cbb0","added_by":"auto","created_at":"2025-12-01 09:50:04","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21276,"visible":true,"origin":"","legend":"","description":"","filename":"Shanksetal2025PVSI.docx","url":"https://assets-eu.researchsquare.com/files/rs-8076104/v1/286c63d2e0708b426903682c.docx"},{"id":96936920,"identity":"c027a20e-c297-42c1-9329-5339e525a338","added_by":"auto","created_at":"2025-11-27 16:50:02","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":175539,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.png","url":"https://assets-eu.researchsquare.com/files/rs-8076104/v1/93f20a2cd5a8c9a51731a565.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Environmental drivers of tick density in UK dairy farms: implications for livestock health and agri-environment policy","fulltext":[{"header":"Background","content":"\u003cp\u003eThe distribution of vector-borne diseases is changing globally, with significant implications for human and animal health. These changes have been associated with multiple, interacting factors that influence vector, pathogen and host dynamics, including climate change, land use change and the encroachment of agriculture into natural ecosystems (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In turn, environmental governance and land management policies are shaping land use transitions, raising concerns that policy-driven landscape change may inadvertently contribute to the emergence and spread of vector borne diseases (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTicks are among the most important arthropod vectors in Europe, transmitting multiple pathogens of both medical and veterinary concern. \u003cem\u003eIxodes ricinus\u003c/em\u003e, the most widespread tick species on the continent, is responsible for the highest burden of tick-borne disease in humans and livestock (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). The abundance and distribution of \u003cem\u003eI. ricinus\u003c/em\u003e varies greatly across different landscapes, reflecting a dependence on suitable microclimates for survival, and availability of hosts to provide blood meals. Microclimate, including a relative humidity of greater than 80% for off-host developmental phases and the availability of vertebrate hosts are both strongly influenced by habitat and landscape structure (9\u0026ndash;12). Small mammals serve as the primary hosts for nymphs and larvae (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), while adult ticks feed most frequently on large herbivores such as deer and cattle. Densities of \u003cem\u003eI. ricinus\u003c/em\u003e are commonly highest in woodland and ecotonal habitats, that support the requisite microclimate and host abundance, but can also survive in open areas such as rough grasslands if conditions are favourable (\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAcross Europe, environment and climate policies promoting afforestation, natural regeneration, and habitat connectivity, such as the EU Biodiversity Strategy for 2030 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), are implicated in creating environments favourable for \u003cem\u003eI. ricinus\u003c/em\u003e survival and influencing tick dispersal through changes in wildlife host distributions (\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Transitions toward sustainable agricultural practices are also central to these strategies, aligning with global initiatives to integrate biodiversity conservation and restoration into farming systems (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Although the biodiversity and climate benefits of these landscape changes are widely acknowledged, their implications for livestock health remain poorly understood, particularly where altered habitats are likely to increase proximity between livestock, wildlife and tick vectors.\u003c/p\u003e\u003cp\u003eFor cattle, the two most important tick-borne diseases in northern Europe are bovine babesiosis, primarily caused by \u003cem\u003eBabesia divergens\u003c/em\u003e, and tick-borne fever, caused by \u003cem\u003eAnaplasma phagocytophilum\u003c/em\u003e, both transmitted by \u003cem\u003eI. ricinus\u003c/em\u003e. Babesiosis is recognised as an emerging infectious disease in Europe, with outbreaks linked to expansion of the vector range and changing host dynamics (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In adult cattle disease can be severe, characterised by fever, haemoglobinuria and depression, and often fatal (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Outbreaks, often associated with previously unexposed cattle or sporadic exposure to infection, result in significant losses (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Tick-borne fever is rarely fatal but contributes to considerable production losses through reduced milk yield, abortions and stillbirths (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Despite these impacts, the environmental and farm-level drivers of the diseases remain poorly characterised. A small number of studies have reported associations between woodland proximity, ecotonal habitats and tick abundance in cattle pastures in France (\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). However, these effects are determined by complex dynamics among habitat structure, host community composition, and local microclimatic conditions. Further research is needed to understand how these factors influence vector \u0026ndash; host -pathogen dynamics across different landscapes and agricultural systems.\u003c/p\u003e\u003cp\u003eThe UK dairy sector provides a valuable case study in this context. Evidence suggests the distribution of \u003cem\u003eI. ricinus\u003c/em\u003e is changing in the UK (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), and tick-borne pathogens are being detected in new areas (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Tick-borne diseases rank among the top three health priorities for extensively grazed livestock in the UK, and are considered as \u0026ldquo;high-impact, low-prevalence\u0026rdquo; conditions by the farming industry (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). At the same time, national policy aims to establish nearly one million hectares of new woodland by 2050, with farmers expected to deliver much of this expansion through agri-environment schemes that incentivise the creation of wildlife-rich habitats (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study investigates the landscape-scale environmental factors influencing the abundance and distribution of ticks on grazing pastures within dairy farms with a recurrent history of bovine babesiosis or tick-borne fever outbreaks in southwest England. We distinguish between within- pasture micro-climate and vegetation effects and those associated with ecotonal habitats and woodland proximity surrounding pastures. A parallel study conducted on the same farms examined the prevalence of subclinical infections with \u003cem\u003eB. divergens\u003c/em\u003e and \u003cem\u003eA. phagocytophilum\u003c/em\u003e in the cattle and is reported elsewhere (Shanks et al., 2025 in review). Here we leverage that data to examine broad scale correlations between farm landscape features, tick abundance and pathogen prevalence in cattle. Finally, we consider the implications of our findings in the context of current agricultural and environmental policies driving landscape change, as well as their relevance for disease control in both livestock and humans.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eFarm selection\u003c/h2\u003e\u003cp\u003eTwelve dairy farms were purposively recruited from the counties of Cornwall, Devon and Dorset in southwest England through local veterinary networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This region accounts for approximately 33% of the national herd and 39% of dairy cattle specifically (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), and has an established presence of \u003cem\u003eI. ricinus\u003c/em\u003e and tick-borne disease in cattle (\u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). The area is predominantly rural, characterised by agricultural land, woodland, heathland, and coastal habitats that support a mixture of livestock farming systems including beef and dairy cattle (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Farms were eligible for inclusion if they (i) did not routinely purchase replacement stock, (ii) had a veterinary diagnosed history of recurrent bovine babesiosis and/or tick-borne fever in the herd, and (iii) maintained a minimum of 50 breeding cows.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eFarm characteristics\u003c/h3\u003e\n\u003cp\u003ePreliminary discussions were conducted with farmers to gather information on general grazing and management practices, and to identify the pastures used for cattle grazing. Herd sizes ranged from 95 to 400 cattle. All farms grazed the milking herd on pasture for at least part of the day or night from spring to autumn and housed indoors during the winter months. Only dairy cattle were present on the study farms; no beef cattle were kept. Four farmers reported winter grazing of sheep, but specific information on which pastures and when grazing occurred was not available. All farmers reported regular deer presence, including sightings in cattle grazed pastures. Acaricides were used for tick control on all but one farm, however, none are licensed for this purpose in cattle in the UK and their use was therefore off-label.\u003c/p\u003e\n\u003ch3\u003eSampling strategy\u003c/h3\u003e\n\u003cp\u003eA sampling framework was designed to capture variation in tick abundance across environmental gradients, incorporating pasture characteristics, boundary features, and fine-scale microhabitat conditions at drag locations.\u003c/p\u003e\u003cp\u003eTo investigate variability in tick distribution across a landscape gradient, the pastures for sampling were selected based on pasture grazing history and proximity to woodland. Specifically, pastures grazed by cows within the previous 12 months were categorised by whether a pasture boundary bordered woodland or not. Six pastures per farm were selected, three with a woodland border if available, and the remainder from non-woodland border pastures. Within each selected pasture, boundaries with the most and least dense bordering woodland were chosen for sampling; where no difference was apparent, two boundaries were randomly selected.\u003c/p\u003e\u003cp\u003eTick sampling was conducted using the blanket-dragging approach (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). A 1m\u003csup\u003e2\u003c/sup\u003e white blanket was pulled slowly across the ground in 10m transects. After each transect, the blanket was inspected, and all life stages of ticks were counted and removed. At each selected pasture boundary, twelve standardised drags were carried out, stratified by distance from the boundary: 0m (immediately adjacent to the physical boundary feature such as hedgerow, fence, or ditch, n\u0026thinsp;=\u0026thinsp;3), 2m (n\u0026thinsp;=\u0026thinsp;3), 4m (n\u0026thinsp;=\u0026thinsp;3), and 15m (n\u0026thinsp;=\u0026thinsp;3), with the latter representing semi-open pasture. An additional nine semi-randomised drags were carried out in each pasture at a distance greater than 15m from all boundaries to represent open pasture. This design captured spatial variation in tick presence relative to pasture edges, where ticks have been found to be concentrated in grazing pastures in a prior study from France (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Sampling took place in May and June 2024, with all drags in a given pasture completed on the same day under dry weather conditions. Questing adults and nymphs were collected from the vegetation. In total, 33 drags were carried out per pasture giving 2376 drags across the study.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eQuesting ticks were assumed to be \u003cem\u003eI. ricinus\u003c/em\u003e based on previous surveillance confirming this as the predominant questing species in southern England (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). \u003cem\u003eDermacentor reticulatus\u003c/em\u003e has also been found in the region (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) however any questing \u003cem\u003eDermacentor\u003c/em\u003e spp. would have been adults, readily distinguished by their larger size and ornate scutum. The nymphs of \u003cem\u003eD. reticulatus\u003c/em\u003e are reportedly nidicolous, whereas adults exhibit exophilic behaviour and are the only stages likely to be collected by blanket-dragging (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eEnvironmental predictors\u003c/h3\u003e\n\u003cp\u003eA hierarchical framework was used to investigate environmental factors driving tick presence/absence and nymph density across three nested spatial scales: transect scale (microhabitat), pasture boundary scale (ecotonal habitat), and pasture scale (landscape context) (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEnvironmental variables measured at three spatial scales to investigate tick presence/absence and nymph density. Subcategories for each variable are listed.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eScale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSubcategories\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eSummary values across all transects (n\u0026thinsp;=\u0026thinsp;2376)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFrequency (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eTransect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLocation relative to pasture boundary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 m, 2 m, 4 m, 15 m, open pasture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVegetation height (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.96\u0026thinsp;\u0026plusmn;\u0026thinsp;12.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u0026ndash;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVegetation density (score 0-100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.35\u0026thinsp;\u0026plusmn;\u0026thinsp;7.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u0026ndash;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNumber of cattle dung piles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.54\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u0026ndash;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDominant vegetation type (% of transects)\u003csub\u003ea\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGrass\u003c/p\u003e\u003cp\u003eHerbaceous/wildflowers\u003c/p\u003e\u003cp\u003eBare ground\u003c/p\u003e\u003cp\u003eBramble/fern\u003c/p\u003e\u003cp\u003eRushes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e92.0\u003c/p\u003e\u003cp\u003e4.1\u003c/p\u003e\u003cp\u003e1.7\u003c/p\u003e\u003cp\u003e1.5\u003c/p\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eBoundary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStructural boundary elements (present/absent)\u003csub\u003eb\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHedgerow\u003c/p\u003e\u003cp\u003eVisible fence\u003c/p\u003e\u003cp\u003eDitch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e81.0\u003c/p\u003e\u003cp\u003e61.9\u003c/p\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDominant habitat type within 10 m outside pasture boundary (% of transects)\u003csub\u003ec\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePasture\u003c/p\u003e\u003cp\u003eTrees\u003c/p\u003e\u003cp\u003eTrack/Road\u003c/p\u003e\u003cp\u003eScrubland\u003c/p\u003e\u003cp\u003eWater\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026ndash;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47.5\u003c/p\u003e\u003cp\u003e26.3\u003c/p\u003e\u003cp\u003e18.3\u003c/p\u003e\u003cp\u003e4.2\u003c/p\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWoodland within 50 m buffer (%) (boundary)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.79\u0026thinsp;\u0026plusmn;\u0026thinsp;15.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u0026ndash;70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ePasture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSaturation deficit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.90\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.30\u0026ndash;13.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWoodland within 50 m buffer (%) (entire pasture)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.22\u0026thinsp;\u0026plusmn;\u0026thinsp;16.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u0026ndash;61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003csub\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sub\u003e \u003cb\u003eUp to three vegetation types were initially recorded for each transect as % cover. For analysis, only the single dominant type was retained as a categorical variable.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003csub\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sub\u003e \u003cb\u003eStructural elements were recorded as present/absent; multiple could be present per recording\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003csub\u003e\u003cb\u003ec\u003c/b\u003e\u003c/sub\u003e \u003cb\u003eHabitat type within 10 m of the pasture boundary was recorded as a single category (pasture, woodland, track/road, scrubland, or water).\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAt the finest resolution, vegetation type, height and density were recorded for each transect to characterize microhabitat features influencing tick survival and questing behaviour (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Ground vegetation height and density were measured using a sward stick, with density expressed as the number of 5cm increments on the stick obscured by vegetation. The three dominant vegetation types and their proportions were recorded for each transect, categorised into five classes: (i) herbaceous plants and wildflowers, (ii) bracken and bramble, (iii) rushes, (iv) grass, and (v) bare ground. Cattle dung piles were counted within each transect to provide an index of cattle abundance, following approaches applied to other host species (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). Cattle can redistribute ticks across pastures through movement, potentially influencing local tick distribution patterns (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). In addition, dung piles could influence tick survival and questing activity by physically covering or obstructing ticks.\u003c/p\u003e\u003cp\u003eAt the pasture boundary scale, structural and contextual features hypothesized to influence tick abundance along pasture edges were recorded. Pasture boundaries were classified by the presence or absence of structural elements (hedgerow, visible fence, ditch) that may provide refuges or pathways for tick hosts (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) or modify micro-climate conditions; more than one element could be present in the same boundary. The dominant habitat within 10m on the opposite side of the boundary at the location of 0m transects was recorded as a single observed category (pasture, trees, water body, scrubland, track/road) to characterise the immediate landscape context influencing ecotonal conditions and host behaviour (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The \u0026lsquo;trees\u0026rsquo; category included both isolated trees and boundaries adjoining larger woodland patches.\u003c/p\u003e\u003cp\u003eTo assess broader landscape influences, the proportion of woodland cover (coniferous and deciduous) was quantified within a 50 m buffer at two spatial scales: (i) around individual pasture boundaries and (ii) around entire pastures. The 50 m buffer was chosen to capture the effect of surrounding woodland (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), while minimising dilution by more distant open habitats and overlap between neighbouring pastures. Data on land cover were extracted from the UK Centre for Ecology and Hydrology Land Cover Map (tiff 2022) (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) using R version 4.4.1(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) and QGIS version 3.40 \u0026lsquo;Bratislava\u0026rsquo;(\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAt the pasture scale, saturation deficit was included as a climatic variable influencing tick desiccation risk and questing activity (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Temperature and humidity were measured at vegetation height immediately before the first drag and after the last drag in each pasture. Saturation deficit was calculated from temperature and humidity using the formula: SD = (1\u0026thinsp;\u0026minus;\u0026thinsp;RH/100) \u0026times; 4.9463 \u0026times; e\u003csup\u003e(0.0621\u003cem\u003eT\u003c/em\u003e)\u003c/sup\u003e where \u003cem\u003eT\u003c/em\u003e is temperature in \u0026deg;C and \u003cem\u003eRH\u003c/em\u003e is relative humidity (%) (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eAll statistical analyses were performed using RStudio 2024.09.01. Generalised Linear Mixed Models (GLMMs) were fitted using the glmmTMB package (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) to investigate the influence of environmental factors on tick presence/absence and nymph density. Model assumptions, including overdispersion, zero-inflation, and outliers were assessed using simulation-based residuals from the DHARMa package (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Collinearity among fixed effects was checked using Variance Inflation Factors (VIF) and model convergence was verified.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eEnvironmental factors influencing tick presence at transect level\u003c/h2\u003e\u003cp\u003eTo examine environmental effects on the presence or absence of questing ticks (nymphs and adults), a binomial GLMM with logit link was fitted to the full dataset of 2376 transects. At the transect level, fixed effects included vegetation height, vegetation density, distance from pasture boundary, dominant vegetation type and the number of cow dung piles. At the pasture level, fixed effects included saturation deficit and the proportion of woodland in a 50m pasture buffer. No boundary-level predictors were included in this model. Random intercepts were fitted for farm (n\u0026thinsp;=\u0026thinsp;12) and pasture (n\u0026thinsp;=\u0026thinsp;72). Backward stepwise model selection was performed using the drop1 function, retaining predictors based on minimisation of Akaike\u0026rsquo;s Information Criterion (AIC) (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). The relative contribution of retained predictors was assessed from their effect sizes, confidence intervals and changes in AIC. Model performance was evaluated using conditional (including random effects) and marginal (fixed effects only) area under the receiver operating characteristic curve (AUC) values, calculated with pROC (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e), and the true skill statistic (TSS) (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEnvironmental factors influencing nymph density at pasture boundaries\u003c/h3\u003e\n\u003cp\u003eTo identify environmental risk factors and pasture boundary characteristics associated with nymph density, a negative binomial GLMM was fitted to nymph counts recorded on 0m transects. At the transect level, fixed effects included vegetation height, vegetation density, distance from pasture boundary, dominant vegetation type, and number of cow dung piles. At the boundary level, fixed effects included presence or absence of structural boundary features, the adjacent habitat type and the proportion of woodland in a 50m boundary buffer. At the pasture level, saturation deficit was included. Random intercepts were fitted for farm (n\u0026thinsp;=\u0026thinsp;12), pasture (n\u0026thinsp;=\u0026thinsp;72), and boundary (n\u0026thinsp;=\u0026thinsp;144). The same AIC-based backward stepwise selection was applied. Model fit was assessed using marginal and conditional R\u0026sup2; (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e), and root mean squared error (RMSE) based on conditional (including random effects) and marginal (fixed effects only) predictions.\u003c/p\u003e\u003cp\u003eTwo-way interaction terms between the proportion of woodland in a 50m boundary buffer and the adjacent habitat type were also evaluated to test whether effects of woodland differed by adjacent habitat. Competing models with and without these interactions were compared using AIC and marginal and conditional R\u003csup\u003e2\u003c/sup\u003e values, calculated with the MuMIn package (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eCattle pathogen prevalence and associations with tick abundance\u003c/h3\u003e\n\u003cp\u003eBroad scale, farm-level associations between cattle \u003cem\u003eA. phagocytophilum\u003c/em\u003e and \u003cem\u003eB. divergens\u003c/em\u003e prevalence and mean tick abundance and woodland cover across sampled grazing pastures per farm were examined using Pearson correlation coefficients. Herd-level prevalence data for adult cattle were obtained from a parallel prevalence study conducted on the same farms during the same grazing season (Shanks et al., 2025 in review). Full details of cattle recruitment, blood sampling, DNA extraction, and PCR assays are reported in that study. For this analysis, PCR results were summarised as the proportion of adult cattle per farm testing positive for \u003cem\u003eA. phagocytophilum\u003c/em\u003e and \u003cem\u003eB. divergens\u003c/em\u003e. Mean tick abundance was calculated as the mean number of ticks per 0 m transect per farm. At the farm level, woodland cover was summarised as the mean proportion of woodland within 50 m buffers around all sampled pastures.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDistribution of questing ticks within dairy pastures\u003c/h2\u003e\u003cp\u003eA total of 1,701 questing ticks were collected over the study period. Overall, ticks were found on 483 transects (20.3%). Nymphs were the most common life stage, comprising 91.3% of the total ticks collected (n\u0026thinsp;=\u0026thinsp;1553), present in 461 (19.4%) transects, followed by adult males (n\u0026thinsp;=\u0026thinsp;91, 5.3%), present in 66 (2.8%) transects and adult females (n\u0026thinsp;=\u0026thinsp;57, 5.4%), present in 47 (2.0%) transects. The maximum number of nymphs recorded in a single transect was 36. Across all transects in the study, the mean nymph density was 0.65 nymphs per transect (SD\u0026thinsp;=\u0026thinsp;2.19). At the pasture boundary (0m distance from pasture boundary, n\u0026thinsp;=\u0026thinsp;432 transects across all farms), the overall mean nymph density was 2.28 nymphs per transect (SD\u0026thinsp;=\u0026thinsp;4.1, max\u0026thinsp;=\u0026thinsp;36), with mean densities declining at increasing distances into the pasture (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In total, ticks were collected from 64 of the 72 sampled pastures (88.9%), with 75% of all ticks collected from 12 (32.4%) pastures. The maximum number of ticks collected in a single pasture was 104.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eEnvironmental variables\u003c/h2\u003e\u003cp\u003eGrass was the dominant vegetation type recorded in 92% of transects across all farms, with herbaceous/wildflowers (4.1%), bare ground (1.7%), bramble/fern (1.5%), and rushes (0.7%) less frequently observed. Hedgerows were recorded as a boundary feature at 81% of 0m transects, visible fences at 61.9% and ditches at 5.3%. More than one of these features was present at 48.1% of 0 m transects (n\u0026thinsp;=\u0026thinsp;208). The dominant habitat within 10m on the opposite side of the boundary at 0m transects was pasture (47.5%), followed by trees (26.3%), track/road (18.3%), scrubland (4.2%), and water (3.7%).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eEnvironmental factors influencing tick presence at transect level\u003c/h2\u003e\u003cp\u003eThe presence of questing ticks at the transect level was significantly associated with distance from the pasture boundary, vegetation height, and the proportion of woodland within a 50 m pasture buffer (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The probability of detecting ticks decreased with increasing distance from the boundary (-0.14, \u0026plusmn; 0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), increased with vegetation height (0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and increased with proportion of woodland within a 50 m buffer around the entire pasture (0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001); values are model estimates\u0026thinsp;\u0026plusmn;\u0026thinsp;SE. Vegetation density, dominant vegetation type, number of cow dung piles and saturation deficit did not have significant impact on presence of questing ticks at transect level (i.e. were dropped during the model selection process, without appreciable increase in AIC (see Additional file 1). Random effects indicated additional variation at the farm (SD\u0026thinsp;=\u0026thinsp;0.17) and pasture (SD\u0026thinsp;=\u0026thinsp;1.20) levels. The final GLMM showed good model performance, having a conditional AUC of 0.900 (95% CI: 0.886\u0026ndash;0.915) and a marginal AUC of 0.833 (95% CI: 0.814\u0026ndash;0.852), with a maximum TSS of 0.66.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFinal binomial GLMM results for the presence of questing ticks (nymphs and adults) across all transects.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFixed effects\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ez- value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDelta AIC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;2.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-9.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetation height\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance from the boundary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-13.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e241.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWood 50m pasture buffer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRandom effects\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eVariance\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePasture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.44\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFixed-effect estimates (logit scale) with standard error (SE), z, p, and Delta AIC from the final model; random-effect variances and standard deviation (SD) are reported for farm and pasture. Predictors were retained based on minimisation of AIC; p-values are reported for inference only.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eEnvironmental factors influencing nymph density at pasture boundaries\u003c/h2\u003e\u003cp\u003eNymph density in transects located 0m from pasture boundaries was positively associated with the proportion of woodland within a 50 m boundary buffer (3.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and to a lesser extent with boundaries adjacent to water (1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38, p\u0026thinsp;=\u0026thinsp;0.001), although water was present in relatively few pastures sampled which may limit the strength of this association (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Boundaries adjacent to track/road showed a marginally negative impact on nymph density (\u0026ndash;0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31, p\u0026thinsp;=\u0026thinsp;0.07), while scrubland and tree adjacent habitats were not significantly different in impact from pasture. Vegetation height, vegetation density, presence of a hedgerow, and dung piles were not significant predictors of nymph density. Random effects indicated additional unmeasured factors affecting nymph density at the farm (SD\u0026thinsp;=\u0026thinsp;0.53), pasture (SD\u0026thinsp;=\u0026thinsp;0.70), and boundary (SD\u0026thinsp;=\u0026thinsp;0.79) levels. The final GLMM explained 27.2% of the variance in nymph density through fixed effects alone (marginal R\u0026sup2;), and 78.4% when random effects were included (conditional R\u0026sup2;; lognormal method). Prediction error was lower for the full model (conditional RMSE\u0026thinsp;=\u0026thinsp;2.12) compared with fixed effects alone (marginal RMSE\u0026thinsp;=\u0026thinsp;3.80). Interaction terms between the proportion of woodland within a 50m boundary buffer and adjacent habitat type were also evaluated to test whether the effect of nearby woodland varied across immediate habitat contexts. Inclusion of these interactions did not substantially improve model fit (ΔAIC\u0026thinsp;\u0026lt;\u0026thinsp;2), increased marginal R\u0026sup2; only slightly (from 0.26 to 0.30) while conditional R\u0026sup2; remained almost unchanged (0.74\u0026ndash;0.80). These terms were therefore not retained in the final model (Additional file 1).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFinal negative binomial GLMM results for nymph density at pasture boundaries (0m transects).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinal GLMM (fixed effects)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEstimate\u0026thinsp;\u0026plusmn;\u0026thinsp;SE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ez- value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDelta AIC\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntercept\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetation height\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVegetation density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHedgerow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWoodland within 50 m boundary buffer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.87\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCattle dung piles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjacent habitat: Scrubland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjacent habitat: Track/road\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjacent habitat: Water\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdjacent habitat: Trees\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRandom effects\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eVariance\u003c/b\u003e\u0026thinsp;\u0026plusmn;\u0026thinsp;\u003cb\u003eSD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePasture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoundary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eFixed-effect estimates (log scale) with SE, z, p, and Delta AIC from the final GLMM are shown with random-effect variances for farm, pasture, and boundary. Predictors were retained based on minimisation of AIC; p-values are reported for inference only. Reference level for adjacent habitat\u0026thinsp;=\u0026thinsp;pasture.\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eCattle pathogen prevalence and associations with tick abundance\u003c/h2\u003e\u003cp\u003eBroad scale correlations at the farm level between herd-level (adult cows) \u003cem\u003eB. divergens\u003c/em\u003e and \u003cem\u003eA. phagocytophilum\u003c/em\u003e prevalence and the farm-level mean tick abundance at 0m transects or woodland cover within 50 m buffers were weak and not statistically significant. Correlation coefficients ranged from \u0026minus;\u0026thinsp;0.36 to 0.07, with wide confidence intervals (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.2). See Additional file 2.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis is the first study to examine the landscape factors driving the abundance of the key tick vector, \u003cem\u003eI. ricinus\u003c/em\u003e, within cattle grazing systems in the UK. By distinguishing the relative contributions of pasture-level vegetation and microclimate from those of ecotonal and broader habitat composition, it establishes a foundation for understanding how environmental structure influences tick hazard in livestock systems. By identifying the habitats and landscape features that sustain high tick densities, the findings provide a critical first step in anticipating how tick-borne disease risks may shift under environmental and climate policies across the UK and European Union that promote woodland expansion, habitat restoration, and landscape connectivity.\u003c/p\u003e\u003cp\u003eTick distribution was highly heterogenous, with strong spatial aggregation both between and within pastures. Densities were concentrated at pasture margins and declined sharply into open pastures, consistent with patterns reported across European agricultural landscapes (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The proportion of woodland cover within 50m buffers around pasture boundaries and entire pastures were the dominant predictors of tick abundance, whereas hedgerow boundaries and immediate adjacency to tree habitats within 10m of pasture boundaries showed no association. This contrasts with studies in UK arable systems and French cattle pastures where hedgerows and tree cover along pasture perimeters have been associated with increased tick presence or abundance (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHedgerows bordered most pasture edges in our study system (81%), reducing statistical power to detect independent effects. In addition, the hedgerows were recorded only as a binary variable, which did not capture structural variation such as width, density and composition, known to influence tick populations (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In hedgerow-dominated dairy landscapes, such binary measures may therefore provide poor indicators of vegetated microsites used by tick hosts. The absence of an effect of adjacency to tree habitats within 10m of pasture boundaries suggests that processes influencing \u003cem\u003eI. ricinus\u003c/em\u003e distribution in grazing pastures operated across broader spatial scales than local boundary features. Woodland extent in 50m buffers captures landscape features such as connectivity and patch size that are known to influence tick populations (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Modelling studies suggest woodlands are a source of ticks for pastures, with migration across ecotones required to sustain the presence of ticks in pastures (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). While evidence for deer transporting ticks into pastures is mixed (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e), regular reported deer sightings in and around cattle pastures in our study are consistent with this potential mechanism. Small mammals also contribute to tick abundance at the woodland \u0026ndash; ecotone - pasture interface, although their dispersal role is complex and species-specific (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). While hedgerows can contribute to habitat connectivity and facilitate host movement between woodland and pastures, they were not detected as important habitats for sustaining tick populations along pasture boundaries in our study landscape. It is important to study hedgerow effects on tick abundance and hazard in other farmland contexts, sampling across different boundary types and integrating more detailed metrics of hedgerow structure.\u003c/p\u003e\u003cp\u003eBy contrast, adjacency to water predicted higher nymph densities in boundary transects. To our knowledge, this is the first study to examine the influence of local water bodies on tick abundance in UK grazing pastures. Studies in other European landscapes have reported \u003cem\u003eI. ricinus\u003c/em\u003e abundance to be high along rivers in wet, humid canopies supporting dense undergrowth with high relative humidity (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). Riparian zones support a high proportion of wildlife (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e), increasing opportunities for host - vector contact and tick dispersion into pasture edges.\u003c/p\u003e\u003cp\u003eOur results indicate that cattle exposure to ticks is likely to be concentrated at pasture edges, particularly those bordering woodland and water bodies. Restricting cattle access to these areas could reduce exposure to ticks, but these areas also provide shade, shelter, and valuable grazing, creating welfare and economic trade-offs (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). Furthermore, complete avoidance of tick exposure is neither feasible nor advised in pasture-based systems within endemic regions. Low-level exposure to infected ticks is thought to support protective immunity to \u003cem\u003eB. divergens\u003c/em\u003e, and controlled exposure remains a recommended strategy for herd management in endemic areas (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). However, key uncertainties remain regarding the spatial distribution of infected ticks within pastures (e.g. whether hazard mirrors patterns in tick abundance), the development and persistence of protective immunity, and how controlled exposure could be implemented at farm level.\u003c/p\u003e\u003cp\u003eTick pathogen data was not available in this study, however, \u003cem\u003eB. divergens\u003c/em\u003e prevalence in questing ticks is typically extremely low across UK and European landscapes (\u003cspan additionalcitationids=\"CR69 CR70\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e), while \u003cem\u003eA. phagocytophilum\u003c/em\u003e prevalence is higher on average (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e). Consequently, while questing nymph density is a useful proxy for cattle exposure, due to the diverse hosts and pathways involved in transmission of \u003cem\u003eB. divergens\u003c/em\u003e and \u003cem\u003eA. phagocytophilum\u003c/em\u003e (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e), infection risk could remain low or inconsistent even in habitats with high nymph densities, limiting the predictive value for assessing farm-level risk. No significant associations were detected between farm-level tick abundance or woodland cover and pathogen prevalence in cattle, though statistical power was limited by the low sample size and the purposive recruitment of herds with a recurrent history of bovine babesiosis or tick-borne fever that likely reduced variability between farms. In addition, infection risk is influenced by multiple processes, including tick pathogen prevalence, multi-year transmission cycles, host immunity, and farm management practices such as acaricide use and grazing patterns that were not captured in this study.\u003c/p\u003e\u003cp\u003eOur findings suggest potential unintended consequences of agri-environment policies that promote woodland expansion and connectivity in farmed landscapes. Increasing woodland extent within grazing systems may enhance habitat suitability for ticks and host species, increasing the likelihood of livestock exposure at the woodland - pasture interface. Multi-year studies integrating tick and cattle sampling, pathogen screening, host activity, and detailed farm management data are required to determine whether landscape changes driven by agri-environment schemes will contribute to measurable increases in tick-borne disease risk, and to inform evidence-based recommendations for disease control at farm level. In the interim, strengthened farm surveillance of tick-borne diseases will be essential to guide management and policy decisions (\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFinally, the implications of this study extend beyond livestock health. High nymph densities in ecotonal habitats within grazing pastures also pose a risk to humans, given \u003cem\u003eI. ricinus\u003c/em\u003e is the primary UK vector of \u003cem\u003eBorrelia burgdorferi\u003c/em\u003e sensu lato, the causative pathogen of Lyme disease, and tick-borne encephalitis virus, both detected in southern England (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). Farmers and recreational users of farmland may therefore be exposed to infected nymphs at pasture margins, reinforcing the continued importance of awareness campaigns promoting personal protective measures.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study provides the first systematic analysis of the influence of local landscape features on tick hazard in UK dairy systems. Tick densities were highly aggregated across pastures and in ecotonal habitats. Woodland cover was the dominant predictor of tick abundance, and adjacency to water bodies also contributed to elevated nymph densities at pasture edges. Hedgerows were not significant predictors of nymph density in our study farms. In a policy context, the findings highlight a critical tension. Woodland creation and connectivity are central to EU and UK environmental targets, delivering clear biodiversity and climate benefits, yet woodland established adjacent to grazed pastures could inadvertently increase livestock exposure to ticks. Translating ecological risk factors into policy recommendations and farm-level disease control interventions is challenging. Further research and stakeholder engagement is needed to co-develop evidence-based recommendations that reconcile biodiversity and climate objectives with the protection of livestock and public health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipating farms were informed that all information provided would remain strictly confidential, be securely disposed of in accordance with the data protection act and that all data would be summarised to ensure no farm would be individually identifiable. The work was carried out under the approval of the University of Liverpool Veterinary Research Ethics Committee: VREC1441\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants consented to have their data published.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data supporting the conclusions of this article are included within the article and its additional files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding for this study was provided by joint funding from the University of Liverpool Faculty of Health and Life Sciences PhD studentship scheme and the Animal and Plant Agency. Additional support for BP and RH was provided by the BBSRC-DEFRA OPTICK project funded by the Biotechnology and Biological Sciences Research Council (BBSRC), Natural Environment Research Council (NERC), UKRI Tackling Infections Strategic Theme and Department for Environment Food \u0026amp; Rural Affairs (Defra) through grant BB/X017974/1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSS Sample collection, data analysis, wrote the first draft; RH data analysis, BP Supervision; NJ Funding acquisition, supervision; JD Supervision.\u0026nbsp;CM, conceptualisation, funding acquisition, project management, supervision, data analysis, contribution to drafts.\u0026nbsp;All authors edited the manuscript and approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the participating farmers for their time, the local veterinarians for their assistance with farm recruitment, Dr Rob Kelly for his clinical epidemiological advice and Abi Joyce for her help with field work.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMedlock JM, Leach SA. 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Ticks and Tick-borne Diseases. 2025;16(5):102523.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"parasites-and-vectors","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"parv","sideBox":"Learn more about [Parasites \u0026 Vectors](http://parasitesandvectors.biomedcentral.com/)","snPcode":"13071","submissionUrl":"https://submission.nature.com/new-submission/13071/3","title":"Parasites \u0026 Vectors","twitterHandle":"@bugbittentweets","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8076104/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8076104/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTicks are important vectors of livestock and human pathogens in Europe. Environmental policies promoting woodland creation and habitat restoration are increasing habitat suitability for \u003cem\u003eIxodes ricinus\u003c/em\u003e but impacts on livestock tick-borne disease risk remain unclear. This study examined how landscape features influence tick distribution on UK dairy farms with a recent history of tick-borne disease.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQuesting ticks were sampled on 72 pastures in 12 dairy farms in southwest England (2,376 transects), stratified by distance from pasture boundaries and adjacency to woodland or non-woodland habitats. Environmental variables were measured at transect, boundary, and pasture scales. Generalised linear mixed models identified predictors of tick presence in pastures, and nymph density at pasture boundaries. Farm-level associations between tick abundance, woodland cover, and cattle pathogen prevalence were assessed descriptively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 1,701 ticks were collected (91.3% nymphs). Ticks were detected on 20% of transects and in 89% of pastures, with densities strongly aggregated at pasture boundaries. The proportion of woodland cover within 50m buffers was the dominant environmental driver at both boundary and pasture scales, with greater cover associated with higher nymph densities and increased probability of tick presence. Boundaries adjacent to water also supported significantly higher nymph densities.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLocal landscape features, particularly woodland cover and small water bodies at boundaries, strongly influence tick distribution in UK dairy pastures. Woodland expansion through environmental schemes may thus be expected to increase tick distribution and densities in farmed landscapes, with implications for livestock exposure and public health.\u003c/p\u003e","manuscriptTitle":"Environmental drivers of tick density in UK dairy farms: implications for livestock health and agri-environment policy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-27 16:49:57","doi":"10.21203/rs.3.rs-8076104/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-29T15:48:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-23T14:49:23+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-18T16:21:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"327009486891170735678459116031012760187","date":"2025-12-18T14:27:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226922948950678279089604375274578378413","date":"2025-12-18T08:46:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"236105228290579194299371107120406963965","date":"2025-11-28T08:33:01+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-20T17:47:31+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-14T15:34:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-13T05:27:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"Parasites \u0026 Vectors","date":"2025-11-10T10:47:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"parasites-and-vectors","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"parv","sideBox":"Learn more about [Parasites \u0026 Vectors](http://parasitesandvectors.biomedcentral.com/)","snPcode":"13071","submissionUrl":"https://submission.nature.com/new-submission/13071/3","title":"Parasites \u0026 Vectors","twitterHandle":"@bugbittentweets","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7079213f-00c6-43fd-9eff-e57ec7843ae9","owner":[],"postedDate":"November 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:01:30+00:00","versionOfRecord":{"articleIdentity":"rs-8076104","link":"https://doi.org/10.1186/s13071-026-07345-w","journal":{"identity":"parasites-and-vectors","isVorOnly":false,"title":"Parasites \u0026 Vectors"},"publishedOn":"2026-03-31 15:57:46","publishedOnDateReadable":"March 31st, 2026"},"versionCreatedAt":"2025-11-27 16:49:57","video":"","vorDoi":"10.1186/s13071-026-07345-w","vorDoiUrl":"https://doi.org/10.1186/s13071-026-07345-w","workflowStages":[]},"version":"v1","identity":"rs-8076104","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8076104","identity":"rs-8076104","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}